In the age of technology, individuals accelerate their biased gathering of information, which in turn leads to a population becoming extreme and more polarized. Here we study a partial-differential-equation model for opinion dynamics that exhibits collective behavior subject to nonlocal interactions. We developed an interaction kernel function to represent biased information gathering. Through a linear stability analysis, we show that biased populations can still form opinionated groups. However, a population that is too heavily biased can no longer come to a consensus, that is, the initial homogeneous mixed state becomes stable. Numerical simulations with biased information gathering show the ability for groups to collectively drift towards one end of the opinion space. This means that a small bias in each individual will collectively lead to groups of individuals becoming extreme together. The characteristic time scale for a group's existence is captured from numerical experiments using the temporal correlation function. Supplementing this, we included a measure of how different each population is after regular time intervals using a form of the Manhattan and Euclidean distance metrics. We conclude by exploring how wall boundary conditions induce pattern formation initially on the most extreme sides of the domain.

Prior studies demonstrate that schizophrenia (SZ) is associated with abnormalities in positive and negative emotional experience that predict clinical presentation. However, it is unclear whether specific discrete emotions within the broader positive/negative categories are driving those symptom associations. Further, it is also unclear whether specific emotions contribute to symptoms in isolation or via networks of emotional states that dynamically interact across time. The current study used network analysis to evaluate temporally dynamic interactions among discrete emotional states experienced in the real world as assessed via Ecological Momentary Assessment (EMA). Participants included 46 outpatients with chronic SZ and 52 demographically matched healthy controls (CN) who completed 6 days of EMA that captured reports of emotional experience and symptoms derived from monetary surveys and geolocation based symptom markers of mobility and home location. Results indicated that less dense emotion networks were associated with greater severity of negative symptoms, whereas more dense emotion networks were associated with more severe positive symptoms and mania. Additionally, SZ evidenced greater centrality for shame, which was associated with greater severity of positive symptoms. These findings suggest that positive and negative symptoms are associated with distinct profiles of temporally dynamic and interactive emotion networks in SZ. Findings have implications for adapting psychosocial therapies to target specific discrete emotional states in the treatment of positive versus negative symptoms.

Collective idea generation and innovation processes are complex and dynamic, involving a large amount of qualitative narrative information that is difficult to monitor, analyze, and visualize using traditional methods. In this study, we developed three new visualization methods for collective idea generation and innovation processes and applied them to data from online social network experiments. The first visualization is the Idea Cloud, which helps monitor collective idea posting activity and intuitively tracks idea clustering and transition. The second visualization is the Idea Geography, which helps understand how the idea space and its utility landscape are structured and how collaboration was performed in that space. The third visualization is the Idea Network, which connects idea dynamics with the social structure of the people who generated them, displaying how social influence among neighbors may have affected collaborative activities and where innovative ideas arose and spread in the social network.

Health Information Exchange (HIE) network allows securely accessing and sharing healthcare-related information among healthcare providers (HCPs) and payers. HIE services are provided by a non-profit/profit organizations under several subscription plans options. A few studies have addressed the sustainability of the HIE network such that HIE providers, HCPs, and payers remain profitable in the long term. However, none of these studies addressed the coexistence of multiple HIE providers in the network. Such coexistence may have a huge impact on the behavior of healthcare systems in terms of adoption rate and HIE pricing strategies. In addition, in spite of all the effort to maintain cooperation between HIE providers, there is still a chance of competition among them in the market. Possible competition among service providers leads to many concerns about the HIE network sustainability and behavior. In this study, a game-theoretic approach to model the HIE market is proposed. Game-theory is used to simulate the behavior of the three different HIE network agents in the HIE market: HIE providers, HCPs, and payers. Pricing strategies and adoption decisions are optimized using a Linear Programming (LP) mathematical model. Results show that the relation between HIEs in the market is crucial to HCP/Payer adoption decision specially to small HCPs. A small change in the discount rate proposed by a competitive HIE provider will highly affect the decision of HCP/payers to join the HIE network. Finally, competition opened the opportunity for more HCPs to join the network due to reduced pricing. Furthermore, collaborative HIEs provided better performance compared to cooperative in terms of profit and HCP adoption rate by sharing their overall costs and revenues.

Abnormalities in positive and negative emotional experience have been identified in laboratory-based studies in schizophrenia (SZ) and associated with poorer clinical outcomes. However, emotions are not static in daily life-they are dynamic processes that unfold across time and are characterized by temporal interactions. Whether these temporal interactions are abnormal in SZ and associated with clinical outcomes is unclear (i.e., whether the experience of positive/negative emotions at time t increases or decreases the intensity of positive/negative emotions at time t+1). In the current study, participants with SZ (n = 48) and healthy controls (CN; n = 52) completed 6 days of ecological momentary assessment (EMA) surveys that sampled state emotional experience and symptoms. The EMA emotional experience data was submitted to Markov chain analysis to evaluate transitions among combined positive and negative affective states from time t to t+1. Results indicated that: (1) In SZ, the emotion system is more likely to stay in moderate or high negative affect states, regardless of positive affect level; (2) SZ transition to co-activated emotional states more than CN, and once emotional co-activation occurs, the range of emotional states SZ transition to is more variable than CN; (3) Maladaptive transitions among emotional states were significantly correlated with greater positive symptoms and poorer functional outcome in SZ. Collectively, these findings clarify how emotional co-activation occurs in SZ and its effects on the emotion system across time, as well as how negative emotions dampen the ability to sustain positive emotions across time. Treatment implications are discussed.

Access to abundant biased information in echo chambers and social bubbles often intensifies opinions to the extremes. The extremization of opinions results in several topics becoming controversial. However, it is very difficult to measure the degree of controversiality of a topic objectively since the controversiality of any topic is subjective and perceived differently from different communities. The absence of an objective measure of controversiality has been a major hindrance in understanding the causes and effects of it. In this work we propose a method to quantify controver-siality of a topic by utilizing eccentricity of opinions on that topic. The eccentricity of an opinion is the amount of strangeness of the opinion relative to other opinions in the social neighborhood. The collective eccentricity of all opinions for a topic works as an indicator of the controversiality of that topic and can be represented by any measure of central tendency. With the help of social network data, we also demonstrate that opinions on several issues related to our routine life show similar trends for diversity though they differ in their controversiality.

It is common in origins of life research to view the first stages of life as the passive result of particular environmental conditions. This paper considers the alternative possibility: that the antecedents of life were already actively regulating their environment to maintain the conditions necessary for their own persistence. In support of this proposal, we describe 'viability-based behaviour': a way that simple entities can adaptively regulate their environment in response to their health, and in so doing, increase the likelihood of their survival. Drawing on empirical investigations of simple self-preserving abiological systems, we argue that these viability-based behaviours are simple enough to precede neo-Darwinian evolution. We also explain how their operation can reduce the demanding requirements that mainstream theories place upon the environment(s) in which life emerged.

Collective motion models most often use self-propelled particles, which are known to produce organized spatial patterns via their collective interactions. However, there is less work considering the possible organized spatial patterns achievable by non-self-propelled particles (nondriven), i.e., those obeying energy and momentum conservation. Moreover, it is not known how the potential energy interaction between the particles affects the complexity of the patterns. To address this, in this paper, a collective motion model with a pairwise potential energy function that conserved the total energy and momentum of the particles was implemented. The potential energy function was derived by generalizing the Lennard-Jones potential to reduce to gravity-like and billiard-ball-like potentials at the extremes of its parameter range. The particle model was simulated under a number of parameterizations of this generalized potential, and the average complexity of the spatial pattern produced by each was computed. Complexity was measured by tracking the information needed to describe the particle system at different scales (the complexity profile). It was found that the spatial patterns of the particles were the most complex around a specific ratio in the parameters. This parameter ratio described a characteristic shape of the potential energy function that is capable of producing complex spatial patterns. It is suggested that the characteristic shape of the potential energy produces complex behavior by balancing the likelihood for particles to bond. Furthermore, these results demonstrate that complex spatial patterns are possible even in an isolated system.

The lack of access to healthcare can lead to poor health outcomes. Factors that prevent patients from getting to their medical care appointments can be associated with an increased risk of adverse health conditions. These factors, such as limited access to transportation and long travel distance, may lead to higher no-show rates and gaps in care. No-shows and gaps in care are claimed to be associated with more emergency department (ED) visits as well as more hospitalizations. In this study, we investigated the impact of travel distance and transportation availability on attending a medical care appointment. Data used in this research were extracted from a community hospital in Broome County, New York. The acquired data spans over the time between January 2021 and July 2022. The response variable in this study is the patient-associated risk score. A bagged random forest meta-ensemble predictive model was developed using a k-fold cross-validation training approach. The model adequacy was assessed using performance measures, including accuracy, sensitivity, and ROC. Results showed that travel distance between a patient’s residential address and their healthcare provider negatively impacted their health outcomes. This finding can help design solutions and interventions such as transportation funding programs and/or expanding bus routes to provide broader transportation coverage.

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label "eccentric" ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two sources of different nature: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals' fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of "likes". Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one's own opinions and facilitates generation of eccentric ideas at a collective level.

Public memories of significant events shared within societies and groups have been conceptualized and studied as collective memory since the 1920s. Thanks to the recent advancement in digitization of public-domain knowledge and online user behaviors, collective memory has now become a subject of rigorous quantitative investigation using large-scale empirical data. Earlier studies, however, typically considered only one dynamical process applied to data obtained in just one specific event category. Here we propose a two-phase mathematical model of collective memory decay that combines exponential and power-law phases, which represent fast (linear) and slow (nonlinear) decay dynamics, respectively. We applied the proposed model to the Wikipedia page view data for articles on significant events in five categories: earthquakes, deaths of notable persons, aviation accidents, mass murder incidents, and terrorist attacks. Results showed that the proposed two-phase model compared favorably with other existing models of collective memory decay in most of the event categories. The estimated model parameters were found to be similar across all the event categories. The proposed model also allowed for detection of a dynamical switching point when the dominant decay dynamics exhibit a phase shift from exponential to power-law. Such decay phase shifts typically occurred about 10 to 11 days after the peak in all of the five event categories.

The ongoing COVID-19 pandemic has inflicted tremendous economic and societal losses. In the absence of pharmaceutical interventions, the population behavioral response, including situational awareness and adherence to non-pharmaceutical intervention policies, has a significant impact on contagion dynamics. Game-theoretic models have been used to reproduce the concurrent evolution of behavioral responses and disease contagion, and social networks are critical platforms on which behavior imitation between social contacts, even dispersed in distant communities, takes place. Such joint contagion dynamics has not been sufficiently explored, which poses a challenge for policies aimed at containing the infection. In this study, we present a multi-layer network model to study contagion dynamics and behavioral adaptation. It comprises two physical layers that mimic the two solitary communities, and one social layer that encapsulates the social influence of agents from these two communities. Moreover, we adopt high-order interactions in the form of simplicial complexes on the social influence layer to delineate the behavior imitation of individual agents. This model offers a novel platform to articulate the interaction between physically isolated communities and the ensuing coevolution of behavioral change and spreading dynamics. The analytical insights harnessed therefrom provide compelling guidelines on coordinated policy design to enhance the preparedness for future pandemics.

Leadership as a social influence process has always involved a complex set of phenomena that demands an interdisciplinary lens. Leadership scholarship has now entered into a digital era. In a digital era, the overall phenomenon is changing, as are the tools through which we study it, demanding a new “lens” through which we view leadership. Yet, this raises the question, to what extent is leadership different in a digital era? In acknowledgement of this trend, a special issue was commissioned at The Leadership Quarterly that sought to stimulate the imagination of leadership scholars and practitioners. In the current work, we begin with a brief review of who, what, when, where and why of digital leadership. We cover leadership in informal contexts (e.g., social media), generalization from face-to-face to virtual contexts, computational modeling, the leveraging of technology (e.g., machine learning; Big Data), as well methodological how-to guides. We then plot a path forward for leadership scholars in the dawn of the digital era.

We formulated and analyzed a set of partial integro-differential equations that capture the dynamics of our adaptive network model of social fragmentation involving behavioral diversity of agents. Previous results showed that, if the agents’ cultural tolerance levels were diversified, the social network could remain connected while maintaining cultural diversity. Here we converted the original agent-based model into a continuous equation-based one so we can gain more theoretical insight into the model dynamics. We restricted the node states to 1-D continuous values and assumed the network size was very large. As a result, we represented the whole system as a set of partial integro-differential equations about two continuous functions: population density and connection density. These functions are defined over both the state and the cultural tolerance of nodes. We conducted numerical integration of the developed equations using a custom-made integrator implemented in Julia. The results obtained were consistent with the simulations of the original agent-based adaptive social network model we previously reported, confirming the robustness of the original finding. Specifically, when the variance of cultural tolerance d is large enough, the population with low d maintains the original clusters of cultures/opinions, while the one with high d tends to come to the center and con-nect culturally distant groups. Parameter dependence of the model behavior was also revealed through systematic numerical experiments.

The global pandemic of COVID-19 over the last 2.5 years have produced an enormous amount of epidemic/public health datasets, which may also be useful for studying the underlying structure of our globally connected world. Here we used the Johns Hopkins University COVID-19 dataset to construct a correlation network of countries/regions and studied its global community structure. Specifically, we selected countries/regions that had at least 100,000 cumulative positive cases from the dataset and generated a 7-day moving average time series of new positive cases reported for each country/region. We then calculated a time series of daily change exponents by taking the day-to-day difference in log of the number of new positive cases. We constructed a correlation network by connecting countries/regions that had positive correlations in their daily change exponent time series using their Pearson correlation coefficient as the edge weight. Applying the modularity maximization method revealed that there were three major communities: (1) Mainly Europe + North America + Southeast Asia that showed similar six-peak patterns during the pandemic, (2) mainly Near/Middle East + Central/South Asia + Central/South America that loosely followed Community 1 but had a notable increase of activities because of the Delta variant and was later impacted significantly by the Omicron variant, and (3) mainly Africa + Central/East Canada + Australia that did not have much activities until a huge spike was caused by the Omicron variant. These three communities were robustly detected under varied settings. Constructing a 3D “phase space” by using the median curves in those three communities for x-y-z coordinates generated an effective summary trajectory of how the global pandemic progressed.

Social fragmentation transition is a transition of social states between many disconnected communities with distinct opinions and a well-connected single network with homogeneous opinions. This is a timely research topic with high relevance to various current societal issues. We had previously studied this problem using numerical simulations of adaptive social network models and found that two individual behavioral traits, homophily and attention to novelty, had the most statistically significant impact on the outcomes of social network evolution. However, our previous study was limited in terms of the range of parameter values examined, and possible interactions between multiple behavioral traits were largely ignored. In this study, we conducted a substantially larger-scale parameter sweep numerical experiment of the same model with expanded parameter ranges by an order of magnitude in each parameter dimension, resulting in a total of 116,640 simulation runs. To capture nontrivial interactions among behavioral parameters, we modeled and visualized the dependence of outcome measures on the model parameters using artificial neural networks. Results show that, while the competition between homophily and attention to novelty is still the primary determinant of social fragmentation, another transition plane emerges when individuals have strong social conformity behavior, which was not previously known. This implies that social fragmentation transition can also occur in the homophily-social conformity trade-off, the two behavioral traits that have very similar microscopic individual-level effects but produce very different macroscopic collective-level outcomes, illustrating the nontrivial macroscopic dynamics of complex collective systems.

The relationship between size and performance of collaborative human small groups has been studied broadly across management, psychology, economics, sociology, and engineering disciplines. However, empirical research findings on this question remain equivocal. Many of the earlier studies centered on empirical human-subject experiments, which inevitably involved many confounding factors. To obtain more theory-driven mechanistic explanations of the linkage between group size and performance, we developed an agent-based simulation model that describes the complex process of collaborative group decision-making on problem-solving tasks. To find better solutions to a problem with given complexity, these agents repeatedly explore and share solution candidates, evaluate and respond to the solutions proposed by others, and update their understanding of the problem by conducting individual local search and incorporating others' proposals. Our results showed that under a condition of ineffective information sharing, group size was negatively related to group performance at the beginning of discussion across each level of problem complexity (i.e., low, medium, and high). However, in the long run, larger groups outperformed smaller groups for the problem with medium complexity and equally well for the problem with low complexity because larger groups developed higher solution diversity. For the problem with high complexity, the higher solution diversity led to more disagreements which in turn hindered larger groups' collaborative problem-solving ability. Our results also suggested that, in small collaborative team settings, effective information sharing can significantly improve group performance for groups of any size, especially for larger groups. This model provides a unified, mechanistic explanation of the conflicting observations reported in the existing empirical literature.

The concept of attractors is considered critical in the study of dynamical systems as they represent the set of states that a system gravitates toward. However, it is generally difficult to analyze attractors in complex systems due to multiple reasons including chaos, high-dimensionality, and stochasticity. This paper explores a novel approach to analyzing attractors in complex systems by utilizing networks to represent phase spaces. We accomplish this by discretizing phase space and defining node associations with attractors by finding sink strongly connected components (SSCCs) within these networks. Moreover, the network representation of phase space facilitates the use of well-established techniques of network analysis to study the phase space of a complex system. We show the latter by introducing a new node-based metric called attractivity which can be used in conjunction with the SSCC as they are highly correlated. We demonstrate the proposed method by applying it to several chaotic dynamical systems and a large-scale agent-based social simulation model.

BACKGROUND: Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose. AIMS: Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables. METHODS: Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms. RESULTS: Machine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H2O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain. CONCLUSIONS: These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed.

Over the past decades, the application of Rasch measurement in language assessment has gradually increased. In the present study, we coded 215 papers using Rasch measurement published in 21 applied linguistics journals for multiple features. We found that seven Rasch models and 23 software packages were adopted in these papers, with many-facet Rasch measurement (n = 100) and Facets (n = 113) being the most frequently used Rasch model and software, respectively. Significant differences were detected between the number of papers that applied Rasch measurement to different language skills and components, with writing (n = 63) and grammar (n = 12) being the most and least frequently investigated, respectively. In addition, significant differences were found between the number of papers reporting person separation (n = 73, not reported: n = 142) and item separation (n = 59, not reported: n = 156) and those that did not. An alarming finding was how few papers reported unidimensionality check (n = 57 vs 158) and local independence (n = 19 vs 196). Finally, a multilayer network analysis revealed that research involving Rasch measurement has created two major discrete communities of practice (clusters), which can be characterized by features such as language skills, the Rasch models used, and the reporting of item reliability/separation vs person reliability/separation. Cluster 1 was accordingly labelled the production and performance cluster, whereas cluster 2 was labelled the perception and language elements cluster. Guidelines and recommendations for analyzing unidimensionality, local independence, data-to-model fit, and reliability in Rasch model analysis are proposed.

The El Farol Bar problem highlights the issue of bounded rationality through a coordination problem where agents must decide individually whether or not to attend a bar without prior communication. Each agent is provided a set of attendance predictors (or decision-making strategies) and uses the previous bar attendances to guess bar attendance for a given week to determine if the bar is worth attending. We previously showed how the distribution of used strategies among the population settles into an attractor by using a spatial phase space. However, this approach was limited as it required N - 1 dimensions to fully visualize the phase space of the problem, where N is the number of strategies available. Here we propose a new approach to phase space visualization and analysis by converting the strategy dynamics into a state transition network centered on strategy distributions. The resulting weighted, directed network gives a clearer representation of the strategy dynamics once we define an attractor of the strategy phase space as a sink-strongly connected component. This enables us to study the resulting network to draw conclusions about the performance of the different strategies. We find that this approach not only is applicable to the El Farol Bar problem, but also addresses the dimensionality issue and is theoretically applicable to a wide variety of discretized complex systems.

Cellular automata (CA) have been lauded for their ability to generate complex global patterns from simple local rules. The late English mathematician, John Horton Conway, developed his illustrious Game of Life (Life) CA in 1970, which has since remained one of the most quintessential CA constructions-capable of producing a myriad of complex dynamic patterns and computational universality. Life and several other Life-like rules have been classified in the same group of aesthetically and dynamically interesting CA rules characterized by their complex behaviors. However, a rigorous quantitative comparison among similarly classified Life-like rules has not yet been fully established. Here we show that Life is capable of maintaining as much complexity as similar rules while remaining the most parsimonious. In other words, Life contains a consistent amount of complexity throughout its evolution, with the least number of rule conditions compared to other Life-like rules. We also found that the complexity of higher density Life-like rules, which themselves contain the Life rule as a subset, form a distinct concave density-complexity relationship whereby an optimal complexity candidate is proposed. Our results also support the notion that Life functions as the basic ingredient for cultivating the balance between structure and randomness to maintain complexity in 2D CA for low- and high-density regimes, especially over many iterations. This work highlights the genius of John Horton Conway and serves as a testament to his timeless marvel, which is referred to simply as: Life.

Many temporal networks exhibit multiple system states, such as weekday and
weekend patterns in social contact networks. The detection of such distinct
states in temporal network data has recently been explored as it helps reveal
underlying dynamical processes. A commonly used method is network aggregation
over a time window, which aggregates a subsequence of multiple network
snapshots into one static network. This method, however, necessarily discards
temporal dynamics within the time window. Here we develop a new method for
detecting dynamic states in temporal networks using information regarding the
timeline of contacts between each pair of nodes. We apply a similarity measure
informed by the techniques of processing time series and community detection to
sequentially discompose a given temporal network into multiple dynamic states
(including repeated ones). Experiments with empirical temporal network data
demonstrated that our method outperformed the conventional approach using
simple network aggregation in revealing interpretable system states. In
addition, our method allows users to analyze hierarchical temporal structures
and to uncover dynamic state at different spatial/temporal resolutions.

The large, positive correlation between speaking time and leader emergence is well-established. As such, some authors have argued for a “babble hypothesis” of leadership, suggesting that only the quantity of speaking, not its quality, determines leader emergence. However, previous tests of this notion may have been problematic. Some studies have asserted a causal effect of speaking time on leader emergence based on experimental studies, but have limited participant communication, access to reliable information, or both. Other studies have used more ecologically valid designs, but have not always controlled for relevant participant traits or roles, suggesting potential endogeneity effects. Testing the babble hypothesis thus requires a study that is both ecologically valid and supports strong inference. The current study fills that gap and finds that speaking time retains its direct effect on leader emergence when accounting for intelligence, personality, gender, and the endogeneity of speaking time.

OBJECTIVE: Anhedonia, traditionally defined as a diminished capacity for pleasure, is a core symptom of schizophrenia (SZ). However, modern empirical evidence indicates that hedonic capacity may be intact in SZ and anhedonia may be better conceptualized as an abnormality in the temporal dynamics of emotion. METHOD: To test this theory, the current study used ecological momentary assessment (EMA) to examine whether abnormalities in one aspect of the temporal dynamics of emotion, sustained reward responsiveness, were associated with anhedonia. Two experiments were conducted in outpatients diagnosed with SZ (n = 28; n = 102) and healthy controls (n = 28; n = 71) who completed EMA reports of emotional experience at multiple time points in the day over the course of several days. Markov chain analyses were applied to the EMA data to evaluate stochastic dynamic changes in emotional states to determine processes underlying failures in sustained reward responsiveness. RESULTS: In both studies, Markov models indicated that SZ had deficits in the ability to sustain positive emotion over time, which resulted from failures in augmentation (ie, the ability to maintain or increase the intensity of positive emotion from time t to t+1) and diminution (ie, when emotions at time t+1 are opposite in valence from emotions at time t, resulting in a decrease in the intensity of positive emotion over time). Furthermore, in both studies, augmentation deficits were associated with anhedonia. CONCLUSIONS: These computational findings clarify how abnormalities in the temporal dynamics of emotion contribute to anhedonia.

Brian Arthur's El Farol bar model of bounded rationality provides a simple computer model of decision making in a complex, dynamic, and self-organized environment. Can systems thinking provide a viable alternative strategy to traditional methods for dealing with these types of problems? Nine different agents, designed from both traditional and systems perspectives, compete in fifteen variants of the El Farol environment and their performance in 4 categories-Winner, Top Performers, Competitive, and Vulnerable-is compared. We show that systems thinking is a competitive strategy that is, at least, on par with traditional strategies and may be less vulnerable to elimination or ruin. However, there are two consequential elements that emerge. First, all strategies have some environments where they succeed and others where they fail. Second, as the population of practitioners adopts these adaptive, systems-based strategies, the environment exhibits new behaviors with a new set of unintended consequences.

Objective: Anhedonia, traditionally defined as a diminished capacity for pleasure, is a core symptom of schizophrenia (SZ). However, modern empirical evidence indicates that hedonic capacity may be intact in SZ and anhedonia may be better conceptualized as an abnormality in the temporal dynamics of emotion.Method: To test this theory, the current study used ecological momentary assessment (EMA) to examine whether abnormalities in one aspect of the temporal dynamics of emotion, sustained reward responsiveness, were associated with anhedonia. Two experiments were conducted in outpatients diagnosed with SZ (n = 28; n = 102) and healthy controls (n = 28; n = 71) who completed EMA reports of emotional experience at multiple time points in the day over the course of several days. Markov chain analyses were applied to the EMA data to evaluate stochastic dynamic changes in emotional states to determine processes underlying failures in sustained reward responsiveness.Results: In both studies, Markov models indicated that SZ had deficits in the ability to sustain positive emotion over time, which resulted from failures in augmentation (ie, the ability to maintain or increase the intensity of positive emotion from time t to t+1) and diminution (ie, when emotions at time t+1 are opposite in valence from emotions at time t, resulting in a decrease in the intensity of positive emotion over time). Furthermore, in both studies, augmentation deficits were associated with anhedonia.Conclusions: These computational findings clarify how abnormalities in the temporal dynamics of emotion contribute to anhedonia.

Gene regulatory network (GRN)-based morphogenetic models have recently gained an increasing attention. However, the relationship between microscopic properties of intracellular GRNs and macroscopic properties of morphogenetic systems has not been fully understood yet. Here, we propose a theoretical morphogenetic model representing an aggregation of cells, and reveal the relationship between criticality of GRNs and morphogenetic pattern formation. In our model, the positions of the cells are determined by spring-mass-damper kinetics. Each cell has an identical Kauffman's NK random Boolean network (RBN) as its GRN. We varied the properties of GRNs from ordered, through critical, to chaotic by adjusting node in-degree K. We randomly assigned four cell fates to the attractors of RBNs for cellular behaviors. By comparing diverse morphologies generated in our morphogenetic systems, we investigated what the role of the criticality of GRNs is in forming morphologies. We found that nontrivial spatial patterns were generated most frequently when GRNs were at criticality. Our finding indicates that the criticality of GRNs facilitates the formation of nontrivial morphologies in GRN-based morphogenetic systems.

A recent conceptual development in schizophrenia is to view its manifestations as interactive networks rather than individual symptoms. Negative symptoms, which are associated with poor functional outcome and reduced rates of recovery, represent a critical need in schizophrenia therapeutics. MIN101 (roluperidone), a compound in development, demonstrated efficacy in the treatment of negative symptoms in schizophrenia. However, it is unclear how the drug achieved its effect from a network perspective. The current study evaluated the efficacy of roluperidone from a network perspective. In this randomized clinical trial, participants with schizophrenia and moderate to severe negative symptoms were randomly assigned to roluperidone 32 mg (n = 78), 64 mg (n = 83), or placebo (N = 83). Macroscopic network properties were evaluated to determine whether roluperidone altered the overall density of the interconnections among symptoms. Microscopic properties were evaluated to examine which individual symptoms were most influential (ie, interconnected) on other symptoms in the network and are responsible for successful treatment effects. Participants receiving roluperidone did not differ from those randomized to placebo on macroscopic properties. However, microscopic properties (degree and closeness centrality) indicated that avolition was highly central in patients receiving placebo and that roluperidone reduced this level of centrality. These findings suggest that decoupling the influence of motivational processes from other negative symptom domains is essential for producing global improvements. The search for pathophysiological mechanisms and targeted treatment development should be focused on avolition, with the expectation of improvement in the entire constellation of negative symptoms if avolition is effectively treated.

We propose a new polynomial-time deterministic algorithm that produces an approximated solution for the travelling salesperson problem. The proposed algorithm ranks cities based on their priorities calculated using a power function of means and standard deviations of their distances from other cities and then connects the cities to their neighbours in the order of their priorities. When connecting a city, a neighbour is selected based on their neighbours' priorities calculated as another power function that additionally includes their distance from the focal city to be connected. This repeats until all the cities are connected into a single loop. The time complexity of the proposed algorithm is (Formula presented.), where n is the number of cities. Numerical evaluation shows that, despite its simplicity, the proposed algorithm produces shorter tours with less time complexity than other conventional tour construction heuristics. The proposed algorithm can be used by itself or as an initial tour generator for other more complex heuristic optimisation algorithms.

Today's society faces widening disagreement and conflicts among constituents with incompatible views. Escalated views and opinions are seen not only in radical ideology or extremism but also in many other scenes of our everyday life. Here we show that widening disagreement among groups may be linked to the advancement of information communication technology by analyzing a mathematical model of population dynamics in a continuous opinion space. We adopted the interaction kernel approach to model enhancement of people's information-gathering ability and introduced a generalized nonlocal gradient as individuals' perception kernel. We found that the characteristic distance between population peaks becomes greater as the wider range of opinions becomes available to individuals or the more attention is attracted to opinions distant from theirs. These findings may provide a possible explanation for why disagreement is growing in today's increasingly interconnected society, without attributing its cause only to specific individuals or events.

Effective teamwork in an initially leaderless group requires a high level of collective leadership emerging from dynamic interactions among group members. Leader emergence is a crucial topic in collective leadership, yet it is challenging to investigate as the problem context is typically highly complex and dynamic. Here, we explore leadership emergence and leadership perception by means of computational simulations whose assumptions and parameters were informed by empirical research and human-subject experiments. Our agent-based model describes the process of group planning. Each agent is assigned with three key attributes: talkativeness, intelligence, and credibility. An agent can propose a suggestion to modify the group plan as a speaker or respond and evaluate others' suggestions and leadership as a listener. Simulation results suggested that agents with high values of talkativeness, intelligence, and credibility tended to be perceived as leaders by their peers. Results also showed that talkativeness may be the most significant and instantaneous predictor for leader emergence of the three investigated attributes: talkativeness, intelligence, and credibility. In terms of group performance, smaller groups may outperform larger groups regarding their problem-solving ability in the beginning, but their performance tends to be of no significant difference in a long run. These results match the empirical literature and offer a mechanistic, operationalized description of the collective leadership processes.

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

We present a theoretical framework that mathematically formulates the evolutionary dynamics of organism-environment couplings using graph product multilayer networks, i.e., networks obtained by “multiplying” factor networks using some graph product operator. In this framework, one factor network represents different options of environments and their mutual physical reachability, and another factor network represents possible types of organisms and their mutual evolutionary reachability. The organism-environment coupling space is given by a Cartesian product of these two factor networks, and the nodes of the product network represent specific organism-environment combinations. We studied a simple evolutionary model using a reaction-diffusion equation on this organism-environment coupling space. We numerically calculated correlations between the inherent fitness of organisms and the actual average fitness obtained from the graph product-based evolutionary model, varying the spatial diffusion rate while keeping the type diffusion rate small. Results demonstrated that, when the spatial diffusion is sufficiently slow, the correlation between inherent and actual fitnesses drops significantly, where it is no longer valid to assume that fitness can be attributed only to organisms.

One of the research questions in ALife that could contribute greatly to social sustainability issues is how dynamic meta-states of a complex system may be sustained through continual adaptive changes, or suppleness (Bedau, 1998). The idea of sustainability by suppleness is fundamentally different from conventional ideas of sustainability by robustness or resilience, and it is directly linked to open-endedness, a topic that has recently attracted significant attention in the ALife community (Taylor et al., 2016). Understanding and implementing mechanisms of suppleness and open-endedness may provide novel perspectives of many of today's socio-economic, socio-ecological and socio-technological problems that call for new strategies to cope with inevitable environmental/contextual changes. This short essay provides a non-exhaustive list of research questions on this topic and encourages ALife researchers to play a leading role in this interdisciplinary collaboration endeavor.

This research proposes a novel technique for distributed control and optimization of the networked systems considering the uncertainties associated with internal complex dynamics and external interactions with the environment. The proposed technique applies a distributed multi-agent framework that minimizes the overall objective of the system subject to the limitations on the shared resources. In this framework, each agent tries to optimize its decisions and improve the learning strategy based on artificial neural networks (ANN) without having access to the statistical distributions of the involved parameters. Comprehensive experiments are implemented to investigate the effects of the learning mechanism and the level of uncertainties. The efficiency of the technique is tested by comparing the proposed technique with the existing traditional network optimization techniques. The proposed technique can be utilized in a variety of applications such as min cost flow problems, disease propagation models, and distributed controls over man-made networks such as supply chain and power grid.

Under preferential attachment (PA) network growth models late arrivals are at
a disadvantage with regard to their final degrees. Previous extensions of PA
have addressed this deficiency by either adding the notion of node fitness to
PA, usually drawn from some fitness score distributions, or by using fitness
alone to control attachment. Here we introduce a new dynamical approach to
address late arrivals by adding a recent-degree-change bias to PA so that nodes
with higher relative degree change in temporal proximity to an arriving node
get an attachment probability boost. In other words, if PA describes a
rich-get-richer mechanism, and fitness-based approaches describe
good-get-richer mechanisms, then our model can be characterized as a
hot-get-richer mechanism, where hotness is determined by the rate of degree
change over some recent past. The proposed model produces much later
high-ranking nodes than the PA model and, under certain parameters, produces
networks with structure similar to PA networks.

Under preferential attachment (PA) network growth models late arrivals are at a disadvantage with regard to their final degrees. Previous extensions of PA have addressed this deficiency by either adding the notion of node fitness to PA, usually drawn from some fitness score distributions, or by using fitness alone to control attachment. Here we introduce a new dynamical approach to address late arrivals by adding a recent-degree-change bias to PA so that nodes with higher relative degree change in temporal proximity to an arriving node get an attachment probability boost. In other words, if PA describes a rich-get-richer mechanism, and fitness-based approaches describe good-get-richer mechanisms, then our model can be characterized as a hot-get-richer mechanism, where hotness is determined by the rate of degree change over some recent past. The proposed model produces much later high-ranking nodes than the PA model and, under certain parameters, produces networks with structure similar to PA networks.

Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.

Network analysis was used to examine how densely interconnected individual negative symptom domains are, whether some domains are more central than others, and whether sex influenced network structure. Participants included outpatients with schizophrenia (SZ; n = 201), a bipolar disorder (BD; n = 46) clinical comparison group, and healthy controls (CN; n = 27) who were rated on the Brief Negative Symptom Scale. The mutual information measure was used to construct negative symptom networks. Groups were compared on macroscopic network properties to evaluate overall network connectedness, and microscopic properties to determine which domains were most central. Macroscopic analyses indicated that patients with SZ had a less densely connected negative symptom network than BD or CN groups, and that males with SZ had less densely connected networks than females. Microscopic analyses indicated that alogia and avolition were most central in the SZ group, whereas anhedonia was most central in BD and CN groups. In addition, blunted affect, alogia, and asociality were most central in females with SZ, and alogia and avolition were most central in males with SZ. These findings suggest that negative symptoms may be highly treatment resistant in SZ because they are not very densely connected. Less densely connected networks may make treatments less likely to achieve global reductions in negative symptoms because individual domains function in isolation with little interaction. Sex differences in centralities suggest that the search for pathophysiological mechanisms and targeted treatment development should be focused on different sets of symptoms in males and females.

Prior studies using exploratory factor analysis provide evidence that negative symptoms are best conceptualized as 2 dimensions reflecting diminished motivation and expression. However, the 2-dimensional model has yet to be evaluated using more complex mathematical techniques capable of testing structure. In the current study, network analysis was applied to evaluate the latent structure of negative symptoms using a community-detection algorithm. Two studies were conducted that included outpatients with schizophrenia (SZ; Study 1: n = 201; Study 2: n = 912) who were rated on the Brief Negative Symptom Scale (BNSS). In both studies, network analysis indicated that the 13 BNSS items divided into 6 negative symptom domains consisting of anhedonia, avolition, asociality, blunted affect, alogia, and lack of normal distress. Separation of these domains was statistically significant with reference to a null model of randomized networks. There has been a recent trend toward conceptualizing the latent structure of negative symptoms in relation to 2 distinct dimensions reflecting diminished expression and motivation. However, the current results obtained using network analysis suggest that the 2-dimensional conceptualization is not complex enough to capture the nature of the negative symptom construct. Similar to recent confirmatory factor analysis studies, network analysis revealed that the latent structure of negative symptom is best conceptualized in relation to the 5 domains identified in the 2005 National Institute of Mental Health consensus development conference (anhedonia, avolition, asociality, blunted affect, and alogia) and potentially a sixth domain consisting of lack of normal distress. Findings have implications for identifying pathophysiological mechanisms and targeted treatments.

Ecological momentary assessment (EMA) was used to examine emotional reactivity and regulation abnormalities during the presence and absence of psychosis. Participants included 28 outpatients with schizophrenia or schizoaffective disorder (SZ) who completed 6 days of EMA. Mathematical models were applied to the EMA data to evaluate stochastic dynamic changes in emotional state and determine how the presence of psychosis influenced the interaction between emotional reactivity and regulation processes across time. Markov chain analysis indicated that although SZ tried to implement emotion regulation strategies frequently during psychotic experiences, those attempts were ineffective at reducing negative emotion from one time point to the next. Network analysis indicated that patients who were less effective at regulating their emotions during psychotic experiences had more dense connections among individual emotions. Findings indicate that psychotic experiences are associated with abnormally strong connections among discrete emotional states that are difficult to regulate despite efforts to do so.

Open-ended evolution requires unbounded possibilities that evolving entities can explore. The cardinality of a set of those possibilities thus has a significant implication for the open-endedness of evolution. I propose that facilitating formation of higher-order entities is a generalizable, effective way to cause a cardinality leap in the set of possibilities that promotes open-endedness. I demonstrate this idea with a simple, proof-of-concept toy model called Hash Chemistry that uses a hash function as a fitness evaluator of evolving entities of any size or order. Simulation results showed that the cumulative number of unique replicating entities that appeared in evolution increased almost linearly along time without an apparent bound, demonstrating the effectiveness of the proposed cardinality leap. It was also observed that the number of individual entities involved in a single replication event gradually increased over time, indicating evolutionary appearance of higher-order entities. Moreover, these behaviors were not observed in control experiments in which fitness evaluators were replaced by random number generators. This strongly suggests that the dynamics observed in Hash Chemistry were indeed evolutionary behaviors driven by selection and adaptation taking place at multiple scales.

Collective design and innovation are crucial in organizations. To investigate how the collective design and innovation processes would be affected by the diversity of knowledge and background of collective individual members, we conducted three collaborative design task experiments which involved nearly 300 participants who worked together anonymously in a social network structure using a custom-made computer-mediated collaboration platform. We compared the idea generation activity among three different background distribution conditions (clustered, random, and dispersed) with the help of the “doc2vec” text representation machine learning algorithm. We also developed a new method called “Idea Geography” to visualize the idea utility terrain on a 2D problem domain. The results showed that groups with random background allocation tended to produce the best design idea with the highest utility values. It was also suggested that the diversity of participants’ backgrounds distribution on the network might interact with each other to affect the diversity of ideas generated. The proposed idea geography successfully visualized that the collective design processes did find the high utility area through exploration and exploitation in collaborative work.

Open-endedness is often considered a prerequisite property of the whole evolutionary system and its dynamical behaviors. In the actual history of evolution on Earth, however, there are many examples showing that open-endedness is rather a consequence of evolution. We suggest that this view, which we call evolved open-endedness (EOE), be incorporated more into research on open-ended evolution. This view should allow for systematic investigation of more nuanced, more concrete research questions about open-endedness and its relationship with adaptation and sustainability.

BACKGROUND: Prior studies using self-report questionnaires and laboratory-based methods suggest that schizophrenia is characterized by abnormalities in emotion regulation (i.e. using strategies to increase or decrease the frequency, duration, or intensity of negative emotion). However, it is unclear whether these abnormalities reflect poor emotion regulation effort or adequate effort, but limited effectiveness. It is also unclear whether dysfunction results primarily from one of the three stages of the emotion regulation process: identification, selection, or implementation. METHOD: The current study used ecological momentary assessment (EMA) to address these questions in the context of everyday activities. Participants included 28 outpatients diagnosed with schizophrenia (SZ) and 28 demographically matched healthy controls (CN) who completed 6 days of EMA reports of in-the-moment emotional experience, emotion regulation strategy use, and context. RESULTS: Results indicated that SZ demonstrated adequate emotion regulation effort, but poor effectiveness. Abnormalities were observed at each of the three stages of the emotion regulation process. At the identification stage, SZ initiated emotion regulation efforts at a lower threshold of negative emotion intensity. At the selection stage, SZ selected more strategies than CN and strategies attempted were less contextually appropriate. At the implementation stage, moderate to high levels of effort were ineffective at decreasing negative emotion. CONCLUSIONS: Findings suggest that although SZ attempt to control their emotions using various strategies, often applying more effort than CN, these efforts are unsuccessful; emotion regulation abnormalities may result from difficulties at the identification, selection, and implementation stages.

Cooperation is ubiquitous at every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. A recently proposed numerical metric, average gradient of selection (AGoS), a useful tool for interpreting and visualizing evolutionary dynamics on networks, allows simulation results to be visualized on a one-dimensional phase space. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we extend AGoS so that it can analyze the evolution of cooperation where mutation may alter strategies of individuals on networks. We show that our extended AGoS correctly visualizes the final states of cooperation with mutation in the individual-based simulations. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the payoff functions, fraction of cooperators, and network structures. Moreover, we found that scale-free networks are the most vulnerable to mutation and thus the dynamics of cooperation are altered from bistability to coexistence on those networks, undergoing an imperfect pitchfork bifurcation.

We studied the long-term dynamics of evolutionary Swarm Chemistry by
extending the simulation length ten-fold compared to earlier work and by
developing and using a new automated object harvesting method. Both macroscopic
dynamics and microscopic object features were characterized and tracked using
several measures. Results showed that the evolutionary dynamics tended to
settle down into a stable state after the initial transient period, and that
the extent of environmental perturbations also affected the evolutionary trends
substantially. In the meantime, the automated harvesting method successfully
produced a huge collection of spontaneously evolved objects, revealing the
system's autonomous creativity at an unprecedented scale.

Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single-cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has not been fully explored. Here we aim at revealing a potential role of criticality of GRNs in morphogenesis, which is hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical intracellular GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogeneous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings correspond to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.

This paper aims to establish theoretical foundations of graph product
multilayer networks (GPMNs), a family of multilayer networks that can be
obtained as a graph product of two or more factor networks. Cartesian, direct
(tensor), and strong product operators are considered, and then generalized. We
first describe mathematical relationships between GPMNs and their factor
networks regarding their degree/strength, adjacency, and Laplacian spectra, and
then show that those relationships can still hold for nonsimple and generalized
GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic
thresholds, modeling propagation in nontrivial space and time, and analyzing
higher-order properties of self-similar networks. Directions of future research
are also discussed.

Many living and non-living complex systems can be modeled and understood as
collective systems made of heterogeneous components that self-organize and
generate nontrivial morphological structures and behaviors. This chapter
presents a brief overview of our recent effort that investigated various
aspects of such morphogenetic collective systems. We first propose a
theoretical classification scheme that distinguishes four complexity levels of
morphogenetic collective systems based on the nature of their components and
interactions. We conducted a series of computational experiments using a
self-propelled particle swarm model to investigate the effects of (1)
heterogeneity of components, (2) differentiation/re-differentiation of
components, and (3) local information sharing among components, on the
self-organization of a collective system. Results showed that (a) heterogeneity
of components had a strong impact on the system's structure and behavior, (b)
dynamic differentiation/re-differentiation of components and local information
sharing helped the system maintain spatially adjacent, coherent organization,
(c) dynamic differentiation/re-differentiation contributed to the development
of more diverse structures and behaviors, and (d) stochastic re-differentiation
of components naturally realized a self-repair capability of self-organizing
morphologies. We also explored evolutionary methods to design novel
self-organizing patterns, using interactive evolutionary computation and
spontaneous evolution within an artificial ecosystem. These self-organizing
patterns were found to be remarkably robust against dimensional changes from 2D
to 3D, although evolution worked efficiently only in 2D settings.

Self-organization has been an important concept within a number of
disciplines, which Artificial Life (ALife) also has heavily utilized since its
inception. The term and its implications, however, are often confusing or
misinterpreted. In this work, we provide a mini-review of self-organization and
its relationship with ALife, aiming at initiating discussions on this important
topic with the interested audience. We first articulate some fundamental
aspects of self-organization, outline its usage, and review its applications to
ALife within its soft, hard, and wet domains. We also provide perspectives for
further research.

Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with psychosis, suggesting that antipsychotics achieve their effect by enhancing a number of central symptoms, which then facilitate reduction of other highly coupled symptoms in a network-like fashion.

Optical inverse multiplexing is a promising technology for high-rate data transmission and rapidly increased demand for capacity. To realize this technology, a key issue is the buffering to alleviate the delay among parallel routes. As described in this paper, delay time characteristics and wavelength increase of optical inverse multiplexing using optical parallel routes in WDM mesh networks are evaluated quantitatively for rapid capacity demand, comparison with the case of whether the existing route is still used or not.

We propose a novel computational method to extract information about
interactions among individuals with different behavioral states in a biological
collective from ordinary video recordings. Assuming that individuals are acting
as finite state machines, our method first detects discrete behavioral states
of those individuals and then constructs a model of their state transitions,
taking into account the positions and states of other individuals in the
vicinity. We have tested the proposed method through applications to two
real-world biological collectives: termites in an experimental setting and
human pedestrians in a university campus. For each application, a robust
tracking system was developed in-house, utilizing interactive human
intervention (for termite tracking) or online agent-based simulation (for
pedestrian tracking). In both cases, significant interactions were detected
between nearby individuals with different states, demonstrating the
effectiveness of the proposed method.

A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern. The complex nature of a stock market challenges us on making a reliable prediction of its future movements. In this paper, we aim at building a new method to forecast the future movements of Standard & Poor's 500 Index (S&P 500) by constructing time-series complex networks of S&P 500 underlying companies by connecting them with links whose weights are given by the mutual information of 60-min price movements of the pairs of the companies with the consecutive 5340 min price records. We showed that the changes in the strength distributions of the networks provide an important information on the network's future movements. We built several metrics using the strength distributions and network measurements such as centrality, and we combined the best two predictors by performing a linear combination. We found that the combined predictor and the changes in S&P 500 show a quadratic relationship, and it allows us to predict the amplitude of the one step future change in S&P 500. The result showed significant fluctuations in S&P 500 Index when the combined predictor was high. In terms of making the actual index predictions, we built ARIMA models with and without inclusion of network measurements, and compared the predictive power of them. We found that adding the network measurements into the ARIMA models improves the model accuracy. These findings are useful for financial market policy makers as an indicator based on which they can interfere with the markets before the markets make a drastic change, and for quantitative investors to improve their forecasting models.

This short article presents a summary of the NetSciEd (Network Science and
Education) initiative that aims to address the need for curricula, resources,
accessible materials, and tools for introducing K-12 students and the general
public to the concept of networks, a crucial framework in understanding
complexity. NetSciEd activities include (1) the NetSci High educational
outreach program (since 2010), which connects high school students and their
teachers with regional university research labs and provides them with the
opportunity to work on network science research projects; (2) the NetSciEd
symposium series (since 2012), which brings network science researchers and
educators together to discuss how network science can help and be integrated
into formal and informal education; and (3) the Network Literacy: Essential
Concepts and Core Ideas booklet (since 2014), which was created collaboratively
and subsequently translated into 18 languages by an extensive group of network
science researchers and educators worldwide.

It is well known that cooperation cannot be an evolutionarily stable strategy for a non-iterative game in a well-mixed population. In contrast, structured populations favor cooperation, since cooperators can benefit each other by forming local clusters. Previous studies have shown that scale-free networks strongly promote cooperation. However, little is known about the invasion mechanism of cooperation in scale-free networks. To study microscopic and macroscopic behaviors of cooperators' invasion, we conducted computational experiments on the evolution of cooperation in scale-free networks where, starting from all defectors, cooperators can spontaneously emerge by mutation. Since the evolutionary dynamics are influenced by the definition of fitness, we tested two commonly adopted fitness functions: accumulated payoff and average payoff. Simulation results show that cooperation is strongly enhanced with the accumulated payoff fitness compared to the average payoff fitness. However, the difference between the two functions decreases as the average degree increases. As the average degree increases, cooperation decreases for the accumulated payoff fitness, while it increases for the average payoff fitness. Moreover, for the average payoff fitness, low-degree nodes play a more important role in spreading cooperative strategies than for the accumulated payoff fitness.

Cooperation is ubiquitous in every level of living organisms. It is known
that spatial (network) structure is a viable mechanism for cooperation to
evolve. Until recently, it has been difficult to predict whether cooperation
can evolve at a network (population) level. To address this problem, Pinheiro
et al. proposed a numerical metric, called Average Gradient of Selection (AGoS)
in 2012. AGoS can characterize and forecast the evolutionary fate of
cooperation at a population level. However, stochastic mutation of strategies
was not considered in the analysis of AGoS. Here we analyzed the evolution of
cooperation using AGoS where mutation may occur to strategies of individuals in
networks. Our analyses revealed that mutation always has a negative effect on
the evolution of cooperation regardless of the fraction of cooperators and
network structures. Moreover, we found that mutation affects the fitness of
cooperation differently on different social network structures.

We propose a slightly revised Miller-Hagberg (MH) algorithm that efficiently
generates a random network from a given expected degree sequence. The revision
was to replace the approximated edge probability between a pair of nodes with a
combinatorically calculated edge probability that better captures the
likelihood of edge presence especially where edges are dense. The computational
complexity of this combinatorial MH algorithm is still in the same order as the
original one. We evaluated the proposed algorithm through several numerical
experiments. The results demonstrated that the proposed algorithm was
particularly good at accurately representing high-degree nodes in dense,
heterogeneous networks. This algorithm may be a useful alternative of other
more established network randomization methods, given that the data are
increasingly becoming larger and denser in today's network science research.

BACKGROUND: The interaction of several sociocultural and environmental factors during an epidemic crisis leads to behavioral responses that consequently make the crisis control a complex problem. METHODS: The system dynamics approach has been adopted to study the relationships between spread of disease, public attention, situational awareness, and community's response to the Ebola epidemic. RESULTS: In developing different simulation models to capture the trend of death and incidence data from the World Health Organization for the Ebola outbreak, the final model has the best fit to the historical trends. Results demonstrate that the increase of quarantining rate over time due to increase in situational awareness and performing safe burials had a significant impact on the control of epidemic. However, public attention did not play a significant role. CONCLUSION: The best fit to historical data are achieved when behavioral factors specific to West Africa like studying the Situational Awareness and Public Attention are included in the model. However, by ignoring the sociocultural factors, the model is not able to represent the reality; therefore, in the case of any epidemics, it is necessary that all the parties and community members find the most significant behavioral factors that can curb the epidemic.

Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby's dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection.

Calculating a product of multiple graphs has been studied in mathematics, engineering, computer science, and more recently in network science, particularly in the context of multilayer networks. One of the important questions to be addressed in this area is how to characterize spectral properties of a product graph using those of its factor graphs. While several such characterizations have already been obtained analytically (mostly for adjacency spectra), characterization of Laplacian spectra of direct product and strong product graphs has remained an open problem. Here we develop practical methods to estimate Laplacian spectra of direct and strong product graphs from spectral properties of their factor graphs using a few heuristic assumptions. Numerical experiments showed that the proposed methods produced reasonable estimation with percentage errors confined within a 10% range for most eigenvalues. (C) 2015 Elsevier B.V. All rights reserved.

This multi-level (individual and collective) study examines collective decision making as it relates to the performance metric of collective decision quality. A collectivistic leadership approach is used, as leaderless collectives engaged in decision making are inherently involved in collective leadership. A multi-level conceptual model for collective decision making is introduced, which incorporates leadership and collective intelligence. Using agent-based simulations and content-coded field study data, results from both methods suggest that there is a positive relationship between individual and collective intelligence, as well as a positive relationship between collective intelligence and collective decision quality. The implications of these and related findings for future collective level research bridging the fields of decision making, leadership, and collective intelligence are discussed. (C) 2016 Elsevier Inc. All rights reserved.

Describing the dynamics of a city is a crucial step to both understanding the human activity in urban environments and to planning and designing cities accordingly. Here, we describe the collective dynamics of New York City (NYC) and surrounding areas as seen through the lens of Twitter usage. In particular, we observe and quantify the patterns that emerge naturally from the hourly activities in different areas of NYC, and discuss how they can be used to understand the urban areas. Using a dataset that includes more than 6 million geolocated Twitter messages we construct a movie of the geographic density of tweets. We observe the diurnal heartbeat of the NYC area. The largest scale dynamics are the waking and sleeping cycle and commuting from residential communities to office areas in Manhattan. Hourly dynamics reflect the interplay of commuting, work and leisure, including whether people are preoccupied with other activities or actively using Twitter. Differences between weekday and weekend dynamics point to changes in when people wake and sleep, and engage in social activities. We show that by measuring the average distances to a central location one can quantify the weekly differences and the shift in behavior during weekends. We also identify locations and times of high Twitter activity that occur because of specific activities. These include early morning high levels of traffic as people arrive and wait at air transportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue of Liberty. We analyze the role of particular individuals where they have large impacts on overall Twitter activity. Our analysis points to the opportunity to develop insight into both geographic social dynamics and attention through social media analysis. (c) 2015 Wiley Periodicals, Inc. Complexity 21: 280-287, 2016

This multi-level (individual and collective) study examines collective decision making as it relates to the performance metric of collective decision quality. A collectivistic leadership approach is used, as leaderless collectives engaged in decision making are inherently involved in collective leadership. A multi-level conceptual model for collective decision making is introduced, which incorporates leadership and collective intelligence. Using agent-based simulations and content-coded field study data, results from both methods suggest that there is a positive relationship between individual and collective intelligence, as well as a positive relationship between collective intelligence and collective decision quality. The implications of these and related findings for future collective level research bridging the fields of decision making, leadership, and collective intelligence are discussed. (C) 2016 Elsevier Inc. All rights reserved.

Networks have become increasingly relevant to everyday life as human society
has become increasingly connected. Attaining a basic understanding of networks
has thus become a necessary form of literacy for people (and for youths in
particular). At the NetSci 2014 conference, we initiated a year-long process to
develop an educational resource that concisely summarizes essential concepts
about networks that can be used by anyone of school age or older. The process
involved several brainstorming sessions on one key question: "What should every
person living in the 21st century know about networks by the time he/she
finishes secondary education?" Different sessions reached diverse participants,
which included professional researchers in network science, educators, and
high-school students. The generated ideas were connected by the students to
construct a concept network. We examined community structure in the concept
network to group ideas into a set of important themes, which we refined through
discussion into seven essential concepts. The students played a major role in
this development process by providing insights and perspectives that were often
unrecognized by researchers and educators. The final result, "Network Literacy:
Essential Concepts and Core Ideas", is now available as a booklet in several
different languages from http://tinyurl.com/networkliteracy .

Describing the dynamics of a city is a crucial step to both understanding the human activity in urban environments and to planning and designing cities accordingly. Here, we describe the collective dynamics of New York City (NYC) and surrounding areas as seen through the lens of Twitter usage. In particular, we observe and quantify the patterns that emerge naturally from the hourly activities in different areas of NYC, and discuss how they can be used to understand the urban areas. Using a dataset that includes more than 6 million geolocated Twitter messages we construct a movie of the geographic density of tweets. We observe the diurnal heartbeat of the NYC area. The largest scale dynamics are the waking and sleeping cycle and commuting from residential communities to office areas in Manhattan. Hourly dynamics reflect the interplay of commuting, work and leisure, including whether people are preoccupied with other activities or actively using Twitter. Differences between weekday and weekend dynamics point to changes in when people wake and sleep, and engage in social activities. We show that by measuring the average distances to a central location one can quantify the weekly differences and the shift in behavior during weekends. We also identify locations and times of high Twitter activity that occur because of specific activities. These include early morning high levels of traffic as people arrive and wait at air transportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue of Liberty. We analyze the role of particular individuals where they have large impacts on overall Twitter activity. Our analysis points to the opportunity to develop insight into both geographic social dynamics and attention through social media analysis. (c) 2015 Wiley Periodicals, Inc. Complexity 21: 280-287, 2016

We propose a new polynomial-time deterministic algorithm that produces an
approximated solution for the traveling salesperson problem. The proposed
algorithm ranks cities based on their priorities calculated using a power
function of means and standard deviations of their distances from other cities
and then connects the cities to their neighbors in the order of their
priorities. When connecting a city, a neighbor is selected based on their
neighbors' priorities calculated as another power function that additionally
includes their distance from the focal city to be connected. This repeats until
all the cities are connected into a single loop. The time complexity of the
proposed algorithm is $O(n^2)$, where $n$ is the number of cities. Numerical
evaluation shows that, despite its simplicity, the proposed algorithm produces
shorter tours with less time complexity than other conventional tour
construction heuristics. The proposed algorithm can be used by itself or as an
initial tour generator for other more complex heuristic optimization
algorithms.

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex.

The nature of concept learning is a core question in cognitive science. Theories must account for the relative difficulty of acquiring different concepts by supervised learners. For a canonical set of six category types, two distinct orderings of classification difficulty have been found. One ordering, which we call paradigm-specific, occurs when adult human learners classify objects with easily distinguishable characteristics such as size, shape, and shading. The general order occurs in all other known cases: when adult humans classify objects with characteristics that are not readily distinguished (e.g., brightness, saturation, hue); for children and monkeys; and when categorization difficulty is extrapolated from errors in identification learning. The paradigm-specific order was found to be predictable mathematically by measuring the logical complexity of tasks, i.e., how concisely the solution can be represented by logical rules.
However, logical complexity explains only the paradigm-specific order but not the general order. Here we propose a new difficulty measurement, information complexity, that calculates the amount of uncertainty remaining when a subset of the dimensions are specified. This measurement is based on Shannon entropy. We show that, when the metric extracts minimal uncertainties, this new measurement predicts the paradigm-specific order for the canonical six category types, and when the metric extracts average uncertainties, this new measurement predicts the general order. Moreover, for learning category types beyond the canonical six, we find that the minimal-uncertainty formulation correctly predicts the paradigm-specific order as well or better than existing metrics (Boolean complexity and GIST) in most cases. (C) 2015 Elsevier Inc. All rights reserved.

Cooperation is ubiquitous in biological and social systems. Previous studies revealed that a preference toward similar appearance promotes cooperation, a phenomenon called tag-mediated cooperation or communitarian cooperation. This effect is enhanced when a spatial structure is incorporated, because space allows agents sharing an identical tag to regroup to form locally cooperative clusters. In spatially distributed settings, one can also consider migration of organisms, which has a potential to further promote evolution of cooperation by facilitating spatial clustering. However, it has not yet been considered in spatial tag-mediated cooperation models. Here we show, using computer simulations of a spatial model of evolutionary games with organismal migration, that tag-based segregation and homophilic cooperation arise for a wide range of parameters. In the meantime, our results also show another evolutionarily stable outcome, where a high level of heterophilic cooperation is maintained in spatially well-mixed patterns. We found that these two different forms of tag-mediated cooperation appear alternately as the parameter for temptation to defect is increased.

Calculating a product of multiple graphs has been studied in mathematics,
engineering, computer science, and more recently in network science,
particularly in the context of multilayer networks. One of the important
questions to be addressed in this area is how to characterize spectral
properties of a product graph using those of its factor graphs. While several
such characterizations have already been obtained analytically (mostly for
adjacency spectra), characterization of Laplacian spectra of direct product and
strong product graphs has remained an open problem. Here we develop practical
methods to estimate Laplacian spectra of direct and strong product graphs from
spectral properties of their factor graphs using a few heuristic assumptions.
Numerical experiments showed that the proposed methods produced reasonable
estimation with percentage errors confined within a +/-10% range for most
eigenvalues.

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex. (C) 2015 Elsevier Inc. All rights reserved.

The nature of concept learning is a core question in cognitive science. Theories must account for the relative difficulty of acquiring different concepts by supervised learners. For a canonical set of six category types, two distinct orderings of classification difficulty have been found. One ordering, which we call paradigm-specific, occurs when adult human learners classify objects with easily distinguishable characteristics such as size, shape, and shading. The general order occurs in all other known cases: when adult humans classify objects with characteristics that are not readily distinguished (e.g., brightness, saturation, hue); for children and monkeys; and when categorization difficulty is extrapolated from errors in identification learning. The paradigm-specific order was found to be predictable mathematically by measuring the logical complexity of tasks, i.e., how concisely the solution can be represented by logical rules.
However, logical complexity explains only the paradigm-specific order but not the general order. Here we propose a new difficulty measurement, information complexity, that calculates the amount of uncertainty remaining when a subset of the dimensions are specified. This measurement is based on Shannon entropy. We show that, when the metric extracts minimal uncertainties, this new measurement predicts the paradigm-specific order for the canonical six category types, and when the metric extracts average uncertainties, this new measurement predicts the general order. Moreover, for learning category types beyond the canonical six, we find that the minimal-uncertainty formulation correctly predicts the paradigm-specific order as well or better than existing metrics (Boolean complexity and GIST) in most cases. (C) 2015 Elsevier Inc. All rights reserved.

Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world's first open-participation research platformfor the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients' online social activities by measuring the numbers of "posts and views" and " helpful marks" each patient obtained. The patients' recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients' recovery. Furthermore, we re-collected the patients' recovery data two months after the first data collection. We found significant correlations between the patients' social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders.

Cooperation is ubiquitous in biological and social systems. Previous studies
revealed that a preference toward similar appearance promotes cooperation, a
phenomenon called tag-mediated cooperation or communitarian cooperation. This
effect is enhanced when a spatial structure is incorporated, because space
allows agents sharing an identical tag to regroup to form locally cooperative
clusters. In spatially distributed settings, one can also consider migration of
organisms, which has a potential to further promote evolution of cooperation by
facilitating spatial clustering. However, it has not yet been considered in
spatial tag-mediated cooperation models. Here we show, using computer
simulations of a spatial model of evolutionary games with organismal migration,
that tag-based segregation and homophilic cooperation arise for a wide range of
parameters. In the meantime, our results also show another evolutionarily
stable outcome, where a high level of heterophilic cooperation is maintained in
spatially well-mixed patterns. We found that these two different forms of
tag-mediated cooperation appear alternately as the parameter for temptation to
defect is increased.

We report a summary of our interdisciplinary research project "Evolutionary
Perspective on Collective Decision Making" that was conducted through close
collaboration between computational, organizational and social scientists at
Binghamton University. We redefined collective human decision making and
creativity as evolution of ecologies of ideas, where populations of ideas
evolve via continual applications of evolutionary operators such as
reproduction, recombination, mutation, selection, and migration of ideas, each
conducted by participating humans. Based on this evolutionary perspective, we
generated hypotheses about collective human decision making using agent-based
computer simulations. The hypotheses were then tested through several
experiments with real human subjects. Throughout this project, we utilized
evolutionary computation (EC) in non-traditional ways---(1) as a theoretical
framework for reinterpreting the dynamics of idea generation and selection, (2)
as a computational simulation model of collective human decision making
processes, and (3) as a research tool for collecting high-resolution
experimental data of actual collaborative design and decision making from human
subjects. We believe our work demonstrates untapped potential of EC for
interdisciplinary research involving human and social dynamics.

We study a mathematical model of social diffusion on a symmetric weighted network where individual nodes' states gradually assimilate to local social norms made by their neighbors' average states. Unlike physical diffusion, this process is not state conservational and thus the global state of the network (i.e., sum of node states) will drift. The asymptotic average node state will be the average of initial node states weighted by their strengths. Here we show that, while the global state is not conserved in this process, the inner product of strength and state vectors is conserved instead, and perfect positive correlation between node states and local averages of their self-neighbor strength ratios always results in upward (or at least neutral) global drift. We also show that the strength assortativity negatively affects the speed of homogenization. Based on these findings, we propose an adaptive link weight adjustment method to achieve the highest upward global drift by increasing the strength-state correlation. The effectiveness of the method was confirmed through numerical simulations and implications for real-world social applications are discussed.

We present NetSci High, our NSF-funded educational outreach program that
connects high school students who are underrepresented in STEM (Science
Technology Engineering and Mathematics), and their teachers, with regional
university research labs and provides them with the opportunity to work with
researchers and graduate students on team-based, year-long network science
research projects, culminating in a formal presentation at a network science
conference. This short paper reports the content and materials that we have
developed to date, including lesson plans and tools for introducing high school
students and teachers to network science; empirical evaluation data on the
effect of participation on students' motivation and interest in pursuing STEM
careers; the application of professional development materials for teachers
that are intended to encourage them to use network science concepts in their
lesson plans and curriculum; promoting district-level interest and engagement;
best practices gained from our experiences; and the future goals for this
project and its subsequent outgrowth.

Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world's first open-participation research platform for the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients' online social activities by measuring the numbers of "posts and views" and "helpful marks" each patient obtained. The patients' recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients' recovery. Furthermore, we re-collected the patients' recovery data two months after the first data collection. We found significant correlations between the patients' social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders.

The present study aims to build a classification model that discriminates between chronological ages of subjects based on resting-state electroencephalography (EEG) data collected from a community sample of 269 children aged 7 to 11. Specifically, spectral power densities in four classical frequency bands: Delta (0.5-3 Hz), Theta (4-7 Hz), Alpha (8-12 Hz) and Beta (14-25 Hz) were extracted for each electrode as features, and fed to three classification algorithms including logistic regression (LR), support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso). In addition, principal component analysis (PCA) was used to reduce the dimensions of the feature space. The results demonstrated that SVM and Lasso evidenced better performance (maximal accuracy = 80.68 +/- 2.01% by SVM and 77.82 +/- 2.11% by Lasso) when applied to original feature space, but LR yielded the best performance with PCA (80.72 +/- 1.73%). The accuracy of binary classification exhibited a decreasing trend with diminishing chronological gaps between the groups.

We derived the critical neighborhood demand in the Schelling's segregation
model by studying the conditions for which a chain reaction of migrations of
unsatisfied agents occurs. The essence of Schelling dynamics was approximated
in two simplified models: (1) a random walk model for the initial stage of the
migrations to illustrate the power-law behavior of chain reaction lengths under
critical conditions, and (2) a two-room model for the whole process to
represent a non-spatial version of segregation dynamics in the Schelling model.
Our theoretical results showed good agreements with numerical results obtained
from agent-based simulations.

An aspatial version for the famous Schelling's segregation model has recently
been proposed, which, called two-room model, is still in an agent-based format
like the original Schelling model. In the present study, we propose a new,
state equation version of the Schelling model. The new equation is based on the
two-room model and is derived in terms of a set of discrete maps. Fixed point
solutions for the new equation are found analytically and confirmed
numerically. Especially, we show that the extremely simple state equations can
reasonably reveal the essence of the Schelling dynamics: integration,
segregation and tipping. In addition to the fixed point solutions, periodic
solutions are identified and conditions of the limit cycles are derived
analytically.

We derived the critical neighborhood demand in the Schelling's segregation model by studying the conditions for which a chain reaction of migrations of unsatisfied agents occurs. The essence of Schelling dynamics was approximated in two simplified models: (1) a random walk model for the initial stage of the migrations to illustrate the power-law behavior of chain reaction lengths under critical conditions, and (2) a two-room model for the whole process to represent a non-spatial version of segregation dynamics in the Schelling model. Our theoretical results showed good agreements with numerical results obtained from agent-based simulations. (C) 2013 Elsevier B. V. All rights reserved.

We study a mathematical model of social diffusion on a symmetric weighted
network where individual nodes' states gradually assimilate to local social
norms made by their neighbors' average states. Unlike physical diffusion, this
process is not state conservational and thus the global state of the network
(i.e., sum of node states) will drift. The asymptotic average node state will
be the average of initial node states weighted by their strengths. Here we show
that, while the global state is not conserved in this process, the inner
product of strength and state vectors is conserved instead, and perfect
positive correlation between node states and local averages of their
self/neighbor strength ratios always results in upward (or at least neutral)
global drift. We also show that the strength assortativity negatively affects
the speed of homogenization. Based on these findings, we propose an adaptive
link weight adjustment method to achieve the highest upward global drift by
increasing the strength-state correlation. The effectiveness of the method was
confirmed through numerical simulations and implications for real-world social
applications are discussed.

Social contagion has been studied in various contexts. Many instances of
social contagion can be modeled as an infection process where a specific state
(adoption of product, fad, knowledge, behavior, etc.) spreads from individual
to individual through links between them. In the meantime, other forms of
social contagion may better be understood as a diffusion process where the
state of an individual tends to assimilate with the social norm (i.e., local
average state) within his/her neighborhood.
Unlike infection scenarios where influence is nonlinear, unidirectional,
fast, and potentially disruptive, social diffusion is linear, bidirectional,
gradual, and converging. The distance between an individual's state and his/her
neighbors' average state always decreases, and thus a homogeneous global state
is guaranteed to be the network's stable equilibrium state in the long run.
This does not sound as intriguing or exciting as infection dynamics, which
might be why there are very few studies on mathematical models of social
diffusion processes.
Here, this study attempts to shed new light on an unrecognized characteristic
of social diffusion, i.e., non-trivial drift it can cause to the network's
global average state. Although somewhat counterintuitive, such global drift is
indeed possible because, unlike physical diffusion processes, social diffusion
processes are not conservational. In what follows, a mathematical model of
social diffusion will be presented to explain the mechanism of this phenomenon,
and some possible collective actions for influencing the direction of global
drift will be proposed. The relevance of social diffusion to individual and
collective improvement will be discussed briefly, with an emphasis on
educational applications.

We studied the roles of morphogenetic principles---heterogeneity of
components, dynamic differentiation/re-differentiation of components, and local
information sharing among components---in the self-organization of
morphogenetic collective systems. By incrementally introducing these principles
to collectives, we defined four distinct classes of morphogenetic collective
systems. Monte Carlo simulations were conducted using an extended version of
the Swarm Chemistry model that was equipped with dynamic
differentiation/re-differentiation and local information sharing capabilities.
Self-organization of swarms was characterized by several kinetic and
topological measurements, the latter of which were facilitated by a newly
developed network-based method. Results of simulations revealed that, while
heterogeneity of components had a strong impact on the structure and behavior
of the swarms, dynamic differentiation/re-differentiation of components and
local information sharing helped the swarms maintain spatially adjacent,
coherent organization.

The Ultimatum Game (UG) is an economic game where two players (proposer and responder) decide how to split a certain amount of money. While traditional economic theories based on rational decision making predict that the proposer should make a minimal offer and the responder should accept it, human subjects tend to behave more fairly in UG. Previous studies suggested that extra information such as reputation, empathy, or spatial structure is needed for fairness to evolve in UG. Here we show that fairness can evolve without additional information if players make decisions probabilistically and may continue interactions when the offer is rejected, which we call the Not Quite Ultimatum Game (NQUG). Evolutionary simulations of NQUG showed that the probabilistic decision making contributes to the increase of proposers' offer amounts to avoid rejection, while the repetition of the game works to responders' advantage because they can wait until a good offer comes. These simple extensions greatly promote evolution of fairness in both proposers' offers and responders' acceptance thresholds.

Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students' academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends' average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

Although cultural integration, or sharing a common corporate culture, is crucial for the success of mergers, previous studies have been limited to firm-level analyses. From a social network perspective, this study explores how cultural integration emerges from the patterns of social interactions among individuals. Using an agent-based model, we investigate the impact of network structures within and between two merging firms on post-merger cultural integration and organizational dysfunctions-individual turnover, interpersonal conflict and organizational communication ineffectiveness-that arise from insufficient cultural integration. The simulation results demonstrate that the highest level of cultural integration is achieved when social ties are more centralized within each merging firm and the social ties between the merging firms are less concentrated on central individuals. Additionally, the results show that within-firm and between-firm network structures significantly affect individual turnover, interpersonal conflict and organizational communication ineffectiveness, and that these three outcome measurements do not vary in tandem.

Generally, phenomena of spontaneous pattern formation are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems (CS) that are both architectured and self-organized. Can we understand their precise self-formation capabilities and integrate them with technological planning? Can physical systems be endowed with information, or informational systems be embedded in physics, to create autonomous morphologies and functions? To answer these questions, we have launched in 2009, and developed through a series of workshops and a collective book, a new field of research called morphogenetic engineering. It is the first initiative of its kind to rally and promote models and implementations of complex self-architecturing systems. Particular emphasis is set on the programmability and computational abilities of self-organization, properties that are often underappreciated in CS science-while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies. [This paper is an extended version of Doursat, Sayama and Michel (2012b) (Chapter 1, in Doursat R et al. (eds.) Morphogenetic engineering: toward programmable complex systems. Understanding complex systems. Springer, 2012a).]

Migration is a fundamental trait in humans and animals. Recent studies investigated the effect of migration on the evolution of cooperation, showing that contingent migration favors cooperation in spatial structures. In those studies, only local migration to immediate neighbors was considered, while long-range migration has not been considered yet, partly because the long-range migration has been generally regarded as harmful for cooperation as it would bring the population to a well-mixed state that favors defection. Here, we studied the effects of adaptive long-range migration on the evolution of cooperation through agent-based simulations of a spatial Prisoner's Dilemma game where individuals can jump to a farther site if they are surrounded by more defectors. Our results show that adaptive long-range migration strongly promotes cooperation, especially under conditions where the temptation to defect is considerably high. These findings demonstrate the significance of adaptive long-range migration for the evolution of cooperation.

This paper presents a review of our recently completed interdisciplinary research project "Evolutionary Perspective on Collective Decision Making" that was conducted through close collaboration between computational, organizational and social scientists at Binghamton University. In this project, we utilized Evolutionary Computation in several non-traditional ways-(1) as a theoretical framework for reinterpreting the dynamics of collective human decision making processes, (2) as a computational simulation model of idea generation and selection, and (3) as a research tool for collecting high-resolution experimental data of actual collaborative design and decision making from human subjects.

Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students' academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends' average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

Although cultural integration, or sharing a common corporate culture, is crucial for the success of mergers, previous studies have been limited to firm-level analyses. From a social network perspective, this study explores how cultural integration emerges from the patterns of social interactions among individuals. Using an agent-based model, we investigate the impact of network structures within and between two merging firms on post-merger cultural integration and organizational dysfunctions-individual turnover, interpersonal conflict and organizational communication ineffectiveness-that arise from insufficient cultural integration. The simulation results demonstrate that the highest level of cultural integration is achieved when social ties are more centralized within each merging firm and the social ties between the merging firms are less concentrated on central individuals. Additionally, the results show that within-firm and between-firm network structures significantly affect individual turnover, interpersonal conflict and organizational communication ineffectiveness, and that these three outcome measurements do not vary in tandem.

Adaptive networks are a novel class of dynamical networks whose topologies
and states coevolve. Many real-world complex systems can be modeled as adaptive
networks, including social networks, transportation networks, neural networks
and biological networks. In this paper, we introduce fundamental concepts and
unique properties of adaptive networks through a brief, non-comprehensive
review of recent literature on mathematical/computational modeling and analysis
of such networks. We also report our recent work on several applications of
computational adaptive network modeling and analysis to real-world problems,
including temporal development of search and rescue operational networks,
automated rule discovery from empirical network evolution data, and cultural
integration in corporate merger.

Self-organization of heterogeneous particle swarms is rich in its dynamics
but hard to design in a traditional top-down manner, especially when many types
of kinetically distinct particles are involved. In this chapter, we discuss how
we have been addressing this problem by (1) utilizing and enhancing interactive
evolutionary design methods and (2) realizing spontaneous evolution of self
organizing swarms within an artificial ecosystem.

Generative Network Automata (GNA) is a powerful tool for the study of adaptive networks. It has the ability to represent a wide range of dynamics by leveraging its inherent generality. The ability to automatically discover underlying dynamics of adaptive network input has been theoretically proposed using GNA. This work tries to answer the question as to whether it is possible to create a practical implementation of GNA for the automatic discovery of dynamical rules that capture the state transition and topological transformation of complex adaptive networks. The results show that our algorithms and software (called PyGNA) correctly identifies the dynamics of a set of simple adaptive networks. Capturing the dynamics of more complex adaptive networks remains a challenge that will require further algorithm improvement.

Migration is a fundamental trait in humans and animals. Recent studies investigated the effect of migration on the evolution of cooperation, showing that contingent migration favors cooperation in spatial structures. In those studies, only local migration to immediate neighbors was considered, while long-range migration has not been considered yet, partly because the long-range migration has been generally regarded as harmful for cooperation as it would bring the population to a well-mixed state that favors defection. Here, we studied the effects of adaptive long-range migration on the evolution of cooperation through agent-based simulations of a spatial Prisoner's Dilemma game where individuals can jump to a farther site if they are surrounded by more defectors. Our results show that adaptive long-range migration strongly promotes cooperation, especially under conditions where the temptation to defect is considerably high. These findings demonstrate the significance of adaptive long-range migration for the evolution of cooperation.

Generally, phenomena of spontaneous pattern formation are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems (CS) that are both architectured and self-organized. Can we understand their precise self-formation capabilities and integrate them with technological planning? Can physical systems be endowed with information, or informational systems be embedded in physics, to create autonomous morphologies and functions? To answer these questions, we have launched in 2009, and developed through a series of workshops and a collective book, a new field of research called morphogenetic engineering. It is the first initiative of its kind to rally and promote models and implementations of complex self-architecturing systems. Particular emphasis is set on the programmability and computational abilities of self-organization, properties that are often underappreciated in CS science-while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies. [This paper is an extended version of Doursat, Sayama and Michel (2012b) (Chapter 1, in Doursat R et al. (eds.) Morphogenetic engineering: toward programmable complex systems. Understanding complex systems. Springer, 2012a).]

This paper presents an agent-based model of cellular proliferation, taxis and spatial diffusion. Our model describes the behavior of individual cells, rather than the aggregate behavior of cell distributions, derived from a set of partial differential equations. The physical motions of cells - taxis toward higher concentration of nutrients and diffusion in space - arc handled in a deterministic way, which is a unique, distinctive feature of our model compared to other stochastic simulation models. The model is tested with some simple assumptions to show interesting self-organizing behaviors.

Examination of temporally changing adaptive social networks has been difficult given the need for extensive and usually real-time data collection. Building from interdisciplinary advances, the authors propose a web search engine-based method (called retrospective relatedness reconstruction or 3R) for collecting approximated historical data of temporally changing adaptive social networks. As quantifying relatedness among people in social networks leads to difficulty in assigning proper weights to relationship ties, 3R offers a means for assessing relatedness between people over time. Additionally, 3R can be applied beyond people relatedness to include word associations. To illustrate these two novel contributions, the authors reconstructed the temporal evolution of a social network from 2005 to 2009 of 92 individuals (key leaders) related to the U.S. financial crisis and also examined the temporal evolution of social sentiment (i.e., fear, shame, blame, confidence) related to the same 92 individuals. We found several illustrative cases where temporal changes in centrality and/or sentiment captured actual events related to these individuals during this time period.

Examination of temporally changing adaptive social networks has been difficult given the need for extensive and usually real-time data collection. Building from interdisciplinary advances, the authors propose a web search engine-based method (called retrospective relatedness reconstruction or 3R) for collecting approximated historical data of temporally changing adaptive social networks. As quantifying relatedness among people in social networks leads to difficulty in assigning proper weights to relationship ties, 3R offers a means for assessing relatedness between people over time. Additionally, 3R can be applied beyond people relatedness to include word associations. To illustrate these two novel contributions, the authors reconstructed the temporal evolution of a social network from 2005 to 2009 of 92 individuals (key leaders) related to the U.S. financial crisis and also examined the temporal evolution of social sentiment (i.e., fear, shame, blame, confidence) related to the same 92 individuals. We found several illustrative cases where temporal changes in centrality and/or sentiment captured actual events related to these individuals during this time period.

Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers' interdisciplinarity at the individual level (diversity of topics related to the researcher) and at the social level (his/her centrality in the researchers' network). We found that visibility boosts by certain research topics were positively correlated with researchers' individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers' social-level interdisciplinarity. Research topics' correlations with researchers' individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of "interdisciplinarity" of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.

A three-dimensional version of the Swarm Chemistry model is presented. Self-organization and morphogenesis of heterogeneous swarms in the 3D model were compared to those in the original 2D version. It was observed that the resulting patterns generally had remarkable robustness against dimensionality change, while some swarms were susceptible to the change. Further experiments showed that it was often sufficient to make minimal parameter adjustments in order to recover the original topological and dynamical properties of those susceptible swarms in 3D, although the dependence on parameters varied case by case with no generic parameter mapping between 2D and 3D.

We present a preliminary study to evolve data sets that maximize performance differences between multiple machine learning classifiers. The aim is to provide useful information towards the decision of which machine learning classifier to use given a particular data set. While literature already exists on comparing multiple classifiers across multiple pre-existing data sets, our approach is novel and unique in that we evolved completely new data sets designed to highlight the performance differences between supervised learning classifiers. By investigating these evolved data sets, we hope to add to the knowledge base concerning which classifiers are appropriate for specific real world classification tasks.

Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers' interdisciplinarity at the individual level (diversity of topics related to the researcher) and at the social level (his/her centrality in the researchers' network). We found that visibility boosts by certain research topics were positively correlated with researchers' individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers' social-level interdisciplinarity. Research topics' correlations with researchers' individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of "interdisciplinarity" of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.

We investigated dynamics of group decision making on complex problems when agents can form mental models of others from discussion history. Results indicated that as the agents' memory capacity increases, the group reaches superficial consensus more easily. Surprisingly, however, the shared mental model of the problem develops only within a limited area of the problem space, because incorporating knowledge from others into one's own knowledge quickly creates local agreement on where relevant solutions are, leaving other potentially useful solutions beyond the scope of discussion. The mechanisms stifling group-level exploration and their implications for decision making research are discussed. (C) 2010 Wiley Periodicals, Inc. Complexity 16: 49-57, 2011

We propose hyperinteractive evolutionary computation (HIEC), a class of IEC in which the user actively chooses when and how each evolutionary operator is applied. To evaluate the benefits of HIEC, we conducted three human-subject experiments. The first two experiments showed that HIEC is associated with a more positive user experience and produced higher quality designs. The third experiment demonstrates the potential of HIEC as a research tool with which one can record the evolutionary actions taken by human users. Implications, limitations, and future directions of research are discussed.

We investigated dynamics of group decision making on complex problems when agents can form mental models of others from discussion history. Results indicated that as the agents' memory capacity increases, the group reaches superficial consensus more easily. Surprisingly, however, the shared mental model of the problem develops only within a limited area of the problem space, because incorporating knowledge from others into one's own knowledge quickly creates local agreement on where relevant solutions are, leaving other potentially useful solutions beyond the scope of discussion. The mechanisms stifling group-level exploration and their implications for decision making research are discussed. (C) 2010 Wiley Periodicals, Inc. Complexity 16: 49-57, 2011

We propose hyperinteractive evolutionary computation (HIEC), a class of IEC in which the user actively chooses when and how each evolutionary operator is applied. To evaluate the benefits of HIEC, we conducted three human-subject experiments. The first two experiments showed that HIEC is associated with a more positive user experience and produced higher quality designs. The third experiment demonstrates the potential of HIEC as a research tool with which one can record the evolutionary actions taken by human users. Implications, limitations, and future directions of research are discussed.

This letter presents a new, artificial-life-based view of the Collatz problem, a well-known mathematical problem about the behavior of a series of positive integers generated by a simple arithmetical rule. The Collatz conjecture asserts that this series always falls into a 4 → 2 → 1 cycle regardless of its initial values. No formal proof has been given yet. In this letter, the behavior of the series is considered an ecological process of artificial organisms (1s in bit strings). The Collatz conjecture is then reinterpreted as the competition between population growth and extinction. This new interpretation has made it possible to analytically calculate the growth and extinction speeds of bit strings. The results indicate that the extinction is always faster than the growth, providing an ecological explanation for the conjecture. Future research directions are also suggested.

Research in shared mental models has Immeasurably aided our understanding of effective teamwork and taskwork However little research has focused on the role that leaders play if any in influencing developing and/or fostering shared mental models and thereby improving team performance We developed an agent-based computational model based on McComb s theory of three-phase mental model development where agents repeatedly share individual opinions (orientation phase) evaluate and respond to the opinions expressed by others (differentiation phase) and modify their understanding of the team based on the responses (integration phase) Leadership and team properties are represented in three experimental parameters social network structure heterogeneity of agents domains of expertise and level of their mutual interest Participative leadership is represented by a fully connected network while Leader-Member exchange (LMX) is represented by a fully connected network of in group members and several out-group members connected only to the leader Our simulation results show that in general participative leadership promotes mental model convergence better than LMX In the meantime the team performance improvement is achieved by participative leadership only when members have both heterogeneous domains of expertise and strong mutual interest In all other conditions participative leadership causes the worst degradation of team performance through team development processes while LMX is the best to minimize such team degradation Implications and suggestions for future research are provided (c) 2010 Elsevier Inc All rights reserved

Research in shared mental models has Immeasurably aided our understanding of effective teamwork and taskwork However little research has focused on the role that leaders play if any in influencing developing and/or fostering shared mental models and thereby improving team performance We developed an agent-based computational model based on McComb s theory of three-phase mental model development where agents repeatedly share individual opinions (orientation phase) evaluate and respond to the opinions expressed by others (differentiation phase) and modify their understanding of the team based on the responses (integration phase) Leadership and team properties are represented in three experimental parameters social network structure heterogeneity of agents domains of expertise and level of their mutual interest Participative leadership is represented by a fully connected network while Leader-Member exchange (LMX) is represented by a fully connected network of in group members and several out-group members connected only to the leader Our simulation results show that in general participative leadership promotes mental model convergence better than LMX In the meantime the team performance improvement is achieved by participative leadership only when members have both heterogeneous domains of expertise and strong mutual interest In all other conditions participative leadership causes the worst degradation of team performance through team development processes while LMX is the best to minimize such team degradation Implications and suggestions for future research are provided (c) 2010 Elsevier Inc All rights reserved

We propose a simple web search engine based method for collecting approximated historical data of temporally changing social adaptive networks, which are rather difficult to obtain experimentally in conventional research methods. In the proposed method, a search query string is combined with additional keywords that specify inclusion/exclusion of specific years to limit the search results to a particular time point. Using the proposed method, we reconstructed the temporal evolution of a social network from 2005 to 2009 of 93 individuals who are important in the US economy. We measured centralities of those individuals for every year and found several illustrative cases where the temporal change of centrality of an individual correctly captured the actual events that are related to him/her over this time period. These results indicate the effectiveness of the proposed method. Limitations and future directions of research are discussed. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

A variety of modeling frameworks have been proposed and utilized in complex
systems studies, including dynamical systems models that describe state
transitions on a system of fixed topology, and self-organizing network models
that describe topological transformations of a network with little attention
paid to dynamical state changes. Earlier network models typically assumed that
topological transformations are caused by exogenous factors, such as
preferential attachment of new nodes and stochastic or targeted removal of
existing nodes. However, many real-world complex systems exhibit both of these
two dynamics simultaneously, and they evolve largely autonomously based on the
system's own states and topologies. Here we show that, by using the concept of
graph rewriting, both state transitions and autonomous topology transformations
of complex systems can be seamlessly integrated and represented in a unified
computational framework.We call this novel modeling framework "Generative
Network Automata (GNA)". In this chapter, we introduce basic concepts of GNA,
its working definition, its generality to represent other dynamical systems
models, and some of our latest results of extensive computational experiments
that exhaustively swept over possible rewriting rules of simple binary-state
GNA. The results revealed several distinct types of the GNA dynamics.

We propose swarm chemistry a new artificial chemistry framework that uses artificial swarm populations as chemical reactants. Reaction in swarm chemistry is not determined by predefined reaction rules as commonly assumed in typical artificial chemistry studies, but is spontaneously achieved by the emergence of a new spatiotemporal pattern of collective behavior through the kinetic interaction between multiple chemical species. We developed a prototype of an interactive simulation tool with which one can explore the dynamics of swarm chemistry using an interactive evolutionary method. Several preliminary results are reported to illustrate the characteristics and effectiveness of this framework, including spontaneous segregation of distinct chemical species, production and restriction of movements, and interactive design of complex biological-looking structures.

We developed Swarm Chemistry 1.2, a new version of the Swarm Chemistry simulator with an enhanced architecture of interactive evolutionary design for exploring heterogeneous self-propelled particle swarm dynamics. In the new version, each evolutionary operator acts locally and visually to part of the population of swarms on a screen, without causing entire generation changes that were used in earlier versions. This new architecture is intended to represent cognitive actions in human thinking and decision making processes more naturally. We tested the effectiveness of the new architecture through an in-class experiment with college students participating as designers as well as evaluators of swarms. We also measured the effects of mixing and mutation operators to the performance improvement of the design processes. The students' responses showed that the designs produced using the new version received significantly higher ratings from students than those produced using the old one, and also that each of the mixing and mutation operators contributed nearly independently to the improvement of the design quality. These results demonstrate the effectiveness of the new architecture of interactive evolutionary design, as well as the importance of having diverse options of action (i.e., multiple evolutionary operators in this context) in iterative design and decision making processes. This work also presents one of the few examples of human-involved experiments on the statistical evaluation of artificial lifeforms, whose quality (such as "livingness") would be hard to assess without using human cognition at this point.

The recent development of economical high-capacity RFID cards has opened up a new opportunity for stigmergic robotic swarms. Through these cards, robotic agents can dynamically exchange more complex, logical information, such as the whole set of their behavioral rules or "culture". To the best of our knowledge, this opportunity has not been explored in swarm robotics and other collective robotics communities. We have developed a prototypical robotic swarm system comprised of 8 low-cost OPEN-ROBOTs with the ability to avoid obstacles and exchange information with low-capacity RFID cards randomly distributed in an environment. To evaluate the effectiveness of our RFID-based cultural transmission technique, we created a realistic computer simulation to test the swarm's competence in mapping a virtual multi-room house covered with 80 low-capacity RFID cards in under one hour. By increasing the probability that a robot adopts a random exploration behavior different from one "marked" on a card, the swarm is able to cover more of an environment with higher consistency between trials. This result indicates that encouraging diversity among agents supports robust emergent behavior and lays the groundwork for future experiments with higher-capacity RFID cards.

Emergent pattern formation in self-propelled particle (SPP) systems is extensively studied because it addresses a range of swarming phenomena that occur without leadership. Here we present a dynamic SPP model in which a sensory blind zone is introduced into each particle's zone of interaction. Using numerical simulations, we discovered that the degradation of milling patterns with increasing blind zone ranges undergoes two distinct transitions, including a spatially non-homogeneous transition that involves cessation of particles' motion caused by broken symmetries in the interaction fields. Our results also show the necessity of nearly complete panoramic sensory ability for milling behavior to emerge in dynamic SPP models, suggesting a possible relationship between collective behavior and the sensory systems of biological organisms.

Emergent pattern formation in self-propelled particle (SPP) systems is extensively studied because it addresses a range of swarming phenomena that occur without leadership. Here we present a dynamic SPP model in which a sensory blind zone is introduced into each particle's zone of interaction. Using numerical simulations, we discovered that the degradation of milling patterns with increasing blind zone ranges undergoes two distinct transitions, including a spatially non-homogeneous transition that involves cessation of particles' motion caused by broken symmetries in the interaction fields. Our results also show the necessity of nearly complete panoramic sensory ability for milling behavior to emerge in dynamic SPP models, suggesting a possible relationship between collective behavior and the sensory systems of biological organisms.

This essay aims to propose construction theory, a new domain of theoretical
research on machine construction, and use it to shed light on a fundamental
relationship between living and computational systems. Specifically, we argue
that self-replication of von Neumann's universal constructors holds a close
similarity to circular computational processes of universal computers that
appear in Turing's original proof of the undecidability of the halting problem.
The result indicates the possibility of reinterpreting a self-replicating
biological organism as embodying an attempt to solve the halting problem for a
{\em diagonal} input in the context of construction. This attempt will never be
completed because of the indefinite cascade of self-computation/construction,
which accounts for the undecidability of the halting problem and also agrees
well with the fact that life has maintained its reproductive activity for an
indefinitely long period of time.

We review recent research which reveals: (1) how spatially distributed populations avoid overexploiting resources due to the local extinction of over-exploitative variants, and (2) how the conventional understanding of evolutionary processes is violated by spatial populations so that basic concepts, including fitness assignment to individual organisms, are not applicable, and even kin and group selection are unable to describe the mechanism by which exploitative behavior is bounded. To understand these evolutionary processes, a broader view is needed of the properties of multiscale spatiotemporal patterns in organism-environment interactions. We discuss measures that quantify the effects of these interactions on the evolution of a population, including multigenerational fitness and the heritability of the environment. (c) 2008 Wiley Periodicals, Inc.

This essay aims to propose construction theory, a new domain of theoretical research on machine construction, and use it to shed light on a fundamental relationship between living and computational systems. Specifically, we argue that self-replication of von Neumann's universal constructors holds a close similarity to circular computational processes of universal computers that appear in Turing's original proof of the undecidability of the halting problem. The result indicates the possibility of reinterpreting a self-replicating biological organism as embodying an attempt to solve the halting problem for a diagonal input in the context of construction. This attempt will never be completed because of the indefinite cascade of self-computation/construction, which accounts for the undecidability of the halting problem and also agrees well with the fact that life has maintained its reproductive activity for an indefinitely long period of time. (c) 2008 Wiley Periodicals, Inc.

We propose a new modeling framework "Generative Network Automata (GNA)" that can uniformly describe both state transitions and autonomous topology transformations of complex dynamical networks. GNA is formulated as an extension of existing complex dynamical network models to include a new set of generative update rules that determine how local network topologies will change based on the current local network states and topologies. This paper introduces basic concepts of GNA, its formal definitions, its generality to represent other dynamical systems models, and some preliminary results of an exhaustive sweep of possible dynamics found in elementary binary GNA with restricted updating rules.

We present new methods of decentralized control and interactive design for artificial swarms of a large number of agents that can spontaneously organize and maintain non-trivial heterogeneous formations. Our model assumes no elaborate sensing, computation, or communication capabilities for each agent; the self-organization is achieved solely by simple kinetic interactions among agents. Specifications of the final formations are indirectly and. implicitly woven into a list of different kinetic parameter settings and their proportions, which would be hard to obtain with a conventional top-down design method but may be designed heuristically through interactive design processes.

We present a general approach for evaluating and visualizing evolutionary dynamics of self-replicators using a graph-based representation for genealogy. Through a transformation from the space of species and mutations to the space of nodes and links, evolutionary dynamics are understood as a flow in graph space. A formalism is introduced to quantify such genealogical flows in terms of the complete history of localized evolutionary events recorded at the finest level of detail. Represented in a multidimensional viewing space, collective dynamical properties of an evolving genealogy are characterized in the form of aggregate flows. We demonstrate the effectiveness of this approach by using it to compare the evolutionary exploration behavior of self-replicating loops under two different environmental settings. © 2006 Massachusetts Institute of Technology.

Almost all complex artifacts nowadays, including physical artifacts such as airplanes, as well as informational artifacts such as software, organizations, business processes and so on, are defined via the interaction of many, sometimes thousands of participants, working on different elements of the design. This collaborative design process is challenging because strong interdependencies between design decisions make it difficult to converge on a single design that satisfies these dependencies and is acceptable to all participants. Current collaborative design approaches are as a result typically characterized by heavy reliance on expensive and time-consuming processes, poor incorporation of some important design concerns (typically later lifecycle issues such as environmental impact), as well as reduced creativity due to the tendency to incrementally modify known successful designs rather than explore radically different and potentially superior ones. © 2006 Springer.

Work to date on computational models of negotiation has focused almost exclusively on defining agreements consisting of one or a few independent issues. The negotiation involved in collaborative design, by contrast, is much more complex, consisting of multiple inter-dependent issues and intractably large design spaces. This paper describes a simulated annealing based approach appropriate for collaborative design negotiations that achieves near-optimal outcomes with binary issue dependencies.

We present a general approach for evaluating and visualizing evolutionary dynamics of self-replicators using a graph-based representation for genealogy. Through a transformation from the space of species and mutations to the space of nodes and links, evolutionary dynamics are understood as a flow in graph space. A formalism is introduced to quantify such genealogical flows in terms of the complete history of localized evolutionary events recorded at the finest level of detail. Represented in a multidimensional viewing space, collective dynamical properties of an evolving genealogy are characterized in the form of aggregate flows. We demonstrate the effectiveness of this approach by using it to compare the evolutionary exploration behavior of self-replicating loops under two different environmental settings.

We propose a new information sharing system, named "BisNet", which automatically gathers information about the bookmarks stored in users' web browsers and helps the users exchange URIs of possibly interesting web pages with others who have similar interest with them. Being different from other typical agent services that gather and provide information according to pre-registered user profiles, BisNet is expected to share more relevant information because of its use of web browser bookmarks that are actively selected and ordered by many humans. To enhance the relevance of information being shared, we developed a novel algorithm for directory evaluation. This algorithm only looks at the local referential structure between bookmark directories and URIs and calculates for each directory the "order index" that represents how well its content URIs are put in order with a focus on specific areas of interest. Then each directory receives new URIs from other related directories with large order indexes. The repetition of such URI exchanges makes the whole directory-URI networks dynamically form directory groups according to the commonness of the URIs they refer to. Our method is unique in that it pays no attention to the actual contents of web pages, and thus is much simpler and faster than other methods based on the result of content analysis. We carried out a field trial that involved 45 people who used a prototype version of BisNet clients. The result indicated that the relevance of shared URIs positively correlated with the "order index" of surrounding related directories, demonstrating the effectiveness of the method we proposed.

We propose a novel artificial-life-oriented media art "RomperSand", which applies a three-dimensional version of the Game of Life (GoL) CA for the construction of an interactive virtual playground. In RomperSand, two distinct sets of state-transition rules are combined together: one for simulating physically plausible motion of virtual sand particles and the other for realizing the GoL-like dynamic behavior of living structures. Players can operate several virtual tools to create, destroy, and interact with these structures. The system was implemented as a Windows application and was tested by several users, gaining positive appreciations from them.

In multiagent system research, the work in building simulation model and the effort in developing analysis methods are closely related because building multiagent models relies heavily on new effective analysis methods while justifying new analysis methods needs the simulation results of multiagent models. MASSE(Multiagent Simulation Systematic Explorer) was proposed as an integrated environment which is aimed at (a) to conduct efficient simulation by intelligent scheduling, (b) to conduct intelligent data analysis and knowledge discovery on simulation data, and (c) to develop such analysis methods themselves. Current implementation of MASSE is described in this manuscript, and its usefulness is shown in three aspects: simple accommodation of existing simulators, satisfactory performance for adopting grid computing technique, and systematic data analysis tool. Collaboration among simulation developers and analysts can be strongly supported by using MASSE to incorporate various simulators and unified method for data analysis. We conclude that the current implementation of MASSE is satisfactory.

In this paper we describe a concept of animated texture using A-life model called "evoloop." The texture transforms interactively according to the input by image recognition. We created an interactive art <living surfaces> using this technique. <Living Surfaces> is an interactive art realizing dynamic transformations of animated textures. On the square canvas, a complicated computer-generated color abstract image is projected, dynamically changing when people move their bodies or small black geometric shapes on it. The evolving patterns generated by "evoloop" appear according to the position of the black shapes. On the rectangle canvas, a three-dimensional creature image is projected, the texture of which reflects the pattern generated on the square canvas.

The traditional construction paradigm of machine and tape is reformulated in a functionally homogeneous space of directed graph structures. Hierarchy-based roles, normally appointed to actors in a construction process, are dissolved and replaced by symmetric, level-less engagement. The separation between static (information carrying) and active (information processing) structures, imposed by mandate of the rules or physics in earlier theoretical models, results instead purely from graph topology. While encompassing traditional machine-tape paradigms as a special case, the formalism is shown to incorporate a wider class of construction relations. Exploiting its flexibility, a representation of a Turing machine is demonstrated, establishing computation universality. The concept of a "Tangled Construction Hierarchy" is introduced.

In this paper we investigate population dynamics, genealogy and complexity-increase of locally interacting populations of cellular automata-based evolving self-replicating loops (evoloops). We outline experiments indicating that the evolutionary growth in complexity, known to be achievable in principle given the complete genetic accessibility granted by universal construction, may be achievable in practice using much simpler replicating structures. By introducing evoloop populations to hostile environments, we demonstrate that selection pressures toward smaller species can be mediated to enable evolutionary accessibility to larger species, which themselves roam a much more vast portion of genetic state-space. We show that this growth in size results from intrinsically biased genealogy inherent in the rules of the evoloop CA, normally suppressed by selection pressures from direct competition favouring the smallest species. This shows that, in populations of simple self-replicating structures, a limited form of complexity-increase may result from a process which is driven by biased genealogical connectivity--a purely emergent property arising out of bottom-up evolutionary dynamics--and not just by adaptation . Implications of this result are discussed and contrasted with other self-replication studies in Artificial Life and Biology.

It is generally believed that self-replication models constructed on cellular automata have quite limited evolutionary dynamics in both diversity and adaptative behavior. Contrary to this view, we show that complex genetic diversification and adaptation processes may occur in self-replicating loop populations. Applying newly developed tools for detailed genetic identification and genealogy tracing to evoloop populations, we uncovered a genotypic permutation space that expands combinatorially with replicator size. Within this space populations demonstrate broad behavioral diversity and non-trivial genetic adaptation, maximizing colony density while enhancing sustainability against other species. We also found a set of non-mutable subsequences enabling genetic operations that alter fitness differentials and promote long-term evolutionary exploration. These results reveal the amazing potential of cellular automata to re-create complex genetic evolution of self-replicators in a simple, deterministic framework.

The concept of self-protection, a capability of an organism to protect itself from exogenous attacks, is introduced into the design of artificial evolutionary systems as a possible method to create and maintain diversity in the population. Three different mechanisms of self-protection are considered and implemented on a cellular-automaton-based evolutionary system, the evoloop. Simulation results imply a positive effect of those mechanisms on diversity maintenance, especially when the self-protection is moderate so that it conserves both the attacker and the attacked. This letter briefly reports the models and the simulation results obtained using those models.

This paper reviews the history of embedded, evolvable self-replicating structures implemented as cellular automata systems. We relate recent advances in this field to the concept of the evolutionary growth of complexity, a term introduced by McMullin to describe the central idea contained in von Neumann's self-reproducing automata theory. We show that conditions for such growth are in principle satisfied by universal constructors, yet that in practice much simpler replicators may satisfy scaled-down - yet equally relevant - versions thereof. Examples of such evolvable, self-replicators are described and discussed, and future challenges identified.

It is widely believed that evolutionary dynamics of artificial self-replicators realized in cellular automata (CA) are limited in diversity and adaptation. Contrary to this view, we show that complex genetic evolution may occur within simple CA. The evolving self-replicating loops ("evoloops") we investigate exhibit significant diversity in macro-scale morphologies and mutational biases, undergoing nontrivial genetic adaptation by maximizing colony density and enhancing sustainability against other species. Nonmutable subsequences enable genetic operations that alter fitness differentials and promote long-term evolutionary exploration. These results demonstrate a unique example of genetic evolution hierarchically emerging from local interactions between elements much smaller than individual replicators. (c) 2004 Wiley Periodicals, Inc.

The concept of "self-protection", a capability of an organism to protect itself from exogenous attacks, is introduced to the design of artificial evolutionary systems as a possible method to create and maintain diversity in the population. Three different mechanisms of setf-protection are considered and implemented on a cellular automata based evolutionary system, the evoloop. Simulation results imply a positive effect of those mechanisms on diversity maintenance, especially when the self-protection is moderate so that it conserves both the attacker and the attacked.

Spatially distributed genetic populations that compete locally for resources and mate only with sufficiently close neighbors, may give rise to spontaneous pattern formation. Depending on the population parameters, like death rate per generation and size of the competition and mating neighborhoods, isolated groups of individuals, or demes, may appear. The existence of such groups in a population has consequences for genetic diversity and for speciation. In this paper we discuss the robustness of demes formation with respect to two important characteristics of the population: the way individuals recognize the demarcation of the local neighborhoods and the way competition for resources affects the birth rate in an overcrowed situation. Our results indicate that demes are expected to form only for sufficiently sharp demarcations and for sufficiently intense competition.

The role of spontaneous pattern formation, the appearance of inhomogeneities that are not directly imposed by external forces, has not been closely examined in the context of the origin and maintenance of genetic diversity in wild populations. Using individual-based computer simulations, we demonstrated that such patterns form in spatially distributed species with local demes under disruptive selection. In our model systems, spatial patterns of genetic diversity arose and changed over time even in the context of a spatially homogenous environment. The spatial distribution and dynamics of the fittest genotypes were controlled by the movement of boundaries between domains of the different genotypes. The rate of diversity decay was dramatically slower than predicted by traditional models. Therefore, spontaneous pattern formation may lead to the maintenance of genetic diversity of a species in a contiguous habitat, despite reproductive mixing. Moreover, the diversity persisted significantly longer in larger habitats and habitats with irregular geographical features. Habitat structure was intimately linked to the preservation of genetic diversity. Spontaneous pattern formation should be considered along with other spatial effects in the design of conservation areas.

We examine the dynamics of evolution in a generic spatial model of a pathogen infecting a population of hosts, or an analogous predator-prey system. Previous studies of this model have found a range of interesting phenomena that differ from the well-mixed version. We extend these studies by examining the spatial and temporal dynamics of strains using genealogical tracing. When transmissibility can evolve by mutation, strains of intermediate transmissibility dominate even though high-transmissibility mutants have a short-term reproductive advantage. Mutant strains continually arise and grow rapidly for many generations but eventually go extinct before dominating the system. We find that, after a number of generations, the mutant pathogen characteristics strongly impact the spatial distribution of their local host environment, even when there are diverse types coexisting. Extinction is due to the depletion of susceptibles in the local environment of these mutant strains. Studies of spatial and genealogical relatedness reveal the self-organized spatial clustering of strains that enables their impact on the local environment. Thus, we find that selection acts against the high-transmissibility strains on long time-scales as a result of the feedback due to environmental change. Our study shows that averages over space or time should not be assumed to adequately describe the evolutionary dynamics of spatially distributed host-pathogen systems. (C) 2003 Elsevier Science Ltd. All rights reserved.

Scale-free networks rely on a relatively small number of highly connected nodes to achieve a high degree of interconnectivity and robustness to random failure, but suffer from a high sensitivity to directed attack. In this paper we describe a parametrized family of networks and analyze their connectivity and sensitivity, identifying a network that has an interconnectedness closer to that of a scale-free network, a robustness to attack closer to that of an exponential network, and a resistance to failure better than that of either of those networks.

Work to date on computational models of negotiation has focused almost exclusively on defining contracts consisting of one or a few independent issues and tractable contract spaces. Many real-world contracts, by contrast, are much more complex, consisting of multiple inter-dependent issues and intractably large contract spaces. This paper describes a simulated annealing based approach appropriate for negotiating such complex contracts that achieves near-optimal social welfares for negotiations with binary issue dependencies.

We present a general method for evaluating and visualizing evolutionary dynamics of self-replicators using a graph-based representation for genealogy. Through a transformation from the space of species and mutations to the space of nodes and links, evolutionary dynamics are understood as a flow in graph space. Mapping functions are introduced to translate graph nodes to points in an n-dimensional visualization space for interpretation and analysis. Using this scheme, we evaluate the effect of a dynamic environment on a population of self-reproducing loops. Resulting images visually reveal the critical role played by genealogical graph space partitioning in the evolutionary process.

Almost all complex artifacts nowadays, including physical artifacts such as airplanes, as well as informational artifacts such as software, organizations, business processes, plans, and schedules, are defined via the interaction of many, sometimes thousands of participants, working on different elements of the design. This collaborative design process is typically expensive and time-consuming because strong interdependencies between design decisions make it difficult to converge on a single design that satisfies these dependencies and is acceptable to all participants. Recent research from the complex systems and negotiation literatures has much to offer to the understanding of the dynamics of this process. This paper reviews some of these insights and offers suggestions for improving collaborative design.

The concept of »self-protection», a capability of an organism to protect itself from exogenous attacks, is introduced to the design of artificial evolutionary systems as a possible method to create and maintain diversity in the population. Three different mechanisms of self-protection are considered and implemented on a cellular automata based evolutionary system, the evoloop. Simulation results imply a positive effect of those mechanisms on diversity maintenance, especially when the self-protection is moderate so that it conserves both the attacker and the attacked.

Work to date on computational models of negotiation has focused almost exclusively on defining agreements consisting of one or a few independent issues. The negotiation involved in collaborative design, by contrast, is much more complex, consisting of multiple inter-dependent issues and intractably large design spaces. This paper describes a simulated annealing based approach appropriate for collaborative design negotiations that achieves near-optimal outcomes with binary issue dependencies.

It is generally difficult for a large population at a fitness peak to acquire the genotypes of a higher peak, because the intermediates produced by allelic recombination between types at different peaks are of lower fitness. In his shifting-balance theory, Wright proposed that fitter genotypes could, however, become fixed in small isolated demes by means of random genetic fluctuations. These demes would then try to spread their genome to nearby demes by migration of their individuals. The resulting polymorphism, the coexistence of individuals with different genotypes, would give the invaded demes a chance to move up to a higher fitness peak. This last step of the process, namely, the invasion of lower fitness demes by higher fitness genotypes, is known as phase III of Wright&apos;s theory. Here we study the invasion process from the point of view of the stability of polymorphic populations. Invasion occurs when the polymorphic equilibrium, established at low migration rates, becomes unstable. We show that the instability threshold depends sensitively on the average number of breeding seasons of individuals. Iteroparous species (with many breeding seasons! have lower thresholds than semelparous species (with a single breeding season). By studying a particular simple model, we are able to provide analytical estimates of the migration threshold as a function of the number of breeding seasons. Once the threshold is crossed and polymorphism becomes unstable, any imbalance between the different demes is sufficient for invasion to occur. The outcome of the invasion, however, depends on many parameters, not only on fitness. Differences in fitness, site capacities, relative migration rates, and initial conditions, all contribute to determine which genotype invades successfully. Contrary to the original perspective of Wright&apos;s theory for continuous fitness improvement, our results show that both upgrading to higher fitness peaks and downgrading to lower peaks are possible.

We present a simple model of distributed multi-agent multi-issued contract negotiation for open systems where interactions are competitive and information is private and not shared. We then investigate via simulations two different approximate optimization strategies and quantify the contribution and costs of each towards the quality of the solutions reached. To evaluate the role of knowledge the obtained results are compared to more cooperative strategies where agents share more information. Interesting social dilemmas emerge that suggest the design of incentive mechanisms.

The notion of fitness is central in evolutionary biology. We use a simple spatially extended predator-prey or host-pathogen model to show a generic case where the average number of offspring of an individual as a measure of fitness fails to characterize the evolutionary dynamics. Mutants with high initial reproduction ratios have lineages that eventually go extinct due to local overexploitation. We propose general quantitative measures of fitness that reflect the importance of time scale in evolutionary processes.

We present a theoretical model of evolution of spatially distributed populations in which organisms mate with and compete against each other only locally. We show using both analysis and numerical simulation that the typical dynamics of population density variation is a spontaneous formation of isolated groups due to competition for resources. The resulting spatial separation between groups strongly affects the process of genetic invasion by local reproductive mixing, and spatially inhomogeneous genetic distributions are possible in the final states. We then consider a specific version of this model in the presence of disruptive selection, favoring two fittest types against their genetic intermediates. This case can be simplified to a system that involves just two nonconserved order parameters: population density and type difference. Since the coexistence of two fittest types is unstable in this case, symmetry breaking and coarsening occur in type difference, implying eventual dominance by one type over another for finite populations. However, such coarsening patterns may be pinned by the spontaneously generated spatial separation between isolated groups. The long-term evolution of genetic composition is found to be sensitive to the ratio of the mating and competition ranges, and other parameters. Our model may provide a theoretical basis for consideration of various properties of spatially extended evolutionary processes, including spontaneous formation of subpopulations and lateral invasion of different types.

The gene centered view of evolution, a framework broadly used in evolutionary theory, assumes that one can assign an effective fitness directly to each allele. This avoids the conceptual and mathematical difficulties which sexual reproduction causes in treating evolutionary processes. We formalize the gene centered view as a dynamic form of the mean field approximation applied to genomic probabilities in reproduction/selection processes. We show that the predictions of the gene centered view are invalid when symmetry breaking and pattern formation occur within a population, and in particular for spatially distributed populations with local mating neighborhoods in the presence of disruptive selection. Our results have significant implications for the fundamental and practical problems of the development and persistence of genetic diversity. They are of great importance for modem efforts in conservation biology to save endangered species which have dramatically reduced genetic diversity.

Collaborative design is challenging because strong interdependencies between design issues make it difficult to converge on a single design that satisfies these dependencies and is acceptable to all participants, Complex systems research has much to offer to the understanding of these dynamics. This paper describes some insights from the complex systems perspective.

We present a simple model of distributed multi-agent multi-issued contract negotiation for open systems where interactions are competitive and information is private and not shared. We then investigate via simulations two different approximate optimization strategies and quantify the contribution and costs of each towards the quality of the solutions reached. To evaluate the role of knowledge the obtained results are compared to more cooperative strategies where agents share more information. Interesting social dilemmas emerge that suggest the design of incentive mechanisms.

A new self-replicating cellular automata (CA) model is proposed as a latest effort toward the realization of an artificial evolutionary system on CA where structural complexity of self-replicators can increase in some cases. I utilize the idea of 'shape encoding' proposed by Morita and Imai (Morita & Imai 1996b) and make the state-transition rules of the model allow organisms to transmit genetic information to others when colliding against each other. Simulations with random initial configuration demonstrate that it is possible that the average length of organisms and the average frequency of brancing per organism both increase, with decreasing self-replication fidelity, and saturate at some constant level. The saturation is caused in part by the fixation of place and shape of organisms onto particular sites. This implies the necessity of introducing some fluidity of site arrangements into the model for further development of evolutionary models using CA-like artificial media.

Sexual reproduction presents significant challenges to formal treatment of
evolutionary processes. A starting point for systematic treatments of
ecological and evolutionary phenomena has been provided by the gene centered
view of evolution which assigns effective fitness to each allele instead of
each organism. The gene centered view can be formalized as a dynamic mean field
approximation applied to genes in reproduction / selection dynamics. We show
that the gene centered view breaks down for symmetry breaking and pattern
formation within a population; and show that spatial distributions of organisms
with local mating neighborhoods in the presence of disruptive selection give
rise to such symmetry breaking and pattern formation in the genetic composition
of local populations. Global dynamics follows conventional coarsening of
systems with nonconserved order parameters. The results have significant
implications for the ecology of genetic diversity and species formation.

The phenomenon of death, or disappearance of life, has two aspects. One is failure in the function of life and the other is dissolution of the structure of life. In order to model the latter aspect and examine the significance of it, the author has contrived a "structurally dissolvable self-reproducing (SDSR) loop"^<2)> by introducing the capability of structural dissolution into Langton's self-reproducing (SR) loop^<1)> in which deal as functional failure has already been installed. To be more specific, a dissolving state '8' was introduced into the set of states of the cellular automata (CA) used for embodying the SR loop, besides other modifications to Langton's transition rules. Through this improvement, the SDSR loop can dissolve its own structure when faced with difficult situations such as a shortage of space for self-reproduction. This mechanism (disappearance of a subsystem of the whole system) induces, for the first time, dynamically-stabel and potentially evolvable behavior into the colony of SDSR loops. In this article, we report in detail the process of implementing the SDSR loop and its behavior, and have some discussions relevant to them.

The phenomenon of death, or disappearance of life, has two aspects. One is failure in the function of life and the other is dissolution of the structure of life. In order to examine the significance of the latter aspect, the author contrived a "structurally dissolvable self reproducing (SDSR) loop" by introducing the capability of structural dissolution into Langton's self-reproducing (SR) loop in which death as functional failure has already been installed. To be more specific, a dissolving state '8' was introduced into the set of states of the CA, besides other modifications to Langton's transition rules. Through this improvement, the SDSR loop can dissolve its own structure when faced with difficult situations such as a shortage of space for self-reproduction. This mechanism (disappearance of a subsystem of the whole system) induces, for the first time, dynamically-stable and potentially evolvable behavior into the colony of SDSR loops.