Decision makers may face situations in which they cannot observe the outcomes that result from their actions. In such decisions, motivations other than the expected utility of outcomes may play a role. The present paper axiomatically characterizes a decision model in which the decision maker cares about whether it can be ex post verified that a good outcome has been achieved. Preferences over acts uniquely characterize a set of events that the decision maker expects to be able to verify in case they occur. The decision maker chooses the act that maximizes the expected utility across verifiable events of the worst possible outcome that may have occurred.  For example, a firm choosing between different carbon emission reduction technologies may find some technologies to leave ex post more uncertainty about the level of emission reduction than other technologies. The firm may care about proving to its stakeholders that a certain amount of carbon reduction has been achieved and may employ privately obtained evidence to do so. It may choose in expectation less efficient technologies if the achieved carbon reduction is better verifiable using the expected future evidence.

The background of the research project is to research models of individual decision making about information that 1) are readily applicable to data and 2) allow for behavioral biases. In the first step, this project obtains a model of stochastic information choice. In this model, the decision maker chooses a sequence of search keywords. The model accounts for a crucial difference between information choice as commonly modeled using information partitions and information choice in practice. Information about whether an event in an information partition is true is equivalent to information whether its negation is true. However, for lists of keywords (for example, a search engine query) there is no meaningful negation.