The mathematical framework of psychological game theory is useful for describing many
forms of motivation where preferences depend directly on one's own or others' beliefs. It …

We study stochastic choice as the outcome of deliberate randomization. We derive a general
representation of a stochastic choice function where stochasticity allows the agent to …

We study a generalization of the classical monopoly insurance problem under adverse
selection (see Stiglitz 1977) where we allow for a random distribution of losses, possibly …

Using a revealed preference approach, we conduct an experiment where subjects make
choices from linear convex budgets in the domain of risk. We find that many individuals …

The expectation is an example of a descriptive statistic that is monotone with respect to
stochastic dominance, and additive for sums of independent random variables. We provide …

A growing literature has shown that people sometimes prefer to randomize between two
options. We investigate how prevalent this behavior is in an experiment using a novel and …

Machine learning algorithms can find predictive signals that researchers fail to notice; yet
they are notoriously hard-to-interpret. How can we extract theoretical insights from these …

Using a novel and simple method we capture ranges of preferences: ranges of values for
which agents prefer neither of two options, but rather to randomize between them. In an …

We study auction design for bidders equipped with non-expected utility preferences that
exhibit constant risk aversion (CRA). The CRA class is large and includes loss-averse …

The mathematical framework of psychological game theory is useful for describing many
forms of motivation where preferences depend directly on own or others' beliefs. It allows for …