Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that...

We report the findings of experiments designed to study how people learn in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their information requirements. Our experimental...

Individual behavioral differences in humans have been linked to measurable differences in their mental activities, including differences in their implicit motives. In humans, individual differences in the strength of motives such as power, achievement and affiliation have been shown to have a...

This paper analyses the formation of international environmental agreements (IEAs) under uncertainty, focusing on the role of learning and risk aversion. It bridges two strands of literature: one focused on the role learning for the success of IEA formation when countries are risk neutral and...

The paper presents an evolutionary model, based on the assumption that agents may revise their current strategies if they previously failed to attain the maximum level of potential payoffs. We offer three versions of this reflexive mechanism, each one of which describes a distinct type:...