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...

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...

In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action-sampling learning, and payof-sampling learning. With this models and together with the models of self-tuning EWA learning and reinforcement learning, we conduct simulations over 12...

We show that for many classes of symmetric two-player games, the simple decision rule "imitate-the-best" can hardly be beaten by any other decision rule. We provide necessary and sufficient conditions for imitation to be unbeatable and show that it can only be beaten by much in games that are of...

We report experiments designed to test between Nash equilibria that are stable and unstable under learning. The “TASP” (Time Average of the Shapley Polygon) gives a precise prediction about what happens when there is divergence from equilibrium under fictitious play like learning processes....

This paper provides a definition of epistemic stability of sets of strategy profiles, and uses it to characterize variants of curb sets in finite games, including the set of rationalizable strategies and minimal curb sets. -- Epistemic game theory ; epistemic stability ; rationalizability ;...

In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from...

We propose a learning dynamic with agents using samples of past play to estimate the distribution of other players' strategy choices and best responding to this estimate. To account for noisy play, estimated distributions over other players' strategy choices have full support in the other...

It is known that there are uncoupled learning heuristics leading to Nash equilibrium in all finite games. Why should players use such learning heuristics and where could they come from? We show that there is no uncoupled learning heuristic leading to Nash equilibrium in all finite games that a...

We apply the dynamic stochastic framework proposed by recent evolutionaryliterature to the class of strict supermodular games when two simplebehavior rules coexist in the population, imitation and myopic optimization.We assume that myopic optimizers are able to see how well their payoff...