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 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....

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...

One reason for firms to voluntarily increase their environmental or social production standards is to prevent consumers from lobbying for stricter mandatory standards. In this sense, voluntary overcompliance serves as a Greek gift, as consumers might be worse off in the end. Strategically, a...

We investigate the role of information feedback in rent-seeking games with two different contest structures. In the stochastic contest a contestant wins the entire rent with probability equal to her share of rent-seeking expenditures; in the deterministic contest she receives a share of the rent...

We conducted controlled laboratory experiments to investigate how humans adapt the decision rule (DR) they use in repeated strategic interactions in light of new information becoming available. We do so by providing -- along different paths -- more and more information over time, so as to...

Mixed Nash equilibria are a cornerstone of game theory, but their empirical relevance has always been controversial. We study in the laboratory two games whose unique NE is in completely mixed strategies; other treatments include the matching protocol (pairwise random vs population...