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 experimentally investigate coordination games in which cognition plays an important role, i.e., where outcomes are affected by the agents level of understanding of the game and the beliefs they form about each others' understanding. We ask whether and when repeated exposure permits agents to...

We consider mixed populations (N=21) of genuine (humans) and artificial (robots) agents, repeatedly interacting in small groups whose composition is changed randomly from round to round. Our purpose is to study the spread of cooperative or non-cooperative behavior in the population over time by...

In clock games, agents receive differently-timed private signals when an asset value is above its fundamental. The price crashes to the fundamental when K of N agents have decided to sell. If selling decisions are private, bubbles can be sustained because people delay selling, after receiving...

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....