Efficiency and Equilibrium in Trial and Error Learning.
In trial and error learning, agents experiment with new strategies and adopt them with a probability that depends on their realized payoffs. Such rules are completely uncoupled, that is, each agent’s behaviour depends only on his own realized payoffs and not on the payoffs or actions of anyone else...
| Main Authors: | , |
|---|---|
| Format: | Working paper |
| Language: | English |
| Published: |
Department of Economics (University of Oxford)
2010
|
| Summary: | In trial and error learning, agents experiment with new strategies and adopt them with a probability that depends on their realized payoffs. Such rules are completely uncoupled, that is, each agent’s behaviour depends only on his own realized payoffs and not on the payoffs or actions of anyone else. We show that by modifying a trial and error learning rule proposed by Young (2009) we obtain a completely uncoupled learning process that selects a Pareto optimal equilibrium whenever a pure equilibrium exists. When a pure equilibrium does not exist, there is a simple formula that relates the long-run likelihood of each disequilibrium state to the total payoff over all agents and the maximum payoff gain that would result from a unilateral deviation by some agent. This welfare/stability trade-off criterion provides a novel framework for analyzing the selection of disequilibrium as well as equilibrium states in finite n-person games. |
|---|