We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to efficient and fair allocations based on a simple convention of courtesy. We prove that following such convention induces a strategy which constitutes an @math -subgame-perfect equilibrium of the repeated allocation game with discounting. Simulation results highlight the effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms, since it achieves more than two orders of magnitude faster convergence, higher efficiency, fairness, and average payoff.
The Minority Game is a physicist's attempt to explain market behaviour by the interaction between traders. With a minimal set of ingredients and drastic assumptions, this model reproduces market ecology among different types of traders. Its emphasis is on speculative trading and information flow. The book first describes the philosophy lying behind the conception of the Minority Game in 1997, and includes in particular a discussion about the El Farol bar problem. Then it reviews the main steps in later developments, including both the theory and its applications to market phenomena. This book gives a colourful and stylized, but also realistic picture of how financial markets operate. Dispersion games are the generalization of the anticoordination game to arbitrary numbers of agents and actions. In these games agents prefer outcomes in which the agents are maximally dispersed over the set of possible actions. This class of games models a large number of natural problems, including load balancing in computer science, niche selection in economics, and division of roles within a team in robotics. Our work consists of two main contributions. First, we formally define and characterize some interesting classes of dispersion games. Second, we present several learning strategies that agents can use in these games, including traditional learning rules from game theory and artificial intelligence, as well as some special purpose strategies. We then evaluate analytically and empirically the performance of each of these strategies.