Imitation in Large Games

TL;DR

This paper explores the role of imitation strategies in large, infinite-duration games on finite graphs, analyzing how different player types evolve and survive using automata-based imitation rules.

Contribution

It introduces a formal framework for studying imitation in large games with automata-based strategies and provides algorithmic results on type survival.

Findings

Automata-based imitation strategies can be effectively modeled in large games.

Algorithmic methods determine the eventual survival of player types.

Imitation can serve as a viable strategy in complex game settings.

Abstract

In games with a large number of players where players may have overlapping objectives, the analysis of stable outcomes typically depends on player types. A special case is when a large part of the player population consists of imitation types: that of players who imitate choice of other (optimizing) types. Game theorists typically study the evolution of such games in dynamical systems with imitation rules. In the setting of games of infinite duration on finite graphs with preference orderings on outcomes for player types, we explore the possibility of imitation as a viable strategy. In our setup, the optimising players play bounded memory strategies and the imitators play according to specifications given by automata. We present algorithmic results on the eventual survival of types.