Multi-agent interaction and nonlinear Markov games

Abstract

The general picture of game theoretic modeling dealt with here is characterized by a set of big players, also referred to as principals or major agents, acting on the background of large pools of small players, the impact of the behavior of each small player in a group on the overall evolution decreasing with the increase of the size of the group. In this Part I approach players in groups are not independent rational optimizers. They are either directly controlled by principals and serve the interests of the latter (pressure and collaboration setting) or they resist the actions of the principals (pressure and resistance setting) by evolving their strategies in an 'evolutionary manner' via interactions with other players subject to certain clear rules, deterministic or stochastic. The examples of the real world problems involved include government representatives (often referred to in the literature as benevolent dictators) chasing corrupted bureaucrats, inspectors chasing tax-paying avoidance, police acting against terrorist groups or models describing the attacks of computer or biological viruses. Other class of examples concerns appropriate (or better optimal) management of complex stochastic systems consisting of large number of interacting components (agents, mechanisms, vehicles, subsidiaries, species, police units, robot swarms, etc), which may have competitive or common interests. Such management can also deal with the processes of merging and splitting of functional units (say, firms or banks) or the coalition building of agents. The actions of the big players effectively control the distribution of small players among their possible strategies and can influence the rules of their interaction.

arXiv commentThis is the final draft of the 1st part of the authors' book 'Many agent games in socio-economic systems: corruption, inspection, coalition building, network growth, security' published by Springer in the Springer Series on Operational Research, 2019

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