Emergent behaviours in multi-agent systems with Evolutionary Game Theory

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. This paper summarises some main research directions and challenges tackled in our group, using methods from EGT and ABM. These range from the introduction of cognitive and emotional mechanisms into agents' implementation in an evolving MAS, to the cost-efficient interference for promoting prosocial behaviours in complex networks, to the regulation and governance of AI safety development ecology, and to the equilibrium analysis of random evolutionary multi-player games. This brief aims to sensitize the reader to EGT based issues, results and prospects, which are accruing in importance for the modeling of minds with machines and the engineering of prosocial behaviours in dynamical MAS, with impact on our understanding of the emergence and stability of collective behaviours. In all cases, important open problems in MAS research as viewed or prioritised by the group are described.

Automated summary

This paper presents a systematic study of the mechanisms of emergence and evolution of collective behaviors in dynamical Multi-Agent Systems (MAS) using Evolutionary Game Theory (EGT) and agent-based modeling and simulation (ABM) techniques. The research covers various aspects, including the introduction of cognitive and emotional mechanisms into MAS, cost-efficient interference for promoting prosocial behaviors in complex networks, regulation and governance of AI safety development ecology, and equilibrium analysis of random evolutionary multi-player games. This paper highlights the importance of EGT for modeling minds with machines and engineering prosocial behaviors in dynamical MAS.