Projects per year
Abstract
We present a detailed analysis of Nash equilibria in multiobjective normalform games, which are normalform games with vectorial payoffs. Our approach is based on modelling each player's utility using a utility function that maps a vector to a scalar utility. For mixed strategies, we can apply the utility function before calculating the expectation of the payoff vector as well as after, resulting in two distinct optimisation criteria. We show that when computing the utility from the expected payoff, a Nash equilibrium can be guaranteed when players have quasiconcave utility functions. In addition, we show that when players have quasiconvex utility functions, pure strategy Nash equilibria are equal under both optimisation criteria. We extend this to settings where some players optimise for one criterion, while others optimise for the second. We combine these results and formulate an algorithm that computes all pure strategy Nash equilibria given quasiconvex utility functions.
Original language  English 

Title of host publication  The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) 
Publisher  International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) 
Publication status  Accepted/In press  5 Mar 2023 
Keywords
 Multiobjective
 Game theory
 Nash equilibrium
Projects
 2 Active

FWOTM1108: Decisionmaking in teamreward multiobjective multiagent domains
1/10/22 → 30/09/25
Project: Fundamental

FWOTM1082: Reinforcement Learning in MultiObjective MultiAgent Systems
1/11/21 → 31/10/23
Project: Fundamental