A utility-based analysis of equilibria in multi-objective normal form games
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Abstract (arXiv)
In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analysed on the basis of the utility that these compromises have for the users of a system, where an agent's utility function maps their payoff vectors to scalar utility values. This utility-based approach naturally leads to two different optimisation criteria for agents in a MOMAS: expected scalarised returns (ESR) and scalarised expected returns (SER). In this article, we explore the differences between these two criteria using the framework of multi-objective normal form games (MONFGs). We demonstrate that the choice of optimisation criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when non-linear utility functions are used.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jan 2020.
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Citing papers per year: 2021: 5; 2022: 0; 2023: 0; 2024: 1; 2025: 2; 2026: 1.
9 papers.
- 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
- 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)cited by 55
- 2025Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weightingcited by 23
- 2021Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Makingcited by 6
- 2024MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learningcited by 5
- 2021Preference Communication in Multi-Objective Normal-Form Gamescited by 3
- 2021Exploring the Impact of Tunable Agents in Sequential Social Dilemmascited by 2
- 2025Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulationcited by 0
- 2026AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RLcited by 0
- 2026AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL30 Jun 2026
- 2025Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting14 Sep 2025
- 2025Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation20 Aug 2025
- 2024MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning23 Jul 2024
- 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)25 Nov 2021
- 2021Preference Communication in Multi-Objective Normal-Form Games17 Nov 2021
- 2021Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making2 Jun 2021
- 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planning17 Mar 2021
- 2021Exploring the Impact of Tunable Agents in Sequential Social Dilemmas28 Jan 2021