arXiv:2605.31330v1 Announce Type: cross Abstract: Institutional incentives are widely used to promote cooperation among autonomous, self-regarding agents, from human societies to multi-agent and AI systems. Existing work typically treats incentive design as a bi-objective problem: minimise institutional cost while achieving a high long-run frequency of cooperation. Whether such schemes also maximise social welfare - total population payoff net of institutional expenditure - has remained largely unexplored. We develop a welfare-centric framework for institutional incentives in finite, well-mixe
The paper addresses the growing complexity of multi-agent and AI systems requiring advanced incentive mechanisms for cooperation and societal alignment, reflecting current research frontiers in AI governance and coordination.
This research provides a framework for optimizing social welfare in AI systems, moving beyond simple cooperation metrics to ensure institutional incentives contribute to overall societal benefit, a critical aspect for the ethical deployment of autonomous agents.
The focus shifts from merely achieving cooperation to actively maximizing social welfare, implying a more nuanced approach to designing incentive structures for AI agents and multi-agent systems.
- · AI ethicists
- · Multi-agent system designers
- · Policymakers in AI governance
- · Generative AI platforms
- · Unregulated AI systems
- · Purely profit-driven AI development
- · Black-box incentive models
Refined incentive structures will be developed for AI agents to prioritize collective good over individual gain.
This could lead to more stable and socially beneficial AI ecosystems, reducing emergent undesirable behaviors.
Long-term, this research may inform the design of economic and social systems where AI agents play a significant role, potentially influencing human societal structures and norms.
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Read at arXiv cs.AI