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Robustness of cooperation-fostering mechanisms under environmental uncertainty

Determine to what extent cooperation-aiding mechanisms for mixed-motive interactions—such as reputation systems that implement indirect reciprocity, partner selection, and trust-building—remain robust and effective at sustaining cooperation when agents face environmental uncertainty that obscures the alignment of their incentives (e.g., in public-goods settings where the underlying returns are uncertain).

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Background

The paper studies emergent cooperation among reinforcement learning agents in environments modeled by the Extended Public Goods Game (EPGG), where the multiplication factor governing public good returns may be uncertain. Prior work shows that social mechanisms including direct and indirect reciprocity, reputation, and partner selection can promote cooperation in mixed-motive settings, but typically assumes clear knowledge of incentives.

The authors highlight that real-world interactions often involve environmental uncertainty that can degrade cooperative outcomes. They explicitly note that the resilience of these cooperation-aiding mechanisms under such uncertainty has not been established, motivating their investigation into how reputation mechanisms and intrinsic rewards perform when agents are uncertain about the degree of incentive alignment.

References

To the best of our knowledge, however, it is still unclear to what extent such mechanisms remain robust even under conditions of environmental uncertainty, which has been shown to act as a detrimental factor in the provision of public goods in human groups . In particular, we are concerned with situations in which computational agents may be uncertain about how (mis-)aligned their interests actually are in the interaction at play: To what extent cooperation-aiding mechanisms remain effective under environmental uncertainty? This is the core research question of the paper.

Emergent Cooperation under Uncertain Incentive Alignment (2401.12646 - Orzan et al., 23 Jan 2024) in Introduction, State of the art