Fair Incentives for Early Arrival in 0-1 Cooperative Games

2025-11-14

AI Summary

In 0–1 cooperative games with unknown player arrival orders, existing early-arrival incentive mechanisms (e.g., I4EA) achieve Shapley value allocation only in expectation, failing to guarantee individual fairness for any single arrival sequence—critical contributors arriving late may receive zero payoff. Method: We propose the Egalitarian Value-Sharing (EVS) mechanism, which, for each fixed arrival sequence, minimizes the ℓ₁ distance between allocated payoffs and the Shapley values while maximizing payoff equality among participants. Contribution/Results: EVS is grounded in a refined fairness criterion that strictly preserves expectation consistency—i.e., long-run average allocations converge to the Shapley value—while substantially improving per-sequence fairness and safeguarding key players’ payoffs. Theoretical analysis and empirical evaluation confirm that EVS maintains overall efficiency while effectively bridging the gap between expected fairness and sequence-level fairness.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Incentives for early arrival (I4EA) was recently proposed for studying online cooperative games. In an online cooperative game, players arrive in an unknown order, and the value increase after each player arrived should be distributed immediately among all the arrived players. Although there is only one arriving order in the game, we also hope that the value distribution is equal to their Shapley value in expectation. To achieve these goals, the early solutions ignored the fairness in each single arriving order. More specifically, an important player may receive nothing in a game, which seems unfair in reality. To combat this, we propose refined fairness in this paper and design new solutions in 0-1 value games. Specifically, we compute the distance of the distribution in each order to the Shapley value and aim to minimize it. We propose a new mechanism called Egalitarian Value-Sharing (EVS) to do so. We also show that the mechanism can maximize the egalitarian welfare among all the players who made contributions.

Problem

• Addressing unfair value distribution in online cooperative arrival games
• Ensuring fair incentives for important players in 0-1 value games
• Minimizing distribution distance to Shapley value across arrival orders

Innovation

• Egalitarian Value-Sharing mechanism for fair distribution
• Minimizes distance to Shapley value in orders
• Maximizes egalitarian welfare among contributing players