Reducing Optimism Bias in Incomplete Cooperative Games

TL;DR

This paper introduces a framework to strategically reveal coalition values in cooperative games to reduce players' over-optimistic expectations, improving decision-making efficiency in AI applications.

Contribution

It proposes methods to optimize the sequence of revealing coalition values, minimizing optimism bias with theoretical analysis and empirical validation.

Findings

Developed analytical tools for optimistic game completions

Created algorithms for offline and online coalition value revelation

Demonstrated improved expectation alignment in practical scenarios

Abstract

Cooperative game theory has diverse applications in contemporary artificial intelligence, including domains like interpretable machine learning, resource allocation, and collaborative decision-making. However, specifying a cooperative game entails assigning values to exponentially many coalitions, and obtaining even a single value can be resource-intensive in practice. Yet simply leaving certain coalition values undisclosed introduces ambiguity regarding individual contributions to the collective grand coalition. This ambiguity often leads to players holding overly optimistic expectations, stemming from either inherent biases or strategic considerations, frequently resulting in collective claims exceeding the actual grand coalition value. In this paper, we present a framework aimed at optimizing the sequence for revealing coalition values, with the overarching goal of efficiently closing the gap between players' expectations and achievable outcomes in cooperative games. Our contributions are threefold: (i) we study the individual players' optimistic completions of games with missing coalition values along with the arising gap, and investigate its analytical characteristics that facilitate more efficient optimization; (ii) we develop methods to minimize this gap over classes of games with a known prior by disclosing values of additional coalitions in both offline and online fashion; and (iii) we empirically demonstrate the algorithms' performance in practical scenarios, together with an investigation into the typical order of revealing coalition values.