Reducing Optimism Bias in Incomplete Cooperative Games
CoRR(2024)
摘要
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.
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