Playing Divide-and-Choose Given Uncertain Preferences
Jamie Tucker-Foltz, Richard Zeckhauser

TL;DR
This paper analyzes the divide-and-choose method for fair division when players have uncertain, Bayesian preferences, revealing strategic differences and optimal division structures under various information and correlation scenarios.
Contribution
It characterizes optimal strategies and equilibria in divide-and-choose with uncertain preferences, providing algorithms and insights into strategic behavior and utility outcomes.
Findings
Optimal divisions depend on preference knowledge and correlation.
Diversification incentives lead to multiple goods being divided at equilibrium.
Utilities vary with the number of goods and information levels.
Abstract
We study the classic divide-and-choose method for equitably allocating divisible goods between two players who are rational, self-interested Bayesian agents. The players have additive values for the goods. The prior distributions on those values are common knowledge. We consider both the cases of independent values and values that are correlated across players (as occurs when there is a common-value component). We describe the structure of optimal divisions in the divide-and-choose game and identify several cases where it is possible to efficiently compute equilibria. An approximation algorithm is presented for the case when the distribution over the chooser's value for each good follows a normal distribution, along with a randomized approximation algorithm for the case of uniform distributions over intervals. A mixture of analytic results and computational simulations illuminates…
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Taxonomy
TopicsEconomic theories and models · Decision-Making and Behavioral Economics · Game Theory and Voting Systems
