Modeling Settlement Bargaining with Algorithmic Game Theory

Abstract

Past computational models of settlement bargaining have lacked explicit game theoretic foundations. Algorithmic game theory, however, offers techniques that can nd perfect Bayesian equilibria even where closed-form mathematical solutions may be intractable. Some recent mathematical models tackle two-sided asymmetric information, including evidentiary signals models, in which the judgment is a sum of both shared and independent private information, and correlated signals models, in which both parties receive noisy signals about the same information. To relax assumptions inherent in these models, this paper employs several progressively more complicated techniques, including iterative elimination of dominated alternatives, no regret learning, and counterfactual regret minimization. Although these algorithms are not guaranteed to produce Nash equilibria in general-sum games like litigation, they nonetheless succeed in producing either exact or close approximate equilibria on discrete versions of the corresponding mathematical models. A single algorithmic game theory model can incorporate a number of features that state-of-the-art mathematical models cannot handle simultaneously, such as two-sided correlated signals of both liability and damages, risk aversion, and options to concede.