# II.B Symmetric Information Models

Models with symmetric information can be divided into two broad categories. The ...rst category includes models of arbitration that typically assume symmetric information between the parties to a dispute and model the behavior of an arbitrator who has less information about the dispute than the parties. The second category includes models of litigation in which excessive optimism is the rationale for some disputes going to court.

Gibbons (1988) models the decision of an arbitrator under both conventional and ...nal-o¤er arbitration structures. The parties to the dispute observe equally noisy signals about the underlying merits of their dispute. The arbitrator observes a di¤erent and noisier signal; both parties know the distribution of the error in the arbitrator’s signal but do not observe the signal directly. In equilibrium, the arbitrator learns from parties’o¤ers and computes her ideal arbitration award, which is a function of the signal and of the average of parties’ o¤ers. When parties’o¤ers di¤er more, these o¤ers provide less precise information to the arbitrator and their average is assigned relatively lower weight in the arbitrator’s ideal award.<sup>2</sup> Also, as the parties’information about the case becomes relatively more important than publicly available information, the award places relatively higher weight on the average o¤er.

This decision rule is consistent with empirical evidence on the e¤ects of parties’o¤ers and arbitrators’awards. Farber and Bazerman (1986) study the arbitration decisions of the National Academy of Arbitrators in 25 cases. They ...nd that arbitrators placed relatively more weight on the known facts of the cases than on the o¤ers made by the parties to the dispute, and placed less weight on parties’ o¤ers as they diverged more. This is consistent with the notion that an arbitrator who mechanically splits the di¤erence between parties’ o¤ers will provide incentives for the parties to take unreasonable or extreme positions. Rather than mechanically splitting the di¤erence, if an arbitrator responds to extreme positions by placing less weight on parties’o¤ers, parties have incentives to submit more reasonable o¤ers.

Boden (1992) works with data from 204 disability claims in Maryland, and estimates the adjudicated award as a function of the disability ratings proposed by the physicians of the claimant and the defense, as well as other known facts about the claimant and the injury. He ...nds that few facts relating to the case add explanatory power to the prediction of the adjudicated award, after including the parties’ positions. Using data on settled claims, he ...nds that when parties settle they place even less weight on the facts of the case than an arbitrator would, so that the compensation paid is practically always the average of the parties’o¤ers. Moreover, Boden ...nds that as parties’positions become more disparate, both adjudicated awards and settlements continue to place a large weight on the o¤ers. Nevertheless, the data imply that physicians

> 2 Given a signal of the underlying facts received by the arbitrator and a position taken by the opposing party, the more aggressive a party’s o¤er is, the lower the weight it will receive in computing the arbitrator’s award, and the less likely it is to be chosen as the award under ...nal-o¤er arbitration.

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