To begin, we review a standard model of litigation decision-making in Section I and propose an extension of this model to include ex post evaluative dispute resolution in Section II. Next, in Section III, we review the analytical concept of the expected value of perfect information as a means of placing a theoretical rational maximum on the value of the information provided by evaluative dispute resolution processes. In Section IV, we review Bayes' theorem and propose this as a rational benchmark for the integration of new information with previously existing subjective probabilities. In Section V, we offer a formal statement of the research questions suggested above, relegating the normative implications of the rational expectations models in favor of a positive analysis of systematic deviation from these rational norms in actual dispute resolution practice. In Section VI, we describe the experiment in some detail. Samples of the instruments utilized are located in the appendix. We discuss the results of our research in Section VII and offer suggestions for future research. In Section VIII, we investigate some heuristics and biases that affect human judgment under uncertainty and consider their applicability in providing explanation for the findings of our research. Finally, we conclude that human decision-making behaviors in negotiation settings do, indeed, conspire to systematically undervalue the informational content of evaluative dispute resolution processes and that, once this information is procured, it is systematically underutilized. Only then do we briefly return to the broader evaluative/facilitative debate, proposing that these findings have important implications that will contribute to the deliberation.
Gregory Todd Jones and Douglas H. Yarn,
Evaluation Dispute Resolution under Uncertainty: An Empirical Look at Bayes' Theorem and the Expected Value of Perfect Information,
2003 J. Disp. Resol.
Available at: https://scholarship.law.missouri.edu/jdr/vol2003/iss2/8