Method vs. Preference Learning In Decision Analytic Preference Assessment

'This award is funded under the American Recovery and Reinvestment Act of 2009

(Public Law 111-5).'

Decision analysis offers a system for helping decision makers understand, assess, and model their preferences. It assumes, however, that people's preferences are coherent and unique. Psychologists and behavioral economists have documented a variety circumstances in which people's choices exhibit biases and inconsistencies particularly when they face novel decision situations. Similar inconsistencies arise when decision analysts work with decision makers to assess their preferences and trade-offs, because the methods the analyst uses are often unfamiliar to the decision makers. In these kinds of situations, decision makers in a sense 'construct' their preferences. Because the construction process can depend to a great extent on the particular questions posed by the analyst, a variety of different biases can arise. Those biases can be amplified in novel decision domains for which the decision maker's preferences are still maturing.

The objective of this research is to understand the interplay between learning about one's preferences in novel domains and learning about the process of assessing one's preferences. Laboratory experiments will provide a basis for developing a behavioral model of the preference-assessment process. In turn, the model will show how to create methods that allow an analyst to assist a decision maker in learning about and expressing his or her preferences in a consistent way in new situations while avoiding biases due to the questions and approaches used. These methods will be field tested in the context of deciding how to restore severely degraded oyster fisheries in North Carolina.