This post is based on the paper: “Multi-attribute utility models as cognitive search engines”, by Pantelis P. Analytis, Amit Kothiyal, and Konstantinos Katsikopoulos that appeared in Judgment and Decision Making, Vol. 9, No. 5, September 2014, pp. 403–419. This post does not look at persistence (post Persistence or delay (post Decision Delay) when you believe that you need more alternatives, but when to quit your search and stop within the available alternatives.
In optimal stopping problems, decision makers are assumed to search randomly to learn the utility of alternatives; in contrast, in one-shot multi-attribute utility optimization, decision makers are assumed to have perfect knowledge of utilities. The authors point out that these two contexts represent the boundaries of a continuum, of which the middle remains uncharted: How should people search intelligently when they possess imperfect information about the alternatives? They pose the example of trying to hire a new employee faced with several dozen applications listing their skills and credentials. You need interviews to determine each candidate’s potential. What is the best way to organize the interview process? First, you need to decide the order in which you will be inviting candidates. Then, after each interview you need to decide whether to make an offer to one of the interviewed candidates, thus stopping your search. The first problem is an ordering problem and the second a stopping problem. If credentials were adequate, you would not need an interview, and if credentials were worthless, you would invite people for interviews randomly.