Drazen Prelec - US grants

This is a collaborative proposal with George Loewenstein of Carnegie-Mellon University (SBR-9520891). The PIs propose to investigate the lack of personal consistency in patience across choice situations. The authors hypothesize that traditional economic discounting models are inadequate to explain such inconsistency. Instead the PIs propose that temporarily high discount rates are promoted by situations in which transient factors influence the desirability of choice alternatives. Such factors include drive states and emotions, for example. The following four hypotheses encompass their theory. First, changes in immediate transient factors have a greater impact on behavior than changes in delayed transient factors. Second, our actual response to a transient factor is greater than anticipated. Third, we underestimate the impact of future transient factors on our future behavior. Fourth, people underestimate the impact of transient factors on the behavior of other persons. The authors propose to do four studies, each of which will manipulate a transient factor. The basic paradigm is to offer participants a choice of an inferior option now or a superior one later. The PIs will observe how the choice behavior is influenced by varying the transient motivation for the options.

Drazen Prelec

Cognitive Resources and Emotion in Time Discounting


It is a basic finding in economics and psychology that people heavily undervalue or discount future events, and we can see evidence of it in many areas of our lives. In financial decisions, many of us find it hard to start to save money for the distant prospect of our retirement or for our children's education. We fail to take proper care of our health, putting off exercise, eating unhealthy snacks, avoiding vaccinations or the dentist, smoking a cigarette or having unprotected sex. Although a great deal of research has demonstrated such discounting, very little is known about why it occurs. Understanding the psychological mechanisms of undervaluation is the aim of the current research.

Preliminary findings indicate that the valuation of a future may combine two processes, an initial a-temporal rapid affective judgment followed by slower cognitive adjustment that incorporates temporal information. Pilot data indicate that operations that disrupt the cognitive component (like being forced to decide under time pressure or while distracted by another task) reduce the amount of discounting. Somewhat surprisingly, giving people more time to think things through increases rather than decreases the appeal of immediate rewards.

We interpret these changes in valuation in light of the psychological literature on the role of affect in decision making. This literature suggests that, while thoughts may be complex, slow and effortful, feelings are simple, rapid and effortless. It is also true that, in making judgments, people will use their feelings as information. Therefore, initial judgments of people or objects are often based first on their feelings about the object and then adjusted more slowly or modified by their thoughts. In our pilot studies, the valuations of far future events changed when cognitive resources were restricted, suggesting that these valuations had a cognitive component that is disrupted by the resource manipulation. The valuations of the near future events were relatively immune to the restriction of cognitive resources, suggesting these valuation judgments are more feelings-based.

With the proposed research, we will extend these preliminary findings to real outcomes and look more closely into the role of affect in the valuation process. These findings will have significant implications for a wide range of decisions. From a normative standpoint, the novel implication of the research is that thinking harder or more thoroughly about a decision may perversely increase the rate at which future outcomes are discounted. The disregard of future outcomes shown therefore may not be simply a case of cognitive myopia, as is commonly thought. On the account proposed here, it is the result of an active cognitive process that counteracts the initial emotional response to a future event.

The proposal objective is to experimentally test and theoretically extend the "Bayesian Truth Serum", a new method for obtaining superior quality information from respondents in opinion research or expert judgment settings. The method rests on a mathematical scoring algorithm that gives respondents an incentive to provide truthful and carefully considered answers to questions, even though only the respondent knows which answer best matches to their truthful opinion. The method could be used to collect truthful expert forecasts of events in the far future (e.g., whether mankind will survive past year 2100), truthful expression of personal tastes or intentions (e.g., intentions to vote), and truthful self-reports of personal behaviors and characteristics (e.g., risky sexual practices).

The main idea behind the scoring algorithm is to give high scores to answers that are more common than predicted, with predictions generated by the same respondents who answer the questions. It follows from Bayesian reasoning that truth-telling maximizes expected score even for a respondent who is sure that their truthful opinion represents a minority view. Furthermore, in situations where there is a single objectively correct answer to a question (i.e., a question dealing with objective knowledge), the scoring algorithm should be able to identify the objectively correct answer even if the majority of respondents endorse a different, incorrect answer.

This research project will investigate how survey accuracy can be improved using the wisdom of crowds. Surveys are important tools for making inferences about current attitudes, opinions, and behaviors as well as making forecasts about future trends. However, they are facing threats to their validity that can decrease the accuracy of their inferences. The project will explore the value of two methods based on the wisdom of crowds for improving survey accuracy. One method entails using respondents' knowledge of their social circles, that is, asking respondents to predict the behavior of friends and family. The other method is a scoring procedure that rewards honest and careful answers. By conducting fine-grained tests of the mechanisms underlying these methods, the project will provide insights regarding how to reduce common threats to the validity of surveys. The project also will inform ways to capture the early indications of opinion change relevant for elections and other societal trends. The results could change the way election polls are conducted and improve election predictions. Data from this project will be made publicly available. The results will be disseminated broadly to scientists, practitioners in survey research, polling companies, politicians, policy makers, public educators, journalists, and the general public.

This research project will examine how political polling accuracy can be improved by incorporating respondents' knowledge of their social-circles and by scoring respondents' answers with the Bayesian Truth Serum algorithm. Social-circle expectation questions ask respondents how their family and friends will vote. Bayesian Truth Serum is a scoring method that can be used to incentivize and selectively weight participants who provide honest and careful answers. The investigators will assess how the combination of both methods can overcome four recognized threats to polling validity: biased samples, careless responding, socially desirable responding, and social dynamics. A longitudinal survey with a probabilistic national sample will be conducted. The survey will consist of three waves before the 2020 U.S. presidential election and one wave after the election. The investigators will compare traditional survey questions with wisdom-of-crowd methods. Benchmarks will include other concurrent polls and forecasting models and eventually the actual election results and a post-election survey.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.