NSF Award
2350258
The next time you need a forecast, stop and ask yourself if you could wait for it. Chances are, especially in this age of accelerations we live in, you want that forecast now. Not in a few minutes. Not in an hour. Certainly not after the forecaster can collect more information. You want it now. You want the best estimate based on the information they have right then. This demand implies that accurate and timely forecasts are valued. How does time pressure impact subjective probability judgments (SPs), how do they change over time, and how much of a trade-off is there between accurate forecasts and timely ones? It is hard to answer these questions because extant theories of SPs have focused on accuracy. Most of them are silent about how SPs evolve as they are constructed with the forecaster's information. This project seeks to answer these questions using computational modeling and behavioral experiments to map the time course of SPs. First, the computational model predicts the SPs people generate in response to questions such as "What is the probability that the University of Kansas men's basketball team will win this year's tournament” and predicts the time it takes people to generate the judgment. Second, a set of empirical studies advance understanding of how people generate the judgments and how time pressure impacts them. The computational framework developed in this project informs the design of prediction polls in terms of number of questions, time pressure, and incentive structures. The project also serves to train students in computational modeling and advance STEM education as the PI integrates data science training within the liberal arts and science curriculum. Developing methods to model response times and judgments create opportunities to expand algorithms' predictive power and provide a channel for social and behavioral scientists to play an active role in developing the field of data science.<br/><br/>This project seeks to provide a dynamic account of how people generate subjective probability (SP) forecasts. This proposal has three aims. The first aim is to develop a computational model of belief formation and the dynamics of SPs that result from this process (Modeling the Dynamics of SPs Aim). Such a model enables to predict SPs, how they change over the briefest of time intervals as people construct a belief and predict how time pressure impacts SPs. But, modeling the dynamics of SPs requires a mechanistic understanding of how belief evolves. To this end, the second aim empirically tests a set of consistency principles of contemporary theories of SPs (Tests of Consistency Principles Aim). These principles state that the evidence or support people recruit about a hypothesis is independent of the alternative hypotheses. Analogous assumptions have been made in the domain of preference, and violations are well established via so-called context effects. These context effects have been diagnostic in identifying the cognitive architecture underlying the construction of preference. Data suggest this may also be the case for the construction of belief. This project rigorously tests these effects across time. Using the computational model of SPs the project also examines the optimal policy for trading off speed and accuracy as forecasters report their SPs over a series of to-be-predicted events. Together, the project empirically-validates a computational framework of SPs that help isolate mechanisms that may inhibit people from giving accurate and timely forecasts; and develop interventions to improve the efficiency of obtaining SPs.<br/><br/>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.
