Failures in Forecasting: An Experiment on Interpersonal Projection Bias
with Tristan Gagnon-Bartsch
Revise and resubmit, Management Science
Using a real-effort experiment, we show that people project their current tastes onto others when forecasting others' behavior, even when their tastes are exogenously manipulated and transparently different. In the first part of our experiment, ``workers'' stated their willingness to continue working on a tedious task. We varied how many initial tasks workers completed before eliciting their willingness to work (WTW); some were relatively fresh when stating their WTW, while others were relatively tired. Later, a separate group of ``predictors''---who also worked on the task---guessed the WTW of workers in each state. We find: (i) tired workers were less willing to work than fresh workers; (ii) predictors (in aggregate) accurately guessed the WTW of workers when they were in the same state as the workers about whom they were predicting, but, (iii) when fresh predictors were guessing about tired workers, they substantially overestimated their WTW, and (iv) when tired predictors were guessing about fresh workers, they underestimated their WTW. Using an additional treatment, we find that workers also mispredicted their own future WTW, and we compare the magnitudes of intra- and interpersonal projection bias.
Reference Dependence and Attribution Bias: Evidence from Real-Effort Experiments
with Tristan Gagnon-Bartsch
American Economic Journal: Microeconomics, forthcoming.
We demonstrate that people's impressions of a real-effort task are shaped by the elation or disappointment they felt when first working on the task. In our experiments, participants learned from experience about one of two unfamiliar tasks, one clearly more onerous than the other. We manipulated participants' initial expectations about which task they would face: some were assigned their task by chance just prior to their initial experience, while others knew in advance which task they would face. In a second session conducted hours later, we elicited their willingness to work again on their previously assigned task. Participants assigned the less-onerous task by chance were more willing to work than those who faced it with certainty; conversely, those assigned the more-onerous task by chance were less willing to work than those who faced it with certainty. These qualitative results---and the fact that differences in willingness to work were observed hours after first impressions were formed---are consistent with a form of attribution bias wherein participants wrongly ascribed sensations of positive or negative surprise to the underlying disutility of their assigned task.
Learning with Misattribution of Reference Dependence
with Tristan Gagnon-Bartsch (lead author)
Journal of Economic Theory, published in 2022.
We examine errors in learning that arise when an agent who suffers attribution bias fails to account for her reference-dependent utility. Such an agent neglects how the sensation of elation or disappointment relative to expectations contributes to her overall utility, and wrongly attributes this component of her utility to the intrinsic value of an outcome. In a sequential-learning environment, this form of misattribution generates contrast effects in evaluations and induces a recency bias: the misattributor's beliefs over-weight recent experiences and under-weight earlier ones. In the long-run, a loss-averse misattributor will grow unduly pessimistic and undervalue prospects in proportion to their variability. Both the short and long-run properties of beliefs under misattribution suggest a tendency to abandon worthwhile prospects when learning from experience. We additionally show how misattribution introduces incentives for familiar forms of expectations management.
A Model of Relative Thinking
with Matthew Rabin and Joshua Schwartzstein
Review of Economic Studies, published in 2021
Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings, the model predicts context effects such as the attraction effect, but predicts meaningful bounds on such effects driven by the intrinsic utility for the choices. In risky environments, a person is less likely to sacrifice utility on one dimension to gain utility on another that is made riskier. For example, a person is less likely to exert effort for a fixed monetary return if there is greater overall income uncertainty. We design and run experiments to test basic model predictions, and find support for these predictions.
A Neurocomputational Model of Altruistic Choice and Its Implications
with Cendri Hutcherson (lead author) and Antonio Rangel
Neuron, published in 2015
We propose a neurocomputational model of altruistic choice and test it using behavioral and fMRI data from a task in which subjects make choices between real monetary prizes for themselves and another. We show that a multi-attribute drift-diffusion model, in which choice results from accumulation of a relative value signal that linearly weights payoffs for self and other, captures key patterns of choice, reaction time, and neural response in ventral striatum, temporoparietal junction, and ventromedial prefrontal cortex. The model generates several novel insights into the nature of altruism. It explains when and why generous choices are slower or faster than selfish choices, and why they produce greater response in TPJ and vmPFC, without invoking competition between automatic and deliberative processes or reward value for generosity. It also predicts that when one’s own payoffs are valued more than others’, some generous acts may reflect mistakes rather than genuinely pro-social preferences.
Pavlovian Processes in Consumer Choice: The Physical Presence of a Good Increases Willingness-to-Pay
with Lindsay M. King, Colin Camerer, and Antonio Rangel
American Economic Review, published in 2010
This paper describes a series of laboratory experiments studying whether the form in which items are displayed at the time of decision affects the dollar value that subjects place on them. Using a Becker-DeGroot auction under three different conditions—(i) text displays, (ii) image displays, and (iii) displays of the actual items—we find that subjects' willingness-to-pay is 40–61 percent larger in the real than in the image and text displays. Furthermore, follow-up experiments suggest the presence of the real item triggers preprogrammed consummatory Pavlovian processes that promote behaviors that lead to contact with appetitive items whenever they are available.
NEW STUFF (IN PROGRESS)
Risk and Loss Attitudes Vary Across Domains
(in submission as short paper)
(One-Sentence Summary) In a simple experiment, I show that people act significantly more loss averse (both in magnitude and economic import) over non-monetary vs monetary outcomes.
Understanding (Biased) Beliefs amongst Breast Cancer Patients
with Daniel Isaac and the support of the Karmanos Cancer Institute
(Two-Sentence Summary) Patient treatment decisions are often shaped by their beliefs about the efficacy and long-term prognosis of the treatment, yet those beliefs are seldom measured. We are measuring these beliefs to better understand issues in adherence and treatment selections.
Heterogeneous Tastes and Social (Mis)Learning
with Tristan Gagnon-Bartsch
(One-Sentence Summary) We provide experimental evidence that heterogenous preferences across agents leads participants to err extracting information after observing others’ actions; this error is consistent with an egocentric bias in the interpretation of others’ actions.
Context-Dependent Choice and Scanner Data
with Michael Conlin, Katie Harris-Lagoudakis, and Andrew VanZytveld
(One-Sentence Summary) We utilize grocery scanner data to examine how the attributes of unchosen alternatives in a category (e.g., eggs) alter a person’s relative preference amongst products in that category.
Testing for Implicit Statistical Biases
with Ned Augenblick
(One-Sentence Summary) Can we detect "true" beliefs about statistical processes in settings where people might cling to biased beliefs but report objective probabilities accurately?