Behavioral Science Workshops

Invited guests, faculty, and students present current research in decision-making and judgment in our workshop series. The emphasis of our workshop series is on behavioral implications of decision and judgment models.

 Workshop Details

• Where: Chicago Booth Harper Center, Classroom C06 - Note this year's workshops will be offered IN-PERSON ONLY
• When: Mondays 10:10–11:30 a.m. (unless otherwise noted)
• "Who can attend: Workshops are open to Roman Family Center faculty, researchers, staff, and students, plus invited guests. Additional requests to attend the workshop are handled on a case-by-case basis. Please email Tricia.Nicholson@chicagobooth.edu if you’d like to attend.

Spring Workshop Series 

Note: Talk titles and abstracts will be added as throughout the quarter.

Monday, March 20, 2023

Stephen Spiller
UCLA

"Differences in Ability Can Masquerade as Differences in Overconfidence"

Abstract: Numerous studies find that measures of overestimation or overplacement of performance are predictive of various outcomes (e.g., status, narcissism, anxiety), indicating they are correlates of positively-biased self-assessments. Such individual differences in overconfidence are frequently measured using either a residual measure or a difference measure. The residual measure measures overconfidence as residuals from a regression of self-evaluations of performance on objective performance. The difference measure measures overconfidence as the difference between self-evaluations of performance and objective performance. Through analysis and simulation, I show that in an unbiased, well-calibrated population, both the residual measure and the difference measure are confounded with ability: whenever ability is associated with an outcome, the residual and difference measures will indicate that apparent overconfidence is also associated with that outcome, even if every individual accurately assesses their own skill and assesses their own performance according to normative principles. Reanalysis of previously collected data indicates that overconfidence is predictive of subsequent performance, consistent with the model’s prediction that it is confounded with ability. I propose an approach to calculate a model-derived null hypothesis. Reported relationships between measures of overconfidence and other constructs may represent relationships with ability rather than positively-biased self-evaluations.

Monday, March 27

PhD Admit Day Presentation

Monday, April 3

Ellen Evers
UC Berkeley

"Guilt about spending as a form of transaction disutility"

Abstract: The way consumers handle money often appears to conflict with theories on how consumers should use money. Despite money being fungible, the way preferences are expressed (e.g., choice, willingness to pay, or willingness to work) often appears to affect preferences. Similarly, the source of money (discretionary income, a surprising windfall, birthday money) also appears to affect what consumers buy with this money. In this paper we present a simple, psychologically intuitive model that we argue can explain and provide insight in these acquisition- and source-effects on preferences, and can be used to generalize current findings and generate new predictions.

Monday, April 10

Julian De Freitas
Harvard

"Public Perception and Autonomous Vehicle Liability"

Abstract: Both the societal and economic benefits of fully automated vehicles will depend in part on whether they are cheaper or more expensive to insure than regular, human-driven vehicles. I will explore how public perception may affect this outcome. 

Monday, April 17

Alex Kale 
University of Chicago, Computer Science

"Data Visualization for Decision Making"

Abstract: When visualization researchers and practitioners talk about the value of data visualization, one of the most commonly cited use cases is helping people make informed decisions. It is often assumed that people making decisions will benefit from mere information exposure, that if one can read data off of a chart, they will be able to use that information for the purpose of decision making. However, relatively little behavioral research on data visualization has applied theories and models from economics to investigate how well chart users can make utility-optimal decisions when relying on different displays. I present an experiment that investigates how well various forms of uncertainty visualization support decision making, specifically an incentivized choice about whether to pay for an intervention. In this study, I looked not only at which visualization designs best support decisions but also how well those same representations support chart users in estimating the effect size of the intervention versus the status quo. Through a combination of hypothesis testing, descriptive behavioral modeling, and qualitative analysis of users’ self-reported chart interpretation strategies, I explain the effectiveness of various visual representations in terms of the heuristics or visual reasoning strategies that people apply when making decisions with uncertainty visualizations. I discuss how incorporating economic formalisms and knowledge of visual reasoning strategies into visualization research can help us build data analysis software that is better aligned with natural tendencies of human judgment and decision making.

Monday, April 24

Ashley Whillans
Harvard

"How Gender and Status Shape Informal Work Negotiations"

Abstract: Most research that explores the role of gender and workplace status in shaping negotiation outcomes has studied formal settings, where there are clearly defined norms or behavioral scripts such as salary negotiations. However, employees frequently engage in negotiations outside of formal settings, and for resources other than money. In this program of research, we explore how gender and workplace status shape people’s willingness to engage in an informal negotiation with implications for productivity and wellbeing: asking for extra time on a work deadline. First, using archival data, surveys, and experiments, we identify a novel factor that predicts gender differences in time stress and burnout. Across academic and professional settings, women are less likely to ask for more time when working under adjustable deadlines (Studies 1-4a). Women’s discomfort in asking for more time on adjustable deadlines uniquely predicts time stress and burnout controlling for marital status, industry, tenure, and delegation preferences (Study 1). Women are less likely to ask for more time to complete their tasks because they hold stronger beliefs that they will be penalized for these requests and worry more about burdening others (Studies 1-2d). We find no evidence that women are judged more harshly than men (Studies 3-5). We also document an organizational intervention: formal processes for requesting deadline extensions reduce gender differences in asking for more time (Studies 6a-7). Second, we conduct three studies (Studies 8-11) in two diverse cultural contexts – the US and Kenya – to explore how other status-linked social identities influence the willingness to engage in informal work negotiations. Consistent with the idea that gender and status are not synonymous, different mediational profiles emerge. Women feel less comfortable asking for time due to a fear of burdening their manager, while lower status individuals feel more concerned with appearing incompetent. When participants are randomly assigned to consider asking for more time or accepting an offer for more time from one’s manager, women (vs. men) and lower status employees feel less comfortable asking vs. accepting an extension. Collectively, these studies shed light on when and how gender and workplace status shape people’s willingness to engage in informal negotiations that could improve the quality of their work and work-life balance. 

Monday, May 1

Simon Dedeo
Carnegie Mellon

"Possibility Architectures: Exploring Human Communication with Generative AI" 

Abstract: What we mean is given not just by what we say, but the things we could have said but didn't. More than that: as we navigate the possibilities before us, we conjure, at a distance, the ones we will later encounter. These emergent architectures, the product of cultural evolution, are crucial to understanding human communication. They appear as everything from verbal tics and cliché to higher-level figurative constructs such as irony, metaphor, and style. They sculpt possibility space on different timescales in ways that answer to cognitive bottlenecks and resource constraints, and social demands ranging from empathetic collaboration to self-censorship. Applying information-theoretic tools to the outputs of GPT-2, ChatGPT, and BERT, we reveal basic patterns in these possibility architectures. Functional, "Bauhaus" prose from the New York Times, for example, arranges possibilities in dense but predictable ways that are very different from the ill-defined Levy-flights of dream journal and the Gothic structure of Cormac McCarthy's No Country for Old Men. Our work reveals new scientific possibilities for Generative AI, and new insights into what — for now — makes us uniquely human. 

Monday, May 8

Dmitry Taubinsky
UC Berkeley 

"A Hybrid Approach to Behavioral Welfare Analysis"

Abstract: We develop an approach to behavioral welfare analysis that combines revealed preference techniques with self-reported subjective well-being techniques. We apply our approach to sharing dilemmas, including ones with an option to avoid sharing opportunities, and find three main results. First, self-reported happiness and satisfaction are not comprehensive measures of welfare, as people’s interpretation of these measures places a large weight on financial consequences but not enough weight on moral experiences like guilt. Second, introducing additional alternatives lowers people’s welfare from behaving more equitably, likely due to negative comparison effects. In particular, our third finding is that standard choice-based analysis of avoidance designs, where people have an option to avoid a sharing opportunity, does not accurately measure people’s welfare from sharing opportunities. This is because the addition of an avoidance option lowers the welfare people derive from existing options—a form of choice-set dependent preferences that choice-based analysis cannot identify. By contrast, our hybrid approach is applicable to situations where people’s choices and welfare are choice-set dependent and where self-reported happiness and satisfaction are not comprehensive measures of welfare. 

Monday, May 15

Neil Lewis Jr.
Cornell

"What We Learn from Where We Live"

Abstract: The United States has long been, and continues to be, a highly segregated society. When societies separate groups of people in the ways that we do in the U.S., that separation has not only economic, political, and sociological consequences, it also affects how people think and communicate about social issues and interventions to address them. In this talk, I will share recent findings from my program of research that has been using the United States as a context to examine how patterns of segregation and other forms of social stratification seep into the mind and affect how people perceive and make meaning of the world around them. I will also discuss the consequences of those meaning-making processes for people’s judgments, motivations, and decisions across multiple domains. I will conclude with implications of this research for social scientific theories, and the practical application of those theories.