Research

Working Papers

Agent Selection and Belief Polarization in Distributive Bargaining
Revise & Resubmit at Management Science
David Hagmann and Daniel Feiler
( Abstract ) (PDF) (OSF)

Costly Distractions: Focusing on Individual Behavior Undermines Support for Systemic Reforms
David Hagmann, Yi-tsen Liao, Nick Chater, and George Loewenstein
( Abstract ) (PDF) (OSF)

Beyond Persuasion: Improving Conversational Quality Around High-Stakes Interpersonal Disagreements
Julia A. Minson, David Hagmann, and Kara Luo
( Abstract ) (PDF) (OSF)

To Know or Not to Know? Cross-cultural Contrasts in Information Preferences
Min-heng Wang, David Hagmann, Greg Chih-Hsin Sheen, Emily Ho, Po-Yi Chen, George Loewenstein
( Abstract ) (PDF)

Hierarchical Instance-Based Learning for Decision Making from Delayed Feedback
Tailia Malloy, David Hagmann, Cleotilde Gonzalez
( Abstract ) (PDF)

Persuasion with Motivated Beliefs
David Hagmann and George Loewenstein
( Abstract ) (PDF)

Publications

Base Rate Neglect as a Source of Inaccurate Statistical Discrimination
Management Science (forthcoming)
David Hagmann, Gwendolin B. Sajons, and Catherine H. Tinsley
( Abstract ) (PDF) (OSF)

Personal Narratives Build Trust Across Ideological Divides
Journal of Applied Psychology (2024)
David Hagmann, Julia A. Minson, and Catherine H. Tinsley
( Abstract ) (PDF) (OSF)

Fear and Promise of the Unknown: How Losses Discourage and Promote Exploration
Journal of Behavioral Decision Making (2022)
Alycia Chin, David Hagmann, and George Loewenstein
( Abstract ) (PDF) (OSF)

Measuring Information Preferences
Management Science (2021)
Emily H. Ho, David Hagmann, and George Loewenstein
( Abstract ) (PDF) (OSF)
(Select Media: Scientific American, Choiceology Podcast)

Nudging Out Support For a Carbon Tax
Nature Climate Change (2019)
David Hagmann, Emily H. Ho, and George Loewenstein
( Abstract ) (PDF) (OSF)
(Select Media: ArsTechnica, Grist)
Honorable Mention for the Behavioral Science and Policy Association, 2022 Best Paper Award

A Behavioral Blueprint For Improving Health Care Policy 
Behavioral Science & Policy (2017)
George Loewenstein, David Hagmann, Janet Schwartz, Keith Ericson, Judd B. Kessler, Saurabh Bhargava, Jennifer Blumenthal-Barby, Thomas D’Aunno, Ben Handel, Jonathan Kolstad, David Nussbaum, Victoria Shaffer, Jonathan Skinner, Peter Ubel, & Brian J. Zikmund-Fisher
( Abstract ) (PDF)

Information Avoidance
Journal of Economic Literature (2017)
Russell Golman, David Hagmann, and George Loewenstein
( Abstract ) (PDF)
(Select Media: American Economic Association, YANSS Podcast)

Polya’s Bees: A Model of Decentralized Decision Making
Science Advances (2015)
Russell Golman, David Hagmann, and John H. Miller
( Abstract ) (PDF)

Warning: You Are About to be Nudged 
Behavioral Science & Policy (2015)
George Loewenstein, Cindy Bryce, David Hagmann, and Sachin Rajpal
( Abstract ) (PDF)

Endogenous Movement and Equilibrium Selection in Spatial Coordination Games
Computational Economics (2014)
David Hagmann and Troy Tassier
( Abstract ) (PDF)

Book Chapters

Conducting “Real Effort” Experiments
Edward Elgar Publishing (forthcoming)
David Hagmann and Luxi Shen
In: Handbook of Experimental Methods in the Social Sciences, edited by Alex Rees-Jones.

The Deep Structure of Deliberate Ignorance: Mapping the Terrain
MIT Press (2021)
Barry Schwartz, Peter J. Richerson, Benjamin E. Berkman, Jens Frankenreiter, David Hagmann, Derek M. Isaacowitz, Thorsten Pachur, Lael J. Schooler, and Peter Wehling
In: Deliberate Ignorance: Choosing Not to Know, edited by Ralph Hertwig and Christoph Engel. Strüngmann Forum Reports, vol. 29, J. R. Lupp, series editor. Cambridge, MA.
(PDF) (Book)

Consortium Co-Authorship

Reproducibility in Management Science
Management Science (2023)
Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali I. Ozkes, and the Management Science Reproducibility Collaboration
( Abstract ) (PDF)

Creative Destruction in Science
Organizational Behavior and Human Decision Processes (2020)
Warren Tierney, Jay H. Hardy III, Charles R. Ebersole, Keith Leavitt, Domenico Viganola, Elena Giulia Clemente, Michael Gordon, Anna Dreber, Magnus Johannesson, Thomas Pfeiffer, Hiring Decisions Forecasting Collaboration, and Eric Luis Uhlmann
( Abstract ) (PDF)

Agent Selection and Belief Polarization in Distributive Bargaining

The selection of an agent precedes most principal-agent relationships, but the consequences of this selection process remain largely unstudied. Across three preregistered experiments (N = 3,190), we show that principals choose agents who make overly aggressive offers. Principals’ selection leads to worse outcomes than if they chose randomly or negotiated on their own behalf, and could be improved by unilaterally selecting less aggressive agents. These less-aggressive agents, however, fail to persist in the market. Principals neglect the increasing polarization of the agent pool, continue to select relatively aggressive agents, and become more polarized in their own beliefs.

Costly Distractions: Focusing on Individual Behavior Undermines Support for Systemic Reforms

Policy challenges can typically be addressed both through systemic changes (e.g., taxes and mandates) and by encouraging individual behavior change. In this paper, we propose that, while in principle complementary, systemic and individual perspectives can compete for the limited attention of people and policymakers. Thus, directing policies in one of these two ways can distract the public’s attention from the other—an “attentional opportunity cost.” In two pre-registered experiments (n = 1,800) covering three high-stakes domains (climate change, retirement savings, and public health), we show that when people learn about policies targeting individual behavior (such as awareness campaigns), they are more likely to themselves propose policies that target individual behavior, and to hold individuals rather than organizational actors responsible for solving the problem, than are people who learned about systemic policies (such as taxes and mandates, Study 1). This shift in attribution of responsibility has behavioral consequences: people exposed to individual interventions are more likely to donate to an organization that educates individuals rather than one seeking to effect systemic reforms (Study 2). Policies targeting individual behavior may, therefore, have the unintended consequence of redirecting attention and attributions of responsibility away from systemic change to individual behavior.

Beyond Persuasion: Improving Conversational Quality Around High-Stakes Interpersonal Disagreements

Heated exchanges over controversial topics can harm relationships while failing to change minds. Across five pre-registered experiments (N = 3,963), we tested a brief intervention to mitigate the interpersonal costs of disagreement and increase the likelihood of future conversations around vaccine hesitancy. Vaccine-supportive participants randomly assigned to training in conversational receptiveness were seen as more reasonable and more trustworthy than those writing in their natural tone, and evaluated their (untrained) counterpart more favorably. They were as persuasive as those who were incentivized to be persuasive only, and their counterparts were more interested in learning their views on other topics. Finally, receiving training in conversational receptiveness and learning that one’s counterpart was similarly trained increased participants’ willingness to discuss vaccines by 50%. We discuss the implications of these findings in the context of the many goals individuals pursue in contentious conversations and the outcomes conflict scholars should consider in future research.

To Know or Not to Know? Cross-cultural Contrasts in Information Preferences

The Information Preferences Scale (IPS) functions as a psychological assessment tool, gauging individuals’ inclination or disinclination to seek more information in the face of potentially negative news, despite its potential value for future decision making and judgment. Although the scale has been applied across various domains of behavioral decision making and social sciences, it has not been cross validated in different languages. This study is, to our knowledge, the first effort to confirm the IPS’s cross-cultural comparability and its convergent validity with the DOSPERT scale. In Study 1, we first investigate whether the IPS exhibits measurement invariance (MI) across cultures, employing samples from both North America and East Asia (utilizing an original dataset collected from Taiwan for this study, with N = 1, 198). Then, we explore whether significant differences in response styles exist between the two samples. Our results, from a multigroup confirmatory factor analysis framework and conducting measurement invariance, suggest the feasibility of comparing information preferences across nations or cultures once partial scalar invariance is established. In addition, we discuss the cross-cultural behavioral implications based on differences in response styles and information preferences. In Study 2, we extended our analysis to a bilingual sample of business school students in Hong Kong (N = 374) and found that both the Chinese and English versions of the IPS reliably predict real-world information-seeking decisions, particularly regarding whether they are perceived by peers as possessing positive traits.

Hierarchical Instance-Based Learning for Decision Making from Delayed Feedback

In real-world decision making, outcomes are often delayed, meaning individuals must make multiple decisions before receiving any feedback. Moreover, feedback can be presented in different ways: it may summarize the overall results of multiple decisions (aggregated feedback) or report the outcome of individual decisions after some delay (clustered feedback). Despite its importance, the timing and presentation of delayed feedback has received little attention in cognitive modeling of decision-making, which typically focuses on immediate feedback. To address this, we conducted an experiment to compare the effect of delayed vs. immediate feedback and aggregated vs. clustered feedback. We also propose a Hierarchical Instance-Based Learning (HIBL) model that captures how people make decisions in delayed feedback settings. HIBL uses a super-model that chooses between sub-models to perform the decision-making task until an outcome is observed. Simulations show that HIBL best predicts human behavior and specific patterns, demonstrating the flexibility of IBL models

Persuasion with Motivated Beliefs

Considerable research finds that people derive utility not only from consumption, but also from their beliefs about themselves and the world. Rather than dispassionately updating their views in response to new information, such belief-based utility leads people to avoid information and use other strategies to protect their existing beliefs. We present a two-stage model of persuasion in the presence of belief-protecting strategies and test it in an incentive compatible persuasion experiment. Persuaders seek to shift receivers’ numeric estimates related to emotionally charged topics, such as abortion and racial discrimination. We manipulate whether the persuader first acknowledges her own lack of certainty and whether she first has an opportunity to build rapport with the receiver, which our theory predicts should enhance persuasiveness, but should be irrelevant or may even go in the opposite direction under the standard account.

Base Rate Neglect as a Source of Inaccurate Statistical Discrimination

Statistical discrimination relies on people inferring unobservable characteristics of group members based on their beliefs about the group. Across four pre-registered experiments (N = 9,002), we show that accurate information about the composition of top performers can induce incorrect beliefs about performance differences across groups when the groups are of unequal size. Because people fail to account for base rates, they underestimate the performance of individuals from smaller groups. As a result, when participants in our experiments receive true information about the gender composition of top performers in a male-dominated candidate pool, they are less likely to hire women, even when there are no gender differences in performance (Study 1). Similarly, they are less likely to hire better-performing non-White candidates when the racial demographics of the candidate pool reflect the US population (Study 4). We show that these choices reflect an error in statistical reasoning, rather than being motivated by a desire to discriminate against any particular group (Study 2). Despite leading to less accurate beliefs, participants disproportionately seek out information about top performers when given the choice, and discrimination thus persists when information selection is endogenous (Study 3).

Personal Narratives Build Trust Across Ideological Divides

Lack of trust is a key barrier to collaboration in organizations and is exacerbated in contexts when employees subscribe to different ideological beliefs. Across five preregistered experiments, we find that people judge ideological opponents as more trustworthy when these opponents support their opinions with self-revealing personal narratives than when they support their opinions with either data or with stories about third parties—even when the content of the messages is carefully controlled to be consistent. Trust does not suffer when explanations grounded in personal narratives are augmented with data, suggesting that our results are not driven by quantitative aversion. Perceptions of trustworthiness are mediated by the speaker’s apparent vulnerability and are greater when the self-revelation is of a more sensitive nature. Consequently, people are more willing to collaborate with ideological opponents who support their views by embedding data in a self-revealing personal narrative, rather than relying on data-only explanations. We discuss the implications of these results for future research on trust as well as for organizational practice.

Fear and Promise of the Unknown: How Losses Discourage and Promote Exploration

Many situations involving exploration, such as businesses expanding into new products or locations, expose the explorer to the potential for subjective losses. How does the potential to experience losses during the course of a search affect individuals’ appetite for exploration? In three incentivized studies, we manipulate search outcomes by presenting participants either with a gain-only environment or a gain-loss environment. The two environments offer objectively identical incentives for exploration: using a framing manipulation, we decrease gain-loss payoffs and provide participants an initial endowment to offset the difference. Participants decide how to explore a one-dimensional space, receiving payoffs based on their location each period. We predict and find that participants are motivated to avoid losses, which increases exploration when they are incurring losses, but decreases exploration when they face the potential for losses. We conclude that exploration is driven by hope of potential gains, constrained by fear of potential losses, and motivated by avoidance of experienced losses.

Measuring Information Preferences

Advances in medical testing and widespread access to the internet have made it easier than ever to obtain information. Yet, when it comes to some of the most important decisions in life, people often choose to remain ignorant for a variety of psychological and economic reasons. We design and validate an information preferences scale to measure an individual’s desire to obtain or avoid information that may be unpleasant but could improve future decisions. The scale measures information preferences in three domains that are psychologically and materially consequential: consumer finance, personal characteristics, and health. In three studies incorporating responses from over 2,300 individuals, we present tests of the scale’s reliability and validity. We show that the scale predicts a real decision to obtain (or avoid) information in each of the domains as well as decisions from out-of-sample, unrelated domains. Across settings, many respondents prefer to remain in a state of active ignorance even when information is freely available. Moreover, we find that information preferences are a stable trait but that an individual’s preference for information can differ across domains.

Nudging Out Support For a Carbon Tax

A carbon tax is widely accepted as the most effective policy for curbing carbon emissions but is controversial because it imposes costs on consumers. An alternative, ‘nudge,’ approach promises smaller benefits but with much lower costs. However, nudges aimed at reducing carbon emissions could have a pernicious indirect effect if they offer the promise of a ‘quick fix’ and thereby undermine support for policies of greater impact. Across six experiments, including one conducted with individuals involved in policymaking, we show that introducing a green energy default nudge diminishes support for a carbon tax. We propose that nudges decrease support for substantive policies by providing false hope that problems can be tackled without imposing considerable costs. Consistent with this account, we show that by minimizing the perceived economic cost of the tax and disclosing the small impact of the nudge, eliminates crowding-out without diminishing support for the nudge.

A Behavioral Blueprint For Improving Health Care Policy

Behavioral policy to improve health and health care often relies on interventions, such as nudges, which target individual behaviors. But the most promising applications of behavioral insights in this area involve more far-reaching and systemic interventions. In this article, we propose a series of policies inspired by behavioral research that we believe offer the greatest potential for success. These include interventions to improve health-related behaviors, health insurance access, decisions about insurance plans, end-of-life care, and rates of medical (for example, organ and blood) donation. We conclude with a discussion of new technologies, such as electronic medical records and web- or mobile-based decision apps, which can enhance doctor and patient adherence to best medical practices. These technologies, however, also pose new challenges that can undermine the effectiveness of medical care delivery.

Information Avoidance

We commonly think of information as a means to an end. However, a growing theoretical and experimental literature suggests that information may directly enter the agent’s utility function. This can create an incentive to avoid information, even when it is useful, free, and independent of strategic considerations. We review research documenting the occurrence of information avoidance, as well as theoretical and empirical research on reasons why people avoid information, drawing from economics, psychology, and other disciplines. The review concludes with a discussion of some of the diverse (and often costly) individual and societal consequences of information avoidance.

Polya’s Bees: A Model of Decentralized Decision-Making

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

Warning: You are About to be Nudged

Presenting a default option is known to influence important decisions. That includes decisions regarding advance medical directives, documents people prepare to convey which medical treatments they favor in the event that they are too ill to make their wishes clear. Some observers have argued that defaults are unethical because people are typically unaware that they are being nudged toward a decision. We informed people of the presence of default options before they completed a hypothetical advance directive, or after, then gave them the opportunity to revise their decisions. The effect of the defaults persisted, despite the disclosure, suggesting that their effectiveness may not depend on deceit. These findings may help address concerns that behavioral interventions are necessarily duplicitous or manipulative.

Endogenous Movement and Equilibrium Selection in Spatial Coordination Games

We study the effects of agent movement on equilibrium selection in network based spatial coordination games with Pareto dominant and risk dominant Nash equilibria. Our primary interest is in understanding how endogenous partner selection on networks influences equilibrium selection in games with multiple equilibria. We use agent based models and best response behaviors of agents to study our questions of interest. In general, we find that allowing agents to move and choose new game play partners greatly increases the probability of attaining the Pareto dominant Nash equilibrium in coordination games. We also find that agent diversity increases the ability of agents to attain larger payoffs on average.

Reproducibility in Management Science

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.

Creative Destruction in Science

Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents’ reasoning about day care options, and gender discrimination in hiring decisions.