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Negotiation, Organizations & Markets

Negotiation, Organizations & Markets

  • Faculty
  • Curriculum
  • Seminars & Conferences
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    • September 2023
    • Article

    A Pull versus Push Framework for Reputation

    By: Jillian J. Jordan

    Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation framework that centers the perspective of evaluators by suggesting that reputation systems can create two fundamentally different incentives for evaluators to reward positive reputations. Evaluators may be pulled towards 'good' actors to benefit directly from their reciprocal cooperation, or pushed to cooperate with such actors by normative pressure. I discuss how psychology and behavior might diverge under pull versus push mechanisms, and use this framework to deepen our understanding of the empirical reputation literature and suggest ways that we may better leverage reputation for social good.

    • September 2023
    • Article

    A Pull versus Push Framework for Reputation

    By: Jillian J. Jordan

    Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation framework that centers the perspective of evaluators by suggesting that reputation...

    • 2023
    • Working Paper

    How People Use Statistics

    By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer

    We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler’s Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.

    • 2023
    • Working Paper

    How People Use Statistics

    By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer

    We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other,...

    • 2023
    • Working Paper

    Channeled Attention and Stable Errors

    By: Tristan Gagnon-Bartsch, Matthew Rabin and Joshua Schwartzstein

    We develop a framework for assessing when somebody will eventually notice that she has a misspecified model of the world, premised on the idea that she neglects information that she deems—through the lens of her misconceptions—to be irrelevant. In doing so, we assess the attentional stability of both general psychological biases—such as naivete about present bias—and empirical misconceptions—such as false beliefs about consumer demand. We explore which combinations of errors and environments allow an error to persist, versus which errors lead people to incidentally learn they have things wrong because even the data they deem relevant tells them that something is amiss. We use the framework to shed light on why fresh eyes are valuable in organizational problems, why people persistently use overly coarse (vs. overly fine) categorizations, why people sometimes recognize their errors in complex environments when they don’t in simple environments, and why people recognize errors in others that they don’t recognize in themselves.

    • 2023
    • Working Paper

    Channeled Attention and Stable Errors

    By: Tristan Gagnon-Bartsch, Matthew Rabin and Joshua Schwartzstein

    We develop a framework for assessing when somebody will eventually notice that she has a misspecified model of the world, premised on the idea that she neglects information that she deems—through the lens of her misconceptions—to be irrelevant. In doing so, we assess the attentional stability of both general psychological biases—such as naivete...

About the Unit

The NOM Unit seeks to understand and improve the design and management of systems in which people make decisions: that is, design and management of negotiations, organizations, and markets. In addition, members of the group share an abiding interest in the micro foundations of these phenomena.

Our work is grounded in the power of strategic interaction to encourage individuals and organizations to create and sustain value (in negotiations, in organizations, and in markets). We explore these interactions through diverse approaches: Although many of us have training in economics, we also have members with backgrounds in social psychology, sociology, and law.

NOM seeks to apply rigorous scientific methods to real-world problems -- producing research and pedagogy that is compelling to both the academy and practitioners.

Recent Publications

A Pull versus Push Framework for Reputation

By: Jillian J. Jordan
  • September 2023 |
  • Article |
  • Trends in Cognitive Sciences
Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation framework that centers the perspective of evaluators by suggesting that reputation systems can create two fundamentally different incentives for evaluators to reward positive reputations. Evaluators may be pulled towards 'good' actors to benefit directly from their reciprocal cooperation, or pushed to cooperate with such actors by normative pressure. I discuss how psychology and behavior might diverge under pull versus push mechanisms, and use this framework to deepen our understanding of the empirical reputation literature and suggest ways that we may better leverage reputation for social good.
Keywords: Reputation; Behavior; Game Theory
Citation
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Related
Jordan, Jillian J. "A Pull versus Push Framework for Reputation." Trends in Cognitive Sciences 27, no. 9 (September 2023): 852–866.

How People Use Statistics

By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
  • 2023 |
  • Working Paper |
  • Faculty Research
We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler’s Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.
Keywords: Decision Choices and Conditions; Microeconomics; Mathematical Methods; Behavioral Finance
Citation
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Bordalo, Pedro, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon, and Andrei Shleifer. "How People Use Statistics." NBER Working Paper Series, No. 31631, August 2023.

Channeled Attention and Stable Errors

By: Tristan Gagnon-Bartsch, Matthew Rabin and Joshua Schwartzstein
  • 2023 |
  • Working Paper |
  • Faculty Research
We develop a framework for assessing when somebody will eventually notice that she has a misspecified model of the world, premised on the idea that she neglects information that she deems—through the lens of her misconceptions—to be irrelevant. In doing so, we assess the attentional stability of both general psychological biases—such as naivete about present bias—and empirical misconceptions—such as false beliefs about consumer demand. We explore which combinations of errors and environments allow an error to persist, versus which errors lead people to incidentally learn they have things wrong because even the data they deem relevant tells them that something is amiss. We use the framework to shed light on why fresh eyes are valuable in organizational problems, why people persistently use overly coarse (vs. overly fine) categorizations, why people sometimes recognize their errors in complex environments when they don’t in simple environments, and why people recognize errors in others that they don’t recognize in themselves.
Keywords: Attentional Stability; Cognition and Thinking; Attitudes; Information; Theory
Citation
Read Now
Related
Gagnon-Bartsch, Tristan, Matthew Rabin, and Joshua Schwartzstein. "Channeled Attention and Stable Errors." Working Paper, August 2023.

Negative Expressions Are Shared More on Twitter for Public Figures Than for Ordinary Users

By: Jonas P. Schöne, David Garcia, Brian Parkinson and Amit Goldenberg
  • July 2023 |
  • Article |
  • PNAS Nexus
Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with tweets’ content. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media—public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers and thus the strength of ties, and proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed to mitigate overexposure to negative content.
Keywords: Social Media; Emotions
Citation
Read Now
Related
Schöne, Jonas P., David Garcia, Brian Parkinson, and Amit Goldenberg. "Negative Expressions Are Shared More on Twitter for Public Figures Than for Ordinary Users." PNAS Nexus 2, no. 7 (July 2023).

How Reputation Does (and Does Not) Drive People to Punish Without Looking

By: Jillian J. Jordan and Nour S. Kteily
  • July 11, 2023 |
  • Article |
  • Proceedings of the National Academy of Sciences
Punishing wrongdoers can confer reputational benefits, and people sometimes punish without careful consideration. But are these observations related? Does reputation drive people to people to “punish without looking”? And if so, is this because unquestioning punishment looks particularly virtuous? To investigate, we assigned “Actors” to decide whether to sign punitive petitions about politicized issues (“punishment”), after first deciding whether to read articles opposing these petitions (“looking”). To manipulate reputation, we matched Actors with co-partisan “Evaluators,” varying whether Evaluators observed i) nothing about Actors’ behavior, ii) whether Actors punished, or iii) whether Actors punished and whether they looked. Across four studies of Americans (total n = 10,343), Evaluators rated Actors more positively, and financially rewarded them, if they chose to (vs. not to) punish. Correspondingly, making punishment observable to Evaluators (i.e., moving from our first to second condition) drove Actors to punish more overall. Furthermore, because some of these individuals did not look, making punishment observable increased rates of punishment without looking. Yet punishers who eschewed opposing perspectives did not appear particularly virtuous. In fact, Evaluators preferred Actors who punished with (vs. without) looking. Correspondingly, making looking observable (i.e., moving from our second to third condition) drove Actors to look more overall—and to punish without looking at comparable or diminished rates. We thus find that reputation can encourage reflexive punishment—but simply as a byproduct of generally encouraging punishment, and not as a specific reputational strategy. Indeed, rather than fueling unquestioning decisions, spotlighting punishers’ decision-making processes may encourage reflection.
Keywords: Opposing Perspectives; Outrage Culture; Signaling; Ideology; Moralistic Punishment; Perspective; Behavior; Reputation; Decision Making
Citation
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Related
Jordan, Jillian J., and Nour S. Kteily. "How Reputation Does (and Does Not) Drive People to Punish Without Looking." Proceedings of the National Academy of Sciences 120, no. 28 (July 11, 2023).

Experimenting with Algorithm Resume Screening

By: Michael Luca, Jesse M. Shapiro, Adrian Obleton, Evelyn Ramirez and Nathan Sun
  • June 2023 |
  • Exercise |
  • Faculty Research
Citation
Related
Luca, Michael, Jesse M. Shapiro, Adrian Obleton, Evelyn Ramirez, and Nathan Sun. "Experimenting with Algorithm Resume Screening." Harvard Business School Exercise 923-050, June 2023.

Amplification of Emotion on Social Media

By: Amit Goldenberg and Robb Willer
  • June 2023 |
  • Article |
  • Nature Human Behaviour
Why do expressions of emotion seem so heightened on social media? Brady et al. argue that extreme moral outrage on social media is not only driven by the producers and sharers of emotional expressions, but also by systematic biases in the way people that perceive moral outrage on social media.
Keywords: Emotion; Perception; Prejudice and Bias; Emotions; Social Media
Citation
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Related
Goldenberg, Amit, and Robb Willer. "Amplification of Emotion on Social Media." Nature Human Behaviour 7, no. 6 (June 2023): 845–846.

Are You Listening to Me? The Negative Link between Extraversion and Perceived Listening

By: Francis J Flynn, Hanne Collins and Julian Zlatev
  • June 2023 |
  • Article |
  • Personality and Social Psychology Bulletin
Extraverts are often characterized as highly social individuals who are highly invested in their interpersonal interactions. We propose that extraverts' interaction partners hold a different view-that extraverts are highly social, but not highly invested. Across six studies (five preregistered; N = 2,456), we find that interaction partners consistently judge more extraverted individuals to be worse listeners than less extraverted individuals. Furthermore, interaction partners assume that extraversion is positively associated with a greater ability to modify one's self-presentation. This behavioral malleability (i.e., the "acting" component of self-monitoring) may account for the unfavorable lay belief that extraverts are not listening.
Keywords: Extraversion; Listening; Self-monitoring; Sociability; Interaction; Interpersonal Communication; Perception
Citation
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Related
Flynn, Francis J., Hanne Collins, and Julian Zlatev. "Are You Listening to Me? The Negative Link between Extraversion and Perceived Listening." Personality and Social Psychology Bulletin 49, no. 6 (June 2023): 837–851.
More Publications

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Harvard Business Publishing

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    15 Rules for Negotiating a Job Offer

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Seminars & Conferences

Sep 27
  • 27 Sep 2023

Aaron Kay, Duke's Fuqua School of Business

Negotiation, Organizations & Markets (NOM) Seminar
→More Seminars & Conferences

Faculty Positions

Harvard Business School seeks candidates in all fields for full time positions. Candidates with outstanding records in PhD or DBA programs are encouraged to apply.
→Learn More

Contact Information

Negotiation, Organizations & Markets Unit
Harvard Business School
Baker Library | Bloomberg Center
Soldiers Field
Boston, MA 02163
NOM@hbs.edu

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