The Emergence of 'Us and Them' in 80 Lines of Code: Modeling Group Genesis in Homogeneous Populations

Kurt  Gray; David G. Rand; Eyal  Ert; Kevin  Lewis; Steve  Hershman; Michael I. Norton

Article | Psychological Science | April 2014

The Emergence of 'Us and Them' in 80 Lines of Code: Modeling Group Genesis in Homogeneous Populations

by Kurt Gray, David G. Rand, Eyal Ert, Kevin Lewis, Steve Hershman and Michael I. Norton

Abstract

Psychological explanations of group genesis often require population heterogeneity in identity or other characteristics, whether deep (e.g., religion) or superficial (e.g., eye color). We use game-theoretical agent-based models to explore group genesis in homogeneous populations and find robust group formation with just two basic principles: reciprocity and transitivity. These emergent groups demonstrate in-group cooperation and out-group defection, even though agents lack common identity. Group formation increases individual payoffs, and group structure is robust to varying levels of reciprocity and transitivity. Increasing population size increases group size more than group number, and manipulating baseline trust in a population has predictable effects on group genesis. An interactive online demonstration enables first-hand exploration of the parameter space (www.mpmlab.org/groups), and available source code (supplementary materials) provides a guide to implementing psychological agent-based models.

Keywords: Groups and Teams;

Format: Print

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Citation:

Gray, Kurt, David G. Rand, Eyal Ert, Kevin Lewis, Steve Hershman, and Michael I. Norton. "The Emergence of 'Us and Them' in 80 Lines of Code: Modeling Group Genesis in Homogeneous Populations." Psychological Science 25, no. 4 (April 2014): 982–990.

From “Chick Beer” to “Dryer sheets for Men,” identity-based labeling is frequently deployed to appeal to people who hold the targeted identity. However, five studies demonstrate that identity appeals can backfire, alienating the very individuals they aim to attract. We begin by demonstrating backlash against identity appeals in the field during the 2016 presidential election (Study 1) and in the lab (Study 2). This (in)effectiveness of identity appeals is driven by categorization threat—feeling unwillingly reduced to a single identity—which is induced when a) the identity deployed is that of a typically marginalized group (Studies 3-4) and b) the appeal evokes a stereotype about that identity (Study 5). Ironically, identity appeals often drive identity-holders away from options they would have preferred in the absence of that appeal.

Working Paper | HBS Working Paper Series | 2019

The Revision Bias

Ximena Garcia-Rada, Leslie John, Ed O’Brien and Michael I. Norton

Things change. Things also get changed—often. Why? The obvious reason is that revising things makes them better. In the current research, we document a less obvious reason: Revising things makes people think they are better, absent objective improvement. We refer to this phenomenon as the revision bias. Nine studies document this effect and provide insight into its psychological underpinnings. In Study 1, MBA students perceived their revised resumes to be of higher quality the more they differed from their original versions, but this perception was not justified: observers judged originals (inaccurately) labeled as revisions to be superior to revisions (inaccurately) labeled as originals. Study 2 pinpoints the direction of the effect: Revisions are appealing, as opposed to originals being unappealing. Moreover, the revision bias holds in a variety of settings in which the revision is devoid of objective improvement—when revisions are trivial (Study 3A), incidental (Study 3B), non-existent (Study 3C), and even objectively worse than the original (Study 3D). Study 4 directly tests the self-fulfilling nature of the revision bias, testing whether mere revision framing leads people to become less critical of the experience—in this study, less sensitive to possible bugs while playing an otherwise identical “revised” video game—and whether this mediates the effect of revision framing on positive evaluations. Studies 5A and 5B offer further support by testing whether the revision bias is accentuated when people engage in a holistic processing style, whether measured as an individual difference (Study 5A) or experimentally induced (Study 5B).

Keywords: Change; heuristics and biases; framing; sequences; judgment; Change; Perception;

Citation:

Garcia-Rada, Ximena, Leslie John, Ed O’Brien, and Michael I. Norton. "The Revision Bias." Harvard Business School Working Paper, No. 19-087, February 2019. View Details

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The development and maintenance of interpersonal relationships require investments of both money and time—resources that are often limited in supply, but in great demand. Indeed, consumers are regularly asked to dedicate their money and time to social engagements, and need to manage these resources efficiently. Therefore, consumers often choose to cite insufficient time or money as an excuse for rejecting social invitations. But how does using the excuse of financial versus time scarcity influence interpersonal relationships? Across eight experiments, we demonstrate that using financial scarcity as an excuse (e.g., “I don’t have money”) increases perceptions of interpersonal closeness and helping behavior compared to using time scarcity as an excuse (e.g., “I don’t have time”). This effect is explained by the fact that time is perceived as a more personally controllable resource than money, resulting in consumers who cite financial (vs. temporal) constraints as being perceived as more trustworthy. However, excuse-givers do not correctly predict this difference in interpersonal outcomes. These findings advance our theoretical understanding of how excuses revolving around resource constraints affect interpersonal perceptions and behaviors and provides practical insights for consumers desiring to minimize social repercussions when turning down social invitations.

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The Emergence of 'Us and Them' in 80 Lines of Code: Modeling Group Genesis in Homogeneous Populations

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