Humans display a reduced set of consistent behavioral phenotypes in dyadic games

Poncela-Casasnovas, Julia; Gutiérrez-Roig, Mario; Gracia-Lázaro, Carlos; Vicens, Julián; Gómez‐Gardeñes, Jesús; Perelló, Josep Maria Mata; Moreno, Yamir; Duch, Jordi; Sánchez, Ángel

doi:10.1126/sciadv.1600451

Cited by 84 publications

(107 citation statements)

References 51 publications

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“…I started to see my daily bonus go down and inevitably began defecting first on the last round, then in the 9th, and finally in the 8th...' Based either on behaviour or on subjects' own self-evaluation, therefore, an experiment conducted for a single session would have substantially overestimated the number of conditional cooperators. Our finding also complements an earlier claim2447 that some individuals exhibit a ‘cooperative phenotype' that is stable across different cooperative games. Whereas these claims refer to correlations between player contributions in one-shot games, however, we find that resilient cooperators retain a highly consistent strategy over many repetitions of the same game; thus the two claims refer to different kinds of inter-temporal consistency.…”

Section: Discussionsupporting

confidence: 87%

Resilient cooperators stabilize long-run cooperation in the finitely repeated Prisoner’s Dilemma

Mao

Dworkin²,

Suri

et al. 2017

Nat Commun

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Learning in finitely repeated games of cooperation remains poorly understood in part because their dynamics play out over a timescale exceeding that of traditional lab experiments. Here, we report results of a virtual lab experiment in which 94 subjects play up to 400 ten-round games of Prisoner's Dilemma over the course of twenty consecutive weekdays. Consistent with previous work, the typical round of first defection moves earlier for several days; however, this unravelling process stabilizes after roughly one week. Analysing individual strategies, we find that approximately 40% of players behave as resilient cooperators who avoid unravelling even at significant cost to themselves. Finally, using a standard learning model we predict that a sufficiently large minority of resilient cooperators can permanently stabilize unravelling among a majority of rational players. These results shed hopeful light on the long-term dynamics of cooperation, and demonstrate the importance of long-run experiments.

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Section: Discussionsupporting

confidence: 87%

Resilient cooperators stabilize long-run cooperation in the finitely repeated Prisoner’s Dilemma

Mao

Dworkin²,

Suri

et al. 2017

Nat Commun

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show abstract

“…These statistical patterns are probably a clue of some interesting sociological and psychological factors driving human behavior. This result confirms that subjects' behavior can be described by a limited number of behavioral phenotypes [12]. To our knowledge, this is the first work that proves the existence of behavioral phenotypes related to the social influence phenomenon.…”

Section: (C) Classes Of Influence Clusteringsupporting

confidence: 83%

On the Social Influence in Human Behavior: Physical, Homophily, and Social Communities

Luceri

Vancheri

Braun

et al. 2017

Studies in Computational Intelligence

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Understanding the forces governing human behavior and social dynamics is a challenging problem. Individuals' decisions and actions are affected by interlaced factors, such as physical location, homophily, and social ties. In this paper, we propose to examine the role that distinct communities, linked to these factors, play as sources of social influence. The ego network is typically used in the social influence analysis. Our hypothesis is that individuals are embedded in communities not only related to their direct social relationships, but that involve different and complex forces. We analyze physical, homophily, and social communities to evaluate their relation with subjects' behavior. We prove that social influence is correlated with these communities, and each one of them is (differently) significant for individuals. We define community-based features, which reflect the subject involvement in these groups, and we use them with a supervised learning algorithm to predict subject participation in social events. Results indicate that both communities and ego network are relevant sources of social influence, confirming that the ego network alone is not sufficient to explain this phenomenon. Moreover, we classify users according to the degree of social influence they experienced with respect to their groups, recognizing classes of behavioral phenotypes. To our knowledge, this is the first work that proves the existence of phenotypes related to the social influence phenomenon.

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“…When we analyse the correlations between the personalized learning paths, students' background information, and their learning outcomes (which have already been recorded by existing MOOC platforms) with machine learning, this envisioned mechanism may offer educational researchers feedback on how to personalize learning for various courses for students from different backgrounds. Over time, this envisioned mechanism can help MOOC educators optimize learning trajectories for different courses based on learning paths which resulted in desirable learning outcomes, possibly with the help of technologies such as behaviour phenotype analytics 17 and transfer learning. 18 Nevertheless, the realization of this vision still has significant challenges.…”

mentioning

confidence: 99%

Towards AI-powered personalization in MOOC learning

Chen

Leung

et al. 2017

npj Science Learn

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Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students' learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed.

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Humans display a reduced set of consistent behavioral phenotypes in dyadic games

Abstract: Lab-in-the-field experiment reveals that humans display a reduced set of consistent behavioral phenotypes in dyadic games.

Cited by 84 publications

References 51 publications

Resilient cooperators stabilize long-run cooperation in the finitely repeated Prisoner’s Dilemma

Resilient cooperators stabilize long-run cooperation in the finitely repeated Prisoner’s Dilemma

On the Social Influence in Human Behavior: Physical, Homophily, and Social Communities

Towards AI-powered personalization in MOOC learning

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