04 Oct Humans display a reduced set of consistent behavioral phenotypes in dyadic games
Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals’ behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals’ actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.
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- Julia Poncela-Casasnovas1,
- Mario Gutiérrez-Roig2,
- Carlos Gracia-Lázaro3,
- Julian Vicens1,4,
- Jesús Gómez-Gardeñes3,5,
- Josep Perelló2,6,
- Yamir Moreno3,7,8,
- Jordi Duch1 and
- Angel Sánchez3,9,10,*
1Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
2Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain.
3Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain.
4Applied Research Group in Education and Technology, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
5Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain.
6UBICS Universitat de Barcelona Institute of Complex Systems, 08028 Barcelona, Spain.
7Department of Theoretical Physics, University of Zaragoza, 50009 Zaragoza, Spain.
8Complex Networks and Systems Lagrange Laboratory, Institute for Scientific Interchange, 10126 Turin, Italy.
9Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain.
10UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain.