Resumen: 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-thefield
experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral
rules dictating individuals’ actions. By analyzing our datawith 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. Idioma: Inglés DOI: 10.1126/sciadv.1600451 Año: 2016 Publicado en: Science 2, 8 (2016), e1600451 ISSN: 0036-8075 Factor impacto JCR: 37.205 (2016) Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 2 / 63 = 0.032 (2016) - Q1 - T1 Factor impacto SCIMAGO: 13.744 - History and Philosophy of Science (Q1) - Multidisciplinary (Q1) - Medicine (miscellaneous) (Q1)