3 files

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

posted on 06.01.2020, 11:41 by Poncela-Casasnovas, Julia, Gutiérrez-Roig, Mario, Gracia-Lázaro, Carlos, Vicens, Julian, Gómez-Gardeñes, Jesús, Perelló, Josep, Moreno, Yamir, Duch, Jordi, Sánchez, Angel

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.


The data from the "dr Brain" experiment is organized in two separated files: drbrain_users.csv
 and drbrain_decisions.csv.

1.)   drbrain_users.csv contains information about the participants of the experiment (or users).
There is one row per user, with the following information about each one of them:

User_ID: unique ID number to identify the user.
Age: user's age
Gender: user's gender
Experiment_number: Number of the experiment the user participated in. For organizational reasons, our research actually was made 45 experiments (or replicas) run over a period of 2 days, each one run with differnt users. A user was only allowed to participate in one experiment. Each experiment included between 10-25 users typically, and they played around 13-18 game rounds, typically. Each round and each couple of users played in different games (that is, different values of S, Sucker's payoff, and T, Temptation to defect, while the values of P=5 , Punishment, and R=10, Reward, were always fixed).
Earnings: number of points the user obtained in total, over all rounds.

2.)   drbrain_decisions.csv  contains the information of the all game rounds for all experiments and all users.
User_ID: unique ID number to identify the user.    
Experiment_number: Number of the experiment the user participated in.
Round_number: Number of the round within a given experiment.
S: Value for the "Sucker's payoff" in the game of that round.
T: Value for the "Temptation to defect" in the game of that round.    
Game: Name of the game corresponding to those values of S and T for that round
Action: Action chosen by the user (C: cooperate, D: defect)
Opponent_ID: ID number of the user's opponent in that round. 
Opponent_Action: Action (C or D) chosen by the user's opponent in that round.


For more details, see our research article:

Humans display a reduced set of consistent behavioral phenotypes in dyadic games.
Julia Poncela-Casasnovas, Mario Gutiérrez-Roig, Carlos Gracia-Lázaro, Julian Vicens, Jesús Gómez-Gardeñes, Josep Perelló, Yamir Moreno, Jordi Duch and Angel Sánchez.
Science Advances Vol. 2, no. 8, 2016.
DOI: 10.1126/sciadv.1600451