Mario Gutiérrez-Roig

Transition from reciprocal cooperation to persistent behaviour in social dilemmas at the end of adolescence

While human societies are extraordinarily cooperative in comparison with other social species, the question of why we cooperate with unrelated individuals remains open. Here we report results of a lab-in-the-field experiment with people of different ages in a social dilemma. We find that the average amount of cooperativeness is independent of age except for the elderly, who cooperate more, and a behavioural transition from reciprocal, but more volatile behaviour to more persistent actions towards the end of adolescence. Although all ages react to the cooperation received in the previous round, young teenagers mostly respond to what they see in their neighbourhood regardless of their previous actions. Decisions then become more predictable through midlife, when the act of cooperating or not is more likely to be repeated. Our results show that mechanisms such as reciprocity, which is based on reacting to previous actions, may promote cooperation in general, but its influence can be hindered by the fluctuating behaviour in the case of children.

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

Lab-in-the-field experiment reveals that 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.

Citizen Science Practices for Computational Social Science Research: The Conceptualization of Pop-Up Experiments

Under the name of Citizen Science, many innovative practices in which volunteers partner with scientist to pose and answer real-world questions are quickly growing worldwide. Citizen Science can furnish ready made solutions with the active role of citizens. However, this framework is still far from being well stablished to become a standard tool for Computational Social Sciences research. We present our experience in bridging Computational Social Sciences with Citizen Science philosophy, which in our case has taken the form of what we call Pop-Up Experiments: Non-permanent, highly participatory collective experiments which blend features developed by Big Data methodologies and Behavioural Experiments protocols with ideals of Citizen Science. The main issues to take into account whenever planning experiments of this type are classified and discused grouped in three categories: public engagement, light infrastructure and knowledge return to citizens. We explain the solutions implemented providing practical examples grounded in our own experience in urban contexts (Barcelona, Spain). We hope that this work serves as guideline to groups willing to adopt and expand such \emph{in-vivo} practices and opens the debate about the possibilities (but also the limitations) that the Citizen Science framework can offer to study social phenomena.

Scaling properties and universality of first-passage time probabilities in financial markets

Financial markets provide an ideal frame for the study of crossing or first-passage time events of non-Gaussian correlated dynamics, mainly because large data sets are available. Tick-by-tick data of six futures markets are herein considered, resulting in fat-tailed first-passage time probabilities. The scaling of the return with its standard deviation collapses the probabilities of all markets examined--and also for different time horizons--into single curves, suggesting that first-passage statistics is market independent (at least for high-frequency data). On the other hand, a very closely related quantity, the survival probability, shows, away from the center and tails of the distribution, a hyperbolic t(-1/2) decay typical of a Markovian dynamics, albeit the existence of memory in markets. Modifications of the Weibull and Student distributions are good candidates for the phenomenological description of first-passage time properties under certain regimes. The scaling strategies shown may be useful for risk control and algorithmic trading.

Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians

Human mobility is becoming an accessible field of study, thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modelling framework based on Langevin dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for some observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach.

Market Imitation and Win-Stay Lose-Shift strategies emerge as unintended patterns in market direction guesses

Decisions made in our everyday lives are based on a wide variety of information so it is generally very difficult to assess what are the strategies that guide us. Stock market provides a rich environment to study how people make decisions since responding to market uncertainty needs a constant update of these strategies. For this purpose, we run a lab-in-the-field experiment where volunteers are given a controlled set of financial information -based on real data from worldwide financial indices- and they are required to guess whether the market price would go “up” or “down” in each situation. From the data collected we explore basic statistical traits, behavioural biases and emerging strategies. In particular, we detect unintended patterns of behavior through consistent actions, which can be interpreted as Market Imitation and Win-Stay Lose-Shift emerging strategies, with Market Imitation being the most dominant. We also observe that these strategies are affected by external factors: the expert advice, the lack of information or an information overload reinforce the use of these intuitive strategies, while the probability to follow them significantly decreases when subjects spends more time to make a decision. The cohort analysis shows that women and children are more prone to use such strategies although their performance is not undermined. Our results are of interest for better handling clients expectations of trading companies, to avoid behavioural anomalies in financial analysts decisions and to improve not only the design of markets but also the trading digital interfaces where information is set down. Strategies and behavioural biases observed can also be translated into new agent based modelling or stochastic price dynamics to better understand financial bubbles or the effects of asymmetric risk perception to price drops.