Citizen Social Lab: a digital platform for human behavior experimentation within a citizen science framework

Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation¿and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with limitations in terms of subject pool or decisions' context, which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate data collection protocols to maintain the same data quality that one can obtain in the laboratories. In this article we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigor, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments, the satisfaction level of participants, or the technical parameters that demonstrate the robustness of the platform and the quality of the data collected

Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework

Sistemes socioeconòmics i financers

Els mercats financers, entre molts altres contextos socials i econòmics, amaguen diverses relacions amb la İ sica estadísƟ ca. Sense anar més lluny, el model matemàƟ c de les coƟ tzacions fi nanceres és el mateix uƟ litzat per a la teoria de gasos o per les parơ cules en suspensió en un líquid. En aquest arƟ cle recorrem la trajectòria de l'anomenada econoİ sica des de 1900 i presentem algunes de les contribucions a la matèria feta per membres de Complexitat.CA

Resource heterogeneity leads to unjust effort distribution in climate change mitigation

Climate change mitigation is a shared global challenge that involves collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together towards a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer −sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two 'generous clusters' and the richest into a 'greedy cluster'. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences

Structure and Traffic on Complex Networks

In a time when large amounts of data about social, economical, technological, and biological systems are produced in a daily bases, complex networks have become a powerful tool to represent the structure of complex systems. The advances in complex networks research have been geared towards the study of two main questions: what can we understand from a complex system by looking at its structure, and more importantly, what is the interplay between the topological and dynamical properties of complex systems. The aim of this dissertation is to review and introduce new tools and methods to measure topological and dynamical properties of complex networks. In particular, it covers two specific problems related with the two previously presented questions: the study of the community structure of complex networks, and the analysis of the dynamical properties of a communication process. The first part of the thesis is focused on the study of the community structure of complex networks, that is, how and why the nodes of the network tend to form groups in which they are highly interconnected. The understanding of this problem is key to characterize the internal organization of complex systems, obtaining better insights about the dynamical behavior of their components. In this part we present an exhaustive review of the community structure identification problem, explaining the limitations of the current existing methods, and we introduce a new method to extract the community structure based on the extremal optimization algorithm. We also present several improvements that increase the efficiency and accuracy of current community identification methods and an exhaustive benchmark of the results obtained when applying this new method to the standard network metrics. These results show that the extremal optimization method and its modifications are one of the fastest and most accurate options to identify the community structure of a network. The second part of the thesis is devoted to the study of some dynamical properties of communication processes over complex networks. Using a simple traffic model we analyze the changes observed on some properties when we introduce congestion in the network: the scaling of the fluctuations and the dynamical robustness. First, we present the scaling of the fluctuations in order to provide a large-scale dynamical characterization of the traffic flow. The idea is that there are a large number of real complex systems that show a scaling relation between the average flux and the variability of this flux. The understanding of the scaling relation presented in the dissertation will help us design better traffic models. And second, we study the dynamical robustness of the traffic, defined as the capability of maintaining the efficiency of the communication when we remove a fraction of nodes of the network. We show that there is a dynamical percolation threshold that splits the network due to the congestion before the topological percolation threshold

Sonido, interacción y redes, febrero 2012

Material docent de la Universitat Oberta de Catalunya.Material docente de la "Universitat Oberta de Catalunya".Learning material of the "Universitat Oberta de Catalunya"

Citizen Social Lab: a digital platform for human behavior experimentation within a citizen science framework

Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation¿and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with limitations in terms of subject pool or decisions' context, which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate data collection protocols to maintain the same data quality that one can obtain in the laboratories. In this article we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigor, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments, the satisfaction level of participants, or the technical parameters that demonstrate the robustness of the platform and the quality of the data collected

Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework

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

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