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 Social Lab: A digital platform for human behaviour 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 varied limitations 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 protocols to maintain the same data quality that one can obtain in the laboratories. Here, 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 rigour, 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 and their satisfaction level, and the parameters that demonstrate the robustness of the platform and the quality of the data collected.Comment: 17 pages, 11 figures and 4 table

Collective navigation of complex networks: Participatory greedy routing

Many networks are used to transfer information or goods, in other words, they are navigated. The larger the network, the more difficult it is to navigate efficiently. Indeed, information routing in the Internet faces serious scalability problems due to its rapid growth, recently accelerated by the rise of the Internet of Things. Large networks like the Internet can be navigated efficiently if nodes, or agents, actively forward information based on hidden maps underlying these systems. However, in reality most agents will deny to forward messages, which has a cost, and navigation is impossible. Can we design appropriate incentives that lead to participation and global navigability? Here, we present an evolutionary game where agents share the value generated by successful delivery of information or goods. We show that global navigability can emerge, but its complete breakdown is possible as well. Furthermore, we show that the system tends to self-organize into local clusters of agents who participate in the navigation. This organizational principle can be exploited to favor the emergence of global navigability in the system.Comment: Supplementary Information and Videos: https://koljakleineberg.wordpress.com/2016/11/14/collective-navigation-of-complex-networks-participatory-greedy-routing

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

Agent-based modeling of human behaviour using PECS model and game theory

In this thesis we present a tool for studying human behavior using agent-based modeling combined with game theory. With the help of the software Netlogo, and using the PECS reference model, we have implemented a tool for studying human behaviour based on decisions done playing to Prisoner's Dilemma, a classical game theory paradox, by using an agent-based model. We have summarized the personality of agents in ve di erent types, and by a given repertoire of possible actions, we have made agents play by using a set of rules for observing its behaviour. The experiment consist on see how agents decide whether cooperate or defect its opponent, based on their initial configuration and the in uence of each component by assigning different weights, and to demonstrate the feasibility of a tool that combine game theory, PECS reference model and agent-based modelling for studying human behaviour and use it as starting point for future studies.En este proyecto presentamos una herramienta para estudiar el comportamiento humano usando un modelado basado en agentes, combinado con la teoría de juegos. Con la ayuda del software Netlogo y usando el modelo de referencia PECS, hemos implementado una herramienta para estudiar el comportamiento humano, basándose en decisiones hechas jugando al Dilema del Prisionero, una paradoja clásica de teoría de juegos, usando un modelado basado en agentes. Hemos concentrado la personalidad de los agentes entre cinco tipos distintos, y dado un repertorio de acciones posibles, hemos hecho jugar a los agentes entre sí, usando un conjunto de reglas para observar su comportamiento. El experimento consiste en ver cómo los agentes deciden entre cooperar o defectar a su oponente, basándose en su configuración inicial y en la influencia de cada componente sobre ellos, asignándoles diferentes pesos, y para demostrar la factibilidad de una herramienta que combina teoría de juegos, el modelo de referencia PECS y modelado basado en agentes para estudiar el comportamiento humano y usarla como punto de partida en futuros estudios.En aquest projecte, presentem una eina per estudiar el comportament humà usant un modelatge basat en agents, combinat amb la teoria de jocs. Amb l'ajuda del programari Netlogo i usant el model de referència PECS, hem implementat una eina per estudiar el comportament humà, basat en el Dilema del Presoner, una paradoxa clàssica de la teoria de jocs, que usa un modelatge basat en agents. Hem concentrat la personalitat dels agents en cinc tipus diferents, amb un repertori d'accions possibles. Hem fet jugar als agents entre si, usant un conjunt de regles per observar el seu comportament. L'experiment consisteix en veure com els agents decideixen cooperar o defectar amb el seu oponent, basant-se en la seva configuració inicial i en la influència de cada component sobre ells, assignant-los diferents pesos, per demostrar la factibilitat d'una eina que combina la teoria de jocs, el model de referència PECS i el modelatge basat en agents per estudiar el comportament humà i fer-la servir com a punt de partida en futurs estudis

Detecting reciprocity at a global scale

Reciprocity stabilizes cooperation from the level of microbes all the way up to humans interacting in small groups, but does reciprocity also underlie stable cooperation between larger human agglomerations, such as nation States? Famously, evolutionary models show that reciprocity could emerge as a widespread strategy for achieving international cooperation. However, existing studies have only detected reciprocity-driven cooperation in a small number of country pairs. We apply a new method for detecting mutual influence in dynamical systems to a new large-scale data set that records state interactions with high temporal resolution. Doing so, we detect reciprocity between many country pairs in the international system and find that these reciprocating country pairs exhibit qualitatively different cooperative dynamics when compared to nonreciprocating pairs. Consistent with evolutionary theories of cooperation, reciprocating country pairs exhibit higher levels of stable cooperation and are more likely to punish instances of noncooperation. However, countries in reciprocity-based relationships are also quicker to forgive single acts of noncooperation by eventually returning to previous levels of mutual cooperation. By contrast, nonreciprocating pairs are more likely to exploit each other’s cooperation via higher rates of defection. Together, these findings provide the strongest evidence to date that reciprocity is a widespread mechanism for achieving international cooperation

Evolutionary dynamics of N-person Hawk-Dove games

In the animal world, the competition between individuals belonging to different species for a resource often requires the cooperation of several individuals in groups. This paper proposes a generalization of the Hawk-Dove Game for an arbitrary number of agents: the N-person Hawk-Dove Game. In this model, doves exemplify the cooperative behavior without intraspecies conflict, while hawks represent the aggressive behavior. In the absence of hawks, doves share the resource equally and avoid conflict, but having hawks around lead to doves escaping without fighting. Conversely, hawks fight for the resource at the cost of getting injured. Nevertheless, if doves are present in sufficient number to expel the hawks, they can aggregate to protect the resource, and thus avoid being plundered by hawks. We derive and numerically solve an exact equation for the evolution of the system in both finite and infinite well-mixed populations, finding the conditions for stable coexistence between both species. Furthermore, by varying the different parameters, we found a scenario of bifurcations that leads the system from dominating hawks and coexistence to bi-stability, multiple interior equilibria and dominating doves

A dynamic over games drives selfish agents to win-win outcomes

Understanding the evolution of human social systems requires flexible formalisms for the emergence of institutions. Although game theory is normally used to model interactions individually, larger spaces of games can be helpful for modeling how interactions change. We introduce a framework for modeling "institutional evolution," how individuals change the games they are placed in. We contrast this with the more familiar within-game "behavioral evolution". Starting from an initial game, agents trace trajectories through game space by repeatedly navigating to more preferable games until they converge on attractor games that are preferred to all others. Agents choose between games on the basis of their "institutional preferences," which define between-game comparisons in terms of game-level features such as stability, fairness, and efficiency. Computing institutional change trajectories over the two-player space, we find that the attractors of self-interested economic agents over-represent fairness by 100% relative to baseline, even though those agents are indifferent to fairness. This seems to occur because fairness, as a game feature, co-occurs with the self-serving features these agents do prefer. We thus present institutional evolution as a mechanism for encouraging the spontaneous emergence of cooperation among inherently selfish agents. We then extend these findings beyond two players, and to two other types of evolutionary agent: the relative fitness maximizing agent of evolutionary game theory (who maximizes inequality), and the relative group fitness maximizing agent of multi-level/group selection theory (who minimizes inequality). This work provides a flexible, testable formalism for modeling the interdependencies of behavioral and institutional evolutionary processes.Comment: 4500 words, 4 figures, 1 supplementary figur