Emerging Cooperation in N-Person Iterated Prisoner's Dilemma over Dynamic Complex Networks

The N-Person Iterated Prisoner's Dilemma (NIPD) is an interesting game that has proved to be very useful to explore the emergence of cooperation in multi-player scenarios. Within this game, the way that agents are interconnected is a key element that influences cooperation. In this context, complex networks provide a realistic model of the topological features found in Nature and in many social and technological networks. Considering these networks, it is interesting to study the network evolution, given the possibility that agents can change their neighbors (dynamic rewire), when non-cooperative behaviors are detected. In this paper, we present a model of the NIPD game where a population of genetically-coded agents compete altogether. We analyze how different game parameters, and the network topology, affect the emergence of cooperation in static complex networks. Based on that, we present the main contribution of the paper that concerns the influence of dynamic rewiring in the emergence of cooperation over the NIPD

Self-Organized Sociopolitical Interactions as the Best Way to Achieve Organized Patterns in Human Social Systems: Going Beyond the Top-Down Control of Classical Political Regimes

The dissertation extrapolates the theory of self-organization in biological organisms to sociopolitical self-organization, in human social systems. It is stated that the latter is the best way to organize human social systems, given their complex nature and the impossibility of the computational dynamics that classical political regimes must perform in order to, unsuccesfully, try to organize human social systems by means of top-down control. Sociopolitical self-organization is presented as the optimal producer of order in human social systems, and it is claimed that anarchic complex networks are the resulting structures.Comment: Originally published in: Repository, Universidad del Rosario Link: http://repository.urosario.edu.co/handle/10336/4387 (2013

Fostering cooperation through dynamic coalition formation and partner switching

In this article we tackle the problem of maximizing cooperation among self-interested agents in a resource exchange environment. Our main concern is the design of mechanisms for maximizing cooperation among self-interested agents in a way that their profits increase by exchanging or trading with resources. Although dynamic coalition formation and partner switching (rewiring) have been shown to promote the emergence and maintenance of cooperation for self-interested agents, no prior work in the literature has investigated whether merging both mechanisms exhibits positive synergies that lead to increase cooperation even further. Therefore, we introduce and analyze a novel dynamic coalition formation mechanism, that uses partner switching, to help self-interested agents to increase their profits in a resource exchange environment. Our experiments show the effectiveness of our mechanism at increasing the agents' profits, as well as the emergence of trading as the preferred behavior over different types of complex networks. © 2014 ACM.The first author thanks the grant Formación de Profesorado Universitario (FPU), reference AP2010-1742. J.Ll.A. and J.A.R-A are partially funded by projects EVE (TIN2009-14702-C02-01), AT (CSD2007-0022), COR (TIN2012-38876-C02-01), MECER (201250E053), and the Generalitat of Catalunya grant 2009-SGR-1434Peer Reviewe

Human behaviour modelling in complex socio-technical systems : an agent based approach

For many years we have been striving to understand human behaviour and our interactions with our socio-technological environment. By advancing our knowledge in this area, we have helped the design of new or improved work processes and technologies. Historically, much of the work in analysing social interactions has been conducted within the social sciences. However, computer simulation has brought an extra tool in trying to understand and model human behaviours. Using an agent based approach this presentation describes my work in constructing computational models of human behaviour for informing design through simulation. With examples from projects in two main application areas of crisis and emergency management, and energy management I describe how my work addresses some main issues in agent based social simulation. The first concerns the process by which we develop these models. The second lies in the nature of socio-technical systems. Human societies are a perfect example of a complex system exhibiting characteristics of self-organisation, adaptability and showing emergent phenomena such as cooperation and robustness. I describe how complex systems theory may be applied to improve our understanding of socio-technical systems, and how our micro level interactions lead to emergent mutual awareness for problem-solving. From agent based simulation systems I show how context awareness may be modelled. Looking forward to the future, I discuss how the increasing prevalence of artificial agents in our society will cause us to re-examine the new types of interactions and cooperative behaviours that will emerge.Depuis de nombreuses années, nous nous sommes efforcés de comprendre le comportement humain et nos interactions avec l'environnement sociotechnique. Grâce à l'avancée de nos connaissances dans ce domaine, nous avons contribué à la conception de technologies et de processus de travail nouveaux ou améliorés. Historiquement, une part importante du travail d'analyse des interactions sociales fut entreprise au sein des sciences sociales. Cependant, la simulation informatique a apporté un nouvel outil pour tenter de comprendre et de modéliser les comportements humains. En utilisant une approche à base d'agents, cette présentation décrit mon travail sur la construction de modèles informatiques du comportement humain pour guider la conception par la simulation. A l'aide d'exemples issus de projets des deux domaines d'application que sont la gestion des crises et de l'urgence et la gestion de l'énergie, je décris comment mon travail aborde certains problèmes centraux à la simulation sociale à base d'agents. Le premier concerne le processus par lequel nous développons ces modèles. Le second problème provient de la nature des systèmes sociotechniques. Les sociétés humaines constituent un exemple parfait de système complexe possédant des caractéristiques d'auto-organisation et d'adaptabilité, et affichant des phénomènes émergents tels que la coopération et la robustesse. Je décris comment la théorie des systèmes complexes peut être appliquée pour améliorer notre compréhension des systèmes sociotechniques, et comment nos interactions au niveau microscopique mènent à l'émergence d'une conscience mutuelle pour la résolution de problèmes. A partir de systèmes de simulation à base d'agents, je montre comment la conscience du contexte peut être modélisée. En terme de perspectives, j'expliquerai comment la hausse de la prévalence des agents artificiels dans notre société nous forcera à considérer de nouveaux types d'interactions et de comportements coopératifs

Learning partner selection rules that sustain cooperation in social dilemmas with the option of opting out

We study populations of self-interested agents playing a 2-person repeated Prisoner’s Dilemma game, with each player having the option of opting out of the interaction and choosing to be randomly assigned to another partner instead. The partner selection component makes these games akin to random matching, where defection is known to take over the entire population. Results in the literature have shown that, when forcing agents to obey a set partner selection rule known as Out-for-Tat, where defectors are systematically being broken ties with, cooperation can be sustained in the long run. In this paper, we remove this assumption and study agents that learn both action- and partner-selection strategies. Through multiagent reinforcement learning, we show that cooperation can be sustained without forcing agents to play predetermined strategies. Our simulations show that agents are capable of learning in-game strategies by themselves, such as Tit-for-Tat. What is more, they are also able to simultaneously discover cooperation-sustaining partner selection rules, notably Out-for-Tat, as well as other new rules that make cooperation prevail

Learning to resolve social dilemmas: a survey

Social dilemmas are situations of inter-dependent decision making in which individual rationality can lead to outcomes with poor social qualities. The ubiquity of social dilemmas in social, biological, and computational systems has generated substantial research across these diverse disciplines into the study of mechanisms for avoiding deficient outcomes by promoting and maintaining mutual cooperation. Much of this research is focused on studying how individuals faced with a dilemma can learn to cooperate by adapting their behaviours according to their past experience. In particular, three types of learning approaches have been studied: evolutionary game-theoretic learning, reinforcement learning, and best-response learning. This article is a comprehensive integrated survey of these learning approaches in the context of dilemma games. We formally introduce dilemma games and their inherent challenges. We then outline the three learning approaches and, for each approach, provide a survey of the solutions proposed for dilemma resolution. Finally, we provide a comparative summary and discuss directions in which further research is needed

Enabling imitation-based cooperation in dynamic social networks

The emergence of cooperation among self-interested agents has been a key concern of the multi-agent systems community for decades. With the increased importance of network-mediated interaction, researchers have shifted the attention to the impact of social networks and their dynamics in promoting or hindering cooperation, drawing various context-dependent conclusions. For example, some lines of research, theoretical and experimental, suggest the existence of a threshold effect in the ratio of timescales of network evolution, after which cooperation will emerge, whereas other lines dispute this, suggesting instead a Goldilocks zone. In this paper we provide an evolutionary game theory framework to understand coevolutionary processes from a bottom up perspective - in particular the emergence of a cooperator-core and defector-periphery - clarifying the impact of partner selection and imitation strategies in promoting cooperative behaviour, without assuming underlying communication or reputation mechanisms. In doing so we provide a unifying framework to study imitation-based cooperation in dynamic social networks and show that disputes in the literature can in fact coexist in so far as the results stem from different equally valid assumptions

An agent-based model for evolution of cooperation with proactive information gathering

This thesis proposes a new model to investigate the impact of proactive information gathering upon the evolution of cooperation among self-interested agents in a multiagent system. It builds upon an existing game-theoretical model of spatially distributed mobile agent population with energy-based individual life cycle, in which individuals keep playing one-shot Prisoner's Dilemma games in neighbourhood encounters. Using proactively gathered information about past behaviour of others, advanced agents in the new model can dynamically adjust their strategies towards different types of opponents. Simulation experiments establish some patterns of how this ability impacts the evolution of cooperation in the presence of varying levels of environmental adversity. The adequacy of the model is demonstrated through a specific design involving two types of advanced agents, one oriented towards cooperation, the other towards defection. The results show that cooperation prevails in a substantially larger area of parameter space than in the basic model without information gathering.multiagent systemcooperationdefectionproactive information gatheringagent-bas