How Evolutionary Dynamics Affects Network Reciprocity in Prisoner's Dilemma

Cooperation lies at the foundations of human societies, yet why people cooperate remains a conundrum. The issue, known as network reciprocity, of whether population structure can foster cooperative behavior in social dilemmas has been addressed by many, but theoretical studies have yielded contradictory results so far—as the problem is very sensitive to how players adapt their strategy. However, recent experiments with the prisoner's dilemma game played on different networks and in a specific range of payoffs suggest that humans, at least for those experimental setups, do not consider neighbors' payoffs when making their decisions, and that the network structure does not influence the final outcome. In this work we carry out an extensive analysis of different evolutionary dynamics, taking into account most of the alternatives that have been proposed so far to implement players' strategy updating process. In this manner we show that the absence of network reciprocity is a general feature of the dynamics (among those we consider) that do not take neighbors' payoffs into account. Our results, together with experimental evidence, hint at how to properly model real people's behaviorThis work was supported by the Swiss Natural Science Foundation through grant PBFRP2_145872 and by Ministerio de Economía y Competitividad (Spain) through grant PRODIEVO.Publicad

On the Causation of the Replicator Dynamics

Here we demonstrate the models formation and research findings of the Replicator Dynamics that interpret the evolutionary process, also the discussion about problem with the models which are commonly used in evolutionary game theory, i.e. the difference between the fitness of population following a certain strategy and the average fitness of the entire population determine the change of population proportion following the strategy between generations, that is to say what happen after one bout are used to interpret the phenomena before the bout. As a modification, we construct a model directly with the numbers of the population following a certain strategy between generations, and prove the characteristics of its key can be discussed according to the model. Key words: adaptation, evolutionary dynamics, Replicator Dynamics Résumé: Ici, nous démontrons la formation des modèles et la conclusion de la recherche de la dynamique de la reproduction qui interprète le procès évolutionniste, et aussi la discussion sur le problème avec les modèles qui sont communément utilisés dans la théorie du jeu évolutionniste, c.-à-d. la différence entre l’aptitude de la population suivant une certaine stratégie et l’aptitude en moyenne de la toute la population déterminent le changement de la proportion de la population suivant la stratégie parmi les générations, c’est-à-dire ce qui se passe après un accès sont utilisé pour interpréter les phénomènes avant un accès. Comme une modification, nous construisons un modèle directement avec les nombres de la population selon une certaine stratégie parmi les générations, et prouvent que les caractéristiques de leur clé peuvent être discuté d’après le modèle. Mots-Clés: adaptation, dynamique évolutionniste, dynamique de reproductio

Online Dispute Resolution Through the Lens of Bargaining and Negotiation Theory: Toward an Integrated Model

[Excerpt] In this article we apply negotiation and bargaining theory to the analysis of online dispute resolution. Our principal objective is to develop testable hypotheses based on negotiation theory that can be used in ODR research. We have not conducted the research necessary to test the hypotheses we develop; however, in a later section of the article we suggest a possible methodology for doing so. There is a vast literature on negotiation and bargaining theory. For the purposes of this article, we realized at the outset that we could only use a small part of that literature in developing a model that might be suitable for empirical testing. We decided to use the behavioral theory of negotiation developed by Richard Walton and Robert McKersie, which was initially formulated in the 1960s. This theory has stood the test of time. Initially developed to explain union-management negotiations, it has proven useful in analyzing a wide variety of disputes and conflict situations. In constructing their theory, Walton and McKersie built on the contributions and work of many previous bargaining theorists including economists, sociologists, game theorists, and industrial relations scholars. In this article, we have incorporated a consideration of the foundations on which their theory was based. In the concluding section of the article we discuss briefly how other negotiation and bargaining theories might be applied to the analysis of ODR

The evolution of prompt reaction to adverse ties

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Evolution of cooperation without reciprocity

A long-standing problem in biological and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. For many situations, kin selection(1) is an adequate explanation, although kin-recognition may still be a problem. Explanations of cooperation between non-kin include continuing interactions that provide a shadow of the future (that is, the expectation of an ongoing relationship) that can sustain reciprocity(2-4), possibly supported by mechanisms to bias interactions such as embedding the agents in a two-dimensional space(4-6) or other context-preserving networks(7). Another explanation, indirect reciprocity(8), applies when benevolence to one agent increases the chance of receiving help from others. Here we use computer simulations to show that cooperation can arise when agents donate to others who are sufficiently similar to themselves in some arbitrary characteristic. Such a characteristic, or 'tag', can be a marking, display, or other observable trait. Tag-based donation can lead to the emergence of cooperation among agents who have only rudimentary ability to detect environmental signals and, unlike models of direct(3,4) or indirect reciprocity(9,10), no memory of past encounters is required.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62686/1/414441a0.pd

Win-stay-lose-learn promotes cooperation in the spatial prisoner's dilemma game

Holding on to one's strategy is natural and common if the later warrants success and satisfaction. This goes. against widespread simulation practices of evolutionary games, where players frequently consider changing their strategy even though their payoffs may be marginally different than those of the other players. Inspired by this observation, we introduce an aspiration-based win-stay-lose-learn strategy updating rule into the spatial prisoner's dilemma game. The rule is simple and intuitive, foreseeing strategy changes only by dissatisfied players, who then attempt to adopt the strategy of one of their nearest neighbors, while the strategies of satisfied players are not subject to change. We find that the proposed win-stay-lose-learn rule promote the evolution of cooperation, and it does so very robustly and independently of the initial conditions. I fact, we show that even a minute initial fraction of cooperators may be sufficient to eventually secure a higly cooperative final state. In addition to extensive simulation results that support our conclusions, we also present results obtained by means of the pair approximation of the studied game. Our findings continue the success story of related win-stay strategy updating rules, and by doing so reveal new ways of resolving the prisoner's dilemma

Learning and Co-operation in Mobile Multi-Robot Systems

Merged with duplicate record 10026.1/1984 on 27.02.2017 by CS (TIS)This thesis addresses the problem of setting the balance between exploration and exploitation in teams of learning robots who exchange information. Specifically it looks at groups of robots whose tasks include moving between salient points in the environment. To deal with unknown and dynamic environments,such robots need to be able to discover and learn the routes between these points themselves. A natural extension of this scenario is to allow the robots to exchange learned routes so that only one robot needs to learn a route for the whole team to use that route. One contribution of this thesis is to identify a dilemma created by this extension: that once one robot has learned a route between two points, all other robots will follow that route without looking for shorter versions. This trade-off will be labeled the Distributed Exploration vs. Exploitation Dilemma, since increasing distributed exploitation (allowing robots to exchange more routes) means decreasing distributed exploration (reducing robots ability to learn new versions of routes), and vice-versa. At different times, teams may be required with different balances of exploitation and exploration. The main contribution of this thesis is to present a system for setting the balance between exploration and exploitation in a group of robots. This system is demonstrated through experiments involving simulated robot teams. The experiments show that increasing and decreasing the value of a parameter of the novel system will lead to a significant increase and decrease respectively in average exploitation (and an equivalent decrease and increase in average exploration) over a series of team missions. A further set of experiments show that this holds true for a range of team sizes and numbers of goals

A comparison of reputation-based trust systems

Recent literature contains many examples of reputation systems which are constructed in an ad hoc way or rely upon some heuristic which has been found to be useful. However, comparisons between these reputation systems has been impossible because there are no established methods of comparing performance. This paper introduces a simulation framework which can be used to perform comparison analysis between reputation models. Two reputation models, one from Abdul-Rahman and Hailes (ARH) [1], and one from Mui, Mohtashemi, and Halberstadt (MMH) [17] are implemented and compared with regard to accuracy, performance and resistance to deception. In order to improve performance in certain cases, MMH is modified to distinguish the concept of “trust” from the concept of “reputation.” Additionally we examine the results of shortening the memory of MMH in order to improve results in environments that are constantly changing

Approaches to multi-agent learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 165-171).Systems involving multiple autonomous entities are becoming more and more prominent. Sensor networks, teams of robotic vehicles, and software agents are just a few examples. In order to design these systems, we need methods that allow our agents to autonomously learn and adapt to the changing environments they find themselves in. This thesis explores ideas from game theory, online prediction, and reinforcement learning, tying them together to work on problems in multi-agent learning. We begin with the most basic framework for studying multi-agent learning: repeated matrix games. We quickly realize that there is no such thing as an opponent-independent, globally optimal learning algorithm. Some form of opponent assumptions must be necessary when designing multi-agent learning algorithms. We first show that we can exploit opponents that satisfy certain assumptions, and in a later chapter, we show how we can avoid being exploited ourselves. From this beginning, we branch out to study more complex sequential decision making problems in multi-agent systems, or stochastic games. We study environments in which there are large numbers of agents, and where environmental state may only be partially observable.(cont.) In fully cooperative situations, where all the agents receive a single global reward signal for training, we devise a filtering method that allows each individual agent to learn using a personal training signal recovered from this global reward. For non-cooperative situations, we introduce the concept of hedged learning, a combination of regret-minimizing algorithms with learning techniques, which allows a more flexible and robust approach for behaving in competitive situations. We show various performance bounds that can be guaranteed with our hedged learning algorithm, thus preventing our agent from being exploited by its adversary. Finally, we apply some of these methods to problems involving routing and node movement in a mobilized ad-hoc networking domain.by Yu-Han Chang.Ph.D