Little Information, Efficiency, and Learning - An Experimental Study

Earlier experiments have shown that under little information subjects are hardly able to coordinate even though there are no conflicting interests and subjects are organised in fixed pairs. This is so, even though a simple adjustment process would lead the subjects into the efficient, fair and individually payoff maximising outcome. We draw on this finding and design an experiment in which subjects re-peatedly play 4 simple games within 4 sets of 40 rounds under little information. This way we are able to investigate (i) the coordination abilities of the subjects depending on the underlying game, (ii) the resulting efficiency loss, and (iii) the adjustment of the learning rule.mutual fate control, matching pennies, fate-control behaviour- control, learning, coordination, little information

Human-agent collectives

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

AN EXAMINATION OF THE STABILITY OF COOPERATION IN A VOLUNTARY COLLECTIVE ACTION: THE CASE OF NONPOINT-SOURCE POLLUTION IN AN AGRICULTURAL WATERSHED

This paper addresses the collective action problem of nonpoint-source pollution control in a small agricultural watershed. At issue is the stability of cooperative behavior among a group of farmers, who have voluntarily agreed to discontinue their use of the herbicide atrazine due to high concentrations of the herbicide in a local water supply. Continued cooperation among the group is threatened by the unexpected cancellation of cyanazine, an inexpensive and widely used alternative to atrazine. With cyanazine no longer available, the farmers will face a significant increase in weed control costs if they continue to use products that do not contain atrazine. Is cooperation among the farmers still possible despite the increased cost of cooperating? This research explores the economic and behavioral factors that influence the collective outcome of this social dilemma. The collective action is modeled as a recurrent coordination problem. The producers (farmers) are engaged in a repeated assurance game with imperfect public information, where producers' choices are driven by the desire to coordinate their actions with the others in the group. A producer's decision to cooperate or defect is based on a threshold approach; if the number of others believed to be cooperating exceeds the level of cooperation required to make cooperation beneficial, then the producer will choose to cooperate. Otherwise, the producer will defect. Since producers are unable to directly observe the choices of the others in the group, each producer must rely on a subjective assessment of the group's behavior based on the realization of the public outcome, the concentration of atrazine in the lake. Producers use a naive Bayesian learning process to update their beliefs about the joint actions of the group. The formal learning process is modeled using a sequential quasi-Bayesian procedure that is consistent with the fictitious play model of learning. The interaction between the producers and the impact of their collective behavior on the levels of atrazine in the lake is formulated as a computational multi-agent system (MAS). The MAS is an artificial representation of the collective action problem that integrates the economic, behavioral and environmental factors that influence the decision-making process of producers. The MAS is used to simulate the evolution of collective behavior among the group and to evaluate the effectiveness of selected incentive mechanisms in preventing the collapse of joint cooperation. The results suggest that without additional incentives, farmers are likely to abandon their voluntary agreement and resume their use of atrazine within the watershed. It is then demonstrated how a combination of policy instruments can be used to alter the underlying game configuration of the collective action problem, resulting in cooperative outcomes. An ambient-based penalty, when used in conjunction with a subsidy payment, is shown to produce divergent incentive structures that shift the classification of the collective action away from a coordination problem with two equilibria to a mixed configuration containing several different game structures and many possible equilibria. This result has important consequences in terms of the evolution of producer behavior and the set of possible collective outcomes. The analysis concludes with an example, which demonstrates that when a mixture of game structures characterizes the collective action, joint cooperation is not a prerequisite to the realization of socially desirable outcomes. By carefully selecting the combination of subsidy payment and ambient penalty, a policy maker can manipulate the underlying structure of the collective action, whereby producers with the smallest impact on water quality choose to defect while all others cooperate.Environmental Economics and Policy,

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

Evolutionary games on graphs

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.Comment: Review, final version, 133 pages, 65 figure