Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal

The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structures emerge. In this report, we examine one particular IPD/CR environment and document the social network methods used to identify population behaviors found within this complex adaptive system. In contrast to the standard homogeneous population of nice cooperators, we have also found metastable populations of mixed strategies within this environment. In particular, the social networks of interesting populations and their evolution are examined.Comment: 37 pages, uuencoded gzip'd Postscript (1.1Mb when gunzip'd) also available via WWW at http://www.cs.wisc.edu/~smucker/ipd-cr/ipd-cr.htm

Hysteresis in an Evolutionary Labor Market with Adaptive Search

This study undertakes a systematic experimental investigation of hysteresis (path dependency) in an agent-based computational labor market framework. It is shown that capacity asymmetries between work suppliers and employers can result in two distinct hysteresis effects, network and behavioral, when work suppliers and employers interact strategically and evolve their worksite behaviors over time. These hysteresis effects result in persistent heterogeneity in earnings and employment histories across agents who have no observable structural differences. At a more global level, these hysteresis effects are shown to result in a one-to-many mapping between treatment factors and experimental outcomes. These hysteresis effects may help to explain why excess earnings heterogeneity is commonly observed in real-world labor markets.Dynamic labor market, Hysteresis (path dependency), Networks, Endogenous Interactions, Agent-based computational economics, Evolutionary game.

From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

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

Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search

This study uses an agent-based computational labor market framework to experimentally study the relationship between job capacity, job concentration, and market power. Job capacity is measured by the ratio of potential job openings to potential work offers, and job concentration is measured by the ratio of work suppliers to employers. For each experimental treatment, work suppliers and employers repeatedly seek preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions modelled as prisoner's dilemma games, and evolve their worksite behaviors over time. The main finding is that job capacity consistently trumps job concentration when it comes to predicting the relative ability of work suppliers and employers to exercise market power.Labor market dynamics, Market power, Capacity, Concentration, Adaptive search, Networks, Endogenous interactions, Agent-based computational economics, Evolutionary game

Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search

This study uses an agent-based computational labor market framework to experimentally study the relationship between job capacity, job concentration, and market power. Job capacity is measured by the ratio of potential job openings to potential work orders, and job concentration is measured by the ratio of work suppliers to employers. For each experimental treatment, work suppliers and employers repeatedly seek preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions modelled as prisoner's dilemma games, and evolve their worksite behaviors over time. The main finding is that job capacity consistently trumps job concentration when it comes to predicting the relative ability of work suppliers and employers to exercise market power. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/tnghome.htmmarket power; agent-based computational economics; evolutionary game; Labor market dynamics; job capacity; job concentration; adaptive search; networks; endogenous interactions

Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search

This study uses an agent-based computational labor market framework to undertake a systematic experimental investigation of the relationship between job capacity, job concentration, and market power. Job capacity is measured by the ratio of total potential job openings to total potential work offers, and job concentration is measured by the ratio of work suppliers to employers. For each setting of the capacity and concentration treatment factors, work suppliers and employers repeatedly seek V- preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions mmodeledas prisoner\u27s dilemma games, and evolve their worksite behaviors over time. The main finding is that job capacity consistently trumps job concentration when it comes to predicting the relative ability of work suppliers and employers to exercise market power. Controlling for job capacity, job concentration has only small unsystematic effects on attained market power levels

Game theoretic modeling and analysis : A co-evolutionary, agent-based approach

Ph.DDOCTOR OF PHILOSOPH