Analysis of game playing agents with fingerprints

Evolutionary computation (EC) can create a vast number of strategies for playing simple games in a short time. Analysis of these strategies is typically more time-consuming than their production. As a result, analysis of strategies produced by an EC system is often lacking or restricted to the extraction of superficial summary Statistics and Probability; This thesis presents a technique for extracting a functional signature from evolved agents that play games. This signature can be used as a visualization of agent behavior in games with two moves and also provides a numerical target for clustering and other forms of automatic analysis. The fingerprint can be used to induce a similarity measure on the space of game strategies. This thesis develops fingerprints in the context of the iterated prisoner\u27s dilemma; we note that they can be computed for any two player simultaneous game with a finite set of moves. When using a clustering algorithm, the results are strongly influenced by the choice of the measure used to find the distance between or to compare the similarity of the data being clustered. The Euclidean metric, for example, rates a convex polytope as the most compact type of object and builds clusters that are contained in compact polytopes. Presented here is a general method, called multi-clustering, that compensates for the intrinsic shape of a metric or similarity measure. The method is tested on synthetic data sets that are natural for the Euclidean metric and on data sets designed to defeat k-means clustering with the Euclidean metric. Multi-clustering successfully discovers the designed cluster structure of all the synthetic data sets used with a minimum of parameter tuning. We then use multi-clustering and filtration on fingerprint data. Cellular representation is the practice of evolving a set of instructions for constructing a desired structure. This thesis presents a cellular encoding for finite state machines and specializes it to play the iterated prisoner\u27s dilemma. The impact on the character and behavior of finite state agents of using the cellular representation is investigated. For the cellular representation resented a statistically significant drop in the level of cooperation is found. Other differences in the character of the automaton generated with a direct and cellular representation are reported

Cheating is evolutionarily assimilated with cooperation in the continuous snowdrift game

It is well known that in contrast to the Prisoner's Dilemma, the snowdrift game can lead to a stable coexistence of cooperators and cheaters. Recent theoretical evidence on the snowdrift game suggests that gradual evolution for individuals choosing to contribute in continuous degrees can result in the social diversification to a 100% contribution and 0% contribution through so-called evolutionary branching. Until now, however, game-theoretical studies have shed little light on the evolutionary dynamics and consequences of the loss of diversity in strategy. Here we analyze continuous snowdrift games with quadratic payoff functions in dimorphic populations. Subsequently, conditions are clarified under which gradual evolution can lead a population consisting of those with 100% contribution and those with 0% contribution to merge into one species with an intermediate contribution level. The key finding is that the continuous snowdrift game is more likely to lead to assimilation of different cooperation levels rather than maintenance of diversity. Importantly, this implies that allowing the gradual evolution of cooperative behavior can facilitate social inequity aversion in joint ventures that otherwise could cause conflicts that are based on commonly accepted notions of fairness.Comment: 30 pages, 3 tables, 5 figure

Learning and innovative elements of strategy adoption rules expand cooperative network topologies

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

About prisoners and dictators: the role of other-self focus, social value orientation, and sterotype primes in shaping cooperative behavior.

Six experiments examined the effects of person factors (i.e., social value orientation and consistency) and situation factors (i.e., stereotype primes) on cooperative behavior in various experimental games. Results indicated that the main and joint influences of person and situation factors on cooperative choices depend on the nature of the game (i.e., prisoner's dilemma or dictator game). Social value orientation, consistency, and primes affect cooperative behavior only in a dictator game, while these factors also lead to rumination about partner's behavioral intentions and personality (and therefore to different cooperative choices) in a prisoner's dilemma game. Differences between these games were explained in terms of the impact they have on other- and self-focus.Choice; Consistency; Dictator game; Effects; Factors; Prisoner's dilemma game; Social Value Orientation; Stereotype Priming; Value;

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

The Present and Future of Game Theory

A broad nontechnical coverage of many of the developments in game theory since the 1950s is given together with some comments on important open problems and where some of the developments may take place. The nearly 90 references given serve only as a minimal guide to the many thousands of books and articles that have been written. The purpose here is to present a broad brush picture of the many areas of study and application that have come into being. The use of deep techniques flourishes best when it stays in touch with application. There is a vital symbiotic relationship between good theory and practice. The breakneck speed of development of game theory calls for an appreciation of both the many realities of conflict, coordination and cooperation and the abstract investigation of all of them.Game theory, Application and theory, Social sciences, Law, Experimental gaming, conflict, Coordination and cooperation