Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository

Genetic Programming: On the Programming of Computers by Means of Natural Selection

This paper draws on the social and behavioral sciences in an endeavor to specify the nature and microfoundations of the capabilities necessary to sustain superior enterprise performance in an open economy with rapid innovation and globally dispersed sources of invention, innovation, and manufacturing capability. Dynamic capabilities enable business enterprises to create, deploy, and protect the intangible assets that support superior long- run business performance. The microfoundations of dynamic capabilities—the distinct skills, processes, procedures, organizational structures, decision rules, and disciplines—which undergird enterprise-level sensing, seizing, and reconfiguring capacities are difficult to develop and deploy. Enterprises with strong dynamic capabilities are intensely entrepreneurial. They not only adapt to business ecosystems, but also shape them through innovation and through collaboration with other enterprises, entities, and institutions. The framework advanced can help scholars understand the foundations of long-run enterprise success while helping managers delineate relevant strategic considerations and the priorities they must adopt to enhance enterprise performance and escape the zero profit tendency associated with operating in markets open to global competition. Copyright  2007 John Wiley & Sons, Ltd.

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Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance

There is strong evidence that people exploit their bargaining power in competitivemarkets butnot inbilateral bargainingsituations. Thereisalsostrong evidence that people exploit free-riding opportunities in voluntary cooperation games. Yet, when they are given the opportunity to punish free riders, stable cooperation is maintained, although punishment is costly for those who punish. This paper asks whether there is a simple common principle that can explain this puzzling evidence. We show that if some people care about equity the puzzles can be resolved. It turns out that the economic environment determines whether the fair types or the selesh types dominate equilibrium behavior.

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A theory of fairness, competition and cooperation

There is strong evidence that people exploit their bargaining power in competitive markets but not in bilateral bargaining situations. There is also strong evidence that people exploit free-riding opportunities in voluntary cooperation games. Yet, when they are given the opportunity to punish free riders, stable cooperation is maintained although punishment is costly for those who punish. This paper asks whether there is a simple common principle that can explain this puzzling evidence. We show that if a fraction of the people exhibits inequality aversion the puzzles can be resolved.

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A Theory of Fairness, Competition and Cooperation

We demonstrate that a simple model, constructed on the premise that people are motivated by both their pecuniary payoff and their relative payoff standing, organizes a large and seemingly disparate set of laboratory observations as one consistent pattern The model is incomplete information but nevertheless posed entirely in terms of directly observable variables The model explains observations from games where equity is thought to be a factor, such as ultimatum and dictator, games where reciprocity is thought to play a role, such as the prisoner's dilemma and gift exchange, and games where competitive behavior is observed, such as Bertrand markets (JEL C78, C90, D63, D64, H41)

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ERC: A Theory of Equity, Reciprocity, and Competition

Subjects in economic laboratory experiments have clearly expressed an interest in behaving unselfishly. They cooperate in prisoners’ dilemma games, they give to public goods, and they leave money on the table when bargaining. While some are tempted to call this behavior irrational, economists should ask if this unselfish and altruistic behavior is indeed self-interested. That is, can subjects’ concerns for altruism or fairness be expressed in the economists’ language of a well-behaved preference ordering? If so, then behavior is consistent and meets our definition of rationality. This paper explores this question by applying the axioms of revealed preference to the altruistic actions of subjects. If subjects adhere to these axioms, such as GARP, then we can infer that a continuous, convex, and monotonic utility function could have generated their choices. This means that an economic model is sufficient to understand the data and that, in fact, altruism is rational. We do this by offering subjects several opportunities to share a surplus with another anonymous subject. However, the costs of sharing and the surplus available vary across decisions. This price and income variation creates budgets for altruistic activity that allow us to test for an underlying preference ordering. We found that subjects exhibit a significant degree of rationally altruistic behavior. Over 98% of our subjects made choices that are consistent with utility maximization. Only a quarter of subjects are selfish money-maximizers, and the rest show varying degrees of altruism. Perhaps most strikingly, almost half of the subjects exhibited behavior that is exactly consistent with one of three standard CES utility functions: perfectly selfish, perfect substitutes, or Leontief. Those with Leontief preferences are always dividing the surplus equally, while those with perfect substitutes preferences give everything away when the price of giving is less than one, but keep everything when the price of giving is greater than one. Using the data on choices, we estimated a population of utility functions and applied these to predict the results of other studies. We found that our results could successfully characterize the outcomes of other studies, indicating still further that altruism can be captured in an economic model.

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Giving According to GARP: An Experimental Test of the Consistency of Preferences for Altruism

Theoretical physics is replete with models. When equations of motion are not available, or not amenable to perturbation theory, or just too hard from which to extract useful information, then physicists turn to models and computation. The Ising model of ferromagnetism is a classic example. A simple nearest neighbor temperature dependent interaction, in two or more dimensions, leads to long-range order and a phase transition at a finite temperature. This model has many locally interacting parts and an emergent behavior (ferromagnetism) at a critical temperature. However, the system never adapts. It does not change the phase transition to a higher temperature or avoid a phase transition altogether. Social systems are always adapting, and this interesting twist produces a vast array of possibilities and forms the basis of much of the discussion in Miller and Page’s book. This book is not a textbook, but rather an essay on complex adaptive systems. The discussions and insights will be better appreciated by readers who have already tried their hand at investigating complex adaptive systems. These systems can be so complex that the best method to discover their properties is to dispatch many computer agents to experience the system’s possibilities. The study becomes more interesting when the agents can alter their actions and the rules of the game. Miller and Page give the simple, but instructive example of forest growth and lightning induced forest fires. If trees grow too rapidly they will cover the allowable space and a fire started anywhere in the forest will spread and destroy the entire forest. A very slow growth will only produce a sparse forest. The authors find a tree growth rate to achieve an optimal stable high forest coverage. Their solution is trumped when altruistic agents are introduced, one for each tree. Some of the agents adapt by not growing a tree in their plot of land (to their personal disadvantage) but the overall global organization is one of firebreaks preventing large scale fires. Adaptation wins! Another model discussed is what physicists call the minority game, that is, making a choice that puts you in the minority. This is perhaps best known through the El Faro example of choosing whether or not to go to Santa Fe’s El Faro bar tonight based on whether it was

Complex Adaptive Systems: An Introduction to Computational Models of Social Life

This paper presents experiments designed to examine the sequential equilibrium reputation hypothesis in the finitely repeated prisoner's dilemma. The authors test the hypothesis by controlling the subjects' ability to build reputations and by manipulating their beliefs that their opponent is irrational or altruistic. The responses of subjects strongly support the sequential equilibrium prediction. The results also suggest an important role for 'homemade altruism,' that is, a natural tendency to cooperate that subjects bring to the experiment from the outside. The authors find that there may be no difference between the beliefs that an opponent is altruistic and the actual chance it is so. Copyright 1993 by Royal Economic Society.

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Rational Cooperation in the Finitely Repeated Prisoner's Dilemma: Experimental Evidence

Economic analysis has largely avoided questions about the way in which economic agents make choices when confronted by a perpetually novel and evolving world. As a result, there are outstanding questions of great interest to economics in areas ranging from technological innovation to strategic learning in games. This is so, despite the importance of the questions, because standard tools and formal models are ill-tuned for answering such questions. However, recent advances in computer-based modeling techniques, and in the subdiscipline of artificial intelligence called machine learning, offer new possibilities. Artificial adaptive agents (AAA) can be defined and can be tested in a wide variety of artificial worlds that evolve over extended periods of time. The resulting complex adaptive systems can be examined both computationally and analytically, offering new ways of experimenting with and theorizing about adaptive economic agents. Many economic systems can be classified as complex adaptive systems. Such a system is complex in a special sense: (i) It consists of a network of interacting agents (processes, elements); (ii) it exhibits a dynamic, aggregate behavior that emerges from the individual activities of the agents; and (iii) its aggregate behavior can be described without a detailed knowledge of the behavior of the individual agents. An agent in such a system is adaptive if it satisfies an additional pair of criteria: the actions of the agent in its environment can be assigned a value (performance, utility, payoff, fitness, or the like); and the agent behaves so as to increase this value over time. A complex adaptive system, then, is a complex system containing adaptive agents, networked so that the environment of each adaptive agent includes other agents in the system. Complex adaptive systems usually operate far from a global optimum or attractor. Such systems exhibit many levels of aggregation, organization, and interaction, each level having its own time scale and characteristic behavior. Any given level can usually be described in terms of local niches that can be exploited by particular adaptations. The niches are various, so it is rare that any given agent can exploit all of them, as rare as finding a universal competitor in a tropical forest. Moreover, niches are continually created by new adaptations. It is because of this ongoing evolution of the niches, and the perpetual novelty that results, that the system operates far from any global attractor. Improvements are always possible and, indeed, occur regularly. The everexpanding range of technologies and products in an economy, or the everimproving strategies in a game like chess, provide familiar examples. Adaptive systems may settle down temporarily at a local optimum, where performance is good in a comparative sense, but they are usually uninteresting if they remain at that optimum for an extended period. A theory of complex adaptive systems based on AAA makes possible the development of well-defined, yet flexible, models that exhibit emergent behavior. Such models can capture a wide range of economic phenomena precisely, even though the development of a general mathematical theory of complex adaptive systems is still in its early stages.' The AAA models complement current theoretical directions; they are

Artificial Adaptive Agents in Economic Theory

A model of learning and adaptation is used to analyze the coevolution of strategies in the repeated Prisoner's Dilemma game under both perfect and imperfect reporting. Metaplayers submit finite automata strategies and update their choices through an explicit evolutionary process modeled by a genetic algorithm. Using this framework, adaptive strategic choice and the emergence of cooperation are studied through ‘computational experiments’. The results of the analyses indicate that information conditions lead to significant differences among the evolving strategies. Furthermore, they suggest that the general methodology may have much wider applicability to the analysis of adaptation in economic and social systems.

John H. Miller

Papers

Giving According to GARP: An Experimental Test of the Consistency of Preferences for Altruism

Complex Adaptive Systems: An Introduction to Computational Models of Social Life

Rational Cooperation in the Finitely Repeated Prisoner's Dilemma: Experimental Evidence

Artificial Adaptive Agents in Economic Theory

The coevolution of automata in the repeated Prisoner's Dilemma