Rick L. Riolo

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 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, possibly supported by mechanisms to bias interactions such as embedding the agents in a two-dimensional space or other context-preserving networks. Another explanation, indirect reciprocity, 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 or indirect reciprocity, no memory of past encounters is required.

Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide

In many domains, agent-based system modeling competes with equation-based approaches that identify system variables and evaluate or integrate sets of equations relating these variables. The distinction has been of great interest in a project that applies agent-based modeling to industrial supply networks, since virtually all computer-based modeling of such networks up to this point has used system dynamics, an approach based on ordinary differential equations (ODE’s). This paper summarizes the domain of supply networks and illustrates how they can be modeled both with agents and with equations. It summarizes the similarities and differences of these two classes of models, and develops criteria for selecting one or the other approach.

Path dependence and the validation of agent-based spatial models of land use

In this paper, we identify two distinct notions of accuracy of land‐use models and highlight a tension between them. A model can have predictive accuracy: its predicted land‐use pattern can be highly correlated with the actual land‐use pattern. A model can also have process accuracy: the process by which locations or land‐use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land‐use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent‐based land‐use model and using multi‐temporal land‐use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty.

Genetic Programming 1998: Proceedings of the Third Annual Conference

Proceedings of the Annual Conferences on Genetic Programming. These proceedings present the most recent research in the field of genetic programming as well as recent research results in the fields of genetic algorithms, artificial life and evolution strategies, DNA computing, evolvable hardware, and genetic learning classifier systems.

Genetic Programming: Theory and Practice

The talks and discussion during the fourth annual Genetic Programming Theory and Practice workshop (GPTPIV), held in Ann Arbor, Michigan, from May 11 to May 13 2006, suggest that the development of GP has crossed a watershed, from an emphasis on exploratory research to focusing on tackling large, real-world applications. Organized by the Center for the Study of Complex Systems (CSCS) of the University of Michigan and supported by Third Millenium, State Street Global Advisors (SSgA), Christopher T. May of Red Queen Capital Management, Michael Korn, and the Biocomputing and Developmental Systems Group of the University of Limerick, the goal of the workshop is to bridge the gap between theory and practice. Paraphrasing the introduction to the first workshop, the goal is “to allow theory to inform practice and practice to test theory.” To that end, the GPTP workshop again assembled a group of leading theoreticians and practitioners to present and discuss their recent work.

An Agent-Based Model of Income Inequalities in Diet in the Context of Residential Segregation

BACKGROUND Low dietary quality is a key contributor to obesity and related illnesses, and lower income is generally associated with worse dietary profiles. The unequal geographic distribution of healthy food resources could be a key contributor to income disparities in dietary profiles. PURPOSE To explore the role that economic segregation can have in creating income differences in healthy eating and to explore policy levers that may be appropriate for countering income disparities in diet. METHODS A simple agent-based model was used to identify segregation patterns that generate income disparities in diet. The capacity for household food preferences and relative pricing of healthy foods to overcome or exacerbate the differential was explored. RESULTS Absent other factors, income differentials in diet resulted from the segregation of high-income households and healthy food stores from low-income households and unhealthy food stores. When both income groups shared a preference for healthy foods, low-income diets improved but a disparity remained. Both favorable preferences and relatively cheap healthy foods were necessary to overcome the differential generated by segregation. CONCLUSIONS The model underscores the challenges of fostering favorable behavior change when people and resources are residentially segregated and behaviors are motivated or constrained by multiple factors. Simulation modeling can be a useful tool for proposing and testing policies or interventions that will ultimately be implemented in a complex system where the consequences of multidimensional interactions are difficult to predict.

Genetic Programming Theory and Practice II

Genetic Programming Theory and Practice.- Discovering Financial Technical Trading Rules Using.- Abstraction GP.- Using Genetic Programming in Industrial Statistical Model Building Population Sizing for Genetic Programming.- Considering the Roles of Structure in Problem Solving by Computer.- Lessons Learned using Genetic Programming in a Stock Picking Context Favourable Biasing of Function Sets.- Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming.- Topological Synthesis of Robust Systems.- Does Genetic Programming Inherently Adopt Structured DesignTechniques?- Genetic Programming of an Algorithmic Chemistry.- ACGP: Adaptable Constrained Genetic Programming.- Searching for Supply Chain Reordering Policies.- Cartesian Genetic Programming and the Post Docking Filtering Problem.- Listening to Data: Tuning a Genetic Programming System.- Incident Detection on Highways.- Pareto-Front Exploitation in Symbolic Regression.- An Evolved Antenna for a NASA Mission.

A Tale of Two Classifier Systems

This paper describes two classifier systems that learn. These are rule-based systems that use genetic algorithms, which are based on an analogy with natural selection and genetics, as their principal learning mechanism, and an economic model as their principal mechanism for apportioning credit. CFS-C is a domain-independent learning system that has been widely tested on serial computers. *CFS is a parallel implementation of CFS-C that makes full use of the inherent parallelism of classifier systems and genetic algorithms, and that allows the exploration of large-scale tasks that were formerly impractical. As with other approaches to learning, classifier systems in their current form work well for moderately-sized tasks but break down for larger tasks. In order to shed light on this issue, we present several empirical studies of known issues in classifier systems, including the effects of population size, the actual contribution of genetic algorithms, the use of rule chaining in solving higher-order tasks, and issues of task representation and dynamic population convergence. We conclude with a discussion of some major unresolved issues in learning classifier systems and some possible approaches to making them more effective on complex tasks.

Beyond Geography: Cooperation with Persistent Links in the Absence of Clustered Neighborhoods:

Electronic communication allows interactions to take place over great distances. We build an agent-based model to explore whether networks that do not rely on geographic proximity can support cooperation as well as local interactions can. Adaptive agents play a four-move Prisoners Dilemma game, where an agents strategy specifies the probability of cooperating on the first move, and the probability of cooperating contingent on the partners previous choice. After playing with four others, an agent adjusts its strategy so that more successful strategies are better represented in the succeeding round. The surprising result is that if the pattern of interactions is selected at random, but is persistent over time, cooperation emerges just as strongly as it does when interactions are geographically local. This has implications for both research on social dynamics, and for the prospects for building social capital in the modern age.

The structure of adaptive competition in minority games

In this paper we present results and analyses of a class of games in which heterogeneous agents are rewarded for being in a minority group. Each agent possesses a number of fixed strategies each of which are predictors of the next minority group. The strategies use a set of aggregate, publicly available information (reflecting the agents’ collective previous decisions) to make their predictions. An agent chooses which group to join at a given moment by using one of his strategies. These games are adaptive in that agents can choose, at different points of the game, to exercise different strategies in making their choice of which group to join. The games are not evolutionary in that the agents’ strategies are fixed at the beginning of the game. We find, rather generally, that such systems evidence a phase change from a maladaptive, informationally efficient phase in which the system performs poorly at generating resources, to an inefficient phase in which there is an emergent cooperation among the agents, and the system more effectively generates resources. The best emergent coordination is achieved in a transition region between these two phases. This transition occurs when the dimension of the strategy space is of the order of the number of agents playing the game. We present explanations for this general behavior, based in part on an information theoretic analysis of the system and its publicly available information. We also propose a mean-field-like model of the game which is most accurate in the maladaptive, efficient phase. In addition, we show that the best individual agent performance in the two different phases is achieved by sets of strategies with markedly different characteristics. We discuss implications of our results for various aspects of the study of complex adaptive systems.