Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

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A Field Guide to Genetic Programming

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

http://www.dpi.inpe.br/cursos/tutoriais/modelagem/referencias/agents_lucc_overview.pdf

Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

This chapter surveys research on agent-based models used in finance. It will concentrate on models where the use of computational tools is critical for the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings.

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Agent-based Computational Finance

Preface (L. Tesfatsion, K.L. Judd). Part 1. ACE research reviews. 1. Agent-based computational economics: A constructive approach to economic theory (L. Tesfatsion). 2. Computationally intensive analyses in economics (K.L. Judd). 3. Agent learning representation: Advice on modelling economic learning (T. Brenner). 4. Agent-based models and human subject experiments (J. Duffy). 5. Economic activity on fixed networks (A.W. Wilhite). 6. ACE models of endogenous interactions (N.J. Vriend). 7. Social dynamics: Theory and applications (H. Peyton Young). 8. Heterogeneous agent models in economics and finance (C. Hommes). 9. Agent-based computational finance (B. LeBaron). 10. Agent-based models of innovation and technological change (H. Dawid). 11. Agent-based models of organizations (M-H. Chang, J.E. Harrington, Jr.). 12. Market design using agent-based models (R. Marks). 13. Automated markets and trading agents (J.J. MacKie-Mason, M. Wellman). 14. Computational methods and models of politics (K. Kollman, S.E. Page). 15. Governing social-ecological systems (M.A. Janssen, E. Ostrom). 16. Computational laboratories for spatial agent-based models (C. Dibble). Part 2. Perspectives on the ACE methodology. Out-of-equilibrium economics and agent-based modeling (W.B. Arthur). Agent-based modeling as a bridge between disciplines (R. Axelrod). Remarks on the foundations of agent-based generative social science (J.M. Epstein). Coordination issues in long-run growth (P. Howitt). Agent-based macro (A. Leijonhufvud). Some fun, thirty-five years ago (T.C. Schelling). Part 3. Guideline for newcomers to agent-based modeling. A guide for newcomers to agent-based modeling in the social sciences (R. Axelrod, L. Tesfatsion).

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Agent-based computational economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.

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Agent-Based Computational Economics: Growing Economies From the Bottom Up

http://www.aiecon.org/staff/shc/pdf/sdarticle_1.pdf

Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market

By studying two well known hypotheses in economics, this paper illustrates how emergent properties can be shown in an agent-based artificial stock market. The two hypotheses considered are the efficient market hypothesis and the rational expectations hypothesis. We inquire whether the macrobehavior depicted by these two hypotheses is consistent with our understanding of the microbehavior. In this agent-based model, genetic programming is applied to evolving a population of traders learning over time. We first apply a series of econometric tests to show that the EMH and the REH can be satisfied with some portions of the artificial time series. Then, by analyzing traders’ behavior, we show that these aggregate results cannot be interpreted as a simple scaling-up of individual behavior. A conjecture based on sunspot-like signals is proposed to explain why macrobehavior can be very different from microbehavior. We assert that the huge search space attributable to genetic programming can induce sunspot-like signals, and we use simulated evolved complexity of forecasting rules and Granger causality tests to examine this assertion.

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On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis

Toward a computable approach to the efficient market hypothesis: An application of genetic programming

https://isiarticles.com/bundles/Article/pre/pdf/16167.pdf

Chia-Hsuan Yeh

Papers

Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market

On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis

Toward a computable approach to the efficient market hypothesis: An application of genetic programming

Examining the effectiveness of price limits in an artificial stock market

Option Pricing with Genetic Programming