A Treatise on the Family. G. S. Becker. Cambridge, MA: Harvard University Press. 1981. Gary Becker is one of the most famous and influential economists of the second half of the 20th century, a fervent contributor to and expounder of the University of Chicago free-market philosophy, and winner of the 1992 Nobel Prize in economics. Although any book with the word "treatise" in its title is clearly intended to have an impact, one coming from someone as brilliant and controversial as Becker certainly had such a lofty goal. It has received many article-length reviews in several disciplines (Ben-Porath, 1982; Bergmann, 1995; Foster, 1993; Hannan, 1982), which is one measure of its scholarly importance, and yet its impact is, I think, less than it may have initially appeared, especially for scholars with substantive interests in the family. This book is, its title notwithstanding, more about economics and the economic approach to behavior than about the family. In the first sentence of the preface, Becker writes "In this book, I develop an economic or rational choice approach to the family." Lest anyone accuse him of focusing on traditional (i.e., material) economics topics, such as family income, poverty, and labor supply, he immediately emphasizes that those topics are not his focus. "My intent is more ambitious: to analyze marriage, births, divorce, division of labor in households, prestige, and other non-material behavior with the tools and framework developed for material behavior." Indeed, the book includes chapters on many of these issues. One chapter examines the principles of the efficient division of labor in households, three analyze marriage and divorce, three analyze various child-related issues (fertility and intergenerational mobility), and others focus on broader family issues, such as intrafamily resource allocation. His analysis is not, he believes, constrained by time or place. His intention is "to present a comprehensive analysis that is applicable, at least in part, to families in the past as well as the present, in primitive as well as modern societies, and in Eastern as well as Western cultures." His tone is profoundly conservative and utterly skeptical of any constructive role for government programs. There is a clear sense of how much better things were in the old days of a genderbased division of labor and low market-work rates for married women. Indeed, Becker is ready and able to show in Chapter 2 that such a state of affairs was efficient and induced not by market or societal discrimination (although he allows that it might exist) but by small underlying household productivity differences that arise primarily from what he refers to as "complementarities" between caring for young children while carrying another to term. Most family scholars would probably find that an unconvincingly simple explanation for a profound and complex phenomenon. What, then, is the salient contribution of Treatise on the Family? It is not literally the idea that economics could be applied to the nonmarket sector and to family life because Becker had already established that with considerable success and influence. At its core, microeconomics is simple, characterized by a belief in the importance of prices and markets, the role of self-interested or rational behavior, and, somewhat less centrally, the stability of preferences. It was Becker's singular and invaluable contribution to appreciate that the behaviors potentially amenable to the economic approach were not limited to phenomenon with explicit monetary prices and formal markets. Indeed, during the late 1950s and throughout the 1960s, he did undeniably important and pioneering work extending the domain of economics to such topics as labor market discrimination, fertility, crime, human capital, household production, and the allocation of time. Nor is Becker's contribution the detailed analyses themselves. Many of them are, frankly, odd, idiosyncratic, and off-putting. …

A Treatise on the Family

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The Theory of Industrial Organization

/pdf/social-capital-a-theory-of-social-structure-and-action-4m7poq6byy.pdf

Social capital: a theory of social structure and action

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.

/pdf/a-field-guide-to-genetic-programming-3wkkmsw81n.pdf

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

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

This paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888912000136

Review: agent-based economic models and econometrics

List of Figures. List of Tables. Preface. 1. An Overview S.-H. Chen. Part I: Introduction. 2. Genetic Algorithms in Economics and Finance A.E. Drake, R.E. Marks. 3. Genetic Programming: A Tutorial S.-H. Chen, et al. Part II: Forecasting. 4. GP and the Predictive Power of Internet Message Traffic J.D. Thomas, K. Sycara. 5. Genetic Programming of Polynomial Models for Financial Forecasting N.Y. Nikolaev, H. Iba. 6. NXCS: Hybrid Approach to Stock Indexes Forecasting G. Armano, et al. Part III: Trading. 7. EDDIE for Financial Forecasting E.P.K. Tsang, J. Li. 8. Forecasting Market Indices Using Evolutionary Automatic Programming O'Neil, et al. 9.Genetic Fuzzy Expert Trading System for NASDAQ Stock Market Timing S.S. Lam, et al. Part IV: Miscellaneous Applications Domains. 10. Portfolio Selection and Management J.G.L. Lazo, et al. 11. Intelligent Cash Flow: Planning and Optimization Using GA M.A.C. Pacheco, et al. 12. The Self-Evolving Logic of Financial Claim Prices T.H. Noe, J. Wang. 13. using GP to Predict Exchange Rate Volatility C.J. Neely, P.A. Weller. 14. EDDIE for Stock Index Options and Futures Arbitrage S. Markose, et al. Part V: Agent-Based Computational Finance. 15. A Model of Boundedly Rational Consumer Choice T. Riechmann. 16. Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market S.-H. Chen, C.-C. Liao. 17. Individual Rationality as a Partial Impediment to Market Efficiency S.-H. Chen, et al. 18. A Numerical Study on the Evolution of Portfolio Rules G. Caldarelli, et al. 19. Adaptive Portfolio Managers in Stock Markets K.Y. Szeto. 20. Learning and Convergence to Pareto Optimality C.R. Birchenhall, J.-S. Lin. Part VI: Retrospect and Prospect. 21. The New Evolutionary Computational Paradigm S.M. Markose. Index.

Genetic Algorithms and Genetic Programming in Computational Finance

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1216&context=managementfacpub

Business intelligence in risk management: Some recent progresses

https://www2.econ.iastate.edu/tesfatsi/ACEHistoricalSurvey.SHCheng2011.pdf

Shu-Heng Chen

Papers

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

Review: agent-based economic models and econometrics

Genetic Algorithms and Genetic Programming in Computational Finance

Business intelligence in risk management: Some recent progresses

Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective