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This chapter discusses an important use of the normal curve model and draws inferences based on a statistic whose sampling distribution is normal. While the sampling distributions of several statistics are of normal form, only the distribution of sample means are being used to illustrate the use of the normal curve model. When one wants to draw an inference about the mean of a single population, the first task is to set up two hypotheses. If σ is known, one can then estimate the variability of the sampling distribution of means by computing σx. This article has not been cited. If the data are discrete, such as the numbers of people falling into various classes, the model will be a discrete probability distribution, but if the data consist of measurements or other numbers that may take any values in a continuum, the model will be a continuous probability distribution. The concept of likelihood, which was introduced by R.
Fisher, plays an important role in all approaches to statistical inference, in particular allowing many of the advantages perceived for Bayesian methods without embracing their controversial prior probabilities. Leigh Tesfatsion and Kenneth L. North-Holland, Amsterdam, the Netherlands, 2006. The current site updates and extends these web support materials beyond the scope of the original handbook guide. Our primary intended audience is graduate students and advanced undergraduate students in the social sciences. These modeling principles help to distinguish ACE from standard economic modeling approaches as well as other forms of ABM.
ABM is well suited for this social science objective. Elsevier, Amsterdam, the Netherlands, 2006, 904pp. What is the Game of Life? Although the Game of Life is not an agent-based model, it is a fascinating illustration of how just three simple behavioral rules can lead to extremely complicated outcomes.
Marchi, Scott, and Scott E. Although slanted towards political science, this wide-ranging ABM survey provides a useful general discussion of ABM capabilities. Norton, New York, NY, pp. ABM using examples from the movies. It concludes by pointing readers to a website where numerous commercial applications of ABM are discussed.
Miller, John, and Scott E. New Edition, Oxford, UK, pp. If you are going to read only one book on evolution, this delightful and insightful book is a good choice. You will be amazed at the implications of the inclusive fitness perspective. In the course of reviewing Daniel C.
Orr provides a provocative wide-ranging discussion of natural selection in relation to both biological and cultural evolution. Oxford University Press, Oxford, UK, pp. Writing in a lively and engaging style, Sigmund provides a non-technical introduction to models of evolution. Topics include population ecology and chaos, random drift and chain reactions, population genetics, evolutionary game theory, and the evolution of cooperation based on reciprocity.
The highlighted pages cover the latter two topics, of most relevance to social scientists. He brings together ideas and techniques from robotics, neuroscience, infant psychology, and artificial intelligence. He addresses a broad range of adaptive behaviors, from cockroach locomotion to the role of linguistic artifacts in higher-level thought. The genetic algorithm is a search technique inspired by the evolutionary effectiveness of mutation and differential reproduction. Each agent might be responding to a fixed environment, or to an every-changing social environment consisting of many agents who are continually adapting to each other.
The article by Rick Riolo in the same issue shows how to incorporate a genetic algorithm in one’s own agent-based model. Using a simple market model for concrete illustration, Vriend demonstrates that substantially different outcomes can result when firms use individual-level genetic algorithm learning versus population-level genetic algorithm learning. Surprisingly, the simplest strategy submitted was the winner in both tournaments. The winning strategy was Tit-For-Tat, the strategy that cooperates on the first move and thereafter does whatever the other player did in the previous move. The selected chapters explain why understanding the IPD is important, how tournament results reveal what it takes to be successful in this context, and why reciprocity works well when paired with a wide range of strategies. Albin, Peter, and Duncan K. Albin and Foley simulate pure exchange among geographically dispersed utility-seeking agents with endowments of two distinct types of goods, and with bounds to rationality and calculation.