Discovering Artificial Economics: How Agents Learn And Economies Evolve

Discovering Artificial Economics is an informal introduction to the ideas of modern systems theory and self-organization as they apply to problems in the economic realm. David Batten interleaves anecdotes and stories with technical discussions, in order to provide the general reader with a good feel for how economies function and change. Using a wealth of examples from evolutionary game theory, to stock markets, to urban and traffic planning, Batten shows how economic agents interact to produce the behavior we have come to recognize as economic life. Despite the book's easy-to-read style, Batten's message is quite profound. Strongly interactive groups of agents can produce unexpected collective behavior, emergent features which are lawful in their own right. These patterns of emergent behavior are the hallmark of a complex, self-organizing economy. Batten discards many traditional axioms of economic behavior. Far from displaying perfectly deductive rationality to achieve a predictable economic equilibrium, his agents face an economy that is open and dynamic. There we find evolution, heterogeneity and instabilities; stochastic and deterministic phenomena; unexpected regularities as well as equally unexpected, large-scale fluctuations. Interacting agents are forced to be intuitive and adaptive, because they must respond to a continuously changing economic landscape. Because complexity theory attempts to study a large number of agents, and their changing interaction patterns, it often gets too difficult for a mathematical solution. Thus, many of the anecdotes and results discussed in the book have emerged from agent-based computer simulations. The message is that the social sciences are poised on the verge of a new scientific era, one in which economists will conduct experiments inside their own computers. Welcome to the new age of Artificial Economics.

Table of Contents:
* List of Illustrations * Preface * Credits * Acknowledgments * 1. Chance and Necessity * "Wetting" the Appetite * Sandpiles, Self-Organization, and Segregation * Power Laws and Punctuated Equilibria * Bulls, Bears, and Fractals * Stasis and Morphogenesis * On Learning Curves * 2. The Road to Know-Ware * What Is Knowledge? * Finding the Road to Know-Ware * The Age of Deception * Seeing the Light at the El Farol * The Emergence of Cooperation * Coevolutionary Learning * 3. Sheep, Explorers, and Phase Transitions * The Fallacy of Composition * Irreducible Interactions * Getting Well Connected * Sheep and Explorers * Are You an Inductive Graph Theorist? * 4. The Ancient Art of Learning by Circulating * Pirenne's Hypothesis * The Mees Analysis * Learning by Circulating * Big Buttons and a Critical Thread * Ephemeral Entrepts * 5. Networks, Boosters, and Self-Organized Cities * The Shortest Network Problem * Pirenne Again? * Selective Urban Growth * One Great Metropolis * Networking Futures * City-Size Distributions Obey Power Laws * Artificial Cities * 6. Traffic Near the Edge of Chaos * The Driver's Dilemma * In Whose Best Interests? * Sheep, Explorers, and Bounded Rationality * Cellular Congestion * Coevolutionary Learning in Congested Traffic * Edge-of-Chaos Management * 7. Coevolving Markets * Are Stock Markets Efficient? * Pattern Recognizers * Scaling the Market's Peaks * Fibonacci Magic * Market Moods * Reading the Market's Mind * How Markets Learn * 8. Artificial Economics * Limits to Knowledge * Adaptive Agents and the Science of Surprise * The New Age of Artificial Economics * Growing a Silicon Society * Some Final Words * Notes * Bibliography * Index