Complexity
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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This article discusses how restructured incentives could have inhibited innovation in ancient China and explain the Needham paradox. Agents in a genetic algorithmic game maximize their payoffs by choosing between innovating and studying the Classics. By restructuring incentives toward studying the Classics, initial spurts of innovation are smothered, resulting in a population with all agents studying the Classics. The incentive structure has a statistically and quantitatively significant impact on the expected average payoffs and the strategy profile of the population: the average payoffs for a regime which rewards innovation fluctuate more but are always higher and the strategy profile is varied. © 2012 Wiley Periodicals, Inc. Complexity, 2012 © 2012 Wiley Periodicals, Inc.