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Date of Award
Robert G. Reynolds
Cultural Algorithms have led to the development of many ways to distribute information within social networks. These mechanisms act by helping the system make decisions about how information is distributed through a population network, and thus are called distribution or decision mechanisms. Many distribution mechanisms have been developed using techniques from auction theory, game theory and various forms of voting construct. Here we discuss several methods of Knowledge distribution collectively called the auction distributions mechanisms and their performance is compared using dynamic complex real-valued functional landscapes. We perform this comparison with regards to robustness, how well the system finds solutions, and resilience, how well the system reacts to changes in the dynamics of the system.
In this paper a new extension of a system called Subcultures called the Subcultured Distribution Mechanism is described. Previous forms of the Subcultures system involved allowing Knowledge Sources within the Belief Space to choose what network was used for their distribution in the Population Space based on the complexity of the problem at hand. Here the Subcultures system is extended to allow the selection of distribution mechanisms along with the network. The new Subcultured Distribution Mechanism is compared with the results of each individual distribution mechanism without a subculture, when applied to a series of dynamic complex optimization problems of varying complexities.
The results suggest that relatively simple mechanism such as Weighted Majority Wins and First Price Auction are sufficient for environments that exhibit low entropic levels of change such as in linear environments. For non-linearly changing environments, First Price Multiround and English Auctions are most of effective. The Subcultured Distribution Mechanism was found to be best suited for complexities where two distribution mechanisms share similar performance, and in the most chaotic environments, both areas where having multiple distribution mechanisms to choose from is advantageous. What these results suggest that while voting approaches work well in predictably changing environments, cultural diversity is a necessity for sustainability in an environment that is changing non-linearly.
We also discuss the development of the Cultural Engine by analyzing the role of robustness and resilience as diagnostic signals. These signals provide insight into the operational health of the Cultural Algorithm by measuring their value prior to future fitness increases, thus determining their suitability as predictors of future performance. We then use this information to discuss how the diagnostic signals can be used to create diagnostic routines that can adjust the Cultural Engine based on its current status.
Kinnaird-Heether, Leonard, "The Impact Of Auction-Based Knowledge Distribution Mechanisms On Optimization Problem Solving In Complex Dynamic Environments With Cultural Algorithms" (2020). Wayne State University Dissertations. 2389.