Deep Social Learning in Dynamic Environments Using Subcultures and Auctions With Cultural Algorithms

Abstract

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 an additional Subcultured Distribution Mechanism is described that works to factor the knowledge distribution mechanism into subnetworks in order to support a “deep social learning” approach. The Subcultured Distribution Mechanism is compared with the results of each individual distribution mechanism without a subculture enhancement, 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 changing environments. For nonlinearly changing environments, First Price Multi-round and English Auctions are most of effective on their own. The Subcultured Distribution Mechanism extension of these mechanisms was found to be best suited for complexities where the two distribution mechanisms had similar performances, and in the most chaotic environments where having multiple distribution mechanisms to choose from was advantageous.