The Impact of Market Preferences on the Evolution of Market Price and Product Quality.
Conference: Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010), Lyon, France, August 30 - September 2, 2010
A significant challenge for firms in an open-competition marketplace is to balance the conflicting attributes of price and quality. Higher quality levels tend to lead to increased product costs, which, depending on market preferences, can trigger an increase in consumer demand. This paper presents a multi-agent model that allows for an exploration of how price and quality evolve as a result of direct market competition between firms. A new competition model, based on price and quality, is defined. Agents compete by determining their price and quality levels with a view to maximizing their profit. Our goal is to examine a range of market configurations and study how agent strategies evolve over time. We focus on those factors which contribute to each agent's survival in this evolutionary setting. We use game theoretic simulation as a basis to examine various agent strategies. A genetic algorithm is used to characterize a changing environment which evolves over time to reflect the emergence of fitter strategy attributes. Individuals can evolve their own market preferences over subsequent generations and adapt to their preferred market strategy. Agent strategies evolve rapidly to reflect the bias of their individual market. The price and quality relationship of a given market is a primary driver of the evolution of agent strategies in that market. Significantly, our results show the emergence of strategies that prefer low price and high quality sensitive markets. This is despite the penalties which are incurred by the higher costs of increased quality. These results have potentially interesting applications to real-world market dynamics, particularly as companies strive to position their products optimally on different markets.
Available from: Karl Mason
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ABSTRACT: Particle swarm optimisation (PSO) is both a heuristic and stochastic optimisation algorithm. The purpose of these algorithms is to give approximate solutions to problems which would be otherwise too difficult to solve. The PSO algorithm optimises the problem space as a result of particles converging on the best known solution after a period of exploration.
This thesis will introduce a PSO variant with Avoidance of Worst Locations (AWL). The motion of the particles in PSO AWL will be different from that of the standard PSO as a result of their ability to remember their worst locations. The particles will use this new information to improve their search of the problem space by spending less time in the worst positions of the problem space. It is found that a subtle influence from the worst location results in the optimum performance. The proposed PSO AWL has a superior performance when compared to the standard PSO and also previous implementations of worst locations. This thesis will also examine the effect of alternative neighbourhood topologies on the performance of each PSO. It is observed that the dynamic topology, which has be dubbed Gradually Increasing Directed Neighbourhoods (GIDN), further augments the performance of PSO AWL.
Each of these PSO variants are then applied to the Dynamic Economic Emissions Dispatch (DEED) problem to compare their effectiveness on constrained multi objective problems. The PSO AWL performed
significantly better than the standard PSO on the DEED problem with each topology. The application of this research to the DEED model demonstrates the impact of these alternative PSO approaches to real world problem domains.
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