Models of market behavior: bringing realistic games to market

Abstract

Modelling markets top-down tends to eliminate the dynamic nature of valuation. As prices constitute emergent properties of market forces and these forces emerge from anticipation and interaction of agents, only by employing games based in discursive systems theory can we detect "systems embedded in systems". Human decision-making has long been described as the convolving of habitual, inferential and affective processes. We have designed a series of simulations that employ neural networks to model the biological processes involved in individual and interactive decision-making. We have also designed models employing these interactions in organizational and market processes. Further, we suggest that observer effects are central to the measurement process in time-series analysis, from series and component definition to experimental design through outcome interpretation. Employing a neural network tool called Differential Filtering, we have suggested that these effects can be understood and, to some extent, vitiated. Finally, we have demonstrated the ability of the brain-emulating networks to detect context and to discover texture in data series, as a solution to problems such as data fusion and data decomposition. We discuss these models in light of modern approaches to complex systems information processing.