Brownian agent-based technology forecasting

Today's innovation process is best characterized by nonlinearity and interaction. Agent-based models build on these concepts, but have not been useful in practice because they are either too complex or too simple to make a good match with reality. As a remedy, we employ a Brownian agent model with intermediate complexity to produce value-added technology forecasting. As an illustration with Korea's software industry data, computer simulation is carried out. Attracted by higher technology value, agents concentrate on specific technology regions, and form co-existing major technology regions of high density. A rough comparison with actual software production data exhibits a fair reflection of reality, and supports the underlying idea that economic motivation of agents should be considered.