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Forecasting the Unpredictable: Simulation Modeling of Consumer Behavior and Market Trends
Abstract
The chapter discusses how simulation-based approaches can redefine consumer behavior and trend forecasts in a world becoming highly dynamic and complex. Based on theoretical models like Behavioral Decision Theory, System Theory, and Diffusion of Innovation, the research addresses the shortcomings of traditional linear forecasting tools and offers to embrace such tools as Agent-Based Modeling (ABM), System Dynamics (SD), and Machine Learning (ML). Using qualitative research and real-life examples of Amazon and Tesla companies, the study reveals the process by which the simulation models embrace emergence behaviours, negative feedback mechanisms, and non-linear connections to understand consumer ecosystems more thoroughly. The results emphasize the fact that simulation is both a tool of forecasting and an asset to gain strategies to perform experiments and tests of policies and adaptive decisions.
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