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Predicting Behavior in Agent-Based Models for Complex Systems

Many important real-world systems feature a population of interacting objects (e.g., financial markets, traffic). These systems often exhibit unexpected macroscopic behavior whose existence would have been hard to predict based solely on a knowledge of the microscopic properties of an individual (e.g., market crashes, traffic jams). Despite the lack of a universally accepted definition of complexity, such real-world systems are typically referred to as ‘complex systems’. When conducting scientific research, we often times find ourselves dealing with such systems, which arise in fields ranging from physics and economics to biology and sociology. Regardless of what we are modeling, we are usually curious about the time evolution of the complex system, and thus would like to make predictions into the future. In this thesis, we focus on cases where the complex system is modeled using a multi-agent framework. We then discuss an optimization method to deduce the finite measure describing the composition of heterogeneous agents in the model. Our primary focus is on agent-based artificial market models for financial time-series, but we also discuss a different application of multi-agent modeling to astrobiology for learning the evolution of protein ecologies and which initial distributions govern the learning process. As multi-agent modeling becomes more computationally feasible due to technological advances, we see how versatile its applications are throughout science.