Sophisticated Behavioral Simulation: A Possible Solution to Problems of Organized Complexity

Simulation technologies have been widely utilized in many scientific research fields such as weather forecasting, fluid mechanics, and biological populations. As a matter of facts, they act as the best tool to handle problems in complex systems where closed-form expressions are unavailable and the target distribution in the representation space is too complex to be fully represented by data-driven learning models, such as deep learning (DL) models. This paper investigates the effectiveness and preference of simulation technologies based on the analyses of scientific paradigms and problems. We revisit the evolution of scientific paradigms from the perspective of data, algorithms, and computational power, and rethink a classic classification of scientific problems which consists of the problems of organized simplicity, problems of disorganized complexity, and problems of organized complexity. These different problems reflect the strengths of different paradigms, indicating that a new simulation technology integrating different paradigms is required to deal with unresolved problems of organized complexity in more complex systems. Therefore, we summarize existent simulation technologies aligning with the scientific paradigms, and propose the concept of behavioral simulation (BS), and further sophisticated behavioral simulation (SBS). They represent a higher degree of paradigms integration based on foundation models to simulate complex social systems involving sophisticated human strategies and behaviors. Beyond the capacity of traditional agent-based modeling simulation (ABMS), BS and further SBS are designed to tackle challenges concerning the complex human system, which can be regarded as a possible next paradigm for science. Through this work, we look forward to more powerful BS and SBS applications in scientific research branches within social science.