Building an 'In Situ-In Silico' Hybrid to Better Understand, Instrument, and Predict Complex Phenomena

Abstract:     
What if advances in multi-agent and computational modeling in silico were married with advances in instrumenting real-world phenomena of interest in situ in near real-time? Specifically, what if business, academic institutions, and government agencies were able to both computationally model and predict complex phenomena while also comparing in near real-time the empirical results of the same phenomena in the real-world? High-performance, standardized approaches to (1) collect in near real-time the instrumented results of real-world, in situ phenomena and (2) compare in parallel multiple computational models to real-world data and identify what parameters the best models share in common - need to exist to achieve this vision of a in silico-in situ hybrid.

Keywords: networks, complexity, in situ, in silico, multi-agent, computational models, sensor technologies

JEL Classifications: D23, D70, D81, D83, O31

Working Paper Series