Modelling and Analysis of Adaptability and Emergent Behavior in a Cryptocurrency Market

Abstract

Understanding how complex system components interact and adapt to environment changes is critical for analyzing their emergent behavior and the various positive and negative effects of that emergent behaviors might have. Several modeling languages and frameworks have been proposed for the modeling of complex adaptive systems but few have been applied in practice beyond simple models such as flocks of birds and predator prey. In this paper, we model the adaptive behavior of various entities in a Bitcoin market. We employ CASTLE, a dedicated framework for the modeling of adaptability in complex adaptive systems. Contrary to existing models where realistic details are not included, we introduce the influence of price speculation and news on trader behavior and experiment with different trader behaviors under varying market conditions. Our analysis of a market of 1,000 initial traders shows the feasibility of our approach but also highlights future research challenges.