Simulation of emergence in artificial societies: a practical model-based approach with the EB-DEVS formalism

TL;DR

This paper introduces EB-DEVS, a formalism for modeling and simulating emergent properties in complex social systems, demonstrating its effectiveness through various case studies and live emergence detection capabilities.

Contribution

It presents EB-DEVS as a novel formalism for live detection of emergence in complex systems, with practical case studies illustrating its advantages and limitations.

Findings

EB-DEVS enables compact modeling of communication structures.

The approach supports live emergence detection in diverse social models.

Multilevel features enhance robustness in complex system simulation.

Abstract

Modelling and simulation of complex systems is key to exploring and understanding social processes, benefiting from formal mechanisms to derive global-level properties from local-level interactions. In this paper we extend the body of knowledge on formal methods in complex systems by applying EB-DEVS, a novel formalism tailored for the modelling, simulation and live identification of emergent properties. We guide the reader through the implementation of different classical models for varied social systems to introduce good modelling practices and showcase the advantages and limitations of modelling emergence with EB-DEVS, in particular through its live emergence detection capability. This work provides case study-driven evidence for the neatness and compactness of the approach to modelling communication structures that can be explicit or implicit, static or dynamic, with or without multilevel interactions, and with weak or strong emergent behaviour. Throughout examples we show that EB-DEVS permits conceptualising the analysed societies by incorporating emergent behaviour when required, namely by integrating as a macro-level aggregate the Gini index in the Sugarscape model, Fads and Fashion in the Dissemination of Culture model, size-biased degree distribution in a Preferential Attachment model, happiness index in the Segregation model and quarantines in the SIR epidemic model. In each example we discuss the role of communication structures in the development of multilevel simulation models, and illustrate how micro-macro feedback loops enable the modelling of macro-level properties. Our results stress the relevance of multilevel features to support a robust approach in the modelling and simulation of complex systems.