Navigating simplicity and complexity of social-ecological systems through a dialog between dynamical systems and agent-based models

TL;DR

This paper presents an iterative method for integrating dynamical systems models and agent-based models to better understand complex social-ecological systems, enhancing insights beyond individual approaches.

Contribution

It introduces a novel dialogue-based procedure for combining DSM and ABM, allowing iterative refinement and broader exploration of social-ecological phenomena.

Findings

Enhanced understanding of poverty traps and innovation in agriculture

Iterative procedure improves model insights and research questions

Method applicable to various social-ecological systems

Abstract

Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.