Abstract
Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations with agent-based models (ABMs) provide a helpful tool. However, the value of ABMs for studying adaptation depends on the availability of methodologies for sensitivity analysis that can quantify resilience and adaptation in ABMs. In this paper we propose a sensitivity analysis methodology that is based on comparing time-dependent probability density functions of output of ABMs with and without agent adaptation. The differences between the probability density functions are quantified by the so-called earth-mover’s distance. We use this sensitivity analysis methodology to quantify the probability of occurrence of critical transitions and other long-term effects of agent adaptation. To test the potential of this new approach, it is used to analyse the resilience of an ABM of adaptive agents competing for a common-pool resource. Adaptation is shown to contribute positively to the resilience of this ABM. If adaptation proceeds sufficiently fast, it may delay or avert the collapse of this system.
Highlights
Many social-ecological systems, which provide important ecosystem services, are under increasing pressure from human activities and environmental changes [1, 2]
For agent-based models (ABMs) to be a useful tool for the assessment of the resilience of social-ecological systems, suitable methodologies for analysing these ABMs are needed
We have proposed a methodology for analysing effects of agent adaptation in ABMs, and showed how this adaptation affects resilience
Summary
Many social-ecological systems, which provide important ecosystem services, are under increasing pressure from human activities and environmental changes [1, 2]. To predict how these systems will respond to pressures, we need to describe their Complex Adaptive System (CAS) characteristics. The system-level behaviour of CAS ‘emerges’ from lower-level interactions and cannot a priori be predicted from the properties of its agents. To properly manage CAS that are under pressure, it is important to understand which properties affect resilience, i.e., the capacity of the system to cope with pressures while maintaining its identity and avoiding drastic changes [4]. In order to predict the occurrence of such transitions, we need to understand the origin and extent of the resilience of CAS [6]
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