Abstract

Single- and multi-layer complex networks have been proven as a powerful tool to study the dynamics within social, technological, or natural systems. An often observed common goal is to optimize these systems for specific purposes by minimizing certain costs while maximizing a desired output. Acknowledging that especially real-world systems from the coupled socio-ecological realm are highly intertwined this work exemplifies that in such systems the optimization of a certain subsystem, e.g. to increase the resilience against external pressure in an ecological network, may unexpectedly diminish the stability of the whole coupled system. For this purpose we utilize an adaptation of a previously proposed conceptual bi-layer network model composed of an ecological network of diffusively coupled resources co-evolving with a social network of interacting agents that harvest these resources and learn each other’s strategies depending on individual success. We derive an optimal coupling strength that prevents collapse in as many resources as possible if one assumes that the agents’ strategies remain constant over time. We then show that if agents socially learn and adapt strategies according to their neighbors’ success, this optimal coupling strength is revealed to be a critical parameter above which the probability for a global collapse in terms of irreversibly depleted resources is high—an effect that we denote the tragedy of the optimizer. We thus find that measures which stabilize the dynamics within a certain part of a larger co-evolutionary system may unexpectedly cause the emergence of novel undesired globally stable states. Our results therefore underline the importance of holistic approaches for managing socio-ecological systems because stabilizing effects which focus on single subsystems may be counter-beneficial for the system as a whole.

Highlights

  • Complex networks have been proven as a powerful framework to study the structure and dynamics in a broad range of real-world systems, ranging from social networks [1,2,3,4] to complex adaptive systems in socio-ecology [5] and multilayer hierarchical structures in infrastucture [6], economy [7] or even the climate system [8, 9]

  • Acknowledging that especially real-world systems from the coupled socio-ecological realm are highly intertwined this work exemplifies that in such systems the optimization of a certain subsystem, e.g., to increase the resilience against external pressure in an ecological network, may unexpectedly diminish the stability of the whole coupled system. For this purpose we utilize an adaptation of a previously proposed conceptual bilayer network model composed of an ecological network of diffusively coupled resources co-evolving with a social network of interacting agents that harvest these resources and learn each other’s strategies depending on individual success

  • Complex networks have been utilized to analyze a broad range of social dynamics [19] and spreading processes [20] that fostered the development of associated conceptual models with foci on complex contagion [21, 22], opinion dynamics [23,24,25] or epidemic spreading [26,27,28]

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Summary

INTRODUCTION

Complex networks have been proven as a powerful framework to study the structure and dynamics in a broad range of real-world systems, ranging from social networks [1,2,3,4] to complex adaptive systems in socio-ecology [5] and multilayer hierarchical structures in infrastucture [6], economy [7] or even the climate system [8, 9]. We exemplify on this potential drawback and show that in a coupled socio-ecological system the optimization of the natural component alone may unexpectedly diminish the stability of the system as a whole and even may cause the existence of undesired stable fixed points corresponding to a global collapse For this purpose we utilize a recently proposed conceptual socio-ecological bi-layer network model consisting of a social layer and an ecological layer [29,30,31]. IV with a summary of the results and an outlook to future work

MODEL DESCRIPTION
Node dynamics and diffusion
Social learning of exploitation strategies
RESULTS AND DISCUSSION
Optimization of resource resilience with infinite social update time
Effect of the diffusive flow with social learning
CONCLUSION & OUTLOOK
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