A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries

Bailey, Richard M., Carrella, Ernesto, Axtell, Robert, Burgess, Matthew G., Cabral, Reniel B., Drexler, Michael, Dorsett, Chris, Madsen, Jens Koed, Merkl, Andreas, and Saul, Steven (2019) A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries. Sustainability Science, 14 (2). pp. 259-275.

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Abstract

Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed by the past experience, must not be unduly constrained; they must allow equally for both the fine-tuning of successful existing approaches and the generation of novel ones in unbiased ways. We study ocean fisheries as an example class of complex human–environmental systems, and present a new model (POSEIDON) and computational approach to policy design. The model includes an adaptive agent-based representation of a fishing fleet, coupled to a simplified ocean ecology model. The agents (fishing boats) do not have programmed responses based on empirical data, but respond adaptively, as a group, to their environment (including policy constraints). This conceptual model captures qualitatively a wide range of empirically observed fleet behaviour, in response to a broad set of policies. Within this framework, we define policy objectives (of arbitrary complexity) and use Bayesian optimization over multiple model runs to find policy parameters that best meet the goals. The trade-offs inherent in this approach are explored explicitly. Taking this further, optimization is used to generate novel hybrid policies. We illustrate this approach using simulated examples, in which policy prescriptions generated by our computational methods are counterintuitive and thus unlikely to be identified by conventional frameworks.

Item ID: 75069
Item Type: Article (Research - C1)
ISSN: 1862-4057
Keywords: Agent-based modelling, Decision-support systems, Fisheries, Optimization, Policy, Simulation, Socio-economic
Copyright Information: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativeco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Date Deposited: 18 Jul 2022 23:43
FoR Codes: 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3005 Fisheries sciences > 300505 Fisheries management @ 100%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180599 Marine systems and management not elsewhere classified @ 100%
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