TY - JOUR
T1 - A computational approach to managing coupled human–environmental systems
T2 - the POSEIDON model of ocean fisheries
AU - Bailey, Richard M.
AU - Carrella, Ernesto
AU - Axtell, Robert
AU - Burgess, Matthew G.
AU - Cabral, Reniel B.
AU - Drexler, Michael
AU - Dorsett, Chris
AU - Madsen, Jens Koed
AU - Merkl, Andreas
AU - Saul, Steven
N1 - Funding Information:
Acknowledgements This work was funded by the Oxford Martin School (within the Sustainable Oceans Programme; PI Bailey) and also by Ocean Conservancy, supported by grants from Packard Foundation, Walton Family Foundation, Gordon and Betty Moore Foundation.
Publisher Copyright:
© 2018, The Author(s).
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
KW - Agent-based modelling
KW - Decision-support systems
KW - Fisheries
KW - Optimization
KW - Policy
KW - Simulation
KW - Socio-economic
UR - http://www.scopus.com/inward/record.url?scp=85048288332&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048288332&partnerID=8YFLogxK
U2 - 10.1007/s11625-018-0579-9
DO - 10.1007/s11625-018-0579-9
M3 - Article
AN - SCOPUS:85048288332
SN - 1862-4065
VL - 14
SP - 259
EP - 275
JO - Sustainability Science
JF - Sustainability Science
IS - 2
ER -