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Abstract System-level integration and optimization of food-energy-water systems (FEWS) require coordination of multiple agencies and decision-makers and incorporating their interdependence. In general, such coordination might be hard to achieve. As a result, the literature on FEWS management either optimizes the operations for one sector (or one decision-maker), or models interdependence among the sectors without optimizing their operations. In this article, we develop a novel multi-agent management optimization approach that is able to incorporate stochasticity and uncertainty in the system’s dynamics and interdependence of the water and energy resources for food production. The proposed method is the first attempt to utilize fundamentals of decision and game theories to optimize operations of multi-agent FEWS. We specifically focus on differentiating between (1) cooperative decision optimization of the operations, where all decision-makers cooperate to achieve the best outcome for the whole system, the social optimum, and (2) non-cooperative decision-making of the agents, the Nash equilibrium. Illustrating with a real-world case study of FEWS in Ventura County, California, we show the difference between the cooperative and non-cooperative decision making in terms of long-term expected cost of managing the system. We further show how the extra costs associated with utilizing the renewable sources of water and energy could be incentivised, so that the non-cooperative solution (the Nash equilibrium) would naturally converge to the best outcome for the whole system (the social optimum).
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Optimizing dynamics of integrated food–energy–water systems under the risk of climate change
Abstract Integrated management of food–energy–water systems (FEWS) requires a unified, flexible and reproducible approach to incorporate the interdependence between sectors, and include the risk of non-stationary environmental variations due to climate change. Most of the recently developed methods in the literature fall short of one or more aspects in such integration. In this article, we propose a novel approach based upon fundamentals of decision theory and reinforcement learning that (1) quantifies and propagates uncertainty, (2) incorporates resource interdependence, (3) includes the impact of uncontrolled variables such as climate variations, and (4) adaptively optimizes management decisions to minimize the costs and environmental impacts of crop production. Moreover, the proposed method is robust to problem-specific complexities and is easily reproducible. We illustrate the framework on a real-world case study in Ventura County, California.
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- Award ID(s):
- 1739676
- PAR ID:
- 10306275
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 14
- Issue:
- 7
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 074010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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