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arXiv · 1607.02376

Multi-Agent System for Groundwater Depletion Using Game Theory

Abstract

Groundwater is one of the most vital of all common pool resources throughout the world. More than half of groundwater is used to grow crops. This research models groundwater depletion patterns within a multi-agent system framework. Irrigators are modeled as agents in the multi-agent system. The irrigation strategies adopted by the agents are investigated using game theory. A set of five irrigators, growing three crops: corn, sorghum and wheat, have been considered in this study. To allow groundwater flow, these agents are assumed to be located in adjoining farm lands. Irrigators are modeled selfish agents that strategize their irrigation patterns in order to maximize their own utilities, i.e. the difference between the total revenue obtained from crop sales and the costs incurred, including groundwater extraction costs. Due to groundwater flow, and have no incentive to conserve groundwater. This leads to unsustainable depletion of the resource under Nash equilibrium, when no irrigator can increase its utility by unilaterally changing its strategy. All parameters in this research are representative of Kansas. Recorded environmental and economic data of the region, along with the DSSAT software, have been used to obtain these futuristic projections. One of the emergent phenomena of the simulations is the adoption of crop rotation patterns by the irrigators to conserve groundwater. The irrigators grow corn, which is a more profitable yet water intensive crop in one year, and in the next, conserve water by growing sorghum instead. Another emergent outcome of this research is the viability of LEMAs. When the irrigators are subject to LEMA-level limits on groundwater use, there is a slight increase in the aggregate utility of the LEMA.

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BibTeXRIS

Ying Huang, Pavel Janovsky, Sanjoy Das, Stephen M. Welch, Scott DeLoach. 2016-07-07. Multi-Agent System for Groundwater Depletion Using Game Theory. https://arxiv.org/abs/1607.02376

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