Problem
Research questions and friction points this paper is trying to address.
groundwater markets
price formation
intertemporal transfer
stochastic allocations
non-cooperative game
Multiperiod Groundwater Markets
🤖 AI Summary
This study investigates endogenous price formation and multi-period pumping strategies in groundwater markets under stochastic allocations and intertemporal water rights banking. It formulates a discrete-time, non-zero-sum, non-cooperative game-theoretic model to capture agricultural agents’ dynamic strategic interactions in water use, trading, and storage. For the first time, the model integrates a water rights banking mechanism with stochastic quotas, thereby endogenizing both market prices and agents’ strategies over multiple periods. A computationally efficient algorithm combining best-response iteration with machine learning techniques is developed to compute the subgame perfect Nash equilibrium. Numerical experiments demonstrate that groundwater recharge dynamics, agents’ risk preferences, and initial allocation levels significantly shape equilibrium prices and extraction strategies, offering theoretical insights for the design of environmental entitlement markets.
📝 Abstract
Motivated by the emergence of local groundwater exchanges, we construct and analyze stochastic models of dynamic groundwater markets. Our primary focus is endogenizing the price formation and groundwater pumping strategies in a closed market with stochastic groundwater allocations and opportunities for intertemporal transfer through rights banking. In our model, several agents, interpreted as farmers or agricultural districts, make competitive decisions on water consumption to produce a basket of goods, as well as on trading allocations among themselves, or banking them for future periods. We define the respective discrete-time non-zero-sum non-cooperative game and construct its sub-game perfect Nash equilibria characterized by the groundwater price process $\{p^\circ(t)\}$. We furthermore construct an algorithm to determine equilibrium strategies and prices through a machine learning approach on top of best-response iterations. Extensive numerical experiments illustrate dynamic phenomena, including the role of groundwater recharge dynamics, agents' risk aversion and groundwater allocations. Our model provides insights into competitive effects in environmental markets with banking features.
Innovation
Methods, ideas, or system contributions that make the work stand out.
dynamic groundwater markets
sub-game perfect Nash equilibrium
rights banking
machine learning algorithm
stochastic allocation
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