Value-based Resource Matching with Fairness Criteria: Application to Agricultural Water Trading

Abhijin Adiga, Yohai Trabelsi, Tanvir Ferdousi, Madhav Marathe, S. S. Ravi, Samarth Swarup, Anil Kumar Vullikanti, Mandy L. Wilson, Sarit Kraus, Reetwika Basu, Supriya Savalkar, Matthew Yourek, Michael Brady, Kirti Rajagopalan, Jonathan Yoder

Research output: Contribution to journalConference articlepeer-review


Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geographic constraints, and fairness. First, we address a matching problem where the goal is to maximize a welfare function in two-sided markets where buyers' requirements and sellers' supplies are represented by value functions that assign prices (or costs) to specified volumes of water. For the setting where the value functions satisfy certain monotonicity properties, we present an efficient algorithm that maximizes a social welfare function. When there are minimum water requirement constraints, we present a randomized algorithm which ensures that the constraints are satisfied in expectation. For a single seller-multiple buyers setting with fairness constraints, we design an efficient algorithm that maximizes the minimum level of satisfaction of any buyer. We also present computational complexity results that highlight the limits on the generalizability of our results. We evaluate the algorithms developed in our work with experiments on both real-world and synthetic data sets with respect to drought severity, value functions, and seniority of agents.

Original languageEnglish
Pages (from-to)13-21
Number of pages9
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
StatePublished - 2024
Event23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024

Bibliographical note

Publisher Copyright:
© 2024 International Foundation for Autonomous Agents and Multiagent Systems.


  • bipartite matching
  • complexity
  • fairness
  • integer linear program
  • Water markets
  • welfare maximization


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