Abstract
The operations of water resources infrastructures, such as dams and diversions, often involve multiple conflicting interests and stakeholders. Among the approaches that have been proposed to design optimal operating policies for these systems, those based on agents have recently attracted an increasing attention. The different stakeholders are represented as different agents and their interactions are usually modeled as distributed constraint optimization problems. Those few works that have attempted to model the interactions between stakeholders as negotiations present some significant limitations, like the necessity for each agent to know the preferences of all other agents. To overcome this drawback, in this paper we contribute a general monotonic concession protocol that allows the stakeholders-agents of a regulated lake to periodically reach agreements on the amount of water to release daily, trying to control lake floods and to supply water to agricultural districts downstream. In particular, we study two specific instances of the general protocol according to their ability to converge, reach Pareto optimal agreements, limit complexity, and show good experimental performance.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
A summary of this work appears, as extended abstract, in the Proceedings of the Autonomous Agents and Multiagent Systems Conference (AAMAS) 2016.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Note that the negotiation protocols are presented referring to utility functions \(\mathcal {U}_{i}\) for uniformity with relevant literature, but in our application we consider cost functions \(\mathcal {J}^{i}_{B_{j}}\). The two representations are related by: \(\mathcal {U}_{i} = 1/\mathcal {J}^{i}_{B_{j}}\).
- 2.
An agreement \(x \in \mathfrak {R}^m\) is Pareto optimal if it is not dominated by any other \(x' \in \mathfrak {R}^m\); where \(x'\) dominates x when, for all i, \(\mathcal {U}_{i}(x') \ge \mathcal {U}_{i}(x)\) and, for at least a \(\bar{i}\), \(\mathcal {U}_{\bar{i}}(x') > \mathcal {U}_{\bar{i}}(x)\).
- 3.
All the utilities functions reported in this section are not related to our application, but have been built manually for illustration purposes.
References
Adams, G., Rausser, G., Simon, L.: Modelling multilateral negotiations: an application to California water policy. J. Econ. Behav. Organ. 30(1), 97–111 (1996)
Altinbilek, D.: Development and management of the Euphrates-Tigris basin. Int. J. Water Resour. Dev. 20(1), 15–33 (2004)
Amigoni, F., Castelletti, A., Giuliani, M.: Modeling the management of water resources systems using multi-objective DCOPs. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 821–829 (2015)
Amigoni, F., Gatti, N.: A formal framework for connective stability of highly decentralized cooperative negotiations. Auton. Agent. Multi-Agent Syst. 15(3), 253–279 (2007)
Anghileri, D., Castelletti, A., Pianosi, F., Soncini-Sessa, R., Weber, E.: Optimizing watershed management by coordinated operation of storing facilities. J. Water Resour. Plann. Manage. 139(5), 492–500 (2013)
Badica, C., Badica, A.: A set-based approach to negotiation with concessions. In: Proceedings of the Balkan Conference in Informatics (BCI), pp. 239–242 (2012)
Berglund, E.: Using agent-based modeling for water resources planning and management. J. Water Resour. Plann. Manage. 141(11), 04015025 (2015)
Block, P., Strzepek, K.: Economic analysis of large-scale upstream river basin development on the Blue Nile in Ethiopia considering transient conditions, climate variability, and climate change. J. Water Resour. Plann. Manage. 136(2), 156–166 (2010)
Castelletti, A., Galelli, S., Restelli, M., Soncini-Sessa, R.: Tree-based reinforcement learning for optimal water reservoir operation. Water Resour. Res. 46(9), W09507 (2010)
Draper, A., Lund, J.: Optimal hedging and carryover storage value. J. Water Resour. Plann. Manage. 130(1), 83–87 (2004)
Elkind, E., Rahwan, T., Jennings, N.: Computational coalition formation. In: Weiss, G. (ed.) Multiagent Systems, pp. 329–380. MIT Press, Cambridge (2013)
Endriss, U.: Monotonic concession protocols for multilateral negotiation. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 392–399 (2006)
Fatima, S., Rahwan, I.: Negotiation and bargaining. In: Weiss, G. (ed.) Multiagent Systems, pp. 143–176. MIT Press, Cambridge (2013)
Gatti, N., Amigoni, F.: An approximate Pareto optimal cooperative negotiation model for multiple continuous dependent issues. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 565–571 (2005)
Giuliani, M., Castelletti, A.: Assessing the value of cooperation and information exchange in large water resources systems by agent-based optimization. Water Resour. Res. 49(7), 3912–3926 (2013)
Giuliani, M., Castelletti, A., Amigoni, F., Cai, X.: Multiagent systems and distributed constraint reasoning for regulatory mechanism design in water management. J. Water Resour. Plann. Manage. 141(4), 04014068 (2015)
Hashimoto, T., Stedinger, J., Loucks, D.: Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour. Res. 18(1), 14–20 (1982)
Lybbert, T., Barrett, C.: Risk responses to dynamic asset thresholds. Appl. Econ. Perspect. Policy 29(3), 412–418 (2007)
Marques, G., Tilmant, A.: The economic value of coordination in large-scale multireservoir systems: the Parana River case. Water Resour. Res. 49(11), 7546–7557 (2013)
Pianosi, F., Castelletti, A., Restelli, M.: Tree-based fitted Q-iteration for multi-objective Markov decision processes in water resource management. J. Hydroinformatics 15(2), 258–270 (2013)
Rausser, G., Simon, L.: A noncooperative model of collective decision making: a multilateral bargaining approach. Technical report, UC Berkeley, Department of Agricultural and Resource Economics (1992). http://escholarship.org/uc/item/1p.67k0dp
Rosenschein, J., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, Cambridge (1994)
Soncini Sessa, R., Castelletti, A., Weber, E.: Integrated and Participatory Water Resources Management: Theory. Elsevier, Amsterdam (2007)
Thoyer, S., Morardet, S., Rio, P., Simon, L., Goodhue, R., Rausser, G.: A bargaining model to simulate negotiations between water users. J. Artif. Soc. Soc. Simul. 4(2) (2001)
Tilmant, A., Beevers, L., Muyunda, B.: Restoring a flow regime through the coordinated operation of a multireservoir system: the case of the Zambezi River basin. Water Resour. Res. 46(7), 1–11 (2010)
Wallace, J., Acreman, M., Sullivan, C.: The sharing of water between society and ecosystems: from conflict to catchment-based co-management. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 358(1440), 2011–2026 (2003)
Yang, Y., Cai, X., Stipanovic̀, D.: A decentralized optimization algorithm for multiagent system-based watershed management. Water Resour. Res. 45(8), 1–18 (2009)
Zeitoun, M., Warner, J.: Hydro-hegemony - a framework for analysis of trans-boundary water conflicts. Water Policy 8(5), 435–460 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Amigoni, F., Castelletti, A., Gazzotti, P., Giuliani, M., Mason, E. (2016). Using Multiagent Negotiation to Model Water Resources Systems Operations. In: Osman, N., Sierra, C. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2016. Lecture Notes in Computer Science(), vol 10002. Springer, Cham. https://doi.org/10.1007/978-3-319-46882-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-46882-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46881-5
Online ISBN: 978-3-319-46882-2
eBook Packages: Computer ScienceComputer Science (R0)