Development and application of coupled system dynamics and game theory: A dynamic water conflict resolution method

Abstract

Conflicts over water resources can be highly dynamic and complex due to the various factors which can affect such systems, including economic, engineering, social, hydrologic, environmental and even political, as well as the inherent uncertainty involved in many of these factors. Furthermore, the conflicting behavior, preferences and goals of stakeholders can often make such conflicts even more challenging. While many game models, both cooperative and non-cooperative, have been suggested to deal with problems over utilizing and sharing water resources, most of these are based on a static viewpoint of demand points during optimization procedures. Moreover, such models are usually developed for a single reservoir system, and so are not really suitable for application to an integrated decision support system involving more than one reservoir. This paper outlines a coupled simulation-optimization modeling method based on a combination of system dynamics (SD) and game theory (GT). The method harnesses SD to capture the dynamic behavior of the water system, utilizing feedback loops between the system components in the course of the simulation. In addition, it uses GT concepts, including pure-strategy and mixed-strategy games as well as the Nash Bargaining Solution (NBS) method, to find the optimum allocation decisions over available water in the system. To test the capability of the proposed method to resolve multi-reservoir and multi-objective conflicts, two different deterministic simulation-optimization models with increasing levels of complexity were developed for the Langat River basin in Malaysia. The later is a strategic water catchment that has a range of different stakeholders and managerial bodies, which are however willing to cooperate in order to avoid unmet demand. In our first model, all water users play a dynamic pure-strategy game. The second model then adds in dynamic behaviors to reservoirs to factor in inflow uncertainty and adjust the strategies for the reservoirs using the mixed-strategy game and Markov chain methods. The two models were then evaluated against three performance indices: Reliability, Resilience and Vulnerability (R-R-V). The results showed that, while both models were well capable of dealing with conflict resolution over water resources in the Langat River basin, the second model achieved a substantially improved performance through its ability to deal with dynamicity, complexity and uncertainty in the river system.

Resolving Transboundary Water Conflicts: Dynamic Evolutionary Analysis Using an Improved GMCR Model

“…If a researcher does not provide reasonable and sufficient preference statements or if the assessment is conducted by different researchers or experts, the preference rankings results are very likely to be different (Zhao and Xu, 2019). In addition, most cooperative or non-cooperative game models focus only on performing qualitative analysis of water conflicts without considering the dynamic evolution of such conflicts (Yuan et al, 2020;Zomorodian et al, 2017).…”

“…As an effective method of resolving conflicts over complicated environmental issues between multiple DMs, game theory can achieve a more realistic simulation of the interest-based decision-making behavior of stakeholders (Dowlatabadi et al, 2020;Madani, 2010;Shi et al, 2016). Although cooperative and non-cooperative game models are generally used to analyze water conflicts, many of them are qualitative analyses that do not consider the dynamic evolutionary characteristics of conflict (Yuan et al, 2020;Zomorodian et al, 2017). The Graph Model for Conflict Resolution (GMCR) (Kilgour et al, 1987), which is a conflict resolution method developed based on classical game theory (Von Neumann and Morgenstern, 1944), can simulate the dynamic evolution of the behavior of DMs (Kilgour and Hipel, 2005).…”

Game theory model for a virtual water strategy: Scenarios under rational and semirational game play

“…These players may form a cooperative game or a non‐cooperative game in which they mainly consider the benefit to themselves over other interests. In accordance with previous game scenarios (Babu & Mohan, 2018; Wang et al, 2018; Xie, 2017; Zomorodian et al, 2018), some basic rules about the model are assumed to make the analysis better defined. The rules are as follows: In order to simplify the analysis, this study builds a game model for two players (regions), which could be extended to multiple players.…”

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

“… Reprinted from [ 47 ] under a CC BY license, with permission from PLOS ONE, original copyright 2017. …”

“…Langat River basin[47]. Reprinted from[47] under a CC BY license, with permission from PLOS ONE, original copyright 2017.https://doi.org/10.1371/journal.pone.0239509.g002 Statistical analysis of the data for both stations.…”