Agent-Based Modeling of Competitive and Cooperative Behavior Under Conflict

Bristow, Michele; Fang, Liping; Hipel, Keith W.

doi:10.1109/tsmc.2013.2282314

Cited by 40 publications

(13 citation statements)

References 37 publications

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“…A typical agent-based model possesses four main components (Macal and North 2010;Bristow et al 2014): (1) a set of agents in which each agent has unique attributes; (2) an environment that agents can interact with as well as other agents; (3) methods that agents use to update their attributes; and (4) an interaction mechanism that controls when and how to interact, and with whom.…”

Section: Methodsmentioning

confidence: 99%

“…Giacomoni et al (2013) examined dynamic interactions among water use, land use, and urbanization progress within a complex adaptive system framework in which ABM is utilized to simulate the water use behavior of each household. Other interesting research topics using ABM techniques include water demand estimation (Athanasiadis et al 2005), water sharing problems (Yang et al 2009;Guiliani and Castelletti 2013;Giuliani et al 2014), and common pool resources management (Schlüter and Pahl-Wostl 2007;Bristow et al 2014). Berglund (2015) provides a comprehensive review of the employment of ABM in water resources management.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Yang et al (2009; utilized decentralized optimization methods to investigate how each agent acts and interacts with others in order to maximize its own benefits in a water sharing problem. Bristow et al (2014) introduced a comprehensive decision-making tool called Graph Model for Conflict Resolution (GMCR) as the interaction mechanism among agents in a common pool resources conflict. In this paper, decentralized optimization methods are utilized as the decision rules within an agent-based model to investigate the changes of water users' behavior in the context of WDM, and their corresponding impacts on individual users and the system as a whole.…”

Section: Introductionmentioning

confidence: 99%

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Agent-Based Modeling Approach to Investigating the Impact of Water Demand Management

Xiao

Fang

Hipel

2018

J. Water Resour. Plann. Manage.

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An agent-based modeling approach is proposed to assess water users' behavior for water demand management (WDM) in a river basin. In this procedure, each agent controls its own strategy regarding whether to conserve or consume more water in order to achieve a better economic return based on an initial allocation scheme. The effects of agents' behaviors on their own economic returns and the aggregated impacts of individual behavior on the system are investigated. A positive incentive given to water conservers encourages agents to implement WDM strategies, which in turn improve water use efficiency. A case study using this new agentbased approach reveals that agricultural users are the main contributors to water conservation.Compensation given to water conservers more than covers the benefit loss from less water consumption, while other users gain benefits from the use of the conserved water. The results also indicate that the implementation of WDM strategies is beneficial for the overall system from both economic and ecological perspectives.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Agent-Based Modeling Approach to Investigating the Impact of Water Demand Management

Xiao

Fang

Hipel

2018

J. Water Resour. Plann. Manage.

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“…For example, it has been shown that social learning among distant individuals increases the level of cooperation, while conformism may reduce the level of cooperation (Molleman et al, 2013;Burton-Chellew et al, 2015). Several agent-based simulation studies have shown the positive impact of increased environmental uncertainty on the sustained level of cooperation (Andras et al, 2003;Andras et al, 2007;Andras, 2008), while many others looked at the various proposed mechanisms responsible for the emergence of cooperation (Axelrod, 1997;Bear and Rand, 2016;Bristow et al, 2014). Simulation studies also confirm that the clustering of agents ready for cooperation increases the level of cooperation in the agent community (Andras, 2016;Mitteldorf and Wilson, 2000).…”

Section: Introductionmentioning

confidence: 99%

Social learning in repeated cooperation games in uncertain environments

András

2018

Cognitive Systems Research

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Cooperation and social learning are fundamental mechanisms that maintain social organisation among animals and humans. Social institutions can be conceptualised abstractly as cooperation games with social learning. In some cases potential cooperation partners may be easily identifiable, while in other cases this is difficult. Real world institutions always operate in uncertain environments. Here we use agent-based simulation to explore the interaction between social learning, cooperation and environmental uncertainty with and without easy to identify cooperation partners. Our agents use a communication language to indicate their cooperation intentions. We discuss the measurement of communication or language complexity metrics, which may be used as correlates of the level of cooperation. The results show that more uncertainty induces more cooperation and that social learning increases the level of cooperation. We show that the positive impact of social learning is bigger in low uncertainty environments than in high uncertainty environments and also in cases where identification of potential cooperation partners is harder. The results suggest that environmental uncertainty, social learning and easy identification of cooperation partners may play alternating roles in the promotion of cooperation in social institutions and the expansion and development of these institutions.

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“…Many works have mentioned the idea of agent based modelling [5][6][7][8]. Different from the traditional model, the agent based modelling is formed by agents that lie in different layers [9].…”

Section: Introductionmentioning

confidence: 99%

Agent Based Fuzzy T-S Multi-Model System and Its Applications

Zhao

2015

Applied Sciences

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Based on the basic concepts of agent and fuzzy T-S model, an agent based fuzzy T-S multi-model (ABFT-SMM) system is proposed in this paper. Different from the traditional method, the parameters and the membership value of the agent can be adjusted along with the process. In this system, each agent can be described as a dynamic equation, which can be seen as the local part of the multi-model, and it can execute the task alone or collaborate with other agents to accomplish a fixed goal. It is proved in this paper that the agent based fuzzy T-S multi-model system can approximate any linear or nonlinear system at arbitrary accuracy. The applications to the benchmark problem of chaotic time series prediction, water heater system and waste heat utilizing process illustrate the viability and the efficiency of the mentioned approach. At the same time, the method can be easily used to a number of engineering fields, including identification, nonlinear control, fault diagnostics and performance analysis.

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Agent-Based Modeling of Competitive and Cooperative Behavior Under Conflict

Cited by 40 publications

References 37 publications

Agent-Based Modeling Approach to Investigating the Impact of Water Demand Management

Agent-Based Modeling Approach to Investigating the Impact of Water Demand Management

Social learning in repeated cooperation games in uncertain environments

Agent Based Fuzzy T-S Multi-Model System and Its Applications

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