An Automated Decision Support System Based on Game Theoretic Optimization for Emergency Management in Urban Environments

Ranganathan, N.; Gupta, U.; Shetty, Rashmi; Murugavel, Ashok K.

doi:10.2202/1547-7355.1236

Cited by 17 publications

(11 citation statements)

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“…These operations are carried out according to drill schedule. We modify Ranganathan's pattern [7] to produce Tables I-III which show the information concerning resource types and availability, emergency requests, emergency priorities, and time needed to reach emergencies from each of the resource centers.…”

Section: A Model Assumptionsmentioning

confidence: 99%

“…Given the TTV vector output, we use (5) to compute five thresholds: m green = 0.4257, m blue = 0.4658, m yellow = 0.5059, m orange = 0.5459, m red = 0.5860. Using these threshold values we apply (7) to calculate the exact Shapley value of the SAS response units, as shown in Figure 4(b). Figure 4(b) shows the five deployment sets for SAS response units when the Shapley value is 1: Z green = {a 1 , a 12 , a 11 , a 2 , a 3 , a 4 , a 5 , a 6 , a 7 , a 8 , a 9 , a 10 }, Z blue = {a 1 , a 12 , a 11 , a 2 , a 3 , a 4 , a 5 , a 6 , a 7 , a 8 , a 9 , a 10 }, Z yellow = {a 2 , a 3 , a 4 , a 5 , a 6 , a 7 , a 8 , a 9 , a 10 }, Z orange = {a 4 , a 5 , a 6 , a 7 , a 8 , a 9 , a 10 }, Z red = {a 7 , a 8 , a 9 , a 10 }.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…The model developed in our study is closely related to the model proposed by Ranganathan et al [7]. They proposed using a game theory model to capture interactions during emergencies.…”

Section: Introductionmentioning

confidence: 99%

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A game theory approach for an emergency management security advisory system

Chen

2008

2008 IEEE International Conference on Intelligence and Security Informatics

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Efficient emergency management must determine how and when to alert and advise the critical and appropriate response units to the danger of terrorist attacks, particularly when available resources are limited. We propose a framework for homeland security advisory system that incorporates two game theory models designed to advise response units and raise the alarm. In the first scheme the interactive behaviors between the elements or participants of the multi-emergency response system and the zone response unit are modeled and analyzed as a non-cooperative game, after which the terrorist threat value is derived from the mixed strategy Nash equilibrium. In the second step the value TTV is used to compute the Shapley value of all response units for five different threat levels; fair response unit allocation based on the Shapley value creates a minimum set of SAS deployment costs. Numerical examples show that the emergency manager can use this framework to quantitatively evaluate the terrorist threat to each response unit and easily discover where response units are most at risk within the five threat levels.Index Terms-terrorist threat value, Nash equilibrium, Shapley value, homeland security advisory system

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Section: A Model Assumptionsmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

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A game theory approach for an emergency management security advisory system

Chen

2008

2008 IEEE International Conference on Intelligence and Security Informatics

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“…Recent research in disaster management indicates the growing use of game-theoretic models (Seaberg et al, 2017). Gupta and Ranganathan (2006); Ranganathan et al (2007); Gupta and Ranganathan (2007); Wang et al (2009) use a non-cooperative game model where the game is played between crisis locations as each location aspires to have the most resources possible. The authors consider a multi-event emergency management system based on a strategic game where the cost matrix for the game is computed based on the criticality of the events and the response time of the resources to reach the crisis location.…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Model Based Resource Allocation During Floods

Majumder¹,

Warier²,

Ghose³

2021

Preprint

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For multiple emergencies caused by natural disasters, it is crucial to allocate resources equitably to each emergency location, especially when the availability of resources is limited in quantity. This paper has developed a multi-event crisis management system using a non-cooperative, complete information, strategic form game model. In the proposed system, each emergency event is assumed to occur in different locations simultaneously. These locations are represented as the players in the game, competing with the other players for an optimal allocation of scarce resources available at different resource stations. The players incur a non-monetary cost for obtaining resource units. The objective of the proposed game is to derive optimal strategies for an effective and fair allocation of resources to the respective players.

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“…These MASs include ERS, HSAS, and security force reallocation systems. However, there are common problems related to extension and scalability that are often encountered [4]. When these game theory models are played by more than four players (i.e., agents), it is not easy to compute the Nash Equilibrium (N.E.)…”

Section: Introductionmentioning

confidence: 99%

A Game Theory Approach for Multi-agent System Resources Allocation against Outside Threats

Wu¹,

Hu²

2019

JRACR

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This study proposes an integrated model for the deployment of multiagent resources for resisting outside threats. The proposed two-stage model applies the divide-and-conquer strategy to solve the resources allocation problem. First, the interactive actions between an external attack and a response agent are modeled as a non-cooperative game, after which the external threat value is derived from the Nash equilibrium. Second, the threat values of all response agents are utilized to compute each agent's Shapley value. Then an acceptable resource allocation of agents based on their expected marginal contribution creates a minimum set of resource deployment costs. The experimental results show that our approach is feasible as a means to mobilize search and rescue resources from a non-affected district and to improve relief efforts against earthquake damage. The Shapley value allocation approach proposed in this study; the percentage of resources allocation of districts is closer to death rate of each district than the proportional division of resources.

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An Automated Decision Support System Based on Game Theoretic Optimization for Emergency Management in Urban Environments

Cited by 17 publications

References 0 publications

A game theory approach for an emergency management security advisory system

A game theory approach for an emergency management security advisory system

Game-Theoretic Model Based Resource Allocation During Floods

A Game Theory Approach for Multi-agent System Resources Allocation against Outside Threats

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