scholarly journals Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shigen Shen ◽  
Longjun Huang ◽  
En Fan ◽  
Keli Hu ◽  
Jianhua Liu ◽  
...  
Keyword(s):  
Trust Management ◽  
Incentive Mechanism ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Evolutionary Process ◽  
Trust Game ◽  
Theoretic Approach ◽  
Evolutionarily Stable Strategies ◽  
Trust Mechanism ◽  
The Incentive Mechanism

A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select actionTrustorMistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select actionTrust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose actionTrustas their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism.

scholarly journals Evolutionary Game Theory: A Generalization of the ESS Definition

2019 ◽  
Vol 21 (04) ◽  
pp. 1950005
Author(s):  
Elvio Accinelli ◽  
Filipe Martins ◽  
Jorge Oviedo
Keyword(s):  
Game Theory ◽  
Strategic Planning ◽  
Evolutionary Game Theory ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Previous Literature ◽  
Evolutionarily Stable Strategies ◽  
Symmetric Games ◽  
Two Populations ◽  
Evolutionarily Stable

In this paper, we study the concept of Evolutionarily Stable Strategies (ESSs) for symmetric games with [Formula: see text] players. The main properties of these games and strategies are analyzed and several examples are provided. We relate the concept of ESS with previous literature and provide a proof of finiteness of ESS in the context of symmetric games with [Formula: see text] players. We show that unlike the case of [Formula: see text], when there are more than two populations an ESS does not have a uniform invasion barrier, or equivalently, it is not equivalent to the strategy performing better against all strategies in a neighborhood. We also construct the extended replicator dynamics for these games and we study an application to a model of strategic planning of investment.

scholarly journals eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

2020 ◽  
Vol 4 (4) ◽  
pp. 37
Author(s):  
Khaled Fawagreh ◽  
Mohamed Medhat Gaber
Keyword(s):  
Random Forest ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Theoretic Approach ◽  
Healthcare Applications ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Game Theoretic ◽  
Game Theoretic Approach

To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.

scholarly journals The New Path to Improve Construction Safety Performance in China: An Evolutionary Game Theoretic Approach

2019 ◽  
Vol 16 (13) ◽  
pp. 2443 ◽  
Author(s):  
Pi ◽  
Gao ◽  
Chen ◽  
Liu
Keyword(s):  
Information System ◽  
Construction Industry ◽  
Construction Projects ◽  
Evolutionary Game ◽  
Safety Information ◽  
Construction Safety ◽  
Safety Performance ◽  
Theoretic Approach ◽  
Economic Activities ◽  
Evolutionarily Stable Strategies

Evidence shows that there are many work-related accidents and injuries happening in construction projects and governments have taken a series of administrative measures to reduce casualties in recent years. However, traditional approaches have reached a bottleneck due to ignoring market forces, and thus new measures should be conducted. This study develops a perspective of safety performance (SP) for construction projects in China and puts forward a conception of the safety information system by using several brainstorming sessions to strengthen the safety supervision of participants in the construction industry. This system provides rating information to the public, and bad performance contractors enter into a blacklist which will influence their economic activities. Considering the limited rationality of government and various contractors, this paper builds a reasonable evolutionary game model to verify the feasibility of the safety information system. The analysis results show that there is not a single set of evolutionarily stable strategies (ESSs), as different situations may lead to different ESSs. The efficiency of applying the safety information system (the blacklist) in the construction industry can be proved by reducing the government’s safety supervision cost and by enhancing construction safety at the same time.

scholarly journals An Incentive Mechanism for Reporting Phishing E-Mails Based on the Tripartite Evolutionary Game Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mengli Wang ◽  
Lipeng Song
Keyword(s):  
Real World ◽  
Incentive Mechanism ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Evolutionary Stable Strategy ◽  
Evolution Process ◽  
Game Model ◽  
Stable Strategy ◽  
Important Link ◽  
E Mail

The human is considered as the important link in the phishing attack, and the e-mail security provider encourages users to report suspicious e-mails. However, evidence suggests that reporting is scarce. Therefore, we study how to motivate users to report phishing e-mails in this paper. To solve the problem, a tripartite evolutionary game model among e-mail security providers, e-mail users, and attackers is constructed. We obtain the desired evolutionary stable strategy through solving the replicator dynamics equations. Moreover, the evolution process to the desired evolutionary stable strategy is derived, which can guide the e-mail security provider to make a reasonable incentive mechanism. Lastly, we experiment with a large real-world e-mail network. The experiment results show that our model is effective and practical.

scholarly journals Packet Forwarding Strategies in Multiagent Systems: An Evolutionary Game Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Yuanjie Li ◽  
Xiaojun Wu
Keyword(s):  
Multiagent Systems ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Game Model ◽  
Packet Forwarding ◽  
Evolutionarily Stable Strategies ◽  
Incentive Mechanisms ◽  
Average Probability ◽  
Save Energy ◽  
Stable Points

In multiagent systems (MASs), agents need to forward packets to each other to accomplish a target task. In this paper, we study packet forwarding among agents using evolutionary game theory under the mechanisms of Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Packet forwarding among agents plays a key role to stabilize the whole MAS. We study the transfer probability of packet forwarding of agents at the idle state or the busy state and computer the probability of the packet forwarding for a MAS. When agents make their decisions to select Forward or No-Forward strategy, a packet forwarding evolutionary game model is built to reflect the utilities of different packet forwarding strategies. Two incentive mechanisms are introduced into the game model. One is to motivate agents to strengthen cooperation; the other is to encourage agents to select the No-Forward strategy to save energy while they are in the busy state. The parameter value that encourages an agent to select the No-Forward strategy is inversely proportional to the average probability of the packet forwarding. The replicator dynamics of agent packet forwarding strategy evolution are given. We propose and prove the theorems indicating that evolutionarily stable strategies (ESSs) can be attained. The results of simulation experiments verify the correctness of the proposed theorems and the effects of the two incentive mechanisms and the probability of packet forwarding, which assures the robustness of evolutionary stable points among agents in MASs.

scholarly journals Evolutionary Game Theoretic Analysis of Low Carbon Investment in Supply Chains under Governmental Subsidies

2018 ◽  
Vol 15 (11) ◽  
pp. 2465 ◽  
Author(s):  
Guang Zhu ◽  
Gaozhi Pan ◽  
Weiwei Zhang
Keyword(s):  
Supply Chains ◽  
Incentive Mechanism ◽  
Rapid Development ◽  
Evolutionary Game ◽  
Investment Strategy ◽  
Free Riding ◽  
Low Carbon ◽  
Evolutionarily Stable Strategies ◽  
Evolutionarily Stable ◽  
Profit Growth

With the rapid development of global industry and economy, excessive carbon dioxide emission has emerged as a critical issue in both developed and developing countries. Using an evolutionary game framework in which game players can adjust their strategies constantly, this paper investigates how to optimize the strategy of low carbon investment for suppliers and manufacturers in supply chains, and discuss the impacts of various factors on evolutionarily stable strategies. Additionally, we examine an incentive mechanism based on governmental subsidies to eliminate free riding and motivate co-investment. Furthermore, a case study and numerical examples are provided for illustration and simulation purposes, leading to several countermeasures and suggestions. Our analytical results show that the strategic choice of low carbon investment is correlated with profit growth coefficients, investment costs and profits from free riding. Investment costs have more significant impacts than other factors on evolutionarily stable strategies, while profit growth coefficients are more important at initial stages in the evolutionary process. The incentive mechanism based on governmental subsidies is an effective solution to motivate co-investment, and governments should take some measures to improve the assess accuracy and supervisory efficiency of investment strategy.

scholarly journals Infect While the Iron is Scarce: Nutrient Explicit Phage-Bacteria Games

2019 ◽  
Author(s):  
Daniel Muratore ◽  
Joshua S. Weitz
Keyword(s):  
Viral Infection ◽  
Nutrient Dynamics ◽  
Evolutionary Game ◽  
Tail Fiber ◽  
Theoretic Approach ◽  
Marine Microbes ◽  
Evolutionarily Stable Strategies ◽  
Game Theoretic ◽  
The Stability ◽  
Fiber Proteins

AbstractMarine microbial primary production is influenced by the availability and uptake of essential nutrients, including iron. Although marine microbes have evolved mechanisms to scavenge sub-nanomolar concentrations of iron, recent observations suggest that viruses may co-opt these very same mechanisms to facilitate infection. The “Ferrojan Horse Hypothesis” proposes that viruses incorporate iron atoms into their tail fiber proteins to adsorb to target host receptors. Here, we propose an evolutionary game theoretic approach to consider the joint strategies of hosts and viruses in environments with limited nutrients (like iron). We analyze the bimatrix game and find that evolutionarily stable strategies depend on the stability and quality of nutrient conditions. For example, in highly stable iron conditions, virus pressure does not change host uptake strategies. However, when iron levels are dynamic, virus pressure can lead to fluctuations in the extent to which hosts invest in metabolic machinery that increases both iron uptake and susceptibility to viral infection. Altogether, this evolutionary game model provides further evidence that viral infection and nutrient dynamics jointly shape the fate of microbial populations.

scholarly journals Research on Optimization of Array Honeypot Defense Strategies Based on Evolutionary Game Theory

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 805
Author(s):  
Leyi Shi ◽  
Xiran Wang ◽  
Huiwen Hou
Keyword(s):  
Replicator Dynamics ◽  
Evolutionarily Stable Strategy ◽  
Evolutionary Game ◽  
Game Model ◽  
Evolutionarily Stable Strategies ◽  
Defense Strategies ◽  
Active Defense ◽  
Defense Technology ◽  
The Stability ◽  
Evolutionarily Stable

Honeypot has been regarded as an active defense technology that can deceive attackers by simulating real systems. However, honeypot is actually a static network trap with fixed disposition, which is easily identified by anti-honeypot technology. Thus, honeypot is a “passive” active defense technology. Dynamic honeypot makes up for the shortcomings of honeypot, which dynamically adjusts defense strategies with the attack of hackers. Therefore, the confrontation between defenders and attackers is a strategic game. This paper focuses on the non-cooperative evolutionary game mechanism of bounded rationality, aiming to improve the security of the array honeypot system through the evolutionarily stable strategies derived from the evolutionary game model. First, we construct a three-party evolutionary game model of array honeypot, which is composed of defenders, attackers and legitimate users. Secondly, we formally describe the strategies and revenues of players in the game, and build the three-party game payoff matrices. Then the evolutionarily stable strategy is obtained by analyzing the Replicator Dynamics of various parties. In addition, we discuss the equilibrium condition to get the influence of the number of servers N on the stability of strategy evolution. MATLAB and Gambit simulation experiment results show that deduced evolutionarily stable strategies are valid in resisting attackers.

Blockchain-based trust management for agricultural green supply: A game theoretic approach

2021 ◽  
pp. 127407
Author(s):  
Yuhan Bai ◽  
Kai Fan ◽  
Kuan Zhang ◽  
Xiaochun Cheng ◽  
Hui Li ◽  
...  
Keyword(s):  
Trust Management ◽  
Theoretic Approach ◽  
Game Theoretic ◽  
Game Theoretic Approach
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