Trust, as a multi-disciplinary research domain, is of high importance in the area of network security and it has increasingly become an important mechanism to solve the issues of distributed network security. Trust is also an effective mechanism to simplify complex society, and is the source to promote personal or social cooperation.From the perspective of network ecological evolution, we propose the model of the P2P Social Ecological Network. Based on GAME theory, we also put forward network trust dynamics and network eco-evolution by analysis of network trust and the development of the dynamics model. In this article, we further analyze the dynamic equation, and the EVOLUTIONARY trend of the trust relationship between nodes using the replicator dynamics principle. Finally, we reveal the law of trust evolution dynamics, and the simulation results clearly describe that the dynamics of trust can be effective in promoting the stability and evolution of networks.

Today, wireless sensor networks are widely employed in various applications, including monitoring environments and tracking objects for military surveillance, industrial applications, and healthcare. Thus, the establishment of security in such networks is of great importance. One of the dangerous attacks against these networks is the Sybil attack. In this attack, a malicious node propagates multiple fake identities simultaneously, which affects routing protocols and many other operations like voting, reputation evaluation, and data aggregation. In this paper, we study the sensor node rate trust decision to defend against Sybil attacks in WSNs and its dynamics that play a key role in stabilizing the whole WSN using EVOLUTIONARY GAME theory. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply the theoretical foundations to devise defiance against Sybil attacks in WSNs. Moreover, we can find out the conditions that will lead SNs to choose the strategy Healthy as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a rate-trust mechanism to satisfy these conditions. And furthermore, the efficiency of algorithms in terms of true detection rate and false detection rate is evaluated through a series of experiments. Experiment results show that the proposed algorithm is able to detect 99.9% of Sybil nodes with a 0.005% false detection rate. Additionally, the proposed algorithm is compared with other algorithms in terms of true detection rate and false detection rate, which shows that the proposed algorithm performs satisfactorily.

Multi rotors UAVs have limited mobility due to the nature of the actuators. As a result, the multi-rotors UAV is unable to track the desired paths in space, and loss of mobility can be a limiting factor. Therefore, a comprehensive control is needed for this type of UAV. This research presents multi-objective optimization for control allocation problems based on EVOLUTIONARY GAME theory to solve the distribution of additional control input on the over-active system in real time on multi-rotor drones. For multi-objective optimization, an EVOLUTIONARY GAME theory-based approach with iterative dynamics is used to find the desired weight using the weighted sum method. The main idea of this method is that the best strategy or dominant solution can be chosen as the solution that remains among other non-dominant solutions. EVOLUTIONARY GAME theory considers strategies as an actor and examines how these strategies can survive using repetitive dynamics with the payment matrix. The numerical simulation results show the optimal weights selected by the EVOLUTIONARY GAME and how the efficiency changes in the dynamic repeater.

Background: Immunotherapy is a recently developed method of cancer therapy, aiming to strengthen a patient’ s immune system in different ways to fight cancer. One of these ways is to add stem cells into the patient’ s body. Methods: The study was conducted in Kermanshah, western Iran, 2016-2017. We first modeled the interaction between cancerous and healthy cells using the concept of EVOLUTIONARY GAME theory. System dynamics were analyzed employing replicator equations and control theory notions. We categorized the system into separate cases based on the value of the parameters. For cases in which the system converged to undesired equilibrium points, “ stem-cell injection” was employed as a therapeutic suggestion. The effect of stem cells on the model was considered by reforming the replicator equations as well as adding some new parameters to the system. Results: By adjusting stem cell-related parameters, the system converged to desired equilibrium points, i. e., points with no or a scanty level of cancerous cells. In addition to the theoretical analysis, our simulation results suggested solutions were effective in eliminating cancerous cells. Conclusion: This model could be applicable to different types of cancer, so we did not restrict it to a specific type of cancer. In fact, we were seeking a flexible mathematical framework that could cover different types of cancer by adjusting the system parameters.

In the current study, we study the EVOLUTIONARY dynamicity of a population, composed of two kinds of individuals namely as type F (Fight) and type Y (Yield) groups which are also distributed randomly. In this model of GAME, individuals are the same as economic agents interacting in pairs and exchanging their money in a random way. Of course, individuals have a fixed behavior during the GAME. In this GAME also, evolution takes place in different periods of time between individuals frequently. We present a continuous nonlinear operator, which describes and fulfills this discrete time EVOLUTIONARY GAME. Furthermore, we set up the adequate mathematical framework to obtain the fixed point of this operator. Based on this equilibrium state of the EVOLUTIONARY operator, it is proved that the possessions of the individuals vanish with time.

This paper implements an approach to examine economic problems in which rational agents interact in dynamic markets. We use EVOLUTIONARY GAME theory and agent-based modeling in tandem as a means to address intertemporal problems that display EVOLUTIONARY attributes. This study examines the behavior of the Organization of Petroleum Exporting Countries (OPEC) in the global oil markets during the 1960s and 1970s, which sought to control global oil markets during this period. . To address this, a symmetric EVOLUTIONARY GAME theory model is used to examine the behavior of OPEC agents as they learned to take control of their resources. An agent-based modeling approach employs computational power to implement the EVOLUTIONARY GAME and provide detailed results. It is shown that OPEC’ s behavior over the period is dependent on the growth of petroleum reserves within the member nations. Increasing realizations of natural resource reserves spur increased rates of learning and experimentation, and this enables the cartel to act cooperatively and capture control of global petroleum markets. If reserves are kept constant, OPEC lingers at a state in which the cartel does not come to dominate world oil markets.

In the real world, diffusion of some contagions at the same time on social network is very important. Due to the large number of contagions that have been diffused on a social network at the same time, in order to reduce the model parameters and also being scalable, the contagions have been grouped and clustered and also all the clusters have been set equal on some of the researches, While this assumption is not acceptable and far from the facts of the real world. To eliminate and fix this issue in this thesis, we try to elaborate the different categories of contagions and consider, analyze and study the whole story as an EVOLUTIONARY GAME theory and will calculate its evolution dynamics of contagions and evolutionarily stable strategy stable strategy. EVOLUTIONARY dynamics and evolutionarily stable of the contagions show the impact of a contagion in the process of diffusion from the point of view of users in two consecutive interval timing and whether it is promoted or suppressed.

This study suggests a new method for analysing the behavioral economics issues in the framework of GAME theory. In this context, bounded rational agents interact with one another in a strategic manner. Therefore, conventional economic modeling techniques are unable to explain these interactions. We use EVOLUTIONARY GAME theory and agent-based modeling as the most suitable tools for studying the dynamic environments of strategic interactions of OPEC members in the period 2015 to 2040. The results of running 4 agent-based behavioral models suggest that despite the split between members (into patient and impatient countries), impatient members enjoy vast oil reserves. But during the EVOLUTIONARY process, members gradually learn how to play the GAME against rival groups. There is thus a possibility that more impatient members of OPEC can gain the upper hand during the GAME against rival groups and undermine their position of market dominance over OPEC.