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.

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.

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 this paper we offer one novel mathematical model of credit lending to customers based on Evolutionary Game Theory. This model presents an efficient and realistic approach. The target of our paper is to explore the Evolutionary Game between banks and customers to grant facilities and credit. We assume that customers are divided into two types. The first type of customers include individual or small and medium enterprises (SME), apply for micro loans from the bank. The second type of customers include corporate banking or large enterprises, apply for large loans from the bank. The relationship between the bank and the customers is a double-sided problem. Banks and customers may trust each other or want to behave opportunistically. This model helps the bank to be able to purposefully decide on the allocation of facilities to customers and find the optimal strategy. This article examines the parameters influencing these strategic decisions. The results show that by increasing or decreasing some of the parameters in the model, banks can be more successful in granting facilities to customers and ultimately increase their profitability.

The use of distributed generation (DG) resources is seen as a key important factor in improving technical and economic aspects of distribution systems. However, little research exists concerning the optimal location and operation of DGs. This paper proposes a novel method to find simultaneously the optimal location and operation of DGs. The method is developed in two phases. Phase 1 deals with the optimal location of DGs using a multiobjective optimization problem in which active power loss reduction, voltage profile improvement, and voltage regulation are the objectives. A MPSO algorithm is developed to solve the multiobjective problem. Phase 2 copes with the optimal income of the DGs’ owners along with the optimal total payment of the distribution company by adopting a bi-level optimization method with two agents. Game Theory is employed to assist in finding optimal contract prices. The performance of the proposed method is evaluated by using two IEEE, 33-bus, standard distribution network. This paper concludes that the proposed method can serve as an accurate tool for practitioners in finding the optimal location and operation strategy of DGs.

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.

Evolutionary Game Theory has been known as the most suitable tool for modeling the dynamics of strategic interactions. In this regard, Evolutionary algorithms present the new approach for learning and decision modeling of bounded rationality factors. The objective of this study is providing the new model of searching for optimal strategies in iterated prisoner's dilemma (IPD) using genetic algorithm. For this purpose, by simulating competition between Iran and Saudi Arabia in OPEC oil Coalition, we used 12 strategy types over 20 runs of genetic algorithm for maximizing individual’s scores and also minimizing competitor fitness scores. Results show that “Tit for Tat” with the highest average fitness in both competitions known as the optimal strategy. The other strategis like; Soft Majority, Trigger & TF2T are next in ranking. The strategy “All D” is known as inefficient strategy in competition with the lowest productivity.

When the monopoly of money creation is removed and private money can be exchanged between people, the issue of Seigniorage share will arise, which is currently conceivable with the advent of cryptocurrencies. The question of the present study is that if we are in a situation where private cryptocurrencies along with money are common in the society with the state publisher, what share of the Seigniorage share will go to the government and what should be the best policy attitude of the central bank in this regard? To answer this question, we used the modeling of Evolutionary Game Theory, the main feature of which is the evolution of a species (in the current research of money). To better approximate the model to reality, the research assumptions were verified by a questionnaire. The research findings show that the central bank's share of the money creation market will fluctuate in a range of about 60 to 80%, and the monetary policymaker's attitude towards cryptocurrencies ultimately determines the share of the private and public sectors in the money creation market. . The main policy implication of this research is that the policymaker's confrontation with the private cryptocurrency and the illegal attitude towards this issue will lead to an increase in the share of the private cryptocurrency in the money market.

Innovations, commercialized by new or old established firms, located at the core of industrial renewal process. The innovation concept has suffered transformations, along with the evolution of the models that try to explain and understand the innovation process. The innovative process corresponds to all activities that generate technological changes and the dynamic interaction between them, not necessarily being novelties. Linier model, Chain-Linked Model and National Innovation Systems (NIS) Approach, are three models that have developed for innovation process. Innovation process can be viewed as Evolutionary process. One can recognize some mechanism for innovation evolution. These are grouped into two classes; those that increase configurations variation and those that decrease it. Emergence of knowledge, knowledge flow and recombination are the mechanism to increase variation of configuration. Internal and external selections are the mechanism to selecting. Innovation operators are Evolutionary operators that create new combinations of configuration and increase variation. This paper develops an Evolutionary cycle in innovation process and extends Evolutionary mechanisms of innovation.