The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games

Nowadays, Evolutionary Game Theory which studies the learning model of players,has attracted more attention than before. These Games can simulate the real situationand dynamic during processing time. This paper creates the Evolutionary MarkovGames, which maps players’ strategy-choosing to a Markov Decision Processes(MDPs) with payoffs. Boltzmann distribution is used for transition probability andthe General Regression Neural Network (GRNN) simulating the strategy-choosing inEvolutionary Markov Games. Prisoner’s dilemma is a problem that uses the methodand output results showing the overlapping the human strategy-choosing line andGRNN strategy-choosing line after 48 iterations, and they choose the same strate-gies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lowerthan similar work and shows a better re

Evolutionary games on graphs

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.Comment: Review, final version, 133 pages, 65 figure

The Theory of the Firm and Its Critics A Stocktaking and Assessment

Ever since its emergence in the 1970s the modern economic or Coasian theory of the firm has been discussed and challenged by sociologists, heterodox economists, management scholars, and other critics. This chapter reviews and assesses these critiques, focusing on behavioral issues (bounded rationality and motivation), process (including path dependence and the selection argument), entrepreneurship, and the challenge from knowledge-based theories of the firm.

BLOCKCHAIN-ENABLED INFORMATION AS A SERVICE AND OPTIMAL FULFILMENT CAPACITY BALANCING IN CYBER PLATFORM-DRIVEN CROWDSOURCED MANUFACTURING

As a new emerging manufacturing paradigm, platform-driven crowdsourced manufacturing utilizes the cooperation between the platform, designer, and service providers to configure and fulfill the supply chain. In this value creation and delivery process, the cyber platform enables and manages the interaction between each participant in the supply chain to respond to varieties of customer needs which lets platform-driven crowdsourced manufacturing become a persuasive approach to seeking manufacturing solutions. This thesis examines platform-driven crowdsourced manufacturing based on two unique perspectives: Information as a Service (IaaS) fulfillment and operational excellence of the platform. From the first perspective, this thesis analyzes the use case of the cyber platform in the platform-driven crowdsourced manufacturing system based on its workflow. An IaaS fulfillment system is designed based on the analysis using blockchain and distributed file-sharing technologies. The proposed system is distributed, which fulfills IaaS by providing secured information upload, sharing, and management services. The decentralization feature of the system reduces the cost of trust for using the system. From the perspective of operational excellence, the thesis models the interactions between users and their decision-making process in the system based on ECC game theory, population dynamics, and the Moran process. Based on the models, an optimization strategy is proposed to manage the fulfillment capacity balance by facilitating the participation level of users.M.S

Statistische Mechanik evolutionärer Dynamik

Evolutionary dynamics is an essential component of a mathematical and computational approach to biology. In recent years the mathematical description of evolution has moved to a description of any kind of process where information is being reproduced in a natural environment. In this manner everything that lives is a product of evolutionary dynamics. Often the behaviour of systems of interacting individuals can be described within game-theoretic models. Evolutionary game theory is such a framework, as a branch of game theory, to study the interaction of non-rational beings like animals or humans under the influence of the changing environment. Virtually all human societies are based on cooperation of many individuals, which is an important precondition for the development of their complexity. In small groups reciprocal altruism can arise from repeated interactions, whereas in larger human communities cooperation can evolve through indirect reciprocity. In this case, individuals cooperate on the basis of a reputation obtained in previous interactions. Such reputation systems have particular interesting applications in the growing field of anonymous trade via online platforms. While different reputation mechanisms and the effect of unintentional errors have recently attracted a lot of interest, the effect of fake reputations - or fraud - has not been taken sufficiently into account, although such fraud might have a detrimental impact on cooperation based on indirect reciprocity. In this thesis a simple model is analyzed in which such an effect is taken into account for the first time. After careful consideration of more complicated models this minimal model has been developed. It has several advantages: First, it is simple enough to allow the analytical calculation of the parameter regions that determine the dynamics of the system. And secondary, it allows a direct comparison with behavioral experiments and could be the basis of a further experimental and mathematical analysis of fraud in reputation systems. The results show that cooperation based on indirect reciprocity is robust with respect to fake reputations and can even be enhanced by them. It has been found that fraud does not necessarily have a detrimental effect on social systems. Furthermore, an extension of the usual replicator dynamics is introduced and a mechanism that works in cyclic games as well as in non-cylic games is developed by introducing a dynamical learning rate. The framework is formulated in finite populations as well as in infinite populations and the relationship between these different approaches is shown. It was shown that a population with such a dynamic learning rate can gain an increased average payoff in transient phases and thereby can also exploit external noise. This mechanism seems to be of particular interest in economic systems.Evolutionäre Dynamik ist eine wesentliche Komponente einer mathematischen und rechnerischen Annäherung an die Biologie. In den letzten Jahren hat sich die mathematische Beschreibung der Evolution stark verändert und liefert nunmehr eine Beschreibung für jeden Prozess, bei dem Information in einer natürlichen Umgebung reproduziert wird. In diesem Sinne ist alles, was lebt, ein Produkt der Evolutionären Dynamik. Häufig kann man das Verhalten interagierender Individuen mit spieltheoretischen Methoden beschreiben. Evolutionäre Spieltheorie, als Zweig der Spieltheorie, bietet den Rahmen, um Interaktionen von nicht rationalen Lebewesen, z.B. von Tieren oder Menschen, unter dem Einfluss einer veränderlichen Umgebung zu untersuchen. Nahezu alle menschlichen Gesellschaften basieren auf Kooperation von vielen Individuen, welche eine wichtige Voraussetzung für die Entwicklung ihrer Komplexität ist. In kleineren Gruppen kann sich gegenseitiger Altruismus aufgrund sich wiederholender Interaktionen entwickeln, während in größeren menschlichen Gemeinschaften Kooperation auch durch indirekte Wechselwirkung entstehen kann. In diesem Fall kooperieren die Individuen auf der Basis ihrer Reputation, die sie in vorausgegangenen Interaktionen erworben haben. Solche Reputationssysteme finden eine besonders interessante Anwendung im wachsenden Feld anonymer Online-Plattformen. Während verschiedene Reputationsmechanismen und der Effekt von unbeabsichtigten Fehlern in letzter Zeit großes Interesse erfuhren, wurde der Einfluß gefälschter Reputation - oder Betrug - kaum betrachtet, obwohl solch ein Betrügen möglicherweise einen schädlichen Einfluss auf auf Kooperation basierende indirekte Reziprozität haben könnte. In dieser Arbeit wird ein einfaches Modell analysiert, indem solch ein Effekt zum ersten Mal betrachtet wird. Ausgehend von komplizierteren Modellen wird ein Minimalmodell entwickelt, welches folgende Vorteile hat: Erstens ist es einfach genug für eine analytische Berechnung der Parameterbereiche, die die Dynamik des Systems bestimmen und zweitens erlaubt es direkte Vergleiche mit Verhaltensexperimenten und könnte so die Basis für weitere experimentelle und mathematische Analysen von Betrügen in Reputationssystemen bilden. Die Ergebnisse zeigen, dass Kooperation, basierend auf indirekter Reziprozität, robust ist bezüglich gefälschter Reputation und sogar unter ihrem Einfuss verstärkt werden kann. D.h, Betrüger haben nicht notwendigerweise einen schädlichen Einfluss auf Sozialsysteme. Desweiteren wurde eine Erweiterung der gewöhnlichen Replikatorgleichungen eingeführt und ein Mechanismus sowohl für zyklische als auch für nicht-zyklische Spiele entwickelt, indem eine dynamische Lernrate eingeführt wurde. Dieser Rahmen wurde in endlichen und unendlichen Populationen formuliert und es konnten die Beziehungen zwischen den unterschiedlichen Ansätzen gezeigt werden. Populationen mit einer dynamischen Lernrate erreichen einen erhöhten mittleren Gewinn auf Transienten und können dabei auch externes Rauschen ausnutzen. Diese Ergebnisse könnten auch für das Verständnis wirtschaftlicher Systeme von Interesse sein

Pharmaceutical enterprises drug quality strategy Moran analysis considering government supervision and new media participation

The improvement of drug quality requires not only the supervision of government, but also the participation of new media. Therefore, this paper considers the impact of government regulation and new media reports on pharmaceutical enterprises, constructs a Moran Process evolutionary game model, and analyzes the evolution trajectory of pharmaceutical enterprises' choice of drug quality improvement strategy and drug cost reduction strategy. We obtain the conditions for the two strategies to achieve evolutionary stability under the dominance of external factors and the dominance of expected returns. To verify the theoretical results, we conduct a numerical simulation by the software MATLAB 2021b. The results show that, first of all, when the government penalty is high, the drug quality improvement strategy tends to become an evolutionary stable solution, increasing the penalty amount will help promote the improvement of drug quality. What's more, when the government penalty is low and the new media influence is low, the drug cost reduction strategy is easier to dominate. The higher the new media influence, the higher the probability that pharmaceutical enterprises choose the drug quality improvement strategy. Thirdly, when the number of pharmaceutical enterprises is lower than a threshold, the drug quality improvement strategy is easier to dominate. Finally, the drug quality improvement strategy is dominant when the quality cost factor is low and the government penalty is high, the drug cost reduction strategy is dominant when the quality cost factor is high and the government penalty is low. Above all, this paper provides countermeasures and suggestions for the drug quality improvement of pharmaceutical enterprises in practice

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Evolutionary Dynamics on Complex Networks

Many complex systems such as the Internet can be represented as networks, with vertices denoting the constituent components of the systems and edges denoting the patterns of interactions among the components. In this thesis, we are interested in how the structural properties of a network, such as its average degree, degree distribution, clustering, and homophily affect the processes that take place on it. In the first part of the thesis we focus on evolutionary game theory models for studying the evolution of cooperation in a population of predominantly selfish individuals. In the second part we turn our attention to an evolutionary model of disease dynamics and the impact of vaccination on the spread of infection. Throughout the thesis we use a network as an abstraction for a population, with vertices representing individuals in the population and edges specifying who can interact with whom. We analyze our models for a well-mixed population, i.e., an infinite population with random mixing, and compare the theoretical results with those obtained from computer simulations on model and empirical networks