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    Game Theory

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    • Introduction to Game Theory
      • 1.1What is Game Theory?
      • 1.2History and Importance of Game Theory
      • 1.3Understanding Basic Terminology
    • Two-Person Zero-Sum Games
      • 2.1Defining Zero-Sum Games
      • 2.2Solving Simple Zero-Sum Games
      • 2.3Strategies and Dominance in Zero-Sum Games
    • Non-Zero-Sum and Cooperative Games
      • 3.1Introduction to Non-Zero-Sum Games
      • 3.2Cooperative Games and the Core
      • 3.3Bargaining & Negotiation Techniques
    • Game Theory in Business and Economics
      • 4.1Market Analysis via Game Theory
      • 4.2Strategic Moves in Business
      • 4.3Auctions and Bidding Strategies
    • Game Theory in Politics
      • 5.1Electoral Systems and Voting Strategies
      • 5.2Power and Conflict Resolution
      • 5.3Foreign Policy and International Relations
    • Psychological Game Theory
      • 6.1Perception, Belief, and Strategic Interaction
      • 6.2Emotions and Decision-Making
      • 6.3Behavioral Biases in Strategic Thinking
    • Games of Chance and Risk
      • 7.1Probability Analysis and Risk Management
      • 7.2Gambler's Fallacy
      • 7.3Risk Tolerance and Decision Making
    • Evolutionary Game Theory
      • 8.1The Origin and Motivation for Evolutionary Game Theory
      • 8.2Evolutionary Stability Strategies
      • 8.3Application of Evolutionary Game Theory
    • Games with Sequential Moves
      • 9.1Extensive Form Representation
      • 9.2Backward Induction
      • 9.3Credible Threats and Promises
    • Game Theory in Social Interactions
      • 10.1Social Rules and Norms as Games
      • 10.2Role of Reputation and Signals
      • 10.3Social Network Analysis
    • Ethics in Game Theory
      • 11.1Fairness Concepts
      • 11.2Moral Hazards and Incentives
      • 11.3Social Dilemmas and Collective Action
    • Technological Aspects of Game Theory
      • 12.1Digital Trust and Security Games
      • 12.2AI and Machine Learning in Game Theory
      • 12.3Online Marketplaces and Digital Economy
    • Applying Game Theory in Everyday Life
      • 13.1Practical Examples of Game Theory at Work
      • 13.2Thinking Strategically in Personal Decisions
      • 13.3Final Recap and Strategizing Your Life

    Evolutionary Game Theory

    The Origin and Motivation for Evolutionary Game Theory

    British theoretical evolutionary biologist and geneticist (1920-2004)

    British theoretical evolutionary biologist and geneticist (1920-2004).

    Game theory, as a field of study, has been applied to a wide range of disciplines, from economics and politics to computer science and biology. One of the most fascinating applications of game theory is in the field of evolutionary biology, leading to the development of a subfield known as Evolutionary Game Theory.

    Understanding the Concept of Evolutionary Game Theory

    Evolutionary Game Theory (EGT) is a modification of classical game theory that considers the dynamics of adaptation in the context of the "game" of survival played by living organisms. Unlike classical game theory, which assumes rational decision-makers, EGT applies to populations of individuals with fixed, inherited strategies. The "payoff" in EGT is not measured in terms of utility or money, but in terms of fitness or reproductive success.

    Historical Development of Evolutionary Game Theory

    The concept of Evolutionary Game Theory was first introduced by John Maynard Smith and George R. Price in the 1970s. They were trying to understand animal behavior, particularly the concept of an "evolutionarily stable strategy" (ESS), a strategy that, if adopted by a population, cannot be invaded by any alternative strategy. This concept was a significant departure from classical game theory, which focused on rational decision-making and equilibrium strategies.

    The Motivation Behind the Application of Game Theory to Evolutionary Biology

    The motivation for applying game theory to evolutionary biology comes from the parallels between economic and biological competition. In both cases, individuals or entities interact, and their success depends on both their strategies and the strategies of others.

    In biology, these interactions often involve competition for resources, mates, or survival itself. By applying game theory, biologists can model these interactions mathematically and predict outcomes based on different strategies. This approach has proven particularly useful in understanding phenomena such as the evolution of cooperation, the balance between selfishness and altruism, and the strategies animals use in conflict situations.

    In conclusion, Evolutionary Game Theory is a powerful tool that has significantly enhanced our understanding of the natural world. By applying the principles of game theory to evolutionary biology, scientists have gained insights into the strategies used by species to survive and reproduce, leading to a deeper understanding of the dynamics of evolution.

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    Next up: Evolutionary Stability Strategies