🆚Game Theory and Economic Behavior Unit 14 – Evolutionary Game Theory & Learning

Foundations of Evolutionary Game Theory

• Evolutionary game theory combines game theory with evolutionary biology to model strategic interactions in populations over time
• Focuses on how strategies evolve and spread through a population based on their relative fitness or payoff
• Assumes individuals inherit strategies from their parents and strategies with higher payoffs tend to become more prevalent
• Introduces the concept of evolutionary stable strategies (ESS) which are strategies that, if adopted by a population, cannot be invaded by any alternative strategy
• Differs from classical game theory by considering a population of players rather than individual rational decision-makers
• Incorporates the role of mutation, selection, and replication in shaping the distribution of strategies in a population
• Provides a framework for understanding the emergence and stability of cooperative behavior in social and biological systems (prisoner's dilemma, hawk-dove game)

Key Concepts and Terminology

• Strategy represents a specific behavior or action plan that an individual adopts in a game or interaction
• Payoff refers to the fitness or reward associated with a particular strategy in a given interaction
• Population consists of a group of individuals who interact with each other and adopt different strategies
• Evolutionary dynamics describe how the frequencies of strategies in a population change over time based on their relative payoffs
• Mutation introduces new strategies into the population through random changes or variations
• Selection favors strategies with higher payoffs, leading to their increased frequency in the population
• Replicator equation is a mathematical model that describes how the frequencies of strategies evolve based on their relative payoffs
• Evolutionarily stable state is a population composition where no mutant strategy can invade and outperform the existing strategies

Evolutionary Stable Strategies (ESS)

• An ESS is a strategy that, if adopted by a population, cannot be invaded by any alternative strategy
• When an ESS is played against itself, it yields a higher payoff than any other strategy played against it
• ESS is a refinement of the Nash equilibrium concept in game theory, taking into account evolutionary dynamics
• In a two-player game, a strategy is an ESS if it is a best response to itself and a better response to any other best response strategy
• ESS can be pure (a single strategy) or mixed (a probability distribution over multiple strategies)
• The concept of ESS helps explain the emergence and stability of certain behaviors in nature (aggressive displays in animals, cooperative hunting)
• ESS provides insights into the long-term outcomes of evolutionary processes and the stability of behavioral patterns

Replicator Dynamics

• Replicator dynamics is a mathematical model that describes how the frequencies of strategies in a population evolve over time
• It assumes that the growth rate of a strategy's frequency is proportional to its relative payoff compared to the average payoff in the population
• The replicator equation is given by: $\frac{dx_i}{dt} = x_i(f_i(x) - \bar{f}(x))$, where $x_i$ is the frequency of strategy $i$, $f_i(x)$ is its payoff, and $\bar{f}(x)$ is the average payoff in the population
• Replicator dynamics can be used to analyze the stability of different population states and identify evolutionarily stable strategies
• It provides a framework for studying the evolutionary dynamics of various games, such as the prisoner's dilemma, hawk-dove game, and coordination games
• Replicator dynamics can be extended to include mutation, finite populations, and structured populations
• It has applications in understanding the evolution of cooperation, language, and cultural traits

Learning Models in Evolutionary Games

• Learning models in evolutionary game theory describe how individuals adapt their strategies based on their experiences and interactions
• Reinforcement learning assumes individuals are more likely to adopt strategies that have yielded higher payoffs in the past
  • Roth-Erev learning model updates propensities of strategies based on their realized payoffs
  • Bush-Mosteller learning model adjusts probabilities of strategies based on their relative performance
• Belief-based learning models assume individuals form beliefs about others' strategies and best respond to those beliefs
  • Fictitious play assumes individuals best respond to the empirical frequency of opponents' past actions
  • Bayesian learning updates beliefs based on observed actions and prior probabilities
• Imitation-based learning models assume individuals copy the strategies of successful or prevalent individuals in the population
  • Proportional imitation rule copies strategies proportional to their relative payoffs
  • Conformist transmission favors adopting the most common strategy in the population
• Learning models can lead to different dynamics and outcomes compared to replicator dynamics, such as cyclic behavior or non-equilibrium states

Applications in Economics and Biology

• Evolutionary game theory has been applied to various economic and biological phenomena
• In economics, it has been used to study the evolution of social norms, conventions, and institutions (bargaining, contract enforcement)
• It provides insights into the emergence of cooperation in social dilemmas, such as public goods provision and common-pool resource management
• Evolutionary game theory has been applied to the study of market competition, firm strategies, and industry dynamics
• In biology, it has been used to understand the evolution of animal behavior, such as mating strategies, parental care, and foraging
• It has been applied to the study of host-parasite interactions, the evolution of virulence, and the dynamics of antibiotic resistance
• Evolutionary game theory has also been used to model the evolution of language, cultural traits, and social learning

Limitations and Criticisms

• Evolutionary game theory relies on simplifying assumptions, such as infinite populations, random matching, and perfect inheritance of strategies
• It often assumes that individuals have complete information about the payoffs and strategies of others, which may not always be realistic
• The replicator dynamics assumes a deterministic and continuous evolution of strategies, which may not capture the stochastic nature of real-world processes
• The concept of ESS has been criticized for its focus on stability rather than the process of reaching an equilibrium
• Evolutionary game theory may not fully account for the role of individual decision-making, learning, and adaptation in shaping evolutionary outcomes
• It has been criticized for its emphasis on static equilibria rather than the dynamic processes of evolution and change
• The applicability of evolutionary game theory to complex real-world situations may be limited by the difficulty of measuring payoffs and identifying relevant strategies

Future Directions and Research

• Incorporating more realistic assumptions, such as finite populations, structured interactions, and stochastic dynamics, into evolutionary game models
• Developing models that integrate evolutionary game theory with other approaches, such as network theory, agent-based modeling, and behavioral economics
• Exploring the co-evolution of strategies and interaction structures, such as the formation and dissolution of social ties
• Investigating the role of individual heterogeneity, such as differences in abilities, preferences, and learning rates, in shaping evolutionary outcomes
• Studying the evolution of higher-order cognitive abilities, such as theory of mind, empathy, and communication, using evolutionary game theory
• Applying evolutionary game theory to the study of collective action problems, such as climate change mitigation and international cooperation
• Integrating insights from evolutionary game theory with empirical research in economics, psychology, and anthropology to better understand human behavior and social dynamics
• Developing new mathematical and computational tools for analyzing complex evolutionary game models and data-driven approaches for parameter estimation and model selection