Evolutionary Games

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Evolutionary Games

• The solution concepts that we have discussed in
  some detail include
• strategically dominant solutions
• equilibrium solutions
• Pareto optimal solutions
• best response solutions
• mixed strategy solutions

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• We now turn attention to another kind of
  equilibrium-based solution. 
• This is a solution that is produced by some form
  of learning or adaptation process. 
• we will focus on the kinds of things that can be
  learned in a population of learning agents. 
• We'll have to be careful because evolution has a
  huge number of things that can affect it mating,
  mutation, environment, catastrophes, other agents
  in the population, etc.  We'll restrict attention
  to just a couple of these factors. 

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Reaching an equilibrium

• the main requirement for reaching an equilibrium
  in learning is that the learning algorithms stop
  changing. 
• This type of equilibrium can be very weak as
  when, for example, a learning agent happens to
  select parameter values that cause another
  learning agent to stop adapting, and vice versa. 
  
• Or, both agents get tired of adapting and just
  "freeze" their solutions even though they may not
  be good solutions.  This type of equilibrium
  may also be weak because even the smallest
  perturbation to this type of equilibrium can
  cause the system to adapt to another solution. 
• A stronger notion of equilibrium is a learned
  solution that is not easily changed by perturbing
  the system. 
• We call such an equilibrium a stable solution.

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• Finally, not every learning process has an
  equilibrium. 
• Since only certain types of learning processes
  and games produce these equilibria, the notion of
  a learning-based equilibrium is not as universal
  as the notion of a Nash equilibrium

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• In evolutionary games, the two main factors that
  contribute to what is learned are
• The types of interactions that occur between the
  agents in a population.
• The rules that are applied to determine which
  strategies within the population are fit and
  therefore likely to be learned by the population.
  

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• Let's begin by using an example. 
• Suppose that we have two large and separate
  groups of agents (males and females) who will be
  playing the battle of the sexes game. 
• Suppose that each of these two groups has a mix
  of agents that either always play cooperate (vote
  for what other wants) or always play defect (vote
  for what it wants) 
•   One agent from each group, one male and one
  female, is selected at random, they each make
  their choice, and they get the reward that
  results.

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• Battle of the Sexes

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• In these images, the x-axis represents the number
  of rounds that the game was played and the y-axis
  represents the percentage of the female group
  (red circles) and of the male group (green
  squares) that play always cooperate.  Note that
  the two graphs represent the two most common
  outcomes --- all the females play always
  cooperate while all the males play always defect
  (top graph), or all the females play always
  defect while all the males play always cooperate
  (bottom graph).  This should make some
  intuitive sense.  If the two groups play a lot,
  then they should learn to settle on one of the
  two Pareto optimal, Nash equilibrium solutions,
  but which solution is chosen depends on the
  initial make-up of the group.  For these
  simulations, the initial population was very
  close to 50/50, but with a small random
  perturbation towards either always defect or
  always cooperate for each group

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Relative Fitness.

• When we look at the strategies, if 1/3 of the
  agents are playing strategy A and getting 1/3 of
  the total utility, they are getting what they
  expected so they shouldnt change.
• HOWEVER, if 1/3 are getting ½ of the total
  utility for all players, they are playing better
  than others. We will do better if we have MORE
  agents like these super achieving agents. But
  how many more?
• The simple thing to do is reset the agents so the
  number of each type of agent exactly matches the
  percent of utility that group achieved in the
  last round.
• When we are happy with the division (no under or
  over achieving group), we are done learning.

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Imitator Dynamics

• Replicator dynamics and random pairings of
  solutions are not the only models for evolution. 
  Thus, they are not the only learning models that
  have some claim to justification. 
• We will explore a different technique for
  selecting the proportion of strategies that
  evolve from one generation to another, but first
  we will need to explore other models for
  selecting which agents interact with each other.

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Playing with Neighbors

• In the previous section, agents were randomly
  paired with other agents from the group.  From an
  evolutionary perspective, it sometimes makes more
  sense to assume that agents are paired with their
  neighbors rather than being randomly paired with
  any other agent.  This pairing with neighbors can
  be implemented in two ways.
• Agents have some way to recognize another agent. 
  If they are randomly paired with another agent
  that they do not like, they can ask to be
  reassigned.  The reassignment will be random, but
  at least they get one chance to reject an
  undesirable agent and they therefore get more
  chances to interact with their friends.
• Agents are physically arranged in group.  For
  example, agents may be arranged on a grid and
  restricted so that they can only interact with
  their immediate neighbors.  These immediate
  neighbors can be defined as those agents to the
  N, S, E, or W of the agent, or to the N, NE, E,
  SE, S, SW, W, or NW of the agent.  For another
  example, agents may be arranged on the perimeter
  of a circle and only able to interact with an
  agent to their right or left.

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• Standard evolutionary game (random interactions)
  ? all Defect
• Modifications- spatial games Interactions no
  longer random, but with spatial neighbours
• Sum scores. Player with highest score of 9 shaded
  takes square (territory, food, mates) in next
  generation
• Some degree of cooperation evolves!

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Imitator Dynamics

• When agents can only play with their neighbors,
  we can introduce a different way (different from
  replicator dynamics) of selecting which
  strategies propagate to the next generation.  One
  way to do this is for an agent to imitate its
  most successful neighbor.  The algorithm for
  doing this goes something like this
• Interact with all of my neighbors (wraping around
  the board as needed), and let all my neighbors
  interact with their neighbors.
• After the interactions with my neighbors are
  complete, identify the interaction strategy from
  my neighbors that was most successful unless my
  current strategy beat all of my neighbors (in
  which case I'll stick to my strategy).
• Change my strategy to the most successful
  strategy of my neighbors -- imitate them -- on
  the next round.
• Imitator dynamics can produce vastly different
  results than replicator dynamics.