1. 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

2. 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.

3. 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.

4. 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

5. In evolutionary games, the two main factors that contribute to what is learned are: 1.The types of interactions that occur between the agents in a population. 2.The rules that are applied to determine which strategies within the population are fit and therefore likely to be learned by the population.

6. 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.

7. Battle of the Sexes DefectCoop Defect1,13,2 Coop2,30,0

8. 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

11. 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 shouldn’t 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.

12. 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.

13. 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.

14. 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!

15. 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.