CS 670 Lab 2: Evoultionary Games
You will conduct a large series of experiments to evaluate what types of strategies evolve in various games. You will perform experiments on the following games:
- Prisoner's Dilemma
- Stag Hunt
- Battle of the Sexes
Use cardinal values for the entries in the payoff matrices
(e.g., use T=5, R=3, P=2, and S=1).
You will consider two types of selection and interaction dynamics: (replicator dynamics, random pairings) (imitator dynamics, lattice pairings).
Use 900 agents in all of your simulations (this translates to a 30 by 30 lattice).
Use agents that have a single state. In other words, use agents that remember the previous action of the other agent and then use this action to determine their next action.
Thus, the set of all agents are:
| Action of other player on previous round | Agent 1 (Always Cooperate) | Agent 2 (Always Defect) | Agent 3 (TfT) | Agent 4 (NotTfT) |
|---|---|---|---|---|
| C | C | D | C | D |
| D | C | D | D | C |
Note that this precludes agents like Win Stay Lose Shift, since I can't have actions that depend on both your previous choice and my previous choice.
Assume that Agent 1 and Agent 3 play C on the first round, and that Agent 2 and Agent 4 play D on the first round.
You will consider iterated games for gamma = 0.95 and gamma = 0.99. For the iterated games, compute V(A|B) prior to running the games. This allows you to turn the iterated games into payoff matrices.
For example, if agent 1 plays agent 2 in the iterated prisoner's dilemma, then the payoff to agent 1 is S / (1-gamma).
Begin with various mixes of agents.
A complete version of the lab specifications can be found in the wiki: