Fictitious Play Algorithm
quantlib-economics
Fictitious Play Algorithm
Find approximate Nash equilibrium using fictitious play learning dynamics. Each player best-responds to the empirical frequency of opponent’s past play. Converges to Nash equilibrium for certain game classes (e.g., zero-sum games, potential games). Returns the limiting mixed strategy profile after specified iterations. Useful for learning dynamics and evolutionary game theory. [Tier: STANDARD, Credits: 2]
POST
Fictitious Play Algorithm
Authorizations
API key for authentication. Get your key at https://api.fincept.in/auth/register
Body
application/json
Payoff matrix for Player 1
Example:
[[3, 0], [5, 1]]Payoff matrix for Player 2
Example:
[[3, 5], [0, 1]]Number of iterations to run
Required range:
x >= 1Example:
1000
Random seed for reproducibility (null for random)
Example:
42
