def OneGeneration(distribution, rounds):
"""Calculate one generation of the reiterated PD-simulation
with exit option. 'distribution' is a 2-tuple that contains
the population shares of CONO and ALL_D player's. 'rounds'
is the number of rounds that are played until the strategy
distribution is updated through replicator dynamics. The
return value is a 2-tuple of the average score for each
strategy.
"""
account = array([0.0, 0.0])
cc = distribution[0]**2 / 2
dd = distribution[1]**2 / 2
cd = distribution[0] * distribution[1]
for i in xrange(rounds):
account[0] += (2*cc*R + cd*S) / distribution[0]
account[1] += (2*dd*P + cd*T) / distribution[1]
poolC = cc * forced_exit * 2 + cd
poolD = dd * 2 + cd
pool = poolC + poolD
cc += poolC**2 / (2 * pool) - cc*forced_exit
dd = poolD**2 / (2 * pool)
cd = poolC * poolD / pool
account[0] /= rounds
account[1] /= rounds
return account
(See also: Brian Skyrms: Evolution of the Social Contract,
Cambridge University Press, 1996, p.11-21.)
"""
try:
from PyPlotter import awtGfx as GfxDriver
except ImportError:
from PyPlotter import wxGfx as GfxDriver
from PyPlotter import Simplex
import Dynamics
from Compatibility import array
payoff_table = array([[1./3, 1./3, 1./3],
[2./3, 0., 0.],
[1./2, 0., 1./2]])
def DemandGame():
"""A population dynamical simulation of the demand game,
demonstrating the use of simplex diagrams.
"""
# Open a window for graphics output.
gfx = GfxDriver.Window(title="Demand Game")
# Generate the appropriate dynamical function from the payoff table.
dynamicsFunction = Dynamics.GenDynamicsFunction(payoff_table, e=0.0)
try:
from PyPlotter import awtGfx as GfxDriver
except ImportError:
from PyPlotter import wxGfx as GfxDriver
from PyPlotter import Graph, Gfx
import Dynamics
from Compatibility import array
# Definition of the Game
T, R, P, S = 6,4,2,1 # payoff parameter for the Prisoner's Dilemma
forced_exit = 0.05 # Chance that cooperation is terminated by external factors
initial_distribution = array([0.5, 0.5])
rounds = 200
generations = 50
def OneGeneration(distribution, rounds):
"""Calculate one generation of the reiterated PD-simulation
with exit option. 'distribution' is a 2-tuple that contains
the population shares of CONO and ALL_D player's. 'rounds'
is the number of rounds that are played until the strategy
distribution is updated through replicator dynamics. The
return value is a 2-tuple of the average score for each
strategy.
"""
account = array([0.0, 0.0])
cc = distribution[0]**2 / 2
if r1[s2[1]] == 1: payment += 0.25
return payment
def GenPayoffTable(playerList):
"""Generates the payoff table for the players in 'playerList'.
"""
table = []
for player1 in playerList:
line = []
for player2 in playerList:
line.append(Payoff(player1, player2))
table.append(line)
return table
payoff_table = array(GenPayoffTable(PlayerNames))
def SignalingGame():
"""Eine populationsdynamische Simulation des 'demand game'.
"""
# Open a window for graphics output.
gfx = GfxDriver.Window(title = "Sender - Receiver Game")
# Generate a dynamics function from the payoff table.
dynFunc = Dynamics.GenDynamicsFunction(payoff_table, e=0.0,noise=0.0)
# Set the graph for plotting the plotting dynamics.
takes the color of the patch that the population as drifted to. After
a while all areas of attraction should have the colour of their
respective attraction point.
"""
try:
from PyPlotter import awtGfx as GfxDriver
except ImportError:
from PyPlotter import wxGfx as GfxDriver
from PyPlotter import Simplex
import Dynamics
from Compatibility import array
payoff_table = array([[1.0 / 3, 1.0 / 3, 1.0 / 3], [2.0 / 3, 0.0, 0.0], [1.0 / 2, 0.0, 1.0 / 2]])
def DemandGame():
"""A population dynamical simulation of the demand game,
demonstrating the use of simplex diagrams.
"""
# Open a window for graphics output.
gfx = GfxDriver.Window(title="Demand Game")
# Generate the appropriate dynamical function from the payoff table.
dynamicsFunction = Dynamics.GenDynamicsFunction(payoff_table, e=0.0)
if r1[s2[1]] == 1: payment += 0.25
return payment
def GenPayoffTable(playerList):
"""Generates the payoff table for the players in 'playerList'.
"""
table = []
for player1 in playerList:
line = []
for player2 in playerList:
line.append(Payoff(player1, player2))
table.append(line)
return table
payoff_table = array(GenPayoffTable(["I3","I4","I1"]))
def SignalingGame():
"""A simulation of the 'sender - receiver game'.
"""
# Open a window for graphics output.
gfx = GfxDriver.Window(title = "Sender - Receiver Game")
# Generate a dynamics function from the payoff table.
dynFunc = Dynamics.GenDynamicsFunction(payoff_table, e=0.0,noise=0.0)
# Set up a simplex diagram for showing the dynamics as a