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import gambit


    

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gambit.__version__


    

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g = gambit.Game.read_game("poker.efg")


    

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# Serialisation of a game
g


    

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# Pythonic interface: access elements of a game via collections
g.players


    

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# Indexing works by text labels, if defined
g.players["Alice"]


    

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# Alice has two information sets - when she has the Ace, and when she has the King
g.players["Alice"].infosets


    

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# Nature is a special player
g.players.chance


    

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g.players.chance.infosets


    

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# Indexing collections can also be done by integer indices as well
g.players.chance.infosets[0].actions


    

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deal = g.players.chance.infosets[0]


    

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# For starters, we have assumed that Ace and King are equally likely
deal.actions["A"].prob


    

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deal.actions["K"].prob


    

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# Compute equilibria of the game via Lemke's algorithm
eqa = gambit.nash.lcp_solve(g)


    

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# We have a list of behaviour profiles
len(eqa)


    

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eqm = eqa[0]


    

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eqm


    

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# Can index behaviour profiles by player
eqm[g.players["Alice"]]


    

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eqm[g.players["Bob"]]


    

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# Can compute various interesting quantities about behaviour profiles as well
eqm.payoff(g.players["Alice"])


    

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# What does Bob believe about Alice's hand when she raises?
eqm.belief(g.players["Bob"].infosets[0].members[0])


    

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from IPython.display import display
probs = [ gambit.Rational(i, 20) for i in xrange(1, 20) ]
bluff = [ ]
belief = [ ]
payoff = [ ]
for prob in probs:
    g.players.chance.infosets[0].actions[0].prob = prob
    g.players.chance.infosets[0].actions[1].prob = 1-prob
    eqa = gambit.nash.lcp_solve(g)
    display("Prob of ace=%.2f" % float(prob), "Alice's strategy with king:", eqa[0][g.players["Alice"].infosets["k"]], "Bob's belief:", eqa[0][g.players["Bob"]])
    bluff.append(eqa[0][g.players["Alice"].infosets["k"].actions["R"]])
    belief.append(eqa[0].belief(g.players["Bob"].infosets[0].members[0]))
    payoff.append(eqa[0].payoff(g.players["Alice"]))


    

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import pylab


    

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pylab.plot(probs, bluff, '-'); pylab.xlabel("Probability Alice gets ace"); pylab.ylabel("Probability Alice bluffs with king")


    

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pylab.plot(probs, payoff, '-'); pylab.xlabel("Probability Alice gets ace"); pylab.ylabel("Alice's equilibrium payoff")


    

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pylab.plot(probs, belief, '-'); pylab.xlabel("Probability Alice gets ace"); pylab.ylabel("Bob's equilibrium belief"); pylab.ylim(0,1)


    

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deal.actions[0].prob = gambit.Rational(1,2)


    

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deal.actions[1].prob = gambit.Rational(1,2)


    

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g


    

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g.outcomes["Alice wins big"]


    

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g.outcomes["Alice wins big"][0] = 3


    

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g.outcomes["Alice wins big"][1] = -3


    

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g.outcomes["Bob wins big"][0] = -3


    

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g.outcomes["Bob wins big"][1] = 3


    

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g


    

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eqa = gambit.nash.lcp_solve(g)


    

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len(eqa)


    

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eqa[0].payoff(g.players["Alice"])


    

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import gambit.gte


    

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print gambit.gte.write_game(g)


    

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