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Unformatted text preview: Economics 405/505 Introduction to Game Theory Prof. Rui Zhao Mixed Strategies Motivating Example: The following Matching Pennies game does not have a pure strategy Nash equilibrium. payoffs Heads Tails Heads 1, 11, 1 Tails1, 1 1, 1 1 Probability 1.1 Probability Distributions If we toss a coin, one of two outcomes { H=Heads, T=Tails } could occur. If the coin is fair, then each outcome occurs with equal likelihood or probabil ity. The vector of probabilities ( 1 2 , 1 2 ) is a probability distribution over the set of outcomes, { H, T } , indicating outcome H occurs with probability 1/2 and outcome T occurs with probability 1/2. We can have different probability distributions over the same set of out comes. For example, if a somewhat biased coin is being tossed so that outcome H occurs with probability 2/3 and outcome T occurs with prob ability 1/3, then we have a probability distribution (abbreviated as prob. dist.) ( 2 3 , 1 3 ) over the same set of outcomes. When we toss a fair die, the prob. dist. over the six outcomes { 1 dot, 2 dots, ..., 6 dots } is ( 1 6 , 1 6 , 1 6 , 1 6 , 1 6 , 1 6 ) . 1.2 Random Variables When we attach numbers to random outcomes, we obtain random variables . For example, consider prob. dist. ( 1 2 , 1 2 ) , on the set of outcomes { H, T } and let X be as follows: X = 1 : if H occurs 0 : if T occurs Then X is a random variable , which can take on two values { 1, 0 } with respective probabilities ( 1 2 , 1 2 ) . Let Y be as follows: Y = 1 : if H occurs 1 : if T occurs 1 Then Y is a random variable , which can take on two values { 1, 1 } with respective probabilities ( 1 2 , 1 2 ) . 1.3 Expected Values of Random Variables The expected value of random variable X defined above can be computed as the following: E ( X ) = 1 1 2 + 0 1 2 = 1 2 . Definition 1 Formally, if X is a random variable which can take on n val ues x 1 ,x 2 ,...,x n with probabilities p 1 ,p 2 ,...,p n respectively, then the expected value of X is E ( X ) = n X i =1 p i x i = p 1 x 1 + + p n x n . In the fair die example, we can define a random variable Z so that Z = 1 if the outcome is 1 dot; Z = 2 if the outcome is 2 dots, etc. Thus Z takes on each of the six different values, 1, 2, ..., 6 with equal probability 1 6 . We have E ( Z ) = 1 1 6 + 2 1 6 + ... + 6 1 6 = 7 2 2 Expected Utility and Mixed Strategies 2.1 Expected Utility (or Payoff) In the fair coin example, suppose further that a person receives a payoff (or utility) u ( H ) = $1 if H occurs and receives payoff u ( T ) = 0 if T occurs. Then the persons payoff is a random variable which takes on values { 1, 0 } with probabilities ( 1 2 , 1 2 ) respectively. The expected payoff of the person is the expected value of his random payoff: E ( u ) = u ( H ) prob. ( H ) + u ( L ) prob. ( L ) = 1 1 2 + 0 1 2 = 1 2 ....
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This note was uploaded on 09/14/2011 for the course ECON 505 taught by Professor Zhao during the Spring '11 term at SUNY Albany.
 Spring '11
 Zhao
 Game Theory

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