Econ 414, Daniel R. Vincent Introduction to Game Theory III Common Knowledge and Payoffs 1 Outline of Lecture a1. The Assumption of Common Knowledge ` Its implication for games ` A puzzle. a2. Payoffs `Uncertainty and Probability `Expected utility. 2 Chap01 1 Econ 414, Daniel R. Vincent 1. Common Knowledge 3 Common Knowledge aWe say that something is “Common Knowledge” if “He knows that she knows that he knows that she knows .... “ ad infinitum. Everyone knows it and everyone knows everyone knows it etc. aIn game theory, we commonly assume that `All the potential players are CK, `The strategies are CK. `The payoffs are CK. aSo everyone knows “the details of the game”. 4 Chap01 2 Econ 414, Daniel R. Vincent Common Knowledge aIs the assumption of Common Knowledge so strong that it rules out most interesting strategic situations? aIn particular, does it rule out any situation where players have some uncertainty? aNot necessarily, all we need is that, at some level, the structure of the game is commonly known. aThe next example illustrates this feature. 5 Example of no CK of a payoff a Here the payoff off player 2 is not fully known. 1 2 (3,x) (0,2) (0,1) (2,5) 6 Chap01 3 Econ 414, Daniel R. Vincent Example: Continued aPlayer 1 cannot determine an optimal strategy without knowing what Player 2’s optimal strategy is. aEven if Player 2 knows x if player 1 does not, he cannot analyze the game well enough to play sensibly. aWe can fix this problem without having to give Player 1 too much information… aWe add an articifical stage where the uncertainty is generated by “Nature”. 7 Example of no CK of a payoff: A potential cure? N a .5 .5 1 2 (3,0) (0,2) (0,1) (2,5) (3,3) (0,2) (0,1) (2,5) 8 Chap01 4 Econ 414, Daniel R. Vincent An example of No CK aHere the “fix” is to assume that the players are in a bigger game, where a random draw is made first determining x. aThe details of the larger game (including the possibility of nature’s random move) is Commonly Known. aNature’s actual choice is not known to player 1 but player 1 will know the probabilities used for the draw. 9 An Example Where Everyone Knowing Everything is Not Common Knowledge. 10 Chap01 5 Econ 414, Daniel R. Vincent The Island People and their Dots a There is an island of preindustrial civilization. The society is made up of five very sensitive people. They meet each morning at the island’s best spring for water. If they do not use this spring, they have to drink the awful water on the other side of the island. a Each person on this island has a fifty percent chance of being born with a blue dot on the forehead. They are all very sensitive about this dot and if they knew they had a dot, they would not be seen in public and would not use the fountain. a Unfortunately, fate would have it that all five people on the island were born with the dot. However, fortunately, there are no mirrors on the island and so they continue to meet every morning at the spring. 11 The Island People and their Dots a One day, Blabbermouth Bob, a visiting anthropologist, happens to say “At least one of you has a blue dot on your forehead.” a Has anything of significance occurred? What will happen? 12 Chap01 6 Econ 414, Daniel R. Vincent The Island People and their Dots a Analysis: Although every person on the island knows the information that Bob has broadcast, it is not common knowledge. That is because, no one can rule out the conjecture that A believes that B believes that A believes that B believes that A believes that there are no blue dots. a Bob’s announcement has ruled this possibility out. What are the possible events? 0 blue dots, 1 blue dot,...,5 blue dots. Bob has ruled out only the first, but he has done it in a way that makes it common knowledge. 13 The Island People and their Dots a If case 1 dot had occurred, then when the announcement is made, the one person with the dot would know it immediately and leave. Since this does not happen, the group meets the next day and it would be common knowledge that only cases 2,3,4, and 5 remain. 14 Chap01 7 Econ 414, Daniel R. Vincent The Island People and their Dots a If case 2 had occurred, then two of the group would see only one dot, infer that they also have a dot and leave immediately. This does not happen so the group meets a third day with cases 3,4 and 5 remaining. Now, if it was case 3, three members would see only two dots and would leave. This does not happen so the fourth day they all come back. On this day, four members would see three dots under case 4, would infer they have the fourth and would leave. Since this does not happen, there is only one case left and on the fifth day no one returns. 15 2. Payoffs 16 Chap01 8 Econ 414, Daniel R. Vincent Payoffs aRecall that one of the three components of a game is a full description of the consequences of every possible play of the game for every player. aThat is, we have to describe how players “care” about the outcomes of a game. 17 Payoffs a Payoffs must capture all aspects of the outcomes of a game that a player may care about. aWe assume that a player is “rational” that is, the player acts to maximize her own expected payoff aDoes this imply we must presume players are selfish? 18 Chap01 9 Econ 414, Daniel R. Vincent Example of the selfish father? Incorrect version? a Father Buy the car No Car Father Drives her (3,3) Friends Drive (0,2) Daughter Careless Careful (0,1) (2,5) 19 The Selfish Father aWe typically expect that parents do not care ONLY about themselves but also their offspring. aDoes this keep us from analyzing games in these situations? aNot necessarily. aIf we wanted to argue that of course the father will prefer his daughter to do well, then we need to incorporate that fact in the payoffs. 20 Chap01 10 Econ 414, Daniel R. Vincent Example of the selfish father? Correct Version? a Father Daughter (3,3) (0,2) (0,1) (5,5) 21 Selfish Players aThe main point is that players can care about each other’s welfare. aIf we want to capture that interdependence, though, we need to be sure to do it in the specification of payoffs. 22 Chap01 11 Econ 414, Daniel R. Vincent More on Payoffs aIn the next lecture, we develop some further tools to help us incorporate payoffs when there is uncertainty in the game. 23 Chap01 12
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