# Game Theory and Algorithms Lecture 10: Extensive Games: Critiques and Extensions

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1 Game Theory and Algorithms Lecture 0: Extensive Games: Critiques and Extensions March 3, 0 Summary: We discuss a game called the centipede game, a simple extensive game where the prediction made by backwards induction contrasts sharply with what happens in practice. Then, we briefly discuss a standard model even more general than extensive with simultaneous moves: games of incomplete information. We give some examples showing how its concepts of randomization and equilibrium are more complex than those encountered previously. Imps and Centipedes The Bottle Imp is a short story by the Scottish writer Robert Louis Stevenson, from 89. The central object of the story is a magical bottle inhabited by an imp who bestows good fortune and wealth upon its owner. There is a catch: each owner of the bottle must sell it before they die or else they will burn in hell; and each sale price must be strictly less than the previous sale price. Legend has it that the imp was the source of power for Napoleon and Captain Cook. In the story, a man buys it, gets a beautiful wife, then sells it, and contracts leprosy. He is able to track down the current owner of the bottle but unfortunately that owner paid only for it. Should the man buy it for and seal his doom, or live in a leper colony? What would you do in this situation? Is it rational to buy the bottle in the first place? We will return to this story later, but for now let us introduce a related one, the centipede game (introduced by Rosenthal in 98). In the centipede game, we have two players with a joint investment. ˆ The two players take alternate turns caring for the investment, which starts off with a value of \$0. ˆ On each turn, either the player can Invest, increasing the value of the investment by one dollar, or else they can Cash In. Lecture Notes for a course given by David Pritchard at EPFL, Lausanne.

2 ˆ If a player cashes in, the game ends: the value of the investment is split equally between the two players, and the player who cashed in gets a \$ bonus. ˆ We limit the investment s value (e.g., at \$00); the next player to move once it reaches this value is forced to Cash In. This is a finite extensive game without simultaneous moves. Let s draw its game tree we will use a limit of \$5 to make the tree smaller, and multiply everything by to remove fractions: I I I I I C C C C C C, 0, 3 4, 3, 5 6, 4 5, 7 The game gets its name since the shape of the tree looks a little bit like a caterpillar. Next, we would like to see what backwards induction predicts in this game. ˆ If the investment reaches \$0 in value, the second player is forced to cash in, giving utilities (5, 7). ˆ If the investment reaches \$8 in value, the first player can either cash in to get utility 6, or invest and get utility 5. So, he would cash in. ˆ Likewise, if the investment reaches \$6 in value, the second player will cash in since 5 4. ˆ Continuing in this way, the only subgame perfect equilibrium is the strategy profile (always cash in, always cash in). So the SPE behaviour would give a payoff of \$ to player and \$0 to player (no matter how large the limit is).. Experiments In experiments, players do not do what SPEs predict (even though we are in the special class without ties where SPEs seemed to be very logical predictors). Here are the results of one particular study []: ˆ The experiment took place in 989 with a maximum investment value of \$6. ˆ The actual cash value given to participants was \$0.0 x instead of x. ˆ Players played about 0 games, never facing the same opponent twice. Roughly, the result was a bell curve: most of the games terminated after 3 or 4 Invest moves, with a smooth decrease to a handful of games stopping immediately or going as long as possible.

4 Such a game is represented with the same sort of game tree used for an extensive game without simultaneous moves; we also require that the edges (actions) are labelled. Next, we draw information sets around some of the internal nodes of the tree. ˆ The information sets are disjoint (i.e. each internal node lies in at most one information set). ˆ Any two nodes in an information set must be labelled by the same player. ˆ Any two nodes in an information set must have the same labels on the edges coming out of them. Then, our model is that each player cannot distinguish nodes in the same information set. That is to say, if nodes h, h both lie in the same information set (say for player i), then any strategy for player i must choose the same action (label) at h and h.. Examples The first example shows that strategic games can be modelled as extensive games with incomplete information. We actually mentioned this in passing in the first lecture: for the taxpayer game, the citizen moves before the government, but the government can t distinguish between the taxpayer s two moves before choosing their own move. p \p A DA L,3 4, DL, 3,3 L DL In the diagrams, we represent information sets with dashed circles. The second example gives an example of a simplified game of poker: A DA A DA,3 4,, 3, 3 ˆ It is a zero-sum game, and the root node is controlled by external randomness. ˆ Both players ante \$, and player is dealt a high or low card. We imagine player as always having a middling card. ˆ Player looks at her card and gets to bet \$ or check. If she checks, the game ends; she wins with a high card and loses with a low card. 4

5 ˆ If player bet, then player can call or fold. Folding loses the \$ ante immediately, while calling means that the stakes are ±\$ depending on the type of player s card. {player high} chance {player low}, check bet bet check, fold call fold call,,,, For this particular game, there is a unique pair of maxminimizing strategies, as follows. Player calls /3 of the time. Player always bets with a high card, and also bets /3 of the time with a low card (intuitively, he bluffs sometimes since otherwise player would never need to call).. Equilibria Equilibrium concepts in games of incomplete information tend to be more complicated. We give one example here, adapted from Osborne & Rubinstein. A B C, L 3, R L R 0, 0,, Consider the strategy profile (A, R). Is it an equilibrium? The reasonableness of playing R for player depends on her estimation of Pr[B]/(Pr[B] + Pr[C]) which is 0/0 if player is always using A. The subgame-perfect concept of checking subgames is not so useful here, since there are no subgames. So equilibrium concepts tend to incorporate new ideas such as beliefs, or 5

6 convergent sequences of strategy profiles, or allowing errors. We mention a few names of these concepts: (weak) sequential equilibria, trembling-hand perfect equilibria, and perfect equilibria, which allow a second most likely choice..3 Mixed Strategies So far, we did not explicitly describe how mixed strategies are modelled in extensive games. There are actually two alternatives: ˆ A player can flip a generalized coin once at the start of the game, and then they pick a pure strategy thereafter. (Probability distribution over strategy functions.) ˆ At each node, a player gets to independently flip a new generalized coin, and makes just one action based on the result of that flip. (Single strategy function whose output is drawn from a distribution, called a behavioural mixed strategy.) These turn out to be equivalent for all games studied before today, but are not equivalent in general. The distinction between the definitions are easiest to see with an example. It also will show that with incomplete information, the two concepts are different, and neither one generalizes the other. A B A B [outcome z] [outcome x] [outcome y] Consider the game depicted above. Player is forgetful whether he has already moved or not. ˆ If player flips a coin before the game starts and thereafter picks a pure strategy A or B uniformly at random, then he gets outcome x half of the time, and outcome z half of the time. ˆ If player flips a coin before each move and picks a uniformly random choice, then he can get to outcome x, y each a quarter of the time, and z half of the time. ˆ Neither model can achieve the distribution on outcomes achieved by the other model. 6

7 There is a natural sufficient condition for the equivalence of the two models: a game has perfect recall if for all pairs h, h in the same information set for some player i, the sequence of prior moves made by player i are the same in h and h. (We assume for this definition that nodes in different information sets get distinct labels for their moves.) For example, the game just pictured does not have perfect recall because of the two nodes in the same information set, one was reached by action sequence, and the other by (A). One nice thing that happens with perfect recall is that we can work on trees in a bottomup fashion. Moreover, we get the equivalence mentioned above. Theorem. In games with perfect recall, randomizing at the start of the game is outcomeequivalent to randomizing each choice separately. The proof idea is to use conditional probability. The next topic will be mechanism design. As a segue from extensive games, we will mention cake-cutting games. References [] R. D. McKelvey and T. R. Palfrey. An experimental study of the centipede game. Econometrica, 60(4):pp , 99. 7

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