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Modelling Large Games by Ehud Kalai Northwestern University.

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Modelling Large Games by Ehud Kalai Northwestern University
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Page 1: Modelling Large Games by Ehud Kalai Northwestern University.

Modelling Large Games

by

Ehud Kalai

Northwestern University

Page 2: Modelling Large Games by Ehud Kalai Northwestern University.

Full paper to come

Related past papers:

Kalai, E., “Large Robust Games,” Econometrica, 72, No. 6, November 2004, pp 1631-1666.

Kalai, E., “Partially-Specified Large Games,” Lecture Notes in Computer Science, Vol. 3828, 2005, 3 – 13.

Kalai, E., “Structural Robustness of Large Games,” forthcoming in the new New Palgrave (available by request).

Page 3: Modelling Large Games by Ehud Kalai Northwestern University.

In semi anonymous gamesmany players structural robustness

Lecture plan:

1. Overview and motivating examples (3 slides).

2. Definition of structural robustness (4 slides).

3. Implications of structural robustness (4 slides).

4. Sufficient conditions for structural robustness (3 slides).

5. More formally (4 slides)

6. Future work (1 slide)

Page 4: Modelling Large Games by Ehud Kalai Northwestern University.

Message and Motivating Examples

Page 5: Modelling Large Games by Ehud Kalai Northwestern University.

In Baysian games with many anonymous players all Nash equilibria are structurally robust.

The equilibria survive changes in the order of play, information revelation, revisions, communication, commitment, delegation, …

Nash modeling of large economic and political systems, games on the Web, etc. is (partially) robust in a strong sense.

Page 6: Modelling Large Games by Ehud Kalai Northwestern University.

Example: Ex-post Nash in Match Pennies

Players: k males and k females.

Strategies: H or T.

Male’s payoff: The proportion of females his choice matches.

Female’s payoff: The proportion of males her choice mismatches.

The mixed strategy equilibrium becomes ex-post Nash as k increases.

Page 7: Modelling Large Games by Ehud Kalai Northwestern University.

Example: Computer choice game

Players: 1,2,…,n

Strategies: I or M

Player’s types: I-type or M-type, iid w.p. .50-.50

Individual’s payoff: .1 if he chooses his computer type (0 otherwise) +.9 x (the proportion of opponents he matches).

The favorite computer equilibrium survives sequential play as n becomes large.

identical payoffs and priors are not needed in the general model

Page 8: Modelling Large Games by Ehud Kalai Northwestern University.

Definitions

Page 9: Modelling Large Games by Ehud Kalai Northwestern University.

Want:A general definition that accommodates both previous robustness notions and more.

Idea of definition:An equilibrium of a one-simultaneous-move Bayesian game G is structurally robust, if it “remains equilibrium” in all “alterations” of G.

“Alterations” of G are described by extensive games, A’ s.

“remains equilibrium” in an alteration A, if every adaptation of to A, , is equilibrium in A.

A

Page 10: Modelling Large Games by Ehud Kalai Northwestern University.

G: any n-person one-simultaneous-move Baysian game.

An alteration of G is any finite extensive game A s.t.

A includes the G players: { A Players} {G players}

Unaltered G types: initially, the G players are assigned types as in G.

Unaltered payoffs: At every final node of A, z, the G-players’ payoffs are the same as in G

Preservation of G choices: Every pure strategy of a G-player i, , has at least one adaptation, , in A.

iaAia

Examples: (1) A game with revision (or one dry run), (2) sequential play

Playing A means playing G: with every final node z of A there is an associated profile of G pure strategies, .

)(za

That is: playing leads to final nodes z with (z) = , no matter what strategies are used by the opponents.

ia iaAia

Page 11: Modelling Large Games by Ehud Kalai Northwestern University.

Given an alteration A and a G-pure-strategy . ia

An adaptation of to A is a strategy of player i in A,that leads to a final nodes z with no matter what strategies are used by the opponents.

iaAia

ii aza )(

Given a G-strategy-profile

An adaptation of is an A-strategy-profile, ,s.t. for every G player i, is an adaptation of .

AAi i

Example: mixed strategies in match pennies.

Given a G-mixed-strategy of player i.iAn adaptation of is an A-strategy, , s.t for every

Aii

ia )()( AAiiii aa A

iaG-pure-strategy : for some .

Page 12: Modelling Large Games by Ehud Kalai Northwestern University.

Definition: An equilibrium of G, is structurally robust if in every alteration A and in every adaptation , every G- player i is best responding, i.e.

A

Ai A

iis best response to .

It is () structurally robust if in every alteration and adaptation as above:

Pr(every G-player is -optimizing at all his positive probability information sets) > 1-.

Page 13: Modelling Large Games by Ehud Kalai Northwestern University.

Implications of structural robustness

Page 14: Modelling Large Games by Ehud Kalai Northwestern University.

1. Play preceded by a dry run: Invariance to revisions, Ex-post Nash and being information proof. No revelation of information, even strategic, can give any player an incentive to revise his choice.

2. Invariance to the order of play in a strong sense.

Page 15: Modelling Large Games by Ehud Kalai Northwestern University.

3. Revelation and delegation.Ex: Computer Choice game with delegation.

Players: the original n computer choosers + one outsider, Pl. n+1.

Types: original players are assigned types as in the CC game.

First: simult. play; each original player chooses between (1) self-play, or (2) delegate-the-play and report a type to Pl. n+1.

Next: simultaneously, self-players choose own computers, Pl. n+1 chooses computers for the delegators.

Payoffs of original computer choosers: as in CC.

Payoff of Pl n+1: 1 if he chooses the same computer for all, 0 otherwise.

There is a new and more efficient equilibrium, but the old favorite computer equilibrium survives.

Page 16: Modelling Large Games by Ehud Kalai Northwestern University.

4. Partially-specified games:

Ex.: Computer Choice game played on the web.

Instructions: “Go to web site xyz before Friday and click in your choice.”

Structural Questions: who are the players? the order of play? monitoring? communications? commitments? delegations? revisions?...

Equilibrium: any equilibrium of the one simultaneous move game can be adapted.

If G is a reduced form of a game U with unknown structure, the equilibria of G may serve as equilibria of U

Page 17: Modelling Large Games by Ehud Kalai Northwestern University.

5. Market games: Nash prices are competitive.

Ex: Shapley-Shubik market game.Players: n traders.

Types: .50-.50 iid prob’s, a banana owner or an apple owner.

Strategies: keep your fruit or trade it (for the other kind).

Proportionate Price: e.g., with 199 bananas and 99 apples traded price=(199+1)/(99+1)=2. (2 bananas for an apple, 0.5 apples for a banana).

Payoff: depends on your type and your final fruit, and on the aggregate data of opponent types and fruit ownership (externalities).

Every Nash equilibrium prices is competitive, i.e., strong rational expectations properties

Page 18: Modelling Large Games by Ehud Kalai Northwestern University.

Partial invariance to institutions: Markets in two island economy

Page 19: Modelling Large Games by Ehud Kalai Northwestern University.

Sufficient conditions for structural robustness

Page 20: Modelling Large Games by Ehud Kalai Northwestern University.

Structural-Robustness Thm (rough statement):

The equilibria of a finite, one-simultaneous-move Bayesian game are (approximately) structurally-robust provided that:

1. The number of players is large.

2. The players’ types are drawn independently.

3. The payoff functions are anonymous and continuous.

The players are only semi anonymous. They may have different payoff functions and different prior type- probabilities (publicly known).

Page 21: Modelling Large Games by Ehud Kalai Northwestern University.

A discontinuous counter example.

Ex: Match the Expert.

Players: 1,2,…,n

P1 Types: “I expert” (informed that I is better) or with equal prob.

“M expert” (informed that M is better).

Players 2,..,n Types: all “non expert” wp 1.

Payoffs: 1 if you choose the better computer, 0 otherwise.

Equilibrium: Pl. 1 chooses the better computer, Pl. 2,3,…,n randomize.

The equilibrium fails to be ex-post Nash (hence, it fails structural robustness), especially when n becomes large.

Page 22: Modelling Large Games by Ehud Kalai Northwestern University.

Counter example with dependent types.

Ex: Computer choice game with noisy dependent information.

Players: 1,2,…,n

Types: wp .50 I is better and (independently of each other) each chooser is told “I better” wp .90 and “M better” wp .10. wp .50 M is better and … .

Payoffs: 1 if you choose the better computer, 0 otherwise.

Equilibrium: Everybody chooses what he is told.

The equilibrium fails to be ex-post Nash (hence, it fails structural robustness), especially when n becomes large.

Page 23: Modelling Large Games by Ehud Kalai Northwestern University.

Formal statement

Page 24: Modelling Large Games by Ehud Kalai Northwestern University.

The modelT – vocabulary of types (finite).A – vocabulary of actions (finite).N – Names of players.

A family F : for any number of players n =1,2,…, F contains infinitely many simul. move Bayesian games G = (N, T= xTi, , A = xAi, u = (u1,…,un)).

The ui’s are uniformly equicontinuous.

N µ N, a set of n-players.

Ti µ T , possible types of player i.

Independent priors,(t)=i iti.

Ai µ A , possible actions of player i.

ui, utility of player i, is a fn of his type-act’n and the empirical dist over

opponents type-actn’s to [0,1], i.e., semi anonymous payoff functions.

Page 25: Modelling Large Games by Ehud Kalai Northwestern University.

Structural Robustness Theorem:

Given the family F and an > 0, there exist positive constants and , <1, s.t. for n=1,2,… all the n-player equilibria of games in F are (, n) structurally robust.

Page 26: Modelling Large Games by Ehud Kalai Northwestern University.

Method of proof

Two steps:

1. By Chernoff bounds: as the number of players increases all the equilibria become (weakly) ex-post Nash at an exponential rate.

2. This implies that they become structurally robust at an exponential rate.

Page 27: Modelling Large Games by Ehud Kalai Northwestern University.

A bit more precisely

Step 1. For an eq’m of the simultaneous move game

Prob(outcome not being weakly -ex-post Nash) < n, with > 0, < 1.

Step 2. For any strategy profile of the simult. move game

If Prob(outcome not being weakly -ex-post Nash) < n,then in any alteration and every adaptation

Prob( some original player not being 2 optimal at some information set) < n n/.

Page 28: Modelling Large Games by Ehud Kalai Northwestern University.

Areas for future work:

• Relaxing the independence condition

What are the weaker conditions we get under reasonable weaker independence assumptions.

• Computing equilibria of large games

Page 29: Modelling Large Games by Ehud Kalai Northwestern University.

Modeling large games

Sampling Models of large games. What are the best parameters to include (e.g., do we really need the prior and utility of every player, or is it better to have the modeler and every player have some aggregate data about the players?).methods help the modelers and players identify the game and equilibria?

Page 30: Modelling Large Games by Ehud Kalai Northwestern University.

Broader issues

Bounded rationality and computational ability in games.

Modified equilibrium notions that incorporate complexity limitations.

Explicit presentations of family of games, and complexity restricted solutions in the data of the game, given the language of the game. This has been done to some degree in cooperative game theory, less so in non cooperative.


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