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Game Theory Dynamic Bayesian Games. Univ. Prof.dr. M.C.W. Janssen University of Vienna Winter semester 2011-12 Week 2 (January 9-10). Difference Dynamics and Statics.
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Game Theory Dynamic Bayesian Games Univ. Prof.dr. M.C.W. Janssen University of Vienna Winter semester 2011-12 Week 2 (January 9-10)
Difference Dynamics and Statics • The only thing to learn in static games with asymmetric information is when types are correlated and then information about own type reveals info about types of other players • Usually, independent types are assumed • In dynamic games with asymmetric information players may learn about types of other players through actions that are chosen before they themselves have to make decisions
Important class of signaling games • In signaling games there are two players, Sender and Receiver • Type of Sender is private information, sender takes an action • Strategy is action depending on type • Receiver takes an action after observing action taken by the sender • Type of sender may be inferred (revealed) on the basis of the action that is actually taken
A simple example 3,2 accept reject U -1,0 apply S 0,0 1/2 N.A good -1,-3 N accept 1/2 apply reject U -1,0 bad S N.A 0,0
What is an equilibrium in this example? • Strategies for both players such that both players strategies are optimal given strategy of the other and the possibly updated beliefs • Pooling and separating equilibria possible (as well as –in more complicated games- semi-pooling or semi-separating equilibria) • Pooling equilibria are equilibria where all types of the sender choose the same action • (Fully) Separating equilibria are equilibria where all types choose different actions from one another (space of actions and types should allow this)
A Separating equilibrium • Proposal: University accepts, student applies if, and only if she is good • Check: Can a player benefit by deviating? • Good student gets positive pay-off in equilibrium, if she deviates she gets 0; • Bad student does not want to apply as this would give a negative pay-off of -1 instead of the equilibrium pay-off of 0; • University can get a pay-off of 2 or -3 when accepting. How to weigh these pay-offs? Bayes’ Rule says that P(good student|application) = 1. Thus, equilibrium pay-off is 2 and deviating gives lower pay-off (of 0) • Thus, this is a separating equilibrium • Are there other separating equilibria?
A Pooling equilibria • Proposal: University rejects, student never applies • Check: Can one of the players benefit by deviating • University always gets pay-off of 0 if student does not apply. Deviating does not improve his situation. • Student does not want to apply (whatever her type) knowing she will be rejected; pay-off of -1 instead of the equilibrium pay-off of 0) • Thus, this is a pooling equilibrium • Are there other pooling equilibria?
The example changed 3,2 accept reject U -1,0 apply S 0,0 1/2 N.A good 1,1 N accept 1/2 apply reject U -1,0 bad S N.A 0,0
Is pooling equilibrium reasonable? • First, it is important to realize that pooling on not applying is still part of an equilibrium • But, in this modified game, it does not seem reasonable. Why? • Rejecting students always gives a pay-off of 0, whereas accepting gives a positive pay-off • Thus, rejecting is an incredible threat • How to get rid of incredible threats? (Usually, impose subgame perfection. But how many subgames are there?)
Perfect Bayes-Nash equilibrium • Refinement of Bayes-Nash equilibrium • Often, in games with private information some information sets are off-the-equilibrium path • When an information set is off-the-equilibrium path, Bayes’ Rule cannot be applied (gives 0/0) • Bayes-Nash equilibrium does not impose any restrictions on strategy after such info set • Perfect Bayes-Nash equilibrium says that (i) some out-of-equilibrium beliefs have to be specified and (ii) given these beliefs, actions have to be optimal
Definition PBNE • A Perfect Bayes-Nash equilibrium is a set of strategies s, one for each player, and out-of-equilibrium beliefs μ(.|a) such that i. each player chooses an optimal strategy given strategies of other players evaluated at updated beliefs iia. μ(.|a) is formed using Bayes’ Rule whenever possible, i.e., if ∑θ p(θ)σ(a|θ) > 0 iib. μ(.|a) is any (arbitrary) probability distribution over type space Θ if ∑θ p(θ)σ(a|θ) = 0
Ruling out pooling equilibrium in modified example • PBNE requires P(good student|apply) to be specified. Let’s say it is μ, where 0≤ μ ≤1 • Thus, P(bad student|apply) = 1 – μ • The expected pay-off for the University of accepting a student is then 2μ + (1-μ), which is positive for any permissible value of μ. • Therefore, University cannot reject a student if it receives an application, as this is not optimal given any out-of-equilibrium belief μ. • With separating equilibrium, one does not need to specify out-of-equilibrium beliefs, as no information set is out-of-equilibrium.
Sequential Rationality • Sequential rationality extends and refines PBNE • Rationality condition of PBNE is extended to hold for any info set • Beliefs condition is refined to include info sets that do not arise from asymmetric information
Example where sequential rationality is needed • Two NE: (T,U) and (B,D) • SPE and PBNE do not have any bite • Still (T,U) seems unreasonable as it is always best for player 2 to choose D • But SPE and PBNE do not require to specify beliefs in this situation 2,2 T 0,0 U M D 0,1 B 1,0 U D 3,1
Reconsider simple example 3,2 accept reject U -1,0 apply S 0,0 1/2 N.A good -1,-3 N accept 1/2 apply reject U -1,0 bad S N.A 0,0 15
Is pooling reasonable? • It satisfies PBNE • Specify out-of-eq beliefs P(good|apply) < 3/5 • It satisfies sequential equilibrium • Sequence σn where both types of students choose to apply with probability 1/n. Updating beliefs gives P(good|apply) = ½ so that university has to reject along the sequence σn • But still it seems that the out-of-equilibrium belief should be P(good|apply) = 1. Why? To apply is a dominated strategy for bad student. (He has never incentive to apply; whereas good student may have incentive if she is accepted) • How to formalize this?
Domination-based beliefs I • Action a is strictly dominated for type θ if there is another action a’ s.t. Mins’S u(a’,s’,θ) > maxsS u(a,s,θ) • For each action aA define Θ(a) = {θ: there is no a’A that strictly dominates a} • A PBE has reasonable beliefs if for all aA with Θ(a) ≠ empty set, μ(θ / a) > 0 only if θΘ(a) • Apply this definition to simple example
Intermediate step to Intuitive Criterion 3,2 accept reject U -1,0 apply exam S 2,1 0,0 1/2 N.A good -1,-3 N accept 1/2 apply reject U -1,0 bad exam S 1,-4 N.A 0,0 18