L 5 - 8, Ch 22, 24, 26, 27, 28 Flashcards

(30 cards)

1
Q

Nature

A

In the extensive form, we model nature as “player 0,” whose decisions are made according to a fixed probability distribution. Because nature is not a strategic player, no payoff numbers are associated with nature. Graphically, nature’s decision nodes—also called chance nodes—are depicted by open circles, to distinguish them from the decision nodes of the strategic players. In games of incomplete information, rational play will require a player who knows his own type to think about what he would have done had he been another type, because even though player 1 knows his nature, player 2 does not

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2
Q

What matters in repeated interactions

A

reputation

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3
Q

Repeated game is

A

a game over several periods of time. It can be infinite or finite.

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4
Q

history at time t

A

Sequence of strategies of each player in each period up until t

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5
Q

The payoff of a finite t-period repeated game is

A

the sum of payoff earned in the t stage games

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6
Q

The payoff of an infinitely repeated game is

A

the discounted sum of payoffs earned in the stage games.

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7
Q

In finite games

A

each strategy combination of round 1 generates a new subgame, hence after round 1, there are as many subgames starting again as strategy combinations in the stage game. These subgames have the same structure as the stage game. The payoff matrix is the same as well. Hence, the subgames reached in period 2 has the same NE as the stage game.

The result is: In finitely repeated game, any sequence of stage game NE is a SPNE of the repeated game.
In finitely repeated games we can use credible threats and promises in period t=1 to support sequentially rational behavior in period t=0 that is not a NE of the stage game

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8
Q

Infinitely repeated games are used to model

A

long-term relationship. Discounting future payoffs allows us to model these situations. Discount factors can be denoted by delta
d= 1 means all future payoffs are valued equally
d= 0 means only today matters, and future payoffs have no value
0<=d<=1

Strategy in an infinitely repeated game may be very complex: we have to specify an action for a player at each IS (infinitely ISs), hence we have to specify an action for a player after every possible history a player could observe in the repeated game.
There are simple strategies as well. Consider a constant strategy that prescribes: always choose a. It describes that a player should choose a at each possible history, hence ignore the history and choose a. Another example is the trigger strategy.

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9
Q

Trigger strategy

A

specifies a history(ies) which trigger a change of behavior. Until a trigger history Is observed, player chooses one action, and after the trigger occurs, he chooses another action.

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10
Q

Trigger strategy consists of

A

A) Cooperative profile – prescription of sequence of actions that don’t form a stage game NE
B) Uncooperative profile – prescription to use stage game NE
C) Trigger from cooperative to uncooperative profile is triggered if any player deviates from the cooperative profile

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11
Q

TS can sustain cooperation in Prisoners dilemma as long as

A

as the players have high enough value for future cashflows and interact repeatedly. The TS is the SPNE

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12
Q

Folk Theorem

A

Consider Infinitely repeated game. Suppose the stage game has a NE that yields payoff w=(w1…w1). Let v=(v1…v1) be any feasible average per-period payoff such as vi>wi for any player i=1,..n. The payoff vector v can be supported as a SPNE outcome if delta is sufficiently close to 1.

This can be achieved by TSs:
In cooperative phase, use any sequence that would yield payoff vector v
Any deviation triggers uncooperative phase - NE of stage game with payoff vector w

If v>w, the uncooperative phase is the threat that sustains v, although it is not NE outcome

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13
Q

Minimum effort is an example of

A

strategic complementarity (the higher the action of the other player, the higher is my best response)

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14
Q

One NE risk dominates another NE in a 2x2 game if

A

the expected payoff of playing the risk dominant equilibrium strategy against a belief (½, ½ = full uncertainty) is higher than the expected payoff of the alternative equilibrium strategy.

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15
Q

Bayes rule

A

how to update beliefs rationally after receiving a signal (partial info) of a type; when there are multiple types and each type has possibly different probability of sending the signal.

Hence Buyes rule is: what is the probability that the signal is a support for a certain type (hypothesis) instead of an alternative hypothesis.

Pr(H|E) = Pr(E|H) Pr(H) / Pr(E|H) Pr(H)+Pr(E|not H) Pr(not H)

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16
Q

Types of incomplete information games

A

Adverse selection

Moral hazard

17
Q

Adverse selection

A

type of the informed player affects the payoffs of the uninformed player; the uninformed player doesn’t observe directly the type, only the action taken by the type. It takes place when the BR of the informed players drives out some types out of the interaction and this impact is negative for economic efficiency.

18
Q

Moral Hazard

A

the action of the informed player affects the payoffs of the uninformed player and the uninformed player does not directly observe this action, only some imprecise signal of the action.

19
Q

Signaling

A

the informed player takes a costly action, more info is revealed if the costs of the action differ across types, so that only certain types send certain signals.

Signaling can work if the player types who hold private info can be distinguished by their strategy. The signal has to be costly, with costs differing across types.

20
Q

Solutions to the adverse selection

A

Signaling

Screening

21
Q

Screening

A

uninformed player designs a set of options giving incentives to the informed player to reveal own type (e.g. deductibles in insurance)

22
Q

Types of Bayesian NE:

A

Pooling and separating

23
Q

Pooling NE

A

if all types of the informed player choose the same action in equilibrium, implying that the uninformed player cannot distinguish the player types of the other players by observing the action chosen in the equilibrium (Aa, Bb)

24
Q

Separating NE

A

if each type of the informed player chooses another action in equilibrium, implying that the uninformed player is able to distinguish the player types of the other player by observing the action chosen in the equilibrium. (Ab, Ba)

25
Perfect Bayesian NE (PBNE) is given by
specifying each player actions that are optimal given the player’s belief and the strategies of other players. Under condition that the belief are updated according to Bayes rule and consistent with actions expected under the PBNE from other players.
26
Steps to Derive the PBNE for sequential game with incomplete info:
1. Specify actions for each player(type) by backwards induction if possible 2. Specify beliefs of the players facing uncertainty 3. Update beliefs (acc to Bayes rule) given the actions expected in equilibrium (when necessary)
27
Teamwork payoffs
payoff 1 = 2(x+y+cxy)-x^2 payoff 2 = 2(x+y+cxy)-y^2 Each player's payoff increases in the input by the other player, due to the complementary parameter "c", and this c is higher, the higher is the other player's action Although the payoff increases in other's action, a player has no say in what the other's action is -> source of inefficiency
28
Bayesian normal form
A normal-form representation of a game with nature. The key idea is that because nature’s moves are predetermined by a probability distribution, we can focus on the strategies of the strategic players and compute payoffs by averaging over the random events in the game.
29
The Nash equilibrium and rationalizability concepts can be applied directly to any game with
Random events. These static concepts are most valid for games in which the players’ actions are taken simultaneously and independently.
30
2 methods of evaluating Bayesian games
1. The first method is to compute rationalizability and Nash equilibrium on the Bayesian normal form. This is recommended for any game that can be represented in matrix form. 2. The second method entails treating the types of each player as separate players. For example, if player 1 is one of two types, L and H then it may be helpful to think of 1L and 1H as distinct players.