Game Theory and Math Economics: A TCS Introduction Christos H. Papadimitriou UC Berkeley christos.
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Transcript of Game Theory and Math Economics: A TCS Introduction Christos H. Papadimitriou UC Berkeley christos.
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Game Theory and Math Economics:A TCS Introduction
Christos H. Papadimitriou
UC Berkeley
www.cs.berkeley.edu/~christos
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Sources
• Osborne and Rubinstein A Course in Game Theory, MIT, 1994
• Mas-Colell, Whinston, and Greene
Microeconomic Theory, Oxford, 1995
• “these proceedings” survey
• http://www.cs.berkeley.edu/~christos/games/cs294.html and …/focs01.ppt
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• Goal of TCS (1950-2000): Develop a mathematical understanding of the
capabilities and limitations of the von Neumann computer and its software –the dominant and most novel computational artifacts of that time
(Mathematical tools: combinatorics, logic)
• What should Theory’s goals be today?
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The Internet
• built, operated and used by a multitude of diverse economic interests
• theoretical understanding urgently needed
• tools: mathematical economics and game theory
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Game Theorystrategies
strategies3,-2
payoffs
(NB: also, many players)
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1,-1 -1,1
-1,1 1,-1
0,0 0,1
1,0 -1,-1
3,3 0,4
4,0 1,1
matching pennies prisoner’s dilemma
chicken
auction
1 … n
1
.
.
nu – x, 0
0, v – y
e.g.
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concepts of rationality
• undominated strategy (problem: too weak)• (weakly) dominating srategy (alias “duh?”) (problem: too strong, rarely exists)• Nash equilibrium (or double best response) (problem: may not exist) • randomized Nash equilibrium
Theorem [Nash 1952]: Always exists....
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if a digraph with all in-degrees 1 has a source,then it must have a sink Sperner’s Lemma
Brouwer’s fixpoint Theorem( Kakutani’s Theorem market equilibrium)
Nash’s Theorem min-max theorem for zero-sum games
linear programming duality
?
P
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Sperner’s Lemma: Any “legal” coloring of the triangulated simplex has a trichromatic triangle
Proof:
!
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Sperner Brouwer
Brouwer’s Theorem: Any continuous function from the simplex to itself has a fixpoint.
Sketch: Triangulate the simplexColor vertices according to “which direction they are
mapped”Sperner’s Lemma means that there is a triangle that
has “no clear direction”Sequence of finer and finer triangulations,
convergent subsequence of the centers of Sperner triangles, QED
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Brouwer Nash
For any pair of mixed strategies x,y (distributions over the strategies) define
(x,y) = (x’, y’), where x’ maximizes
payoff1(x’,y) - |x – x’|2,
and similarly for y’. Any Brouwer fixpoint is now a Nash equilibrium
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Nash von Neumann
If game is zero-sum, then double best response is the same as max-min pair
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The critique of mixed Nash
• Is it really rational to randomize?
(cf: bluffing in poker, IRS audits)
• If (x,y) is a Nash equilibrium, then any y’ with the same support is as good as y.
• Convergence/learning results mixed
• There may be too many Nash equilibria
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is it in P?
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The price of anarchy
cost of worst Nash equilibrium“socially optimum” cost
[Koutsoupias and P, 1998]
routing in networks= 2 [Roughgarden and Tardos, 2000]
Also: [Spirakis and Mavronikolas 01,Roughgarden 01, Koutsoupias and Spirakis 01]
The price of the Internet architecture?
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• More problems: Nash equilibria may be “politically incorrect:” Prisoner’s dilemma
• Repeated prisoner’s dilemma?
• Herb Simon (1969): Bounded Rationality
“the implicit assumption that reasoning and
computation are infinitely cheap
is often at the root of negative results in Economics”
• Idea: Repeated prisoner’s dilemma played by memory-limited players (e.g., automata)?
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c d c d
c d c d
d
d
cc
c
c cc c,d
c, d
dd
d
d
tit-for-tat punish once
switch on dpunish forever
Theorem: These are the only undominated 2-state strategies
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how about f(n)-g(n) equilibria?
g(n)
f(n)n
n
Theorem [PY94]:
• (d - d)n
• complicated N.e. with payoffs 3 -
• tit-for-tat
~2n
~2n
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mechanism design(or inverse game theory)
• agents have utilities – but these utilities are known only to them
• game designer prefers certain outcomes depending on players’ utilities
• designed game (mechanism) has designer’s goals as dominating strategies
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mechanism design (math)
• n players, set K of outcomes, for each player i a possible set Ui of utilities of the form u: K R+
• designer preferences P: U1 … Un 2K
• mechanism: strategy spaces Si, plus a mapping G: S1 … Sn K
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Theorem (The Revelation Principle): If there is a mechanism, then there is one in which all agents truthfully reveal their secret utilities.
Proof: common-sense simulation
Theorem (Gibbard-Satterthwaite): If the sets of possible utilities are too rich, then only dictatorial P’s have mechanisms.
Proof: Arrow’s Impossibility Theorem
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• but… if we allow mechanisms that use Nash equilibria instead of dominance, then almost anything is implementable
• but… these mechanisms are extremely complex and artificial
(TCS critique would be welcome here…)
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• but… if outcomes in K include payments (K = K0 Rn ) and utilities are quasilinear (utility of “core outcome” plus payment) and designer prefers to optimize the sum of core utilities, then the Vickrey-Clarke-Groves mechanism works
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e.g., Vickrey auction
• sealed-highest-bid auction encourages gaming and speculation
• Vickrey auction: Highest bidder wins,
pays second-highest bid
Theorem: Vickrey auction is a truthful mechanism.
Theorem: It maximizes social benefit and auctioneer expected revenue.
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e.g., shortest path auction
6
6
3
4
5
11
10
3
pay e its declared cost c(e),plus a bonus equal to dist(s,t)|c(e) = - dist(s,t)
ts
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Theorem: Resulting mechanism is truthful and maximizes social benefit
Theorem [Suri & Hershberger 01]: Payments can be computed by one shortest path computation.
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e.g., 2-processor scheduling [Nisan and Ronen 1998]
• two players/processors, n tasks, each with a different execution time on each processor
• each execution time is known only to the appropriate processor
• designer wants to minimize makespan ( = maximum completion time)• each processor wants to minimize its own
completion time
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Idea: Allocate each task to the most efficient processor (i.e., minimize total work). Pay each processor for each task allocated to it an amount equal to the time required for it at the other processor
Fact: Truthful and 2-approximate
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Theorem (Nisan-Ronen) : No mechanism can achieve ratio better than 2
Sketch: By revelation, such a mechanism would be truthful.
wlog, Processor 1 chooses between proposals of the form (partition, payment), where the payment depends only on the partition and Processor 2’s declarations
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Theorem (Nisan-Ronen, continued):
Suppose all task lengths are 1, and Processor 1 chooses a partition and a payment
If we change the 1-lengths in the partition to and all others to 1 + , it is not hard to see that the proposals will remain the same, and Processor 1 will choose the same one
But this is ~2-suboptimal, QED
Also: k processors, randomized 7/4 algorithm.
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e.g., pricing multicasts [Feigenbaum, P., Shenker, STOC2000]
30
2170
52
21 40
32
costs
{23, 17, 14, 9}
{14, 8}{9, 5, 5, 3}
{17, 10}
{11, 10, 9, 9}
{}
utilities of agents in the node(u = the intrinsic value of the informationto agent i, known only to agent i)
i
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We wish to design a protocol that will result in the computation of:
• x (= 0 or 1, will i get it?)
• v (how much will i pay? (0 if x = 0) )
protocol must obey a set of desiderata:
i
i
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• 0 v u,
• lim x = 1
• strategy proofness: (w = u x v ) w (u …u …u ) w (u … u'…u )
• welfare maximization
ui xi – c[T] = max
marginal cost mechanism
u ii
i i
i i
defi i
i 1 i n 1 i n
i
• budget balance
v = c ( T [x])
Shapley mechanism
i
i
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But…In the context of the Internet, there is another desideratum:
Tractability: the protocol should require few (constant? logarithmic?) messages per link. This new requirement changes drastically the space of available solutions.
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• 0 v u
• lim x = 1
• strategy proofness: (w = u x v ) w (u …u …u ) w (u … u'…u )
• welfare maximization
w = max
marginal cost mechanism
u ii
i i
i i
defi i
i 1 i n 1 i n
i
• budget balance
v = c ( T [x])
Shapley mechanism
i
i
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c
W1
W2
W3
W = u + W c, if > 0 0 otherwise
i j
Bottom-up phase
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c A
D
D
D = min {A, W}
v = max {0, u D}ii
Top-down phase
Theorem: The marginal cost mechanism is tractable.
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Theorem: “The Shapley value mechanism is intractable.”
Model: Nodes are linear decision trees, and they exchangemessages that are linear combinations of the u’s and c’s
{u < u < … < u }1 2 n
agents drop out one-by-one
c1
c2
cn
It reduces to checking whether Au > Bc by two sites, one of which knows u and the other c, where A, B are nonsingular
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Algorithmic Mechanism Design
• central problem• few results outside “social welfare
maximization” framework (n.b.[Archer and Tardos 01])
• VCG mechanism often breaks the bank• approximation rarely a remedy (n.b.[Nisan and
Ronen 99, Jain and Vazirani 01])• wide open, radical departure needed
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algorithmic aspects of auctions
• Optimal auction design [Ronen 01]
• Combinatorial auctions [Nisan 00]
• Auctions for digital goods
• On-line auctions
• Communication complexity of combinatorial auctions [Nisan 01]
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coalitional games
Game with players in [n]v (S) = the maximum total payoff of all players in S, under worst case play by [n] – S
How to split v ([n]) “fairly?”
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first idea: the core
A vector (x1, x2,…, xn) with i x i = v([n])is in the core if for all S we have
x[S] v(S)
Problem: It is often empty
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second idea: the Shapley value
xi = E(v[{j: (j) (i)}] - v[{j: (j) < (i)}])
Theorem [Shapley]: The Shapley value is theonly allocation that satisfies Shapley’s axioms.
e.g., power in the UN Security Council;splitting the cost of a trip
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third idea: bargaining setfourth idea: nucleolus
.
.
.seventeenth idea: the von Neumann-
Morgenstern solution
[Deng and P. 1990] complexity-theoreticcritique of solution concepts
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• also an economic problem• surrendering private information is either good or
bad for you• personal information is intellectual property
controlled by others, often bearing negative royalty• selling mailing lists vs. selling aggregate
information: false dilemma• Proposal: evaluate the individual’s contribution
when using personal data for decision-making
some thoughts on privacy
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e.g., marketing survey [Kleinberg, Raghavan, P 2001]
customers
possibleversions of
product
“likes”• company’s utility is proportional to the majority• customer’s utility is 1 if in the majority
• how should all participants be compensated?
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the internet game
3, 2
5, 9
1, 1
2, 0
3, 1
7, 4
3, 1
2, 2 3, 6
capacity of the internalnetwork to carry traffic(edges have capacity)
intensity of traffic to/from this node,distributed to othernodes proportionatelyto their intensity
1, 4
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v[S] = value of total flow that can be handled by the subgraph
induced by S
• Compute the Shapley flow
• Find a flow in the core
• Under what circumstances is the core nonempty? Contains all maximal flows?
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Game Theory and Math Economics:
• Deep and elegant
• Different
• Exquisite interaction with TCS
• Relevant to the Internet
• Wide open algorithmic aspects
• Mathematical tools of choice
for the “new TCS”