Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of...

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Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria
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Page 1: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Algorithmic Game Theoryand Internet Computing

Amin Saberi

Stanford University

Computation of Competitive Equilibria

Page 2: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Outline

History

Economic theory and equilibria (existence, dynamics, stability)

An algorithmic approach: computation, polynomial time computability

Page 3: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

A bit history Rabbi Samuel ben Meir (12th century, France): 2nd century

text: “You shall have inspectors of weights and measures but not inspectors of prices.” Commentary (Aumann): If one seller charges too high a price, then another will undercut him.

Adam Smith (1776): Capital flows from low-profit to high-profit industries (demand function implicit?)

Page 4: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

The beginning of analytical work

Standard analysis demand functions: Cournot (1838) supply functions: Jenkin (1870) excess demand: Hicks (1939).

Dynamics in 1870’s: Is out-of-equilibrium behavior modeled by demand and supply?

Page 5: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Walras, Fisher, Pareto, Hicks Walras [1871, 1874]:

first formulator of competitive general equilibrium theory. Recognized need for stability (how to get into equilibrium)His name: tatonnements (gropings).

Page 6: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Walras, Fisher, Pareto, Hicks Walras [1871, 1874]:

first formulator of competitive general equilibrium theory. Recognized need for stability (how to get into equilibrium)His name: tatonnements (gropings).

Fisher (1891): tried to compute the equilibrium prices

Page 7: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

First computational approach!

Fisher (1891): Hydraulic apparatus for calculating equilibrium

Page 8: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Walras, Fisher, Pareto, Hicks Walras [1871, 1874]: tatonnements

Pareto (1904): Pointed out that even a simple economy requires a large set of equations to define equilibrium. Argued that market was an effective way to solve large systems of equations, better than an “ordinateur” (his word in the French translation). I believe this is the word now used to translate, “computer.”

Page 9: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Walras, Fisher, Pareto, Hicks Walras [1871, 1874]: tatonnements

Fisher (1894), Pareto (1904): Markets and computation

Hicks (1939): convergence and “Hicksian” condition on the Jacobian of the excess demand functions (the determinants of the minors be positive if of even order and negative if of odd order)

Page 10: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Samuelson and successors Samuelson [1944]: Hicksian conditions neither necessary

nor sufficient for stability.

Metzler [1945]: if off-diagonal elements of Jacobian are non-negative (commodities are gross substitutes), then Hicksian conditions are sufficient.

Arrow [1974]: Hicksian conditions were actually equivalent to the statement that the real roots of the Jacobian are negative.

Page 11: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Arrow, Debreu and… Arrow-Hurwicz et. al. papers [1977]: Sufficient

conditions for stability of Samuelson-Lange systemGross substitution implies that Euclidean norm decreases

Will talk about these dynamics in details in the next lecture

Arrow-Debreu: existence of equilibrium prices (will show a variation of Debreu’s proof)

Page 12: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

End of the program? Scarf’s example, Saari-Simon Theorem: For any dynamic

system depending on first-order information (z) only, there is a set of excess demand functions for which stability fails.

Uzawa: Existence of general equilibrium is equivalent to fixed-point theorem (will show in this lecture)

Linear complementarity Programs (LCP) and algorithms:Scarf, Eaves, Cottle…(later in the quarter)

Page 13: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Outline

History

Economic theory and equilibria (existence, dynamics, stability)

An algorithmic approach: computation, polynomial time computability

Page 14: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

New applications: Internet, Sponsored search, combinatorial auctions

Computation as a lense!

First papers: Megiddo 80’s, DPS 01prices and ND communication complexity

Lots of new algorithm: convex programs combinatorial algorithms

Last 10 years

Page 15: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

n buyers, with specified money m divisible goods (unit amount) Buyers have CES utility functions:

Contains several interesting special cases: = 1 linear = 0 Cobb-Douglas = -1 Leontief (rate allocation in a network)

A CES Market

Page 16: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

n buyers, with specified money m divisible goods (unit amount) Buyers have CES utility functions:

Contains several interesting special cases: = 1 linear = 0 Cobb-Douglas = -1 Leontief (rate allocation in a network)

A CES Market

Page 17: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

n buyers, with specified money mi

m divisible goods (unit amount) Buyers have CES utility functions:

Find prices such that buyers spend all their money Market clears

Market Equilibrium

Page 18: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Buyers’ optimization program:

Global Constraint:

Market Equilibrium

Page 19: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

The space of feasible allocations is:

How do you aggregate the utility functions U1, U2, … Un ?

Eisenberg-Gale’s convex program

Page 20: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

The space of feasible allocations is:

How do you aggregate the utility functions U1, U2, … Un ?

First observation: Adding them up is not the answer!

Eisenberg-Gale’s convex program

Page 21: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Buyer i should not gain (or loose) by Doubling all uij s

By splitting himself into two buyers with half of the money

Eisenberg-Gale’s convex program

Page 22: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Buyer i should not gain (or loose) by Doubling all uij s

By splitting himself into two buyers with half of the money

Eisenberg-Gale’s solution:

Eisenberg-Gale’s convex program

Page 23: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Eisenberg-Gale’s convex program

Page 24: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Optimum dual: Equilibrium prices (also unique)

Gives a poly-time algorithm for computing the equilibrium

Eisenberg-Gale’s convex program

Page 25: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Optimum dual: Equilibrium prices (also unique)

Gives a poly-time algorithm for computing the equilibrium

Market is “proportionally” fairfor every other allocation achieving

Eisenberg-Gale’s convex program

Page 26: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Optimum dual: Equilibrium prices (also unique)

Gives a poly-time algorithm for computing the equilibrium

The program works for all homogenous utility functions, generalized to homothetic KVY 03(homothetic: U(f(y)) U is homogeneous of degree one and f is a monotone)

Eisenberg-Gale’s convex program

Page 27: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Application: Congestion Control

;3

2;

3

1

Maximize

321

321

xxx

xxx

x1

x2x3

121 xx 131 xx

Page 28: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Congestion Control

21 pp$

$$

321 Maximize xxx Find the right prices in a Leontief market

p1 = p2 = 3/2

Page 29: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Primal-dual scheme

primal: packet rates at sources dual: congestion measures (shadow prices)

A market equilibrium in a distributed setting!

Kelly, Low, Doyle, Tan, ….

Congestion Control

Page 30: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Exchange Economy

Agents buy and sell at the same time:

Page 31: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Exchange Economy

Agents buy and sell at the same time:

-1 -1 0 1

At least as hard as solving Nash Equilibria

(CVSY 05)

Polynomial-time algorithms known (DPSV 02, J 03, CMK 03 , GKV 04, ...

OPEN!!

Page 32: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Nash = Leontief

Use LCP as an intermediate step:

Finding the solution of LCP for H > 0

Nash equilibria for a symmetric game H

x is equilibrium if:

Page 33: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Nash = Leontief

Finding the solution of LCP for H > 0

Leontief: H the rate matrix; agent i owns good ix is at equilibrium if:

Page 34: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Open Questions

Exchange economies with -1 < < -1

Markets with indivisible goods Price equilibria; proportional fair allocation

Core of a Game: LP-based algorithm for transferable payoff Still open for NTU games

Page 35: Algorithmic Game Theory and Internet Computing Amin Saberi Stanford University Computation of Competitive Equilibria.

Nash = Leontief

In Leontief markets, agents consume goods in fixed proportions:

Let H > 0 be the utility matrix. Assume agent i owns good i

x is an equilibrium if