Adrien Blanchet – TSE (GREMAQ, Universite Toulouse 1...

EQUILIBRE DE NASH & TRANSPORT OPTIMAL

Adrien Blanchet – TSE (GREMAQ, Universite Toulouse 1 Capitole)

Modelisation with optimal transport (ANR TOMMI)

In collaboration with P. Mossay & F. Santambrogio and G. Carlier

EQUILIBRE DE NASH & TRANSPORT OPTIMAL LJK GRENOBLE – 3-4 OCTOBRE 2013 1 / 22

INTRODUCTION

PLAN

1 INTRODUCTION

2 THE MODELS

Model I: one type of agentModel II: agent with types

3 MAIN RESULTS

4 CONNEXION WITH OPTIMAL TRANSPORT

5 IDEA OF THE PROOF

6 DISCUSSIONS


INTRODUCTION

NON-COOPERATIVE GAMES

GAME THEORY

The players choose actions in a given set. The payoff of the agent i depends on her action ai andthe actions of all the other players a−i . We denote the payoff Π(ai , a−i).A player can also play in mixed strategy, i.e. to play a strategy ej with a probability xj . This mixedstrategy is thus given by a vector (x1, · · · , xN).If the strategy y of Player 2 is known we say that Player 1 is in best reply against y if her action x∗

is such thatx∗ = ArgmaxxΠ(x, y) .

A pair (x, y) is a Nash equilibirum if each agent is in best reply against the other player’s action(i.e. all the agents have no incentive to relocate).

NASH (1950)

“The theory of non-cooperative games is based on the absence of coalitions in that it is assumed

that each participant acts independently, without collaboration and communication from any of the

others. ”

→ Existence of equilibria in a non-cooperative n-persons game (n ∈ N).


INTRODUCTION

CONTINUUM OF PLAYERS

VON NEUMANN-MORGENSTERN (1944)

“An almost exact theory of a gas, containing about 1025 freely moving particles, is incomparably

easier than that of the solar system, made up of 9 major bodies.”

“It is a well known phenomenon in many branches of the exact and physical sciences that very

great numbers are often easier to handle than those of medium size. This is of course due to the

excellent possibility of applying the laws of statistics and probabilities in the first case.”

“When the number of participants becomes really great, some hope emerges that the influence of

every particular participant will become negligible, and that the above difficulties may recede and a

more conventional theory become possible.”

SCHMEIDLER (1973)

“Non-atomic games enable us to analyze a conflict situation where the single player has noinfluence on the situation.”

→ Existence of an equilibria in a non-atomic game with an arbitrary finite number of purestrategies. See also [Mas-Colell, 1984].


THE MODELS

PLAN

1 INTRODUCTION

2 THE MODELS


3 MAIN RESULTS


5 IDEA OF THE PROOF

6 DISCUSSIONS


THE MODELSMODEL I: ONE TYPE OF AGENT

A MODEL WITH ONE TYPE OF AGENT

Consider a non-cooperative anonymous game with a continuum of agents (= “mean field game” inPierre-Louis Lions’ terminology).

COST FUNCTION

The agent has to take action in a compact metric action space Y . Given an action distributionν ∈ P(Y ) the agent taking action y incurs the cost

Π(y , ν) := V [ν](y) .



EXTERNALITIES

RIVALRY/CONGESTION

The utility of the agent decreases when the number of players who choose the same actionincreases.

Examples:

Consumption of the same public good (motorway game),

Food supply in an habitat decreases with the number of its users (ex. Sticklebacks (Milinsky)).

INTERACTIONS

The utility of the agents increases because some other agents play a similar action.

Examples:

Location to go shopping,

Quality of a product in a differentiated industry.

EXTERNALITIES IN [BECKMANN, 1976]’S MODEL

Congestion: the agents benefit from social interactions but there is a cost to access to distantagents,

Interaction: more populated areas lead to higher competition for land.



AN URBAN REGION MODEL

Let K be a convex domain of Rd and ν the density of agents. We assume that ν is a probabilitydensity.

INDIRECT COST FUNCTIONAL

Consider

V [ν](y) := f [ν(y)]︸︷︷︸

congestion

+

∫

K

φ(|y − z|)ν(z) dz

︸︷︷︸

interaction

+ A(y)︸︷︷︸

amenities

.

where

f is the competition for land. We assume that f is an increasing function.

φ is the travelling cost. We assume that φ is a non-negative and radially symmetriccontinuous function.

A is an external potential. We assume that A is a continuous function bounded from below.

NASH EQUILIBRIUM

The probability ν ∈ P(Y ) is a Nash equilibrium if:

{V [ν](y) = V ν-a.e. y ,

V [ν](y) ≥ V a.e. y ∈ Y .


THE MODELSMODEL II: AGENT WITH TYPES

GENERALISATION

Consider now that the agents have a given type x in a compact metric space X . Given an actiondistribution ν ∈ P(Y ), the type-x agent taking action y incurs the cost

Π(x, y , ν) .

Assume

COST IN A SEPARABLE FORM

Π(x, y , ν) := c(x, y) + V [ν](y) .

NASH EQUILIBRIUM

The probability γ ∈ P(X × Y ) is a Nash equilibrium if:

its first marginal is µ ,

its second marginal ν is such that there exists a function ϕ such that

{Π(x, y , ν) = ϕ(x) γ-a.e. (x, y),

Π(x, y , ν) ≥ ϕ(x) a.e. (x, y) ∈ X × Y .


MAIN RESULTS

PLAN

1 INTRODUCTION

2 THE MODELS


3 MAIN RESULTS


5 IDEA OF THE PROOF

6 DISCUSSIONS


MAIN RESULTS

MAIN RESULTS

EXISTENCE AND UNIQUENESS [B., MOSSAY & SANTAMBROGIO, 2012] AND [B. & CARLIER, 2012]

There exists a unique Nash equilibrium.

Our results apply to

POTENTIAL GAMES (SEE [MONDERER-SHAPLEY, 1996] FOR A FINITE NUMBER OF PLAYERS)

There exists a functional E such that V [ν] is the first variation of E i.e.

V [ν] =δE

δν.

Under the assumptions:

E displacement convex and coercive. Ex.: φ convex symmetric and the congestion functionsatisfies the Inada condition.

c satisfies a generalised Spence-Mirrlees condition i.e. for every x , y 7→ ∇x c(x, y) isinjective. Ex. c smooth and strictly convex.

For sake of simplicity, we assume from now on

c(x, y) =|x − y |2

2.


CONNEXION WITH OPTIMAL TRANSPORT

PLAN

1 INTRODUCTION

2 THE MODELS


3 MAIN RESULTS


5 IDEA OF THE PROOF

6 DISCUSSIONS


CONNEXION WITH OPTIMAL TRANSPORT

OPTIMAL TRANSPORT: THE MONGE-KANTOROVICH DISTANCE

CONNECTION WITH OPTIMAL TRANSPORT

Let γ ∈ P(X × Y ) be a Nash equilibrium of second marginal ν. Then γ is a solution to theKantorovich problem, i.e. γ is a solution to

minΠX γ=µ,ΠY γ=ν

∫∫

X×Y

c(x, y) dγ(x, y) =: Wc(µ, ν)

Proof: Let η be of first marginal µ and second marginal ν then we have

∫∫

X×Y

c(x, y) dη(x, y) ≥

∫∫

X×Y

(ϕ(x) − V [ν](y)) dη(x, y)

=

∫

X

ϕ(x) dµ(x) −

∫

Y

V [ν](y) dν(y) =

∫∫

X×Y

c(x, y) dγ(x, y) .

PURITY OF THE EQUILIBRIUM

If µ does not give weight to points then any Nash equilibrium is pure.


IDEA OF THE PROOF

PLAN

1 INTRODUCTION

2 THE MODELS


3 MAIN RESULTS


5 IDEA OF THE PROOF

6 DISCUSSIONS


IDEA OF THE PROOF

MAIN IDEA

VARIATIONAL PROBLEM

infν∈P(Y )

{1

2W2

2 (µ, ν) + E[ν]

}

(1)

where

E[ν] =

∫

K

F (ν(x)) dx +

∫

K

A(x) dν +1

2

∫∫

K2φ(|x − y |)ν(x)ν(y) dx dy .

andwhere F is an antiderivative of f and the Monge-Kantorovich distance is defined by

W22(µ, ν) := min

ΠX γ=µ,ΠY γ=ν

∫∫

X×Y

|x − y |2

2dγ(x, y)

EQUIVALENCE BETWEEN EQUILIBRIUM AND MINIMISER

γ ∈ P(X × Y ) is a Nash equilibrium if and only if

ν is a minimiser of (1),

γ is a solution to the Kantorovich problem.


IDEA OF THE PROOF

OPTIMAL TRANSPORT: PURITY OF THE EQUILIBRIUM

Let µ ∈ P(X) and ν ∈ P(Y ). A measurable function T : X → Y pushes-forward µ onto ν, andwe denote T#µ = ν, if

∀ζ ∈ C0b (Y ),

∫

X

ζ [T (x)] dµ(x) =

∫

Y

ζ(y) dν(y) .

BRENIER’S THEOREM (CPAM, 1991)

There exists a unique optimal transport map T solution to the Kantorovich problem. Moreover it isa solution to the Monge problem

infT :T#µ=ν

∫

X

|x − T (x)|2 dµ(x) = W22(µ, ν) .


IDEA OF THE PROOF

OPTIMAL TRANSPORT: UNIQUENESS

DISPLACEMENT INTERPOLATION, SEE MCCANN (ADV. MATH., 1997)

Let T be the optimal transport map which transports ρ0 dx onto ρ1 dy . The displacement

interpolation between ρ0 and ρ1 is

ρt = [(1 − t)id + tT ] #ρ0 .

DISPLACEMENT CONVEXITY

A functional G is displacement convex if for all ρ0 ∈ P(X), ρ1 ∈ P(Y )

G[ρt ] ≤ (1 − t)G[ρ0] + tG[ρ1].

CRITERIA OF DISPLACEMENT CONVEXITY, SEE MCCANN (ADV. MATH., 1997)

Assume that

F (0) = 0 and r 7→ rd F (r−d ) is convex non-increasing,

A is convex then V [ν] is displacement convex.

φ is convex then W[ν] is displacement convex.

then E is displacement convex.


DISCUSSIONS

PLAN

1 INTRODUCTION

2 THE MODELS


3 MAIN RESULTS


5 IDEA OF THE PROOF

6 DISCUSSIONS


DISCUSSIONS

COMPUTATION OF THE EQUILIBRIUM

A PARTIAL DIFFERENTIAL EQUATION FOR THE EQUILIBRIUM

Let u be a solution to the following Monge-Ampere equation

µ(x) = det(D2u(x)) exp

(

−|∇u(x)|2

2+ x · ∇u(x) − u(x) −

∫

Y

φ(∇u(y),∇u(z)) dµ(z)

)

then ϕ(x) = u(x) + |x|2/2 is the optimal transport which transport µ onto ν so that ν = ϕ#µ.

0 2 4 6 8 10 12 14 160.0

0.5

1.0

1.5

2.0

2.5

3.0

FIGURE: The distribution µ in dash line and ν in the case f (x) = x8 , φ(z) = 10−4|z|2 and A = (x − 10)4 .


DISCUSSIONS

WELFARE ANALYSIS

SOCIAL WELFARE

∫∫

X×Y

Π(x, y , ν) dγ =

∫∫

X×Y

|x − y |2

2dγ +

∫

Y

[

f [ν(y)] +

∫

Y

φ(y , z) dν(z) + A(y)

]

dν(y).

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.50.0

0.1

0.2

0.3

0.4

0.5

0.6

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.50

100

200

300

400

500

600

FIGURE: Left: the optimum (red) and the equilibrium (blue). Right: tax at the equilibrium. Cost of anarchy ∼ 1.8.

TAX TO RESTORE EFFICIENCY

Tax[ν](y) = ν(y)f [ν(y)] − F [ν(y)] +1

2

∫

Y

φ(y , z) dν(z) .


DISCUSSIONS

DYNAMICAL PERSPECTIVE

The agents start with a distribution of strategies and adjust it over time by choosing

MINIMISING SCHEME

νk+1 ∈ argminν

{1

2τW2

2 (νk , ν) + E[ν]

}

.

This scheme converges in some sense to the

CONTINUOUS EVOLUTION EQUATION

∂ν

∂t+ div (−ν∇V [ν]) = 0,

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.50.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.50.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

FIGURE: Convergence and stabilisation toward the equilibrium in the case of a logarithmic congestion, cubic

interaction, and a potential A(x) := (x − 5)3 with 1l[0,1] as initial guess (left) and made of two bumps (right).


DISCUSSIONS

Merci pour votre attention


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Adrien Blanchet – TSE (GREMAQ, Universite Toulouse 1...

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