Transcript Economic Models of Network Formation Networked Life CIS 112 Spring 2008 Prof. Michael Kearns Background and Motivation • First half of course: – common or “universal” structural.

Economic Models of
Network Formation
Networked Life
CIS 112
Spring 2008
Prof. Michael Kearns
Background and Motivation
• First half of course:
– common or “universal” structural properties of “natural” networks
• small diameter, high clustering, heavy-tailed degree distributions,…
– development of statistical models of network formation
• Watt’s “Caveman/Solaria”, alpha model, pref. att., Kleinberg’s model…
– analyzed simple “transmission” dynamics
• disease/fad spread, forest fires, PageRank,…
• Second half of course:
– examination of “rational” dynamics in networks
• interdependent security games, exchange economies,…
– interaction of rational dynamics with statistical formation models
• e.g. when network is formed via pref. att., what will price variation be?
• This lecture: let network formation be “rational” as well
• A very recent topic
Example: A Centrality Game
•
•
•
•
•
•
Let’s consider a simply stated network formation game
Have N players, consider them vertices in the network
Each player has to decide which edges to build or buy
Assume a fixed cost c to build an edge
Player’s goal: be as “central” in the network as possible
Cost to player i:
– cost(i) = S j distance(i,j) + c x (# edges bought by i)
– distance (i,j) = shortest-path distance between i and j in the network
jointly formed by all the players’ edge purchases
– players want to minimize their own cost
– so need to balance edge costs vs. centrality
– will shortly examine a variant of this cost function
Comments and Clarifications
• Formalizing as a one-shot game
– contrast with “gradual” or incremental statistical formation models
– could imagine multi-round or stage game; more complex
• Each player has a huge choice of actions
– action for player i: any subset S_i of all the N-1 edges i could buy
– number of choices for S_i = 2^(N-1)
– cost of choosing S_i = c|S_i|
• Are assuming that if i buys edge to j, j (and all others) can “use” or
benefit from this edge (“unilateral” edge formation)
• Joint action for all N players:
– choice of edge sets for all players: S_1, S_2, …, S_N
• Let G = G(S_1,S_2,…,S_N) be the resulting overall graph/NW
Nash Networks
• Q: How can we view this game a NW formation model?
• A: View the NWs generated as being the Nash equilibria
• More precisely: say that G can be “formed” by the game if:
– G = G(S_1,S_2,…,S_N) for some choices for the S_i
– S_1,S_2,…,S_N form a Nash equilibrium of the shortest-paths game
– so, no player i can improve cost(i) by unilaterally:
• dropping an edge they bought and saving the cost c
• adding an edge they didn’t buy and paying the cost c
• Contrast:
– stochastic formation models (Erdos Renyi, pref. att., etc.)
• examine the “likely” structural properties of random networks
– game-theoretic formation models
• examine the structural properties of equilibrium networks
Properties of Equilibria
• Questions we might ask in NW Life:
–
–
–
–
what are the diameters of the equilibrium graphs?
what do their degree distributions look like?
what are their clustering coefficients?
Etc.
• Not much known precisely
• We’ll examine two network formation games:
– NW formation game based on Kleinberg’s model
– NW formation game based on bipartite Milk-Wheat market
Kleinberg’s Model
• Similar in spirit to the a-model
• Start with an n by n grid of vertices (so N = n^2)
– add local connections: all vertices within grid distance p (e.g. 2)
– add distant connections:
• q additional connections
• probability of connection at distance d: ~ 1/d^r
– so full model given by choice of p, q and r
– large r: heavy bias towards “more local” long-distance connections
– small r: approach uniformly random
• Kleinberg’s question:
– what value of r permits effective search?
• Assume parties know only:
– grid address of target
– addresses of their own direct links
• Algorithm: pass message to neighbor closest to target
An Economic Variation on Kleinberg
• Again have N players/vertices, but arrange them in a grid
• Grid connections provide free connectivity
• Instead of variable probabilities for long-distance edges, introduce
variable costs:
–
–
–
–
–
let cost to i to purchase edge to j = g(i,j)^a
g(i,j) = grid or “Manhattan” distance from i to j
a = some constant value
so cost grows with distance on grid, at a rate determined by value a
player’s overall cost function:
• edge costs + sum of distances to others (centrality)
• So now just have another centrality network formation game
• Another striking “tipping point”:
– for any a <= 2, all Nash equilibria have constant diameter
• i.e. diameter does not grow with N!
– for any a > 2, all Nash equilibria have unbounded diameter
• i.e. diameter grows with N
– Nash equilibria seem to be “regular”, but we don’t know much…
What About Navigation?
Economic Formation + Economic Dynamics
• Recall our simple 2-good exchange economy model:
–
–
–
–
–
–
start with a bipartite network between “Milks” and “Wheats”
Milks start with 1 unit milk, but value only wheat
Wheats start with 1 unit wheat, but value only milk
prices = proposed rates of exchange
price p means party is willing to exchange their 1 unit for p of other
equilibrium prices: prices for each party such that
• all parties behave “rationally” = trade only with “best price” neighbor(s)
• everyone is able to trade away their initial endowment
– at equilibrium, party charging p only trades with parties charging 1/p
– equilibrium prices = equilibrium wealths
• Before we examined wealth distribution for given networks
Price Variation vs.
a and n
n=1
n = 250, scatter plot
n=2
Exponential decrease with a; rapid decrease with n
(Statistical) Structure and Outcome
•
Wealth distribution at equilibrium:
•
Price variation (max/min) at equilibrium:
•
Random graphs result in “socialist” outcomes
•
Price variation in arbitrary networks:
– power law (heavy-tailed) in networks generated by preferential attachment
– sharply peaked (Poisson) in random graphs
– grows as a root of n in preferential attachment
– none in random graphs
– despite lack of centralized formation process
–
–
–
–
characterized by presence/absence of a perfect matching
alternately: an expansion property
theory of random walks
economic vs. geographic isolation
Economic Formation + Economic Dynamics
• Now imagine that network is not given, but must be bought/built
• Each player i (buyer or seller) chooses a set S_i of trading partners on
the opposing side (sellers or buyers)
–
–
–
–
–
assume each edge costs c to purchase, where c can be paid in milk or wheat
edge purchased by one party can be used “for free” by other party
once all parties have decided what edges to buy, have some graph G
at price equilibrium of G, player i receives wealth W_i
overall payoff to player i:
• W_i – c x (#edges purchased by i)
• Can again view this as a network formation model:
– possible networks = Nash equilibria of the above game
– i.e. a choice of edge sets S_i bought for each player i such that no party
can unilaterally improve their overall payoff by dropping or adding an edge
Price Variation?
•
•
•
•
•
•
•
Can price/wealth variation still be present? How much?
What do the equilibrium networks look like?
Suppose G = G(S_1,S_2,…,S_N) is a Nash equil. of this game
Let W_min < 1 be smallest wealth (w/o edge costs)
Let c be the cost of an edge
Then must have W_min > 1 – c (same as c > 1 – W_min)
This inequality is tight
– can construct networks where W_min = 1-c
• So rational NW formation eradicates inequality…
– …up to the cost to buy an edge
Network Structure
• If G is a Nash equilibrium of this game, then:
– G equals its “exchange subgraph” --- no “unused” edges
– G consists of (possibly multiple) connected components
• each component has uniform prices p, 1/p
– don’t know much yet about structure within components
• some components may have a range of degrees