1. Introduction to Game Theory and Networks Networked Life CSE 112 Spring 2007 Prof. Michael Kearns

2. Game Theory A mathematical theory designed to model: –how rational individuals should behave –when individual outcomes are determined by collective behavior –strategic behavior Rational usually means selfish --- but not always Rich history, flourished during the Cold War Traditionally viewed as a subject of economics Subsequently applied by many fields –evolutionary biology, social psychology Perhaps the branch of pure math most widely examined outside of the “hard” sciences

3. Prisoner’s Dilemma Cooperate = deny the crime; defect = confess guilt of both Claim that (defect, defect) is an equilibrium: –if I am definitely going to defect, you choose between -10 and -8 –so you will also defect –same logic applies to me Note unilateral nature of equilibrium: –I fix a behavior or strategy for you, then choose my best response Claim: no other pair of strategies is an equilibrium But we would have been so much better off cooperating… cooperatedefect cooperate-1, -1-10, -0.25 defect-0.25, -10-8, -8

4. Penny Matching What are the equilibrium strategies now? There are none! –if I play heads then you will of course play tails –but that makes me want to play tails too –which in turn makes you want to play heads –etc. etc. etc. But what if we can each (privately) flip coins? –the strategy pair (1/2, 1/2) is an equilibrium Such randomized strategies are called mixed strategies headstails heads1, 00, 1 tails0, 11, 0

5. The World According to Nash A mixed strategy is a distribution on the available actions –e.g. 1/3 rock, 1/3 paper, 1/3 scissors Joint mixed strategy for N players: a vector P = (P[1], P[2],… P[N]): –P[i] is a distribution over the actions for player i –assume everyone knows all the distributions P[j] –but the “coin flips” used to select from P[i] known only to i “private randomness” –two digressions: mixed strategy simulation in Kings and Pawns? can people randomize? P is an equilibrium if: –for every player i, P[i] is a best response to all the other P[j] Nash 1950: every game has a mixed strategy equilibrium –no matter how many rows and columns there are –in fact, no matter how many players there are Thus known as a Nash equilibrium A major reason for Nash’s Nobel Prize in economics

6. Facts about Nash Equilibria While there is always at least one, there might be many –zero-sum games: all equilibria give the same payoffs to each player –non zero-sum: different equilibria may give different payoffs! Equilibrium is a static notion –does not suggest how players might learn to play equilibrium –does not suggest how we might choose among multiple equilibria Nash equilibrium is a strictly competitive notion –players cannot have “pre-play communication” –bargains, side payments, threats, collusions, etc. not allowed Computing Nash equilibria for large games is difficult

7. Digression: Board Games and Game Theory What does game theory say about richer games? –tic-tac-toe, checkers, backgammon, go,… –these are all games of complete information with state –incomplete information: poker Imagine an absurdly large “game matrix” for chess: –each row/column represents a complete strategy for playing –strategy = a mapping from every possible board configuration to the next move for the player –number of rows or columns is huge --- but finite! Thus, a Nash equilibrium for chess exists! –it’s just completely infeasible to compute it –note: can often “push” randomization “inside” the strategy

8. Games on Networks Matrix game “networks” Vertices are the players Keeping the normal (tabular) form –is expensive (exponential in N) –misses the point Most strategic/economic settings have much more structure –asymmetry in connections –local and global structure –special properties of payoffs Two broad types of structure: –special structure of the network e.g. geographically local connections –special payoff functions e.g. financial markets

9. Case Study: Interdependent Security Games on Networks

10. The Airline Security Problem Imagine an expensive new bomb-screening technology –large cost C to invest in new technology –cost of a mid-air explosion: L >> C There are two sources of explosion risk to an airline: –risk from directly checked baggage: new technology can reduce this –risk from transferred baggage: new technology does nothing –transferred baggage not re-screened (except for El Al airlines) This is a “game”… –each player (airline) must choose between I(nvesting) or N(ot) partial investment ~ mixed strategy –(negative) payoff to player (cost of action) depends on all others …on a network –the network of transfers between air carriers –not the complete graph –best thought of as a weighted network

11. The IDS Model [Kunreuther and Heal] Let x_i be the fraction of the investment C airline i makes p_i: probability of explosion due to directly checked bag S_i: probability of “catching” a bomb from someone else –a straightforward function of all the “neighboring” airlines j –incorporates both their investment decision j (x_j) and their probability or rate of transfer to airline i Payoff structure (qualititative, can be made quantitative): –increasing x_i reduces “effective direct risk” below p_i… –…but at increasing cost (x_i*C).. –…and does nothing to reduce effective indirect risk S_i, which can only be reduced by the investments of others –network structure influences S_i Typical strategic incentives: –when your neighbors are under-investing, your incentive to invest is low basic problem: so much indirect risk already that you can’t help yourself much –when your neighbors are all fully investing, your incentive to invest is high because your fate is in your own control now --- can reduce your only remaining source of risk What are the Nash equilibria? –fully connected network with uniform transfer rates: full investment or no investment by all parties!

12. Abstract Features of the Game Direct and indirect sources of risk Investment reduces/eliminates direct risk only Risk is of a catastrophic event (L >> C) –can effectively occur only once May only have incentive to invest if enough others do! Note: much more involved network interaction than info transmittal, message forwarding, search, etc.

13. Other IDS Settings Fire prevention –catastrophic event: destruction of condo –investment decision: fire sprinkler in unit Corporate malfeasance (Arthur Anderson) –catastrophic event: bankruptcy –“investment” decision: risk management/ethics practice Computer security –catastrophic event: erasure of shared disk –investment decision: upgrade of anti-virus software Vaccination –catastrophic event: contraction of disease –investment decision: vaccination –incentives are reversed in this setting

14. An Experimental Study Data: –35K N. American civilian flight itineraries reserved on 8/26/02 –each indicates full itinerary: airports, carriers, flight numbers –assume all direct risk probabilities p_i are small and equal –carrier-to-carrier xfer rates used for risk xfer probabilities The simulation: –carrier i begins at random investment level x_i in [0,1] –at each time step, for every carrier i: carrier i computes costs of full and no investment unilaterally adjusts investment level x_i in direction of improvement (gradient)

15. Network Visualization Airport to airport Carrier to carrier

16. least busy most busy level of investment simulation time The Price of Anarchy is High

17. Tipping and Cascading

18. Necessary Conditions for Tipping