Economics, Computer Science and Policy

1. Economics, Computer Science and Policy By: Michael Kerns (University of Pennsylvania) Proc: Issues on Science and Technology, Winter, 2005 Krishna Venkatasubramanian CSE 591: Embedded Networks Monday 29th January, 2007

2. Overview • Introduction • Economics IN Computer Science • Computer Science IN Economics • Economics Models: • Interdependent Security Games (IDS) • Case Study: • Computer Security Problem • Airline Security Problem • Effects on Policy

3. Introduction • Many diverse problems have both computational and economics (game-theoretic) properties. Example: • Vaccination against diseases • Spam prevention • Explosive in airline baggage • Example: • Game-theoretic/ economic • Decide on strategy to vaccinate based on much how others have decided on vaccination (prevalent level of infection in the population. • Computational • Decide to vaccinate based on chances of infection spreading from people in contact. (weighted graphs)

4. Economics IN Computer Science • Biggest Example: Internet • Decentralized • Comprised of autonomous entities (ASU, AT&T, other ISP) • Diverse incentives, sizes, goals • Major problems (Spam, Virus) • Economic in nature. • Technology does not provide complete solution • Example of Economic model based solution: • Technology suggests: “ask ISP to filter spam” • ISPs charge based on volume of data carried and so have no economic incentive to stop spam • Solution: “charge money for every email, s.t., bulk mail is expensive, single mail is not.” • How to implement economic solutions – CHALLENGING!!!

5. Computer Science IN Economics • Game theoretic models for modern problems have very large scale, cannot be enumerated in traditional forms (matrices). • Computational Algorithms • Designed for large dimension problems • Heuristic approaches for intractable problems • Allow game theoretic models to scale up • Example: • Evaluating distribution of wealth, variation in prices based on connectivity patterns of good exchange.

6. Game Theory: Definitions • Game: Interactive situation specified by: set of players, their possible courses of actions, and possible payoffs. • Strategy: For a player a strategy is a plan describing player decisions in each possible situation that might arise. • Payoff: Is the result w.r.t. a player for choosing a strategy. It depends on strategies of other players and represented as a matrix. • Outcome: A specific combination of strategy choices made by players in a game. • Equilibrium (Nash): Is reached when; all players have chosen a strategy, and no player can gain from unilaterally switching to another strategy.

7. Strategy Payoffs Prisoner’s Dilemma Players Equilibrium Outcomes

8. Interdependent Security Games • Developed By: • Howard Kunreuther (U. Penn) • Geoffrey Heal (Columbia U.) • Models: • Risk Management Scenarios • Main Idea: • Interdependent security (IDS) games model situations where each player has to determine whether or not toinvest in protection or security against an uncertain event knowing that there is some chance s/he will be negatively impacted by others who do not follow suit 1 • Diverse Applications • Network security • Baggage screening • Vaccinations against diseases 1http://www.schneier.com/blog/archives/2005/09/research_in_beh.html

9. Interdependent Security Games • DECISION: Each player in the game has an investment decision to make • RISK: for each player is either direct or indirect • Direct risk is due to player’s own inactions/actions • Indirect risk due to rest of the player’s actions/inactions • CHOICE: Rational players choose decision based on both direct and indirect risk • RESULT: Equilibrium is reached when all players are rational, i.e. makes decisions based on direct/indirect risks faced.

10. Security Example Hard-drive • SCENARIO • A large hard-drive shared by you + n networked users • Needs protection from malicious software • DECISION • Whether you should download updates to security software • RISK • Direct: you not updating security software • Indirect: other users not updating their software • CHOICE • Update software • Don’t update software • DECISION • Equilibrium, when everyone updates or no one updates U1 U3 U2 Security Software Internet U1 • Networked structure between parties symmetric • All parties are affected by same amount by action or inaction, one or more users U3 U2 Same effect

11. Baggage Screening Example • SCENARIO: • Air carriers screen luggage for detecting explosives • All screening needs to meet minimum FDA requirements • Luggage of two types: • Direct checked in baggage • Transferred baggage • DECISION: • Should an airline invest in improved screening its directchecked baggage • RISK: • Direct: from directly checked baggage having explosive • Indirect: from transferred baggage having explosive

12. Baggage Screening Example Contd.. Most Transfers • PROPERTIES: • Networked interactions between airlines based on transfer of baggage • Not all airlines transfer to one another, therefore strong asymmetry • Networked structure has strong influence on the outcome • Incentive for investing depends upon incentives of others • Computational means required to understand behavior of IDS model 5 Link if transfer 7 6 8 9 Carriers Least Transfers Security Level Requirement Minimum = 5 Maximum = 10

13. Simulation Parameters • Large scale simulation of IDS done with airline example. • 35,562 records of flight itineraries examined for Aug 26, 2002. • Transfers 122 air carriers appeared in data set. • Carrier’s business was computed using total flight legs in which the carrier appears. From To Carrier LA PHX Leg 1 South West Alaskan Leg 2 LA SF SF Seattle South West Leg 3 Transfer

14. Simulation Model • Parameters: • D(A): direct risk to airline A, probability of A checking in a explosive. • Dependent upon customer base, geographic region • Can be derived from statistics of security breaches at airports with maximum direct checked volume for A. • T(A,B): indirect risk to A due to transfers from B; probability of an explosive in bag transferred from B. • Different for different pairs • Depends upon volume of transfer • I(A): quantifies the required investment for A • E(A): cost of in-flight explosion for A • Payoff for a given carrier ‘A’ is computed based on current investment of other carriers weighted by their probability of transferring baggage to A. • Payoff POSITIVE: INCENTIVE TO INVEST (Carriers adjust to this value) • Payoff NEGATIVE: DISINCENTIVE TO INVEST Common Defaults Based on actual transfer data

15. Visualization Largest number of transfers occur between busiest carriers Most busy carriers Most busy carriers Least busy carriers Least busy carriers Transfer between 36 busiest airlines

16. Results (price of anarchy) level of investment Simulation time least busy Last to converge Don’t invest most busy

17. Results: Tipping Point Tipping Point Cascading behavior

18. Results: Condition for Tipping Tipping Point not reached

19. Effect on Policy • Policy directly affect by the confluence of economic and computational models • Example: • Policy for spam reduction based on economic models. • Policy of subsidization of air carriers • However, convincing policy makers inspite of political, budgetary constraints a challenge.

20. Useful Links • Game Theory Links: http://www.gametheory.net/lectures/level.pl • Introduction to Game Theory: http://www.pitt.edu/~jduffy/econ1200/Lectures.htm