Risk attitudes, normal-form games, dominance, iterated dominance

1. Risk attitudes, normal-form games, dominance, iterated dominance Vincent Conitzer conitzer@cs.duke.edu

2. Risk attitudes • Which would you prefer? • A lottery ticket that pays out $10 with probability .5 and $0 otherwise, or • A lottery ticket that pays out $3 with probability 1 • How about: • A lottery ticket that pays out $100,000,000 with probability .5 and $0 otherwise, or • A lottery ticket that pays out $30,000,000 with probability 1 • Usually, people do not simply go by expected value • An agent is risk-neutral if she only cares about the expected value of the lottery ticket • An agent is risk-averse if she always prefers the expected value of the lottery ticket to the lottery ticket • Most people are like this • An agent is risk-seeking if she always prefers the lottery ticket to the expected value of the lottery ticket

3. Decreasing marginal utility • Typically, at some point, having an extra dollar does not make people much happier (decreasing marginal utility) utility buy a nicer car (utility = 3) buy a car (utility = 2) buy a bike (utility = 1) money $200 $1500 $5000

4. Maximizing expected utility utility buy a nicer car (utility = 3) • Lottery 1: get $1500 with probability 1 • gives expected utility 2 • Lottery 2: get $5000 with probability .4, $200 otherwise • gives expected utility .4*3 + .6*1 = 1.8 • (expected amount of money = .4*$5000 + .6*$200 = $2120 > $1500) • So: maximizing expected utility is consistent with risk aversion buy a car (utility = 2) buy a bike (utility = 1) money $200 $1500 $5000

5. Different possible risk attitudes under expected utility maximization utility • Green has decreasing marginal utility → risk-averse • Blue has constant marginal utility → risk-neutral • Red has increasing marginal utility → risk-seeking • Grey’s marginal utility is sometimes increasing, sometimes decreasing → neither risk-averse (everywhere) nor risk-seeking (everywhere) money

6. What is utility, anyway? • Function u: O →  (O is the set of “outcomes” that lotteries randomize over) • What are its units? • It doesn’t really matter • If you replace your utility function by u’(o) = a + bu(o), your behavior will be unchanged • Why would you want to maximize expected utility? • For two lottery tickets L and L’, let pL + (1-p)L’ be the “compound” lottery ticket where you get lottery ticket L with probability p, and L’ with probability 1-p • L ≥ L’ means that L is (weakly) preferred to L’ • (≥ should be complete, transitive) • Expected utility theorem. Suppose • (continuity axiom) for all L, L’, L’’, {p: pL + (1-p)L’ ≥ L’’} and {p: pL + (1-p)L’ ≤ L’’} are closed sets, • (independence axiom – more controversial) for all L, L’, L’’, p, we have L ≥ L’ if and only if pL + (1-p)L’’ ≥ pL’ + (1-p)L’’ then there exists a function u: O →  so that L ≥ L’ if and only if L gives a higher expected value of u than L’

7. Normal-form games

8. Rock-paper-scissors Column player aka. player 2 (simultaneously) chooses a column Row player aka. player 1 chooses a row A row or column is called an action or (pure) strategy Row player’s utility is always listed first, column player’s second Zero-sum game: the utilities in each entry sum to 0 (or a constant) Three-player game would be a 3D table with 3 utilities per entry, etc.

9. “Chicken” • Two players drive cars towards each other • If one player goes straight, that player wins • If both go straight, they both die D S S D D S D not zero-sum S

10. Rock-paper-scissors – Seinfeld variant MICKEY: All right, rock beats paper!(Mickey smacks Kramer's hand for losing)KRAMER: I thought paper covered rock.MICKEY: Nah, rock flies right through paper.KRAMER: What beats rock?MICKEY: (looks at hand) Nothing beats rock.

11. Dominance • Player i’s strategy sistrictly dominates si’ if • for any s-i, ui(si , s-i) > ui(si’, s-i) • siweakly dominates si’ if • for any s-i, ui(si , s-i) ≥ ui(si’, s-i); and • for some s-i, ui(si , s-i) > ui(si’, s-i) -i = “the player(s) other than i” strict dominance weak dominance

12. Prisoner’s Dilemma • Pair of criminals has been caught • District attorney has evidence to convict them of a minor crime (1 year in jail); knows that they committed a major crime together (3 years in jail) but cannot prove it • Offers them a deal: • If both confess to the major crime, they each get a 1 year reduction • If only one confesses, that one gets 3 years reduction confess don’t confess confess don’t confess

13. “Should I buy an SUV?” accident cost purchasing cost cost: 5 cost: 5 cost: 5 cost: 8 cost: 2 cost: 3 cost: 5 cost: 5

14. Mixed strategies • Mixed strategy for player i = probability distribution over player i’s (pure) strategies • E.g. 1/3 , 1/3 , 1/3 • Example of dominance by a mixed strategy: 1/2 1/2

15. Checking for dominance by mixed strategies • Linear program for checking whether strategysi*is strictly dominated by a mixed strategy: • normalize to positive payoffs first, then solve: • minimize Σsipsi • such that: for any s-i, Σsi psiui(si, s-i) ≥ ui(si*, s-i) • Linear program for checking whether strategysi*is weakly dominated by a mixed strategy: • maximize Σs-i(Σsipsiui(si, s-i)) – ui(si*, s-i) • such that: • for any s-i, Σsipsiui(si, s-i) ≥ ui(si*, s-i) • Σsi psi= 1 Note: linear programs can be solved in polynomial time

16. Iterated dominance • Iterated dominance: remove (strictly/weakly) dominated strategy, repeat • Iterated strict dominance on Seinfeld’s RPS:

17. Iterated dominance: path (in)dependence Iterated weak dominance is path-dependent: sequence of eliminations may determine which solution we get (if any) (whether or not dominance by mixed strategies allowed) Iterated strict dominance is path-independent: elimination process will always terminate at the same point (whether or not dominance by mixed strategies allowed)

18. Two computational questions for iterated dominance • 1. Can a given strategy be eliminated using iterated dominance? • 2. Is there some path of elimination by iterated dominance such that only one strategy per player remains? • For strict dominance (with or without dominance by mixed strategies), both can be solved in polynomial time due to path-independence: • Check if any strategy is dominated, remove it, repeat • For weak dominance, both questions are NP-hard (even when all utilities are 0 or 1), with or without dominance by mixed strategies [Conitzer, Sandholm 05] • Weaker version proved by [Gilboa, Kalai, Zemel 93]