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Pushing the Envelope: new research topics at the interface of cs and econ/gt Yoav Shoham Stanford University (many debts are due)

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Presentation on theme: "Pushing the Envelope: new research topics at the interface of cs and econ/gt Yoav Shoham Stanford University (many debts are due)"— Presentation transcript:

1 Pushing the Envelope: new research topics at the interface of cs and econ/gt Yoav Shoham Stanford University (many debts are due)

2 Stanford, April 2007BAGT Symposium2 Primary areas of interaction so far Computing solution concepts, primarily NE Multi-agent learning Compact games (graphical games, MAIDs, game networks, local-effect games, social networks, …) Mechanism design, in particular auctions

3 Stanford, April 2007BAGT Symposium3 Talk Outline Computing solution concepts, primarily NE –The role of NE unclear Multi-agent learning –Ditto Compact games (graphical games, MAIDs, game networks, local-effect games, social networks, …) –Other forms of compactness, and what about coalitional games? Mechanism design, in particular auctions –Behavioral Mechanism design Beyond GT: Algorithmic Institutional Design

4 Stanford, April 2007BAGT Symposium4 A game with a trivial, unique NE HeadsTails Heads 1,-1-1,1 Tails -1,11,-1 RockPaperScissors Rock 0,0-1,11,-1 Paper 1,-10,0-1,1 Scissors -1,11,-10,0 Matching PenniesRochambeau (Rock-Paper-Scissors)

5 Stanford, April 2007BAGT Symposium5 A game with a trivial, unique NE HeadsTails Heads 1,-1-1,1 Tails -1,11,-1 RockPaperScissors Rock 0,0-1,11,-1 Paper 1,-10,0-1,1 Scissors -1,11,-10,0 Matching PenniesRochambeau (Rock-Paper-Scissors) (www.worldrps.com)

6 Stanford, April 2007BAGT Symposium6 A game with a trivial, unique NE HeadsTails Heads 1,-1-1,1 Tails -1,11,-1 RockPaperScissors Rock 0,0-1,11,-1 Paper 1,-10,0-1,1 Scissors -1,11,-10,0 Matching PenniesRochambeau (Rock-Paper-Scissors) (www.worldrps.com) Lesson: Nash equilibrium not necessarily instructive

7 Stanford, April 2007BAGT Symposium7 Some Intuition about Learning LeftRight Up 1,03,2 Down 2,14,0 Stackelberg Game

8 Stanford, April 2007BAGT Symposium8 Some Intuition about Learning LeftRight Up 1,03,2 Down 2,14,0 Stackelberg Game Lesson: cant separate learning from teaching

9 Stanford, April 2007BAGT Symposium9 The typical GT work on MAL Define a certain learning procedure (or dynamics) –fictitious play –rational learning –no-regret learning Prove conditions under which it converges in the limit –to NE, Correlated NE, etc –either in actual strategy or in empirical frequency –And almost always in self play

10 Stanford, April 2007BAGT Symposium10 Five Distinct Research Agendas in MAL Computation: Quick-and-dirty method for (e.g.) NE Social science: How people (institutions, animals…) learn. Game theory puritanism: Equilibria of learning strategies. Distributed control: Learning in common-payoff games. Targeted learning: Learning when you have some sense of how your opponents might behave.

11 Stanford, April 2007BAGT Symposium11 Lesson: Need to take NE with a grain of salt Beautiful, clever Makes it hard to back off from assumptions of perfect rationality; can we have an alternative, constructive game theory? In any event, best response computation merits as much attention as eqm

12 Stanford, April 2007BAGT Symposium12 Talk Outline Computing solution concepts, primarily NE –The role of NE unclear Multi-agent learning –Ditto Compact games (graphical games, MAIDs, game networks, local-effect games, social networks, …) –Other forms of compactness, and what about coalitional games? Mechanism design, in particular auctions –Behavioral Mechanism design Beyond GT: Algorithmic Institutional Design

13 Stanford, April 2007BAGT Symposium13 On compact representations Compact representations are fine; need more –Programming constructs in strategy descriptions (programmatic rationality) –Partial games (e.g., logic-based game description) What about coalitional games?

14 Stanford, April 2007BAGT Symposium14 Marginal Contribution Nets Games represented by sets of rules pattern value { a & b & c } 5 Value of a group S equals the sum of the values of the rules S satisfies v(S) = r : S satisfies r} v(r) Focus on conjunction & negation in pattern

15 Stanford, April 2007BAGT Symposium15 Conciseness of MC-Nets Theorem MC-Nets generalize the multi-issue representation of [CS04] Theorem MC-Nets generalize the graphical representation of [DP94]

16 Stanford, April 2007BAGT Symposium16 Computational Leverage Shapley value can be efficiently computed in MC-nets –Exploiting Additivity and Symmetry Determining membership in core is hard, but one can determine membership in time exponential in treewidth –Determining emptiness, or finding an arbitrary member of a non-empty core, are no harder

17 Stanford, April 2007BAGT Symposium17 Talk Outline Computing solution concepts, primarily NE –The role of NE unclear Multi-agent learning –Ditto Compact games (graphical games, MAIDs, game networks, local-effect games, social networks, …) –Other forms of compactness, and what about coalitional games? Mechanism design, in particular auctions –Behavioral Mechanism design Beyond GT: Algorithmic Institutional Design

18 Stanford, April 2007BAGT Symposium18 Recall some results from auction theory Informal observations –Dutch = First-price, sealed bid –English Second-price, sealed bid (cf. proxy bidding) –Japanese English –Second-price and Japanese have dominant strategies For precise analyses, need to distinguish between –Common values and independent values (winners curse) –Risk averse, risk-neutral and risk-seeking bidders Formal results speak to: –Whether an auction is incentive compatible –Whether the auction is efficient –Whether the auction is revenue maximizing

19 Stanford, April 2007BAGT Symposium19 Example of BMD: Online marketing The X5 story What are we optimizing for? Behavioral requirements (BMD) (ack: Moshe Tennenholtz) –# sign-ups –# return visits (magic number: 5) –Message injection –Product education –Truthful consumer surveys Yields a new perspective on existing mechanisms Suggests new mechanisms

20 Stanford, April 2007BAGT Symposium20 Some new truths about auctions, from the perspective of marketing First-price sealed-bid auction Dutch auction Second-price sealed-bid auction English auction Dominant-strategy mechanisms can be suboptimal Barter- and multiple-currency markets might trump markets with universal currency

21 Stanford, April 2007BAGT Symposium21 Some new, marketing-oriented mechanisms Tournament auction –Infinitely many equilibria Average-price auction –Giving the little guy a chance Team bidding –Cooperation Community auction –Coopetition Online collectibles –The marketing advantages of barter systems Preference auction –Win-win for the auctioneer and buyers

22 Stanford, April 2007BAGT Symposium22 Tournament auction A series of sealed-bid auctions; X% make it to the next day; person with highest remaining points wins.

23 Stanford, April 2007BAGT Symposium23 Tournament auction Other activities added to basic tournament auction

24 Stanford, April 2007BAGT Symposium24 Inserting a population game into an auction Capturing information about consumers and their views of others; the latter is particularly truthful.

25 Stanford, April 2007BAGT Symposium25 Average Price Game The consumer who bids closest to the average of all bids wins the prize.

26 Stanford, April 2007BAGT Symposium26 Team Bidding Bidders form teams and pool their bids.

27 Stanford, April 2007BAGT Symposium27 … Cariocas Community Auction A global bid triggers the close of multiple auctions. Community Auction

28 Stanford, April 2007BAGT Symposium28 Online collectibles Online collection of digital objects, initially assembled by various online activities.

29 Stanford, April 2007BAGT Symposium29 Online collectibles … and then exchanged via online barter

30 Stanford, April 2007BAGT Symposium30 Main takeaways Marketing considerations completely change the rules of the game. Some lessons of BMD: –new design criteria –new perspectives on existing mechanisms –new mechanisms Many applications beyond marketing. Example: Captchas, ESP A lot more work is needed before this becomes a science

31 Stanford, April 2007BAGT Symposium31 Talk Outline Computing solution concepts, primarily NE –The role of NE unclear Multi-agent learning –Ditto Compact games (graphical games, MAIDs, game networks, local-effect games, social networks, …) –Other forms of compactness, and what about coalitional games? Mechanism design, in particular auctions –Behavioral Mechanism design Beyond GT: Algorithmic Institutional Design

32 Stanford, April 2007BAGT Symposium32 Algorithmic Institutional Design (ack: Mike Munie) What is better: The EE or CS qual structure at Stanford? Similar for job interviews, admissions, consumer surveys, etc Reminiscent of, but distinct from, the secretary problem The answer: Depends on what youre optimizing for. And even given that, depends.

33 Stanford, April 2007BAGT Symposium33 Formal Model, continued

34 Stanford, April 2007BAGT Symposium34 Results Multiple versions –Single prof? –Single student? –Parallel or sequential? Sample results –Even in simplest case, selecting an optimal set of questions is NP- Hard, and is not submodular, so there is a not an obvious approximation algorithm –Sequentiality can be maximally helpful –In the multiagent setting, even deciding between committee structures is NP-Hard –*Seems* like there are well behaved special cases

35 Stanford, April 2007BAGT Symposium35 Talk Outline Computing solution concepts, primarily NE –The role of NE unclear Multi-agent learning –Ditto Compact games (graphical games, MAIDs, game networks, local-effect games, social networks, …) –Other forms of compactness, and what about coalitional games? Mechanism design, in particular auctions –Behavioral Mechanism design Beyond GT: Algorithmic Institutional Design

36 Stanford, April 2007BAGT Symposium36 thank you!


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