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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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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

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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

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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)

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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)

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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

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

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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

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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

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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.

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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

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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

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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?

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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

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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]

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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

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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

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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

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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

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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

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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

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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.

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

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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.

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

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

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

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

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

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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

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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

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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.

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Stanford, April 2007BAGT Symposium33 Formal Model, continued

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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

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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

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Stanford, April 2007BAGT Symposium36 thank you!

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