pushing the envelope: new research topics at the interface of cs and econ/gt

Pushing the Envelope:new research topics at the interface of cs and econ/gt

Yoav Shoham

Stanford University

(many debts are due)

Stanford, April 2007 BAGT Symposium 2

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


Talk Outline


– The role of NE unclear


– Ditto


– Other forms of compactness, and what about coalitional games?


– Behavioral Mechanism design

• Beyond GT: Algorithmic Institutional Design


A game with a trivial, unique NE

Heads Tails

Heads 1,-1 -1,1

Tails -1,1 1,-1

Rock Paper Scissors

Rock 0,0 -1,1 1,-1

Paper 1,-1 0,0 -1,1

Scissors -1,1 1,-1 0,0

Matching Pennies Rochambeau (Rock-Paper-Scissors)



Heads Tails

Heads 1,-1 -1,1

Tails -1,1 1,-1

Rock Paper Scissors

Rock 0,0 -1,1 1,-1

Paper 1,-1 0,0 -1,1

Scissors -1,1 1,-1 0,0


(www.worldrps.com)



Heads Tails

Heads 1,-1 -1,1

Tails -1,1 1,-1

Rock Paper Scissors

Rock 0,0 -1,1 1,-1

Paper 1,-1 0,0 -1,1

Scissors -1,1 1,-1 0,0


(www.worldrps.com)Lesson: Nash equilibrium not necessarily instructive


Some Intuition about Learning

Left Right

Up 1,0 3,2

Down 2,1 4,0

Stackelberg Game


Some Intuition about Learning

Left Right

Up 1,0 3,2

Down 2,1 4,0

Stackelberg Game

Lesson: can’t separate learning from teaching


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.


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


Talk Outline




– Ditto







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?


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


Conciseness of MC-Nets

Theorem MC-Nets generalize the multi-issue representation of [CS04]

Theorem MC-Nets generalize the graphical representation of [DP94]


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


Talk Outline




– Ditto







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 (winner’s 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”


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


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


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


Tournament auction

A series of sealed-bid auctions; X% make it to the next day; person with highest remaining points wins.


Tournament auction

Other activities added to basic tournament auction


Inserting a population game into an auction

Capturing information about consumers and their views of others; the latter is particularly truthful.


Average Price Game

The consumer who bids closest to the average of all bids wins the

prize.


Team Bidding

Bidders form teams and pool their bids.


… Cariocas’ Community Auction

A “global bid” triggers the close

of multiple auctions.

Community Auction


Online collectibles

Online collection of digital objects, initially assembled by various

online activities.


Online collectibles

… and then exchanged via online

barter


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


Talk Outline




– Ditto







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 you’re optimizing for. And even given that, depends.


Formal Model, continued


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


Talk Outline




– Ditto







thank you!

pushing the envelope: new research topics at the interface of cs and econ/gt

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