Jan 16, 2006Auctions in SCM1 Auction descriptions Decision-theoretic approach Collusive and...

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Jan 16, 2006 Auctions in SCM 1

Auctions in SCM

•Auction descriptions•Decision-theoretic approach•Collusive and no-collusive game-theoretic approach

Jan 16, 2006 Auctions in SCM 2

Trading Agent Competition: Supply Chain Management game

• 6 software agents compete to run profitable PC assembly business for 220 days– Bidding for components from 8 suppliers– Bidding for orders from 100’s of customers

(simultaneously)– Managing production & delivery planning

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Auction Descriptions

•Cheat-sheets

•Agent valuations

•Analogs

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Supplier auction Periodic-clear, multi-unit, bizzaro-

price auction

• Bid structure: component type, quantity, date and reserve price

• Information: 20-day summary, offer price, date & quantity

• Clearing rules: ...• The good: Offers of components of type t,

satisfying reserve price, satisfying one or both of date and quantity

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Supplier pricing

• i = days in advance• d = date• i*Cac

d = naïve estimate of supply• Cavl' = supply - demand (not counting RFQs from

agents with less reputation) up to date d+i• delta = 0.5

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Reputation

• Reputation is ordered : offered ratio.

• Includes a prior of 2000 ordered & offered + 100 per day.

• Renormalized on the range [0,0.9] for IMD & Pintel, [0,0.45] for all others, but limited to 1.

• For each RFQ, offered = max (smallest offer, ordered, 0.2*requested )

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Customer auctionSimultaneous/sequential, reverse,

single-unit, 1st-price auction• The good (fully disclosed): Right to sell 1-20 computers

of specified type for bid price, or be penalized 5-15% of the reserve price every day late to a maximum of 5 days (after which the order is cancelled)

• Bid structure: price, date and quantity• Clearing rules: Lowest price that satisfies date, quantity

& reserve price• Information: Whether or not you win, max and min

winning bid over auctions for that type of PC, 20 day summary

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20-day summary

• Suppliers– Total ordered/shipped for each class (eg,

CPU)– Mean production capacity for each class– Mean price for each component

• Customers– Total requested/ordered for each SKU– Mean price for each SKU

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Valuation and exposure definitions

• Super-additive valuation: The value of a set of goods is greater than the sum of the values of those goods

• Sub-additive valuation: The value of a set of goods is less than the sum of the values of those goods

• Exposure: The risk of winning some sub-optimal set of goods

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Exposure in SCM

• In isolation, every good has negative value:– Components cost money to store– Customers charge for missed shipments

• Super-additive: Only "matched sets" (components and orders) can turn profit

• Sub-additive: Too many matched sets will overwhelm production capacity and cause loss

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Analogs

• Compare supplier auctions and 2nd price– Payment independent of reserve price– Reserve price is a bound on payment– Probability of winning increases monotonically

with reserve price– Therefore, Dominant Strategy Truthful? (At

least for the reserve price)

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Decision theoretic approach to customers

• Customer side seems to come down to conditional distribution modeling– P ( winning | bid price , state)– State includes auction parameters and known

facts about the world (eg, recent prices, 20-day reports)

• Then bid to maximize valuation– Naïve P()=1 approach– Expectation approach

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Decision theoretic approach to suppliers

• Reserve price is DS Truthful?

• Large quantities bids can be risky to reputation

• Effect of local price fluctuations is exaggerated

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Non-Collusive Approaches

•Disrupting markets

•Disrupting agents

•Risk-attitudes

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Disrupting markets

• Increasing demand in supplier markets– Limited scope: doesn’t affect customer

reserve price (or late penalty) or storage costs

• High- or low-balling customer auctions (exploiting other agents exposure risks)

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Disrupting agents algorithms

• Crashing unreliable agents (making the impossible happen)

• Preventing convergence (adding noise to the available information)

• Exploiting simplistic models (oscillating strategies)

• What about between-game learning (human and machine)?

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Reconsidering reputation

• Agents can compute their own reputations exactly and can deliberately “manage” them

• Reputation has monotonic, but “relative” value (best can’t improve, worst can’t get worse)

• An opportunity for active learning?

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Externalities & Risk-attitudes

• Outcome space can be profitability or it can be the final (relative) rankings– Agent has negative externalities against

another’s benefit

• Utility from ranking– Implies actual value of money is a sum of step

functions– Observation of rankings will be very noisy

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Collusive Approaches

•FCC Spectrum Auction–Applications to TAC

•Set-your-hair-on-fire Collusion

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FCC spectrum auctionSimultaneous, single-good, English

auctions• The good: Exclusive rights to broadcast on

a given frequency range in a given US city

• Bid structure: “Real-value” jump bids are allowed

• Information rules: Bidders are not anonymous

• Clearing rules: Auctions all clear at once

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How to collude in the FCC auction

• Agents decide on a distribution "outside" of the mechanism

• Defection is punished by threat or retaliation bids on multiple goods held by the defector

• Communication through the identity of the bidder and possibly the timing or value of the bid (nothing else needed)

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Cost/benefit of collusion

• Cost: “Freedom” to bid on any auction you wish

• Benefit: “Protection” from the full costs of market competition

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Bad news: prisoner’s dilemma

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Outcome of collusion

• Robust: cartel was a small subset of actual bidders (6 out of 153)

• Profitable: member of the cartel paid significantly less ($2.50/person vs. $4.34/person) for more (476 out of 1479)

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Differences from SCM

• Auctions are sealed bid

• Winner and winning bid aren't announced

• Auctions close periodically

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Communication options for SCM

• Outside channels: "In poor taste"

• Supplier auctions: Difficult and expensive

• Customer auctions: The min-bid for a type of PC– Cost to send– Very limited bandwidth– Shared bandwidth

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Enforceability in SCM

• Defector-detection is difficult because of anonymity and sealed-bid– Probabilistic inference?

• Is enforcement necessary?– Not if the cartel all represent one entity– Multiple agents can advance and cartels

would benefit from advancing collectively

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Set-your-hair-on-fire collusion

• Ignoring cost, an agent can disrupt supplier and customer markets indefinitely

• Agents that can anticipate (or request) disruptions have a significant advantage

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What if Collusion is illegal?

• Set-their-hair-on-fire collusion!– Our martyr agent tries to help another team

instead

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Thanks!