Reducing Costly Information Acquisition in Auctions Kate Larson, University of Waterloo

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Reducing Costly Information Acquisition in
AuctionsKate Larson, University of Waterloo

• Presented by David Thompson,
• University of British Columbia
• July 10, 2006

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Overview

• Deliberative Agents
• Auctions and Deliberative Bidders
• Optimal Search
• Larsons Auction
• Results

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Deliberative Agents

• Can deliberate (to gain information) as well as
  bidding like a normal agent

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Deliberative Agents Properties

• R Resources dedicated to deliberation on each
  possible problem
• cost function mapping resource allocations to
  cost in utility
• A Algorithms provide solutions to problems
• PP Performance profiles describe how
  allocating resources to an algorithm affect the
  quality of solution it returns

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Deliberative Agents Anytime Algorithms

• All algorithms are assumed to have the anytime
  property (similar to local search)
• Can be stopped at anytime (or work with any
  amount of resources)
• Always return a solution
• Increasing time/resources always produces a
  weakly better solution

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Auctions and Deliberative Bidders

• Agents pay deliberation costs
• Strategy space is expanded to include
  deliberation actions (equilibria in this space
  deliberation equilibria)
• Agents may want to deliberate about each others
  valuations (strategic deliberation)

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Auctions Desirable Properties

• Deliberation-proof agents have no incentive to
  strategically deliberate
• Non-misleading agents have no incentive to act
  inconsistently with their valuation
• Preference-formation independence auction
  doesnt depend on cost functions, algorithms or
  performance profiles
• This combination is impossible (result from a
  previous paper), drop preference-formation
  independence

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Optimal Search

• An abstract problem from Operations Research
• n boxes, each with contents of different values
• fi(v), distribution over value of box i
• costi, cost of opening box i
• Agent gets to keep 1 box (after exploring)

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Optimal Search Solution

• Assign each box a cutoff value Ki, where agent is
  indifferent to opening box i
• Selection Rule open box with highest cut-off
  value
• Stopping Rule stop when the maximum observed
  reward is greater than cutoff of all unopened
  boxes

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Larsons Auction

• Using knowledge of agents algorithms and
  performance profiles, calculate cutoffs for each
  agent and order them
• At stage t, the first t bidders participate in a
  2nd price auction with a reserve price
• Reserve prices are set to produce a
  non-misleading Bayes-Nash equilibrium (acting as
  a proxy for bidders t1..n)

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Larsons Auction Properties

• Non-misleading by reserve-price design
• Deliberation-proof
• Agents have no incentive to deliberate before
  they can bid
• Earlier agents have already demonstrated
  unexpectedly low valuations (by not buying)
• On expectation, later agents wont affect the
  outcome (the auction will close)

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Experimental Results Efficiency(Uniform Costs)
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Experimental Results Efficiency(Informative
Costs)
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Experimental Results Cost of Deliberation vs.
2nd Price Auction (Uniform Costs)
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Experimental Results Cost of Deliberation vs.
2nd Price Auction (Informative Costs)
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Thank You.