A Principled Study of Design Tradeoffs for Autonomous Trading Agents

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A Principled Study of Design Tradeoffs for
Autonomous Trading Agents

• Ioannis A. Vetsikas
• Bart Selman
• Cornell University

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Agents Preferences

• Bidders have preferences for bundles of items
• Complementarities
• Combination of goods is valued more than sum of
  values of individual goods V(a,b)V(a)V(b
  )
• e.g. having a VCR and a TV together
• Substitutability
• Combination of goods is valued less than sum of
  values of individual goods V(a,b)
  )
• e.g. having a Dell or a Gateway computer
• One formulation Combinatorial Auctions

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Bidding in Simultaneous Auctions

• Goods are traded independently
• Different rules for each auction (potentially)
• Main issue Participants need to speculate on
  behavior of other agents
• How aggressively does one bid, when and what for?
• Having a plan flexible enough to handle
  contingencies
• Best solution is relative to other players
  strategies

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Trading Agent Competition (TAC)

• General problem capturing several issues of
  bidding in simultaneous auctions
• Provides a universal testbed for researchers
• Travel agents
• Working on behalf of 8 customers each
• Arranging for a trip to Tampa
• round-trip flight tickets
• hotel accommodations
• entertainment tickets
• GOAL Maximize profit

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TAC
url//www.sics.se/tac
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White Bear General
Architecture

• Follows the SMPA architecture (loosely)
• While (not end of game)
• Get price quotes
• Calculate estimates statistics
• Planner (Formulate desired plan)
• Bidder (Bid to implement plan)
• Plan how many goods of each type it is
  desirable to allocate to each customer

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Decomposing the Problem
Agent
Optimizer / Planner
Partial Bidding Strategy 1
Partial Bidding Strategy 2
Partial Bidding Strategy k
Auction Type 1
Auction Type 2
Auction Type k
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Agent Components

• OPTIMIZER
• INPUT Price information from bidders
  (and client preferences from original game
  data)
• OUTPUT Quantities of each good to be bought
• METHOD Solve optimization problem
• BIDDERS (for each auction type)
• INPUT Quantities to be bought and
  pricing information from auctions
• OUTPUT Bid Price and Bid Placement Time
• METHOD Determine strategies and experiment to
  find best strategy profile

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Determining Partial Strategies

• Determine boundary strategies
• E.g. minimum and maximum price for the bid, if
  bid price is the issue
• Determine intermediate strategies
• By modifying boundary strategies
• By combining boundary strategies
• By using a strategy that constitutes an
  equilibrium for a simpler but similar game

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Bidding Strategies Hotels

• ISSUE Bid Price
• Dilemma
• If not aggressive, could get outbid and lose
  rooms needed
• will get outbid by other agents and lose utility
  for not implementing the plan and for unused
  resources
• If too aggressive, prices will skyrocket and the
  agents score will get hurt more than other
  agents scores
• All agents scores are hurt
• But this hurts the agent more, since rooms it
  desires will have an increased price

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Bidding Strategies Hotels (cont.)

• Low aggressiveness (boundary str.)
• Bids higher than the current ask price by an
  increment
• High aggressiveness (boundary str.)
• Bids for all rooms progressively closer to the
  marginal utility
• Medium aggressiveness (intermediate str.)
• Combines two previous strategies
• For critical rooms (rooms with high marginal
  utility) the bid is close to the marginal utility
• For all other rooms it bids an increment above
  the current price (the increment increases as
  time passes)

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Bidding Plane Tickets

• ISSUE Time of Bid Placement
• Dilemma
• To bid early in order to get the cheapest tickets
• Or to bid later in order not to limit its options
• Solution
• Bid for some of the tickets at the beginning
• Bids for the rest after some hotel room auctions
  have closed
• Strategies Which tickets are bought at the
  beginning

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Bidding Plane Tickets (cont.)

• Late Bidder (boundary str.)
• Buy at the beginning only tickets that are
  certain to be used
• Buying nothing at the beginning is a clearly
  inefficient strategy in this setting, so it is
  not used as a boundary strategy
• Early Bidder (boundary str.)
• Buy all tickets at the beginning
• Strategic Bidder (intermediate str.)
• Modifies Early Bidder boundary strategy
• Uses Strategic Demand Reduction Weber 97
• Buy all tickets at the beginning, except the ones
  that are highly likely not to be used

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Exploring Strategy Space

• Determine the best partial strategy for one
  particular auction type
• Keep all other partial strategies fixed
• Use a fixed number of agents using intermediate
  strategies
• Vary the mixture of agents using boundary
  strategies
• Explore strategy space systematically
• Use several experiments to evaluate the
  strategies for different auction types
• Use the best partial strategies found in the
  previous experiments as the strategies that are
  kept fixed in each experiment
• Stop when experiments converge

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Experiment 1
A mildly aggressive agent usually performs
better than agents with high or low aggressiveness
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Experiment 2
The strategically bidding agents perform best
overall
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Experiment 3
The medium aggressiveness agent performs best
overall However the difference is not
always significant
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Some Comments

• Overall the medium and high aggressiveness
  versions perform the best
• But the medium aggressiveness agent is more
  consistent in general
• Overall the strategic agent versions perform the
  best
• The early bidder is significantly better than the
  late bidder
• In general you win when you are going against
  the tide, i.e. being aggressive when most other
  agents are not

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White Bear General
Observations

• Planner is adaptive, versatile, fast and robust
• Agent uses both principled methods and approaches
  guided by the knowledge acquired by observing the
  behavior of the games and combines both
  seamlessly
• The agent used in TAC was the strategic agent
  with medium aggressiveness
• Agent White Bear always ranks in the top three
  agents in all the competition rounds of the
  Trading Agent Competition

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TAC 2002 Final Scores

• 19 institutions in the preliminaries
• 16 in the semi-finals
• 8 in the finals
• White Bear was 1st in the final

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Related Work

• Examined the behavior of agents bidding for N
  similar items in an Nth price auction to find
  Bayes-Nash equilibria for the bid prices
• Examined the effect that better price prediction
  has on the performance of the agent
• Using historical price information definitely
  improves performance
• More intelligent price prediction showed minimal
  improvement
• Examined ways to reduce the number of games per
  experiment needed in order to derive accurate
  conclusions