1. A Principled Study of Design Tradeoffs for Autonomous Trading Agents Ioannis A. Vetsikas Bart Selman Cornell University

2. 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})<V({a})+V({b}) e.g. having a Dell or a Gateway computer One formulation: Combinatorial Auctions

3. 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

4. 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

5. TAC url://www.sics.se/tac

6. 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

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

8. 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

9. 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

10. 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 agent’s 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

11. Bidding Strategies – Hotels (cont.) 1.Low aggressiveness : (boundary str.)  Bids higher than the current ask price by an increment 2.High aggressiveness : (boundary str.)  Bids for all rooms progressively closer to the marginal utility 3.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)

12. 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

13. Bidding – Plane Tickets (cont.) 1.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 2.Early Bidder: (boundary str.)  Buy all tickets at the beginning 3.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”

14. 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”

15. Experiment 1 A mildly aggressive agent usually performs better than agents with high or low aggressiveness

16. Experiment 2 The strategically bidding agents perform best overall

17. Experiment 3 The medium aggressiveness agent performs best overall However the difference is not always significant

18. 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

19. 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

20. TAC 2002 Final Scores #AgentScore 1WhiteBear3556 2Southampton3492 3Thalis3351 4UMBCTAC3321 5Walverine3316 6livingagents3310 7kavayaH3250 8cuhk3248 19 institutions in the preliminaries 16 in the semi-finals 8 in the finals White Bear was 1 st in the final

21. 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