Price Prediction in a Trading Agent Competition

1. Price Predictionin a Trading Agent Competition

2. Introduction • TAC Travel-Shopping Game • Price Prediction • Approaches to Price Prediction • Evaluating Prediction Quality • Limitations

3. Introduction • The TAC-02 presented a challening market game in the domain of travel shopping. • Many market decision problems invole some anticipation of forecast. • We unaware of studies exploring the problem in a context reminiscent of multi-auction environments.

4. TAC Travel-Shopping Game 1 • Traders assemble flights,hotels,and entertainment into trips. • Clients are described by their preferred • Arrival and departure days (pa and pd). • The premium (hp) they are willing to pay to stay at the “Towers”(T) hotel rather than “Shanties”(S). • Three different types of entertainment

6. Flights • Consists of an inflight day I and outflight day j, 1≦I＜j≦5 • Flights in and out each day are sold independently • Price determined by a stochastic process • Initial price for each flight is ~U[250,400]

7. Hotels • There are 16 rooms available in each hotel each night and these are sold through ascending 16th-price auctions. • Each minute,starting at 4:00,one of the hotel auctions is selected at random to close,with the others remaining active and open for bids.

8. Entertainment • Agents receive an initial random allocation of entertainment tickets (indexed by type and day) • They may allocate to their own clients or sell to other agents through continuousdouble auctions.

9. TAC Travel-Shopping Game 2 A feasible client trip r is define by ◎an inflight day inr, outflight day outr ◎hotel type (Hrwhich is 1 if T and 0 if S) ◎entertainment surplus ψ(r)

10. Price Prediction 1 • Anticipating hotel prices is a key element in a severtal decisions facting a TAC agent. 1.Selectingtrip itineraries: Flight price;Hotel price 2.Biddingpolicy: The resulting clearing price

11. Price Prediction 2 • Divide price prediction into two phases: 1.Initial: Bidding policy; Trip choices 2.Interim: Revision of bids as the hotel auctions start to close.

12. Approaches to Price Prediction 1

13. Approaches to Price Prediction 2 • Historical Averaging • Machine Learning • Competitive Analysis

14. Historical Averaging 1 • Most agents took a relatively straightforward approach to initial price prediction .estimating the hotel clearing price according to observed historical average. • For example, harami calculates the mean hotel prices for the preceding 200 games, and uses this as its initial prediction. • Given a dataset, agents tend to use the sample meanor distribution itself as estimate,at least the baseline.

15. The approach taken by Southampton TAC 1.divided into“competitive”,”non-competitive”,and “semi-competitive” 2.Specified a reference price for each type and day of hotel in each game category. 3.Choose a category for any game based on its monitoring of recent game history.

16. Machine Learning 1 • Employed it derive relationships between observable parameters and resulting hotel prices. • Game-specific features provide potentially predictive information,enabling the agent to anticipate hotel price directions before they are manifest in price quotes themselves.

17. The approach taken by kavayaH • It uses neural networks trained via backpropagation. • It has a separate network for each hotel. • The inputs for each network are based on the initial flight price.

18. Competitive Analysis • To presume that they are well-approximated by a competitive economy. • It calculate the Walrasian competitive equilibrium of the TAC economy. • Taking into account the exogenously determined fight prices,Walverine finds a set of hotel prices that support such an equilibrium,and returns these values as its prediction for the hotel’s final prices.

19. Evaluating Prediction Quality 1 EUCLIDEAN Distance Lower values of d are preferred,and for any p,d(p,p)=0.

20. Evaluating Prediction Quality 2 Expected value of perfect prediction (EVPP)

22. Evaluating Prediction Quality 3

23. Limitations • First:we have focused exclusively on initial price prediction ignoring interim prediction task • Second:Can not be general • Third:Complete seperation is not possible in principle.