Computational Mechanisms for Multi-Attribute Exchange Markets

1. Computational Mechanisms for Multi-Attribute Exchange Markets Eugene Fink Part I: Research interests and projects Part II: Automated exchange system

2. 1992–1998 Ph.D. Student 1999–2003 Assistant Professor Part I: Research Interests and Projects

3. Major Minor Research interests • Artificial intelligence • Machine learning • Algorithm theory • Computational geometry

4. Representation changes Medical expert systems Multi-attribute exchanges Approximate data matching Indexing of time series Meshing in three dimensions Generalized convexity Research projects • Artificial intelligence • Machine learning • Algorithm theory • Computational geometry

5. Automated simplificationof search problems. • Integrated library of search and learning algorithms • Top-level module that selects appropriate algorithms for each given problem • Automated problem reformulation, to im- prove efficiency of the selected algorithms top-level control reformulated problems search modules learning modules Representation changes Artificial intelligence and learning

6. Automated selection of cancerpatients for experimental treat-ment procedures. Joint work with the Moffitt Cancer Center. Medical expert systems Artificial intelligence • Guiding a nurse through the related questions • Identifying the appropriate medical tests • Reducing the cost of the selection process

7. Fast identification of approximatematches in massive structured data. Joint work with Carnegie MellonUniversity and the DYNAMiXTechnologies Corporation. Approximate data matching Artificial intelligence, learning, and algorithms • Indexing of alphanumeric data • Search for approximate matches • Application to homeland security

8. Indexing and retrieval oftime series by their majorminima and maxima. 3 1 1 3 Indexing of time series Artificial intelligence and algorithms 4 • Fast compression of time series • Hierarchical indexing of compressed series • Search for series similar to a given pattern

9. Representation of spatial objects bytriangle and quadrilateral meshes. Joint work with the SandiaNational Laboratories and USFCivil Engineering Department. Meshing in three dimensions Algorithms and computational geometry • Indexing in three dimensions • Search for optimal meshes • Repair of meshes with holes

10. Exploration of mathematical and computational properties ofrestricted-orientation convexity. • Definition of generalized convex sets in terms of their intersections with lines from some fixed set of orientations • Study of convex sets in higher dimensions Generalized convexity Computational geometry

11. Joint work with the DYNAMiXTechnologies Corporation. Part II: Automated Exchange System for Multi-Attribute Markets

12. Outline • Motivation • Main concepts • Architecture • Best-price matches • Quality functions • Performance • Conclusions

13. Combine the speed of the stock exchange with the flexibility of eBay + Motivation • Build an automated • exchange for trading • goods and services

14. Previous work • Combinatorial auctions • Standardized exchanges

15. Combinatorial auctions • Complex goods • Asymmetry between • buyers and sellers • Illiquid

16. Standardized exchanges • Simple goods • Symmetry between • buyers and sellers • Liquid

17. Research goals Build an automated exchangefor non-standardized goods. • Allow complex constraints in the specification of desirable trades • Support fast-paced trading for markets with millions of orders • Include optimization techniques to maximize traders’ satisfaction

18. Outline • Motivation • Main concepts • Architecture • Best-price matches • Quality functions • Performance • Conclusions

19. Main concepts • Market • Orders • Matches • Fills • Attributes

20. Market All items that can be traded form amarket. Example: All conceivable vehicles compose a car market.

21. Orders An order is asubsetof the market along with aprice function. Example: Any Mustang or Corvette ; Mustang for $28,000 or Corvette for $32,000 , –$1 for every ten miles.

22. Price Sell order Orders Price Buy order

23. Matching A buy order matches a sell order if: There is item buy-items  sell-items, such that buy-price(item) sell-price(item).

24. Sell order Price Matching Price Buy order

25. Sell order Fill Matching Price Buy order

26. : • fill-item buy-items  sell-items • fill-price buy-price(fill-item) • fill-price sell-price(fill-item) Fills A fill is defined by a specific item and its price , which must match the buy order and the sell order

27. Fill: Mustang $29,000 Fills Buy order: Sports car $30,000 Sell order: Mustang $28,000 or Corvette $32,000

28. Attributes A specific market is defined by a list of attributes. Example: A used-car market is a set of all possible vehicles, defined by model,year, and mileage.

29. An item specification may include lists of values and numeric ranges. Example: Sell order Model: Mustang Year: 2003 Mileage: 0 Buy order Model: Mustang Year: 2001..2003 Mileage: 0..10K Attributes A trader specifies acceptableitems by attribute values.

30. Attributes Sell order Model: Mustang Year: 2003 Price Year Buy order Model: Mustang Year: 2000..2003 03 02 01 00 Model Camry Mustang Corvette

31. Outline • Motivation • Main concepts • Architecture • Best-price matches • Quality functions • Performance • Conclusions

32. Traders enter orders through the interface ma-chines, which send the orders to the matcher. fills orders orders fills orders fills Architecture The system consists of a central matcher anduser interfaces that run on separate machines. Matcher User interface User interface User interface

33. Data structures The matcher includes a centralstructure for indexing of orders. • Tree of fully specified orders, which are called index orders • Unordered list of the other orders, which are called nonindex orders

34. Model Mustang Corvette Year Year 1996 1997 1985 1986 1988 Mileage Mileage Mileage Mileage Mileage 50K 1996 Mustang, 50K miles, $7,000 Indexing tree

35. Camry Year 1999 2000 1988 Mileage Mileage Mileage 10K 180K 20K 1999 Camry, 10K miles, $18,000 2000 Camry, 180K miles, $5,000 1988 Corvette, 20K miles, $4,000 Indexing tree Model Mustang Corvette Year Year 1996 1997 1986 Mileage Mileage Mileage 50K 1996 Mustang, 50K miles, $5,000

36. Process all new orders Re-match all old nonindex orders Trading cycle The matcher alternates between processingnew orders and identifying matches for old nonindex orders.

37. Process Processing For every new order: Search for matches in the indexing tree. If it is not filled, and it is an index order, then add it to the indexing tree. If it is not filled, and it is a nonindex order, then add it to the unordered list. Re-match

38. Process Re-matching For every old nonindex order: Search for matches in the indexing tree, among new index orders. If it is filled, then remove it from the unordered list. Re-match

39. Process Drawbacks Re-match • The system does not find matches between two nonindex orders • The system delays matches between a new index order and an old nonindex order

40. Outline • Motivation • Main concepts • Architecture • Best-price matches • Quality functions • Performance • Conclusions

41. Optimal matches The system searches for the best-price matches in the indexing tree. • Depth-first search • Best-first search

42. Model Mustang Camry Corvette Year Year Year 1996 1999 2000 1988 Mileage Mileage Mileage Mileage 50K 10K 180K 20K 1996 Mustang, 50K miles, $7,000 1999 Camry, 10K miles, $18,000 2000 Camry, 180K miles, $5,000 1988 Corvette, 20K miles, $4,000 Best price Depth-first search Buy Order:Any car madeafter 1990

43. Depth-first search Drawback: If there are many matching leaves, the search takes a long time. Solution: Limit the number of matches.

44. Model Mustang Camry Corvette Year Year Year 1996 1999 2000 1988 Mileage Mileage Mileage Mileage 50K 10K 180K 20K 1996 Mustang, 50K miles, $7,000 1999 Camry, 10K miles, $18,000 2000 Camry, 180K miles, $5,000 1988 Corvette, 20K miles, $4,000 Best price among found matches Depth-first search With limited number of matches Buy Order:Any car madeafter 1990 Limit: 2 matches Actual best price

45. Depth-first search With limited number of matches Drawback: The system may not find the best-price match. Solution: Apply best-first search • : • Store the best price for each subtree • Use these prices to guide the search

46. Model Mustang Camry Corvette $7,000 $4,000 $5,000 Year Year Year 1996 1999 2000 1988 $18,000 $7,000 $5,000 Mileage Mileage Mileage Mileage $4,000 50K 10K 180K 20K 1996 Mustang, 50K miles, $7,000 1999 Camry, 10K miles, $18,000 2000 Camry, 180K miles, $5,000 1988 Corvette, 20K miles, $4,000 Best prices for subtrees $4,000

47. Model $4,000 Mustang Camry Corvette $7,000 $4,000 $5,000 Year Year Year 1996 1999 2000 1988 $18,000 $7,000 $5,000 Mileage Mileage Mileage Mileage $4,000 50K 10K 180K 20K 1996 Mustang, 50K miles, $7,000 1999 Camry, 10K miles, $18,000 2000 Camry, 180K miles, $5,000 2000 Camry, 180K miles, $5,000 1988 Corvette, 20K miles, $4,000 Best price Search for the best price Buy Order:Any car madeafter 1990

48. Search for the best price • Advantages: • The system finds the best match • It is much faster than DFSwithoutalimit on the number of matches • Drawback: • It is usually slower than DFSwith a small limit on the number of matches

49. Outline • Motivation • Main concepts • Architecture • Best-price matches • Quality functions • Performance • Conclusions

50. Quality Price Attributes Buy order Quality functions A trader can specify a quality function that ranks the acceptable transactions. The transaction quality may depend on an item and its price.