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Modeling an Agent-Oriented E-Commerce Business Model Based on the NYSE Specialist System

This paper by Kenneth Griggs and presented by Alexander Sverdov explores an innovative agent-oriented business model for e-commerce inspired by the NYSE specialist system. It outlines various auction types, including English, Dutch, and continuous double auctions, and examines the role of specialists in managing stock trading. The proposal introduces an agent architecture consisting of four agent types—trading, principal, notification, and representation agents—designed to replicate the functions of NYSE specialists, ensuring efficient market operations and interactions without direct human intervention.

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Modeling an Agent-Oriented E-Commerce Business Model Based on the NYSE Specialist System

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Presentation Transcript

  1. Review of: An Agent Oriented Business Model for E-Commerse based on the NYSE Specialist system, paper by Kenneth Griggs Presented by: Alexander Sverdov

  2. The Paper Outline • Description of different auctions. • Description of NYSE Specialist role. • Proposal of an agent architecture that serves the same purpose as NYSE Specialists.

  3. English Auction • Reserve Price (or bid floor) below which no bids are accepted. • Auctioneer guides the bidding process upwards until a final highest bid is accepted.

  4. Dutch Auction • Reverse of an English Auction. • Auctioneer states a high starting price. • The Auctioneer proceeds to reduce the price by a fixed amount until a bid is made. • The first bid is taken.

  5. Continuous Double Auction • Multiple buyers and sellers compete in the market. • Bids and Asks are cleared continuously.

  6. Specialist in CDA at NYSE • Specialists are employees of independent specialist firms. • Manage trading of a certain stock. • Governed by stock exchange rules.

  7. Illustration

  8. Specialists • Play the role of Market Makers. • Always willing to buy and sell at a “fair” price (even if they don’t own shares). • Ensure orderly market; control the rising/lowering of stock prices---by possibly buying/selling against the market trends (by using their own capital to cushion sharp price changes). • Can act as regular traders.

  9. Roles of Specialists • Agents: buy/sell shares on customer’s request (act as floor broker). • Auctioneer: Provide a market for a security. Always be ready to buy/sell. • Catalyst: The specialist keeps track of all known interest in the stock, and alerts interested parties. • Principal: Buy and sell stock for their own account (with some rules).

  10. Specialist Book • Maintain all transactions. • All market and limit orders. • Provides a unique view of the market.

  11. Intelligent Software Agents • Autonomy: agents can operate without direct intervention by humans or others. • Social ability: agents can interact with other agents and/or humans. • Reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it.

  12. Intelligent Software Agents, Cont. • Pro-activeness: agents can exhibit goal-directed behavior by taking the initiative. • Mobility: agents can move to other environments. • Temporal continuity: agents are continuously running processes.

  13. Proposed Architecture

  14. Architecture Details

  15. More Detailed Details

  16. Four Agent Types • Trading Agent • Principal Agent • Notification Agent • Representation Agent

  17. Trading Agent • Invokes trading rules • Matches orders • Maintains Bid/Ask spread • Records transactions to the tape • Updates Inventory • Notifies other agents when required • Records direct buyer-seller trades

  18. Principal Agent • Performs an analysis using data from trade repository. • Invokes principal behavior rules. • Requests trades from the trading agent.

  19. Notification Agent • Catalyst function. • Notifies possible buyers and sellers of market conditions • Maintains and updates the notification DB and the tape. • Requests trades from the trading agent. • Notifies the representation agent when applicable.

  20. Representation Agent • Interacts with represented buyers and sellers. • Requests trades from trading agent. • Negotiates commissions • Updates trade repository

  21. Database, Knowledge, etc. • The system maintains information about all participants, and all the relevant stock data. • Trading rules are handled using an expert system. • Principal behavior is determined by neural network or some heuristic system.

  22. Things to be addressed • Consistent agent & market semantics. • A deeper understanding of specialist knowledge and functions (process model) • Agent development tools. • A high level agent scripting language. • An analysis of knowledge representation techniques to be used by the agents (rule-based, Expert System shell, neural net, genetic algorithm, etc.)

  23. Conclusion • The paper provides a first attempt at modeling the NYSE specialist role using an agent based system. • The paper does not describe an implementation, but rather a possible design for such a system.

  24. The End.

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