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Agent Negotiation via Auctions

Agent Negotiation via Auctions. Tracy Mullen IST, Penn State tmullen@ist.psu.edu. Outline. Market/Negotiation overview Computational Market Systems Blue Skies/Mobile economy University of Michigan Digital Library (UMDL) Information Economy Auction Manager middleware

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Agent Negotiation via Auctions

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  1. Agent Negotiation via Auctions Tracy Mullen IST, Penn State tmullen@ist.psu.edu

  2. Outline • Market/Negotiation overview • Computational Market Systems • Blue Skies/Mobile economy • University of Michigan Digital Library (UMDL) Information Economy • Auction Manager middleware • Buyer/Seller’s choice bundles • Market policies • Case Study: Bandwidth Exchanges • Future Directions

  3. Why markets? • Ronald Coase: The Nature of the Firm (1937) • Alternative modes organizing transactions: • Markets: decentralized, price signals • Firms: hierarchies • Why do we have any firms? Why don’t we have just one mega-firm?

  4. Why markets? • Ronald Coase: The Nature of the Firm (1937) • Alternative modes organizing transactions: • Markets: decentralized, price signals • Firms: hierarchies • Why do we have any firms? Why don’t we have just one mega-firm? • TRANSACTION COSTS • Other factors related to transaction costs: price discovery costs, information asymmetries, uncertainty, limits of 3rd party enforcement

  5. One View of Commerce Fundamentals Step 1 What do I want? Where is it? Step 2 How much is it? What should I bid? Step 3 What should I pay? How should I pay? Discover Negotiate Exchange Infrastructure

  6. More Details • Discover: • Advertisements • Junk mail/coupons • Catalogs • Browse/Shop • Consumer Reports • Negotiate: • Price tag • Barter • Auction • Stock Market • Exchange: • Payment type: • $$/check/credit card • Delivery options • Follow up care Business Models/Social & Legal Environment Interconnection Medium

  7. Physical => Internet • Spatial restrictions of current physical markets often no longer apply. • Participants no longer have to be spatially co-located. • Lower transaction costs lead to new marketplaces: E-trade, eBay, Priceline.com, DemandLine.com. • Computational power/real-time communication lead to: • lower information manipulation costs, lower transaction costs. • automated search and negotiation tools. • Standardized commodities & customized products => mass customization: • information/digital products: personalized newspapers, online flexible subscription models. • non-digital products: bundling of travel packages.

  8. Negotiation configurations Buyer Seller Buyer Seller Buyer Seller Buyer Seller Auction Buyer Seller Buyer Seller

  9. Some configuration issues • Scale up • # of agents, # of messages • vs. auction bottlenecks • Distributed auction approach:Ygge, Power Load Management, ICMAS 96 • Security • Buyer/seller trust • Andersson, Sandholm, Leveled Commitment Contracts, AAAI 1998 • Trusting the auctioneer • Franklin, Reiter, The Design and Implementation of a Secure Auction Server, IEEE Trans. on Info Theory, 96

  10. Negotiation Mechanisms: Auctions • What is an auction? • Set of rules for determining price and/or allocation • Enforces a protocol • McAfee & McMillian, Auctions and Bidding, JEL 87 • Auction framework provides structured, yet flexible market infrastructure which promotes automated negotiation: • Mediated vs. Unmediated • Buyers do not have to separately find & contact every seller • Price vs. Barter • Price minimizes communication between agents • Formal vs. Informal • Standardized offers simplify communication between agents

  11. Why Mediation? • Manages communication, information • Encapsulates negotiation rules • Source of constraint, structure • Enforcement • Not an agent: No discretion! agent agent agent Mediator agent agent

  12. Parametrized Auction Specification

  13. Internet Bandwidth • Smart markets in network bandwidth: Varian, MacKie-Mason • Message packet includes a willingness-to-pay/bid • Network interface admits packets in descending order of their bids, until congestion bound reached • All packets priced at congestion cost -- the amount that highest denied service packet bid (Vickrey Auction) • There is no reason for Messages not to honestly bid their willingness-to-pay: incentive compatible, efficient

  14. Second Price (Vickrey) Auction Case 0: You value the item at $3 and you bid $3 $3 $2.75 $3.25 $2.5 $3 B. Item is sold for $3 to bidder who values it more than you do A. You get item the at $2.75

  15. $3 $2 But... • Does this mean you should really bid your true willingness-to-pay? Let’s suppose: • You value the item at $2, but you bid $3 • You value the item at $3, but you bid $2 • What happens?

  16. Second Price (Vickrey) Auction Case 1: You value the item at $2, but you bid $3 $3 $2.5 $2 You might get item at $2.5, more than it’s worth to you

  17. $3 $2.5 $2 Second Price (Vickrey) Auction Case 2: You value the item at $3, but you bid $2 You might lose item to competitor for $2, when you could have had it for $2.5

  18. Mechanism Design • Market Allocation Mechanism: a communication process whereby dispersed knowledge is coordinated and used to determine a collective resource allocation. • An allocation mechanism is defined by: • the interaction protocol (e.g. set of allowable messages and protocol) • what kinds of bids/offers can agents make • what kinds of information about price quotes, other bidders, etc. can agent’s request. • the rules that define the allocation outcome • when does the final allocation get decided • what is the price/quantity allocation based on the current set of bids

  19. Mechanism Properties • Incentive Compatible • No agent has anything to gain by departing from the mechanism interaction rules. • Pareto Optimality • No other allocation can make an agent better off without making at least one other agent worse off. • Privacy Preserving • Don’t need to have agents send entire set of preferences and budgets to central planner. • Individual Rationality • Agents benefit by participating • Information Viability • Messages can’t be huge.

  20. Combinatorial Auctions • Combinatorial auctions • FCC plan to use a combinatorial auction for their 3G spectrum auction in 2002 • Combinatorial auctions are useful when there are synergies involved buying different items. • For FCC: spectrum/BW packages, say across different geographical regions, MHZ, QoS, etc. • Ex: Suppose you have 3 goods: Northeast spectrum (NE), Middle Atlantic spectrum (MA), Southeast spectrum (SE) • Possible bundles are NE, MA, SE, [NE, MA], [NE,SE], [MA, SE], [NE,MA,SE]

  21. A Few Risks/Concerns • Possibility for strategic maneuvers • Inherent “threshold” or “free rider” problems • Company A values NE license at $50, Company B values MA license at $50, Company C values {NE, MA} package at $90. Company A and B can jointly outbid Company C, however one may end up paying more than its fair share. E.g., Company A bids $50, while Company B bids $40. Company B is a “free rider”. • Defaulting/withdrawing bids • A single bid default in package bidding can affect the award of many other licenses and be used strategically. • In spectrum auction: FCC Commission recommends stringent default penalties. • Added auction bidding complexity • E.g., FCC restricted set of possible spectrum packages • Reduces complexity of both bidding and determining auction winners. • Also creates some (debated) concern over whether the limited choice favors firms with certain kinds of business plans.

  22. Related Research Questions • Auction Model • How robust are these auctions against collusion/strategic manipulation among bidders? • What information do bidders need to provide to the auction? • How can we either minimize the information requirements and/or simplify them? • Are there ways to simplify the number of combinations offered, based on networking knowledge and/or buyer/seller’s choice type constructs? • How can computational efficiency be increased? • Buyer/seller behavior and strategies • What kinds of strategies are required for buyers/sellers to participate successfully? How complicated are these strategies? Can this be done using agents? • How much information do the participants need to gather? • Should there be auction stopping and pacing rules?

  23. Computational Market Systems • In a computational economy, markets coordinate the activities of individual agents each acting in their own self-interest. • Individual user preferences regarding goods and services, as well as their quality and cost, are summarized and communicated via price. • A computational market system is the: • Set of interaction protocols • Infrastructure services • System policies that implement a computational economy. Distributed Resource Allocation Problem E-Commerce

  24. Site 1 Internet Router 0 Site 2 Resource Allocation: Blue-Skies Economy Should a mirror site be established on the LAN? Consumer @site 1 Internet LAN Delivery(I,1) Carrier(0,1) Transport(I,1) Carrier(I,0) Network Resources Blue-Skies Transport(I,2) Carrier(0,2) Consumer @site 2 Delivery(I,2)

  25. Steps in Designing an Economy • Define Goods • Goods define the problem search space • Homogeneity vs. preserving important differences • Define Producers • Maximize profits under given technology • Carrier: Quadratic cost technology • E.g., x = y^2 + y + 1 (111 units input -> 10 units output) • Transport: arbitrageur y = min(x1, x2) • Define Consumers • Maximize utility subject to budget constraint • Pre-existing utility or model via econ framework • Endowments  material balance constraints • Utility func params  found via competitive equil conditions • Price = marginal cost • Marginal utility ratio = price ratio: MU1/MU2 = p1/p2 • Solve optimization problem via markets

  26. UMDL Overview • Provide library services in distributed network environment • Information agents buy and sell information services • Requirements: support dynamic, open, large-scale system • Distributed agent architecture, commerce framework Collection Interface Agents User Interface Agents Mediators Information Sources Information Consumers

  27. Information Goods and Services • Bundling: subscription, per-issue, per-article • Timeliness: pre-publication, immediately, delayed • Terms: redistribute, read-only • To Whom: individual, library, group Problem: Goods and services can vary across many dimensions in ways not determined at design time. Magazine Dimensions: Approach: Flexible supporting infrastructure based on Ontologies and Auctions.

  28. Auction Auction Auction UMDL Commerce Infrastructure Query Seller 1. How do I describe what I want to sell? 2. Where do I go to sell it? 3. Match me with a buyer at a price 4. Transact for service Service Classifier Auction Manager 2. Where do I go to buy it? 1. How do I describe what I want to buy? 3. Match me with a seller at a price User

  29. Market Management Services • Market Matching • Automate finding service markets for agents • Notification of new markets of interest to agents • Arbitrage between related markets for liquidity and to keep prices in line • Complex goods: selectable bundles • Market Policy • Market creation and selection issues • Implemented via rules or incentives • Data Collection and Information Dissemination • Data can be used to measure system welfare, assess auction charging policy • Post market information for agents

  30. Market Policies • Support and uphold established business practices • Libraries, publishing, financial, business • Endogeneous market creation/adaption policies  market structure • Account for system externalities such as market creation costs • Infrastructure costs • Agent decision complexity costs

  31. Market Creation Policy • Agents decide • Internalize costs to system via auction fees • Auction Manager recommends • Supply defaults based on market policy, service characteristics, current market configuration • Testbed for evaluating different policies for market creation • General question: are there system policies that result in better economic performance?

  32. Computational Market System Summary • Demonstrate use of economic analytic tools for system design • Design and implementation of generic negotiation framework in UMDL • Identify and implement several market management services • Market middleware: Auction Manager • Managing the scope of markets: policies, matching, arbitrage • Formal representation for describing service selection options/rules.

  33. Internet Auctions and Agents • How do we design economic mechanisms and agents to operate in an Internet economy? • What happens when humans and computational agents participate in the same mechanism? • Can we design auctions that level the playing field between humans and agents? (Do we want to?) • Design of auction to be “transparent” • Provide mediators to reduce information gathering requirements • ?? • Policy rules for deploying/adapting mechanism • What kinds of tradeoffs between computational efficiency, economic efficiency, profit maximization, fairness are necessary

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