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Optimal Clearing of Supply/Demand Curves Ankur Jain, Irfan Sheriff, Shashidhar Mysore {ankurj, isheriff, shashimc}@cs.ucsb.edu Computer Science Department UC Santa Barbara T. Sandholm and S. Suri. Optimal clearing of supply/demand curves. In AAAI-02 workshop on Agent-Based Technologies for B2B Electronic Commerce, Edmonton, Canada, 2002. Market Clearing Preliminaries Supply Curve Suppose seller has Q identical units to sell. Supply curve denotes the supply at price p as s(p). Upward sloping curve. Quantity s(p). p Price Market Clearing Preliminaries… Demand Curves Suppose there are N buyers – each with a demand curve . Demand at price p is d(p). Downward sloping curves. Quantity d(p) p Price Auctions, reverse auctions and exchanges Forward Auctions – One Seller, Multiple buyers. Reverse Auction – One buyer, Multiple sellers. Exchanges – Multiple buyers, Multiple sellers. Variations Piecewise linear vs. Linear curves. Non-Discriminatory (uniform) vs. Discriminatory pricing. Main Results Market type Curve type Computational complexity Non discriminatory markets Piecewise Linear O( nk log(nk) ) Discriminatory markets Linear Discriminatory markets Piecewise Linear NP-Complete O( n logn ) Objective To study optimal clearing of supply/demand curves with multiple indistinguishable units such that the auctioneer’s profit is maximized. Market clearing algorithms This project involves the implementation of the following algorithms : Non discriminatory (ND) auctions. ND reverse auctions. ND exchanges. Discriminatory reverse auctions. Discriminatory auctions. ND Auctions Quantity Uniform clearing price, One seller - multiple buyers. n curves, with maximum k linear pieces each. Feasibility Σsi(p*ask) = Σdj(p*bid). Goal is to maximize p*bid(Σdj(p*bid)) – p*ask(Σsi(p*ask)) d2 d1 s1 Price P* bid Steps • Compute Aggregate curve. • For each linear piece solve for maximum revenue auction. • Suppose p*bid is the unit price with maximum revenue. • Clear each buyer at p*bid i.e., d(p*bid) units. Quantity ND Auctions Profit Price O(nk log( nk )) Strategy – • Sort nk breakpoints O(nklog(nk)) • Aggregate – Linesweep O(nk) • Envelope – Linesweep O(nk) • Decompose into K trapezoids, find maximum revenue O(K) • Clear each buyer at p*bid ND Reverse auction Quantity Uniform clearing price, One buyer multiple sellers - n curves, with maximum k pieces each Maximize third party (who runs the market) profit Profit Price o(nk log( nk )) Strategy – • Sort nk breakpoints O(nk log(nk)) • Aggregate – Linesweep O(nk) • Envelope – Linesweep O(nk) • Decompose into K trapezoids, find maximum revenue O(K) • Clear each seller at p*ask ND Exchanges Quantity Maximize third party (who runs the market) profit Feasibility – Σsi (p*ask) = Σdj(p*bid) Goal is to maximize p*bid(Σdj(p*bid)) – p*ask(Σsi(p*ask)) Profit Price O(nk log( nk )) Strategy – • Sort nk breakpoints O(nk log(nk)) • Aggregate – Linesweep O(nk) • Envelope – Linesweep O(nk) • Decompose into K trapezoids, find maximum revenue O(K) • Clear each seller at p*ask, buyer at p*bid Discriminatory Reverse Auction Non uniform clearing price, multiple sellers - One buyer - wants to buy Q units Minimize total cost for the buyer Clearing Problem Sellers have upward sloping supply curve, Minimize q ai pi bi &, ai 0, bi 0 ( pi qi ) s. t. q ai pi bi & qi Q Using Lagrangian Multipliers 2Q bi ai bi pi 2ai 2 bi ai qi 2 2 Discriminatory Reverse Auction … Quantity Strategy – • Arrange sellers by their minimum feasible price (bi/ai) – O(nlogn) • Incrementally add sellers and check for feasibility and minimum cost constraint O(1) • Suppose minimum total cost occurs with Si sellers, solve for clearing price and quantity for each seller. Q Price Discriminatory Auction Non uniform clearing price, One seller – Multiple buyers (has Q units) Maximize total revenue for the seller Each buyer is represented by a downward sloping demand curve, Maximize ( pi qi ) s. t. q ai pi bi & qi Q Unconstrained solution – Sell exactly ½ bi units to buyer i. If Q < ½ Σbi then Using Lagrangian Multipliers bi 2Q p bi i 2ai 2 ai bi ai qi 2 2 Discriminatory Auction … Quantity Strategy – • Initialize (pi,qi) = bi/2ai, bi/2) • If Σ(qi) <=Q, done • Choose l with min pi (say pl), increase each bid’s unit price by pl, if feasible, compute lagrangian and output each buyer’s quantity • Otherwise, remove buyer l from the market and repeat above steps Q Price Re-cap of Main Results Market type Curve type Computational complexity Non discriminatory markets Piecewise Linear O( nk log(nk) ) Discriminatory markets Linear Discriminatory markets Piecewise Linear NP-Complete O( n logn ) Demo …