1. Methodology of Exchange Design John Ledyard and Preston McAfee Caltech and Yahoo!

2. Introduction There is a large literature on the design of selling mechanisms. – Builds on theory, experiment, and other practical tests. – Has led to a practical methodology for the choice of selling methods Little has been written on practical exchange design. Exchange examples – Flow: a group of sellers sells a continuing sequence of differentiated goods to a group of buyers – Stock: A group of traders is continuously rebalancing asset positions 9/3/2009Cornell University2

3. Introduction (continued) An exchange maps expressed preferences into allocations and $ – It is a mechanism, which may be iterative and reactive – The process of expressing preferences and the mapping into allocations is exchange design Different from auction design – Competition between multiple sellers – Goals: efficiency not revenue, exchange profits – Auction methods (e.g. ascending prices) may not be applicable – Replacing brokers – network externalities A single seller can replace own brokers with auction. – New issue – who to charge? 9/3/2009Cornell University3

4. Introduction (continued) Not much literature – Theory VCG Myerson-Satterthwaite, Gresik-Satterthwaite, General equilibrium – Experiment and Practice One-sided lessons – We are putting together a bibliography – Please send references Today: - A rough “state of the art” commentary 9/3/2009Cornell University4

5. Summary (In case I don’t get to the end.) Expressively Easy – Design a language for expressing desired trades that accommodates important distinctions. – Understanding what participants actually value is critical to a successful design. Strategically Simple – Design trading algorithms so that a straight-forward strategy performs reasonably well. – Permitting iterative adjustment of bids can simplify strategies but should be binding. Functionally Fair – The exchange design should not be tilted towards one type of participant. – Exchange and traders must keep commitments. 9/3/2009Cornell University5

6. A running example – RECLAIM The Cap – maximal pollution levels by year The Permits – (year, cycle, zone) – Years: (initial) 1994-2010 – Cycle 1 – Jan to Dec, Cycle 2 – July to June – Zone 1 – inland, Zone 2 – coastal – Declining aggregate amount, total 50%. – Example: To cover pollution in Feb 2008 an inland firm can use either (2008, 1, 1), (2007, 2,1), (2008, 1, 2), or (2007, 2,2) A trader’s problem is to decide whether to buy and sell permits or to install abatement equipment covering 20 years, one needs to negotiate over quantities and prices of 80 different permits. 9/3/2009Cornell University6

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9. Converging slowly when thin 9/3/2009Cornell University9

10. (N=40) A little faster when much thicker 9/3/2009Cornell University10

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13. (N=12) What a CVM can do to a thin market! 9/3/2009Cornell University13

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18. A real application - bonds Allowed more order types – Downward sloping demand (diminishing MU) – Upward sloping demand (quantity discounts) – ORs of ANDS, ANDs of ORs, etc. Size and difficulty of the real problem – 200,000 variables, 300,000 constraints 2,000 bonds 50,000 bids (many contingencies allowed = {0,1}) – Relaxed algorithm (LP) took 20 minutes – Needed a solution in 7 minutes – Could get 85% of best known bound 90% of the time 9/3/2009Cornell University18

19. Expressively Easy Intentionally design a language for expressing trades that accommodates important distinctions. Understanding what participants actually value is critical to a successful design. – The exchange is replacing brokers who “know their clients” Different but similar products can be treated as identical to simplify – Issue: exogenous or endogenous? Different buyers can have different interfaces and bid formats – Spot buyers vs. impression buyers – Portfolio balancers vs. single issue speculator 9/3/2009Cornell University19

20. Strategically Simple Design trading algorithm so that a straight-forward strategy performs reasonably well – Dominant strategy is simple but may cost in efficiency VCG vs McAfee vs Uniform Price Call – Algorithmic complexity can make sensible participation difficult and should be minimized Generalized Uniform Price Call Market works very well with single-minded traders. – Open question: what if they are not single-minded? Conjecture from BFL: still ok. If prices depend primarily on the marginal traders then most have incentive to “honestly” report willingness to pay and accept. – Pay what you bid is not a particularly good approach. – Prices can be set in a relatively coarse manner without significant efficiency loss Permitting iteration of bids simplifies but bids should be binding Information – Generally want individual bid information not available – Do want aggregate information, like prices, available – With combinatorics, fitting in is important so providing individual information can be valuable. Endogenous sunshine seems to work here. 9/3/2009Cornell University20

21. Functionally Fair Exchange neutrality – Exchange design should not be tilted towards one type of participant. – Example: Max stated surplus and not sellers surplus Commitments – It is crucial that commitments be filled. Traders: Deliver promised assets and cash. – Can enforce with escrow, etc. Exchange: Stick to the stated rules. – Bad examples: Enron, ACE, ….. Balanced “revenue model” – Modest levels of revenue can be raised with a straight percentage charge (and can be incorporated in pricing information). – Large revenue should be collected with value-add pricing to cause less damage to efficiency. 9/3/2009Cornell University21

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