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  1. A Unified Framework for Dynamic Pari-mutuel Information Market Yinyu Ye Stanford University Joint work with Agrawal (CS), Delage (EE), Peters (MS&E), and Wang (MS&E)

  2. Outline • Information Market • Pari-mutuel Information Market • LP Pari-mutuel Mechanism • Dynamic Pari-mutuel Mechanism • Sequential Convex Pari-mutuel Mechanism • Desired Properties of SCPM and New Mechanism Design • Extensions to General Trading Market

  3. What is Information Market • A place where information is aggregatedvia market for the primary purpose of forecasting events. • Why: • Wisdom of the Crowds: Under the right conditions groups can be remarkably intelligent and possibly smarter than the smartest person. James Surowiecki • Efficient Market Hypothesis: financial markets are “informationally efficient”, prices reflect all known information

  4. Sport Betting Market • Market for Betting the World Cup Winner • Assume 5 teams have a chance to win the World Cup: Argentina, Brazil, Italy, Germany and France

  5. Options for the Market • Double Auction: Let participants trade directly with one another • Requires participants to find someone to take the other side of their order (i.e.: the complement of the set of teams which they have selected) • Appropriate method for markets with small number of states and large number of participants • Centralized Market Maker • Introduce a market maker who will accept or reject orders that he receives from market participants • Market organizer may be exposed to some risk • This approach works better in thinly traded markets • Greater liquidity can be induced by allowing multi-lateral order matching • Lower transaction costs (no search costs for the participants) • Problem: How should the market organizer fill orders in such a manner that he is not exposed to any financial risk?

  6. Central Organization of the Market • Belief-based • Central organizer will determine prices for each state based on his beliefs of their likelihood • This is similar to the manner in which fixed odds bookmakers operate in the betting world • Generally not self-funding • Pari-mutuel • A self-funding technique popular in horseracing betting.

  7. Horse 1 Horse 2 Horse 3 Bets Total Amount Bet = $6 Outcome: Horse 2 wins Winners earn $2 per bet plus stake back: Winners have stake returned then divide the winnings among themselves Pari-mutuel Market Model I • Definition • Etymology: French pari-mutuel, literally, mutual stakeA system of betting on races whereby the winners divide the total amount bet in proportion to the sums they have wageredindividually (after deducting management expenses). • Example: Parimutuel Horseracing Betting

  8. World Cup Betting Market • Market for World Cup Winner • We’d like to have a standard payout of $1 per share if a participant has a winning order. • Combinatorial Orders

  9. Pari-mutuel Market Model II • Combinatorial Betting Language in the Market • N possible states of the world (one will be realized) • n participants who, traderk, submit orders to a market maker containing the following information: • ai,k - state bid (either 1 or 0) • πk– bid price per share • lk– limit on share quantity • Market maker will determine the following: • xk– order fill/# of awarded shares • pi – state price/beliefs/probabilities • Call or dynamic auction mechanism is used. • If an order is accepted and correct state is realized, market maker will pay the winning order a fixed amount $1 per share.

  10. Call Auction Mechanisms Dynamic Market Makers 2002 – Bossaerts, Fine, and Ledyard Issues with double auctions that can lead to thinly traded markets Call auction mechanism can solve this problem 2003 – Fortnow, Killian, Pennock and Wellman Solution technique for the call auction mechanism 2005 – Lange and Economides Non-convex call auction formulation with unique state prices 2005 – Peters, So and Y Convex programming of call auction with unique state prices 2003 – Hanson Combinatorial information market design 2004, 2006 – Pennock, Chen, and Dooley Dynamic Pari-mutuel market 2007 – Chen and Pennock Cost function based market 2007 – Peters, So and Y Dynamic market-maker implementation of call auction mechanism Research Evolution

  11. LP Pari-mutuel Market Mechanism Boosaerts et al. [2001], Lange and Economides [2001], Fortnow et al. [2003], Yang and Ng [2003], Peters et al. [2005], etc An LP pricing mechanism for the call auction market

  12. World Cup Betting Results Orders Filled State Prices

  13. Other Issues • How to make state prices unique • How to create initial funding to the market • How to incorporate the market maker’s own belief into the market Non-convex formulation with unique state prices/beliefs by Lange and Economides [2005]

  14. Expected extra profit Worst-case Profit Monetary profit retained by market maker on state i Market maker’s belief on state i Belief-Based and Risk Neutral This mechanism has a fixed price i for all i

  15. Expected extra profit Worst-case Profit Belief-Based and Risk Averse b is a combination weight factor in [0 1]

  16. Expected total objective Monetary profit retained by market maker Market maker’s belief on state i Convex Pari-mutuel Market Mechanism Peters et al. [2005], etc Theorem(Peters et al. 2005) Convex programming of call auction has unique state prices p(b) that are identical to those of the non-convex formulation of Lange and Economides (2005).

  17. Utility-Maximization Interpretation Let the concave and increasing utility function be ln(.), and i be the market maker’s probability belief on state i. Then, the objective of the market maker is the worst case profit combined with an expected utility value of the contingent state realization. Here, b is a positive combination weight factor:

  18. Dynamic Pari-mutuel Market Model • Traders come one by one with order (a, , l) • Market maker has to make an order-fill decision as soon as an order arrives • may need to accept bets that do not have a matching bet yet. • Market maker still hopes • to pay the winners almost completely from the stakes of losers • to update state prices reflect the traders' aggregated belief on outcome states

  19. Desirable Properties of Mechanisms • Efficient computation for price update • Truthfulness (in myopic sense) • Bidding true value of a bet should be dominant strategy for each trader (if he or she is a one-time trader) • Properness • A dominant strategy for traders is to place bets on outcome states so that resulting price reflects his or her true belief • stronger condition than truthfulness • Bounded worst-case loss • Net amount the market maker may have to pay the winners at the end • Risk attitude of the market-maker • Market organizer takes certain risk when accepting bets that are not matched by the current bets in the system • The risk attitude of market maker determines the dynamics of market • extreme risk averseness implies that no bet will ever get accepted.

  20. Background: Existing Mechanisms I • Market Scoring Rule • Traders report their beliefs/prices, p, on outcome states directly • Payment is determined by a scoring rule, si( p ), on reported price vector p in the probability simplex S={ p  0: ∑ pi=1 } For some positive constant b: Logarithmic Market Scoring Rule (LMSR)Hanson [2003] Quadratic Market Scoring Rule (QMSR)

  21. Market Scoring Rule Suppose constant b=0.1 and you bet the distribution p=(0.2, 0.3, 0.2, 0.25, 0.05) on the five teams. Then, if Brazil wins, your reward for each share under (LMSR) is 0.1ln(.3) + 1 = .87

  22. Background: Existing Mechanisms II • Cost-Function Based Scoring Rule(Chen and Pennock 2007) • Trader submits an order quantity characterized by the vector v, where vi representsthe number of shares that the trader desires over state i • The total fee charge to the trader where C( q ) is a cost function of the current outstanding share quantity vector q. • Instantaneous price vector would be ∇C( q ) reflecting aggregated beliefs/probabilities.

  23. Background: Existing Mechanisms III Theorem(Chen and Pennock 2007) Every scoring rule admits a cost-function representation, C(q), where • LMSR: • QMSR: Note that the quadratic cost scoring rule cannot guarantee the price/probability vector nonnegative

  24. Sequential Convex Pari-mutuel Mechanism(Peters et al. 2007) for an arrival order (a,, l ) where q is the current outstanding share quantity vector, e is the vector of all ones, and x it the order fill variable. Prices are the optimal Lagrange multipliers of the convex optimization problem Background: Existing Mechanisms IV

  25. Background: Existing Mechanisms V It turns out that one can use the KKT optimality conditions to create a quick update scheme to solve the SCPM model for an arrival order, instead of needing to solve the full convex program each time. Theorem(Peters et al. 2007) The SCPM problem can be solved in double-logarithmic time, that is, log log(1/ε) arithmetic operations. The computational complexity of the three described mechanisms are essentially identical.

  26. Questions • What are the common features and differences among these mechanisms? • Why some properties are satisfied or unsatisfied by a mechanism? • What type of cost-functions imply a valid scoring rule? • How to compare and rationalize different mechanisms • Is there new and better mechanism yet to be discovered?

  27. In this Work A unified framework is developed that • subsumes existing mechanisms • establishes necessary and sufficient conditions for satisfying certain desirable properties • provides a tool for designing new mechanisms with all desirable properties

  28. Generalized Sequential Convex Pari-mutuel Mechanism for an arrival order (a,, l ) where q is the current outstanding share quantity vector, e is the vector of all ones, x it the order fill variable, and u(s) is any (expected) concave and increasing value function of slack shares retained by the market maker. Unified Pari-mutuel Market Mechanism

  29. Market maker maximization principle: the unified framework is to balance market maker’s revenue from the arrival trader and (future) value Prices/beliefs are the optimal Lagrange multipliers of the convex optimization problem with maximizers (x*,s*,z*), and they are Prices in SCPM

  30. Every scoring rule or cost function mechanism is the SCPM corresponding to a specific concave and increasing value function. Conversely, every concave and increasing value function in SCPM induces a scoring rule or cost function mechanism and can be truthfully implemented. The properties of the value function and its derivatives, such as boundedness, smoothness, span, etc, determine other desired or undesired properties of the mechanism, such as the worst-case loss, properness, risk-attitude, etc. The Main Results

  31. LMSR: QMSR*: Log-SCPM: Value Functions of Existing Mechanisms

  32. Other Utilities • Linear-SCPM: • Min-SCPM: • Exp-SCPM:

  33. Truthfulness • Our unified framework is an affine maximizer of the form so that the general VCG scheme can be applied: let (x*,z*) be the maximizer of above, charge the trader by • Corollary (Agrawal et al. 2009)For fixed a and l, the one time trader will truthfully bet , his or her valuation of one share of a, in general SCPM.

  34. Efficient Implementation • The VCG scheme involves solving a convex optimization problem as mentioned earlier, it can be solved efficiently in double-logarithmic time.

  35. Properness I • Definition The scoring rule s(.) is properif the optimal strategy for a selfish trader is to report his or her private beliefr, that is, In the cost-function market model, C(.) is proper if The scoring rule is strictly proper if r is the only maximizer.

  36. Properness II • Proper Market Scoring Rule→SCPM Theorem(Agrawal et al. 2009)Any proper market scoring rule with cost function C( q ) can be formulated as SCPM with u( s ) = - C( - s ) • SCPM →Proper Market Scoring Rule Theorem(Agrawal et al. 2009)The SCPM gives a proper market scoring rule if ∇u(.)spans the simplex S; and a strictly proper rule if u(.) is smooth. The SCPM also gives an implicit cost function:

  37. Properness III • LMSR: Strictly proper • QMSR: Strictly proper • Log-SCPM: Strictly proper • Linear-SCPM: Not proper • Min-SCPM: Proper but not strictly • Exp-SCPM: Strictly proper

  38. DefinitionWorst-case net amount that market maker may have to pay the winners. For outstanding share quantitiesq, the traders have paid Thus, the worst case loss of the market maker is given by a convex optimization problem Bounds on Worst-Case Loss I

  39. LMSR: QMSR: Log-SCPM: unbounded Linear-SCPM: unbounded Min-SCPM: 0 (extreme risk averse) Exp-SCPM: Bounds on Worst-Case Loss II

  40. The return for market maker is random depending on the actual realization of states in question. Let c be the money collected so far and qi be the number of shares already sold on state i . Then, on accepting new order (a,, l ) with x shares, the return in statei is Theorem(Agrawal et al. 2009)The SCPM with concave and increasing value function is equivalent to choosing x in order to minimize a convex risk measure on random return z(i). Moreover, any convex risk measure can be used to construct an SCPM model with a corresponding concave value function. Controllable Risk Measure I

  41. The risk measure used is of form which considers the worst distribution p in terms of tradeoff between expected return and a penalty functionL(p). For many popular mechanisms, penalty functionL(p) represents divergence from a prior distribution, which presents an learning interpretation of various value functions used in SCPM. Controllable Risk Measure II

  42. LMSR: QMSR:has no controllable risk measure! This is due to the fact that the function is not monotone and it leads to negative prices Log-SCPM: Linear-SCPM: unbounded Min-SCPM: 0 Exp-ECPM: Controllable Risk Measure III

  43. Neither existing mechanism is “perfect”:LMSR has a unboundedworst-case loss if the number of states is large, and Log-SCPM is even worse. QMSRhas no controllable risk measure and it even leads to negative “probabilities”, while Min-SCPMis overly conservative. To illustrate this point, we consider an information market where the number of states is exponentially large. Design of New Mechanism I

  44. Realized Permutation Matrix Horses Ranks Horses Bid Matrix Ranks Horses Ranks Permutation Betting Market (Agrawal et al. 2008) Proportional Betting Market Reward = $3

  45. Design of New Mechanism II Is there a “perfect” market mechanism? The answer is “yes” and the design tool is the unified SCPM. Quad-SCPM:

  46. Desirable Properties of Quad-SCPM (Agrawal et al. 2009) • Efficient computation for price update: yes • Truthfulness (Myopic):yes • Properness:yes andstrictly • Bounded worst-case loss: identical to QMSR • Controllable risk measure of market-maker: yes

  47. General Trading Market: LP Mechanism bi: initial supply quantity of resource i; aik: demand rate of trader k on resource i; k: revenue per share from trade k; xk: decision variable of order fill for trader k.

  48. Sequential or Dynamic Trading Market • Traders come one by one; buy or sell, orcombination, with combinatorial bid(s) (A,, l) • Market maker has to make an order-fill decision as soon as an order arrives • Market maker still hopes • to maximize revenue or minimize regret • to enforce truthful bid prices  • to control “risk”

  49. Arrival multiple bids (A,, l) from a trader where q is the resource quantity committed/sold toearlier traders, x it the order fill variable vector for the new orders, andu(s)is any concave and increasing value function of slack resource quantity,s, retained by the market maker. Sequential Trading Market Mechanism

  50. Market maker maximization principle: to balance the immediate earning, x, and future revenue, u(s), with reserve prices p=∇u (b – q ). New (reserve) pricesare the optimal Lagrange multipliers of the convex optimization problem with maximizers (x*,s*), and they are Prices in Trading Market Mechanism