Robin Hanson invented a wonderful market maker well suited for use in prediction market applications with a long name: the *logarithmic market scoring rule market maker*, which I’ll abbreviate as LMSR. (In fact, Hanson invented an entire class of *market scoring rule market makers*, but the logarithmic variant seems the most useful.) Hanson’s two papers on the subject are excellent, but Hanson does not spend a lot of time explaining how LMSR functions as a market maker in the typical sense. Instead, Hanson mostly emphasizes a second, alternate way of thinking about his market maker, as a “sequential shared scoring rule”, which I will not try to explain here. Hanson prefers to describe trader behavior in terms of “changing the price” instead of “buying and selling shares”. In my opinion, most people who encounter LMSR for the first time don’t quite see how beautifully and naturally LSMR can be used as a market maker in a standard prediction market setting. In fact, I am embarrassed to admit that upon my own first reading of Hanson’s papers, I did not fully “get it”. It took my seeing LMSR implemented in practice, by Todd Proebsting at Microsoft Research for Microsoft’s internal prediction markets, to realize how elegantly LMSR can be used as a market maker in an otherwise typical prediction market. LMSR is now being used in several places, including an implementation at InklingMarkets with a wonderfully intuitive interface, the Washington Stock Exchange, BizPredict, and (reportedly) at YooNew. Net Exchange was one of the first to use LMSR, though they seem to favor Hanson’s “change the price” interface over the more widespread “buy and sell shares” interface. As Chris Masse is quick to point out, LMSR has achieved much more widespread use than my own competing invention, the dynamic parimutuel market maker, which so far is being used in only one place: our own Yahoo! Tech Buzz Game.

In this post I will try to explain how to implement LMSR in a way that I believe most people familiar with prediction markets will understand. This interpretation of LMSR is not new: it’s the way Proebsting thinks about LMSR and it’s implicit “between the lines” in Hanson’s papers. But I haven’t seen this interpretation of LMSR written up anywhere, so I’m hoping that others can benefit from this explanation. The following understanding of LMSR was developed over the past few months together with my colleague Yiling Chen.

Suppose there are two outcomes that traders can buy or sell shares of (bet on or against) such that one and only one of the two outcomes is guaranteed to eventually occur. For example, the two outcomes could be “a Democrat wins the 2008 US Presidential election” and “a Democrat does not win the 2008 US Presidential election”. Each share is worth exactly $1 if and only if the trader is correct. In other words, one share of “Democrat wins” pays $1 if, in 2008, a Democrat actually wins the election, and is worthless otherwise. The following description can be easily generalized to any number of (disjoint and exhaustive) outcomes, including the case of combinatorial markets, but for ease of exposition I’ll stick to the two-outcome case.

The market maker keeps track of how many shares have been purchased by traders in total so far for each outcome: that is, the number of *shares outstanding* for each outcome. Let q1 and q2 be the number (“quantity”) of shares outstanding for each of the two outcomes. The market maker also maintains a *cost function* C(q1,q2) which records how much money traders have collectively spent so far, and depends only on the number of shares outstanding, q1 and q2. For LMSR, the cost function is:

C = b * ln(e^{q1/b}+e^{q2/b})
where “ln” is the natural logarithm function, “e” is the constant e=2.718…, and “b” is a parameter that the market maker must choose. The parameter “b” controls the maximum possible amount of money the market maker can lose (which happens to be b*ln2 in the two-outcome case). The larger “b” is, the more money the market maker can lose. But a larger “b” also means the market has more liquidity or depth, meaning that traders can buy more shares at or near the current price without causing massive price swings.

Traders arrive one at a time and tell the market maker how many shares they want to buy or sell of each outcome. Traders say, for example, “I want to buy 13 shares of outcome 1 — how much will that cost?”, or “I want to sell 250 shares of outcome 2 — how much will you pay me?”. The market maker uses the cost function to answer these questions. The cost to buy 13 shares of outcome 1 is simply C(q1+13,q2) – C(q1,q2). The “cost” to sell 250 shares of outcome 2 is C(q1,q2-250) – C(q1,q2), which will be a negative number (negative cost), meaning that the seller receives money in return for the shares. In general, if a trader wants to buy or sell shares of either or both outcomes so as to change the number of shares outstanding from (q1,q2) to (q1*,q2*), then he or she must pay C(q1*,q2*) – C(q1,q2) dollars. If this amount is negative it means the trader receives money instead of paying money.

Here’s a simple example. Suppose b=100 and no one has purchased any shares yet, so q1=q2=0. A trader arrives who wants to buy 10 shares of outcome 1. The trader must pay:

C(10,0)-C(0,0) = 100 * ln(e^{10/100}+e^{0}) – 100 * ln(e^{0}+e^{0}) = $5.12
Now suppose that at some time later, the number of shares outstanding for outcome 1 is q1=50 and the number of shares outstanding of outcome 2 is q2=10. Now the same trader above returns to the market and wants to sell her 10 shares. The trader’s “payment” is:

C(40,10)-C(50,10) = 100 * ln(e^{40/100}+e^{10/100}) – 100 * ln(e^{50/100}+e^{10/100}) = -$5.87
This is a negative number so it means the trader receives $5.87. So in the end the trader made a round-trip profit of $0.75.

That’s it! Well, almost. If the market maker wants to quote a “current price”, he can. The current price for outcome 1 is:

price1 = e^{q1/b}/(e^{q1/b}+e^{q2/b})
and similarly for price2. But note that the current price only applies for buying a miniscule (infinitesimal, in fact) number of shares. As soon as a trader starts buying, the price immediately starts going up. In order to figure out the total cost for buying some number of shares, we should use the cost function C, not the price function. (If you remember your calculus: The total cost for buying k of shares of outcome 1 is the integral of the price function from q1 to q1+k. The price function (“price1”) is the derivative of the cost function C with respect to q1, and the cost function is the integral of the price function.)

Finally, although I won’t go into the details here, one can generalize the above so that the market maker can handle *limit orders*, for example an order to “buy up to 100 shares of outcome 1, each at price less than or equal to $0.80”. But if unfilled limit orders like this are allowed to persist, the market maker logic can get a little complicated.

As I mentioned, Hanson actually invented an entire class of market makers: he shows how to turn any proper scoring rule into a market maker. Yiling Chen and I have derived the cost and price functions corresponding to the quadratic scoring rule. It turns out, however, that the quadratic scoring rule market maker is not very interesting or useful in practice. I’ll save the details for another day. We’re also working on additional classes of market makers that do seem useful, results we hope to report on soon [update: see our paper “A utility framework for bounded-loss market makers”].