Policy disputes arise at all scales of governance: in clubs, non-profits, firms, nations, and alliances of nations. Both the means and ends of policy are disputed. While many, perhaps most, policy disputes arise from conflicting ends, important disputes also arise from differing beliefs on how to achieve shared ends. In fact, according to many experts in economics and development, governments often choose policies that are “inefficient” in the sense that most everyone could expect to gain from other feasible policies. Many (...) other kinds of experts also see existing policies as often clearly inferior to known alternatives. (shrink)

Since market scoring rules have become popular as a form of market maker, it seems worth reviewing just what such mechanisms are intended to do. The main function performed by most market makers is to serve as an intermediary between people who prefer to trade at different times. Traders who have the same favorite times to trade can show up together to an ordinary continuous double auction, and then make and accept offers to trade. But when traders have different favorite (...) times, a market maker can help them by first making offers that some of them will accept, and then later making opposite offers which others will accept. By adjusting prices in his favor, a market maker can even profit from providing this service. By making offers, however, a market maker opens himself up to the risk of losing to informed traders who know more than he about asset values. It is a complex and difficult task to choose the price and duration of offers in order to profit the most from intermediary trades while suffering the least from informed trades. This task requires subtle judgments about the relative fraction of informed and intermediary trades at different times, prices, quantities, and trading histories. No simple algorithm could reasonably claim to do this task optimally. Very active markets have little need for market makers, as anyone can trade at anytime. In markets with large but sporadic trades, a human will likely find it profitable to apply their considerable intelligence to the complex task of market making. The question is what to do for smaller less-active markets, which cannot afford such human attention. Trading may simply not happen there if no intermediary can be found to make such markets. A computer program with less than human intelligence that attempts to make markets runs the risk of being out-smarted by human traders. Humans might even figure out how to turn that program into a money pump, giving up cash each time it is run through some cycle of trades.. (shrink)

We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. (...) Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting is not a WPBE. In the infinite-stage model with unconditionally independent signals, there does not exist an equilibrium in which all information is revealed in a finite amount of time. We propose a simple discounted market scoring rule that reduces the opportunity for bluffing strategies. We show that in any WPBE for.. (shrink)

While a simple information market lets one trade on the probability of each value of a single variable, a full combinatorial information market lets one trade on any combination of values of a set of variables, including any conditional or joint probability. In laboratory experiments, we compare the accuracy of simple markets, two kinds of combinatorial markets, a call market and a market maker, isolated individuals who report to a scoring rule, and two ways to combine those individual reports into (...) a group prediction. We consider two environments with asymmetric information on sparsely correlated binary variables, one with three subjects and three variables, and the other with six subjects and eight variables (and so 256 states). (shrink)

People have long noticed that speculative markets, though created for other purposes, also do a great job of aggregating relevant information. In fact, it is hard to find information not embodied by such market prices. This is, in part, because anyone who finds such neglected information can profit by trading on it, thereby reducing the neglect.1 So far, speculative markets have done well in every known head-to-head field comparison with other forecasting institutions. Orange juice futures improved on National Weather Service (...) forecasts,2 horse race markets beat horse race experts,3 Oscar markets beat columnist forecasts,4 gas-demand markets beat gas-demand experts,5 stock markets beat the official NASA panel at fingering the guilty company in the Challenger accident,6 election markets beat national opinion polls,7 and corporate sales markets beat official corporate forecasts.8 Recently, some have considered creating new markets specifically to take. (shrink)