1. Manipulators Increase Information Market Accuracy Robin Hanson and Ryan Oprea George Mason University

2. PAM Concerns Terrorists themselves could drive up the market for an event they are planning and profit from an attack, or even make false bets to mislead intelligence authorities. U.S. Senators Wyden and Dorgan, Press Release, July 28, 2003. Would-be assassins and terrorists could easily use disinformation and clever trading strategies to profit from their planned misdeeds while distracting attention from their real target. Steven Pearlstein, Washington Post, July 30, 2003. Trading... could be subject to manipulation, particularly if the market has few participants – providing a false sense of security or... alarm.... the lack of intellectual foundation or a firm grasp of economic principles - or the pursuit of other agendas - has led to a proposal that almost seems a mockery of itself. Joseph Stiglitz, Los Angeles Times, July 31, 2003.

3. Manipulation Fears (PAM) Bad guys gain $ by giving info, changing acts –More plausible if bet on specific details, thick market PAM not on specifics, max gain/trade < $100 –A good deal for us if give few $, gain much info –Terror & corporate sabotage now effect big markets Bad guys lose $ to obscure market info –If slow adjust to track record, worst case is no info $1M PAM worth it if 0.1% chance gain 0.1% of $400B/yr –We see little effect in lab, field experiments –If small, “noise traders” attract others, net add info

4. Market Microstructure Theory Example – Kyle ’85 –X - Informed trader(s) – risk averse –Y - Noise trader – fool or liquidity pref –Market makers – no info, deep pockets If many compete, set Price = E[value|x+y] Info markets – use risk-neutral limit –If Y larger, X larger to compensate, and more info gathered, so more accuracy! –Trading on any consideration other than asset value is noise trading!

5. A Graphical Model of Bias Private Info on Asset Value Private Info on Desire to Bias Expected value No desire to bias Lower than expected Higher than expected Want lower priceWant higher price Joint distribution of private info

6. Simple Bias Equilibrium Private Info on Asset Value Private Info on Desire to Bias No net sale Buy 2 Buy 1 Buy 3 Sell 1 Sell 2 Assume net orders monotone in asset info + bias desire × price effect Then this is how price should vary with net orders Counter-balanced by anti-bias example An example of successful bias

7. The Game Time Traders pick Traders see Manipulator sees Traders, Manipulator pick Market Maker sees Market Maker picks All get payoffs EffortBiasCluesQuantityTotalPrice

8. Normality Gives Linearity

9. Theorems

10. Ave Harm Not = Ave Error? Imagine manipulator raised accuracy when it matters, lowered otherwise Example: estimate of chance of terror attack, but not size of attack Fix: Make market price more linear in harm are concerned about

11. Conclusion Standard models include noise trades Accuracy improves with more noise Manipulators are noise traders Worry if ave harm not = ave error?

12. Price Manipulation Model ManipulatorMarket maker Informed trader Equilibrium Noise trader