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from Google Ai:

The purpose of the Policy Analysis Market (PAM)—a project under DARPA's FutureMAP (Futures Markets Applied to Prediction) program launched in 2001—was to use online prediction markets to aggregate dispersed expert knowledge and forecast geopolitical events, economic instability, and terrorism in the Middle East. [1, 2, 3]

Core Objectives

• Aggregate Information: Harness the wisdom of crowds and specialized experts to predict future geopolitical outcomes better than traditional, centralized intelligence analysis. [1, 2]
• Avoid Strategic Surprise: Create early-warning indicators for political shifts, military actions, and economic trends by incentivizing accurate forecasting through a futures-market pricing mechanism. [1, 2, 3]

Controversy and Cancellation

• Public Backlash: The project was canceled in 2003 after members of Congress discovered it and condemned it as a grotesque "federal betting parlor" that allowed individuals to profit from predicting assassinations and terrorist attacks. [1, 2, 3]
• Political Fallout: Despite defenders arguing that prediction markets are effective analytical tools, the intense political optics led the Pentagon to shut down the initiative. [1, 2]

If you would like, I can provide more details on:

• How prediction markets function as information-aggregating tools
• Later DARPA programs that revived similar forecasting or social science tracking concepts (like SCORE or NGS2)

later programs

Following the high-profile cancellation of FutureMAP in 2003, DARPA and its sister intelligence agency, IARPA, continued to seek ways to crowdsource, automate, and quantify predictions. To avoid public backlash, they pivoted away from overt geopolitical "betting parlors" and focused instead on data mining, AI algorithms, and evaluating the credibility of academic research. [1, 2, 3]

1. The Direct Successor: IARPA ACE and IC Prediction Market

Because the concept of prediction markets remained highly respected by intelligence professionals, the newly formed Intelligence Advanced Research Projects Activity (IARPA) officially picked up the torch. [1]

• IC Prediction Market (ICPM): Launched in 2009, this internal, secure prediction market allowed verified intelligence community analysts to trade on geopolitical outcomes, macroeconomic data, and foreign policy events without public optics. It operated successfully for over a decade before concluding in 2020. [1]
• Aggregating Crowdsourced Evaluation (ACE) / The Good Judgment Project: Running alongside the ICPM, this program aimed to improve the accuracy of probabilistic forecasts. It famously funded the research that discovered "Superforecasters"—ordinary individuals who could out-predict professional intelligence analysts by leveraging structured training and cognitive diversity.

2. DARPA SCORE (Systematizing Confidence in Open Research and Evidence)

Launched in 2019, DARPA’s SCORE program represents a highly sophisticated evolution of the original FutureMAP methodology. [1]

• The Goal: The Department of Defense relies heavily on social and behavioral sciences (SBS) to plan military deterrence and stability operations. However, academic research suffers from a massive "reproducibility crisis". [1, 2]
• The Mechanism: SCORE generated quantitative "confidence scores" to predict if an academic claim would hold up under independent replication. [1]
• The Result: SCORE paired human prediction markets with machine learning algorithms. The program confirmed that aggregated human forecasting markets remained the single most accurate method for predicting whether a scientific finding was true or reproducible. [1, 2]

3. DARPA NGS2 (Next Generation Social Science)

Instead of just asking humans to guess what will happen, the Next Generation Social Science (NGS2) program focused on the underlying mechanisms of human behavior. [1]

• The Goal: To build automated, web-based toolkits capable of modeling and predicting how collective identities form, fracture, or destabilize.
• The Focus: Moving prediction into the realm of controlled, large-scale digital experiments to find the "causal mechanisms" of social phenomena, rather than relying strictly on historical correlation