Aggregative Contingent Estimation (ACE) was a program of the Office of Incisive Analysis (OIA) at the Intelligence Advanced Research Projects Activity (IARPA). [1] [2] The program ran from June 2010 until June 2015. [3]
The broad program announcement for ACE was published on June 30, 2010. [4] ACE funded the Aggregative Contingent Estimation System (ACES) website and interface on July 15, 2011. [5] They funded The Good Judgment Project some time around July 2011. [6] ACE has been covered in The Washington Post'' [7] and Wired Magazine . [8] The program was concluded by late 2015. [9] The program manager was future IARPA director Jason Gaverick Matheny. [10]
The official website says that the goals of ACE are "to dramatically enhance the accuracy, precision, and timeliness of intelligence forecasts for a broad range of event types, through the development of advanced techniques that elicit, weight, and combine the judgments of many intelligence analysts." [1] The website claims that ACE seeks technical innovations in the following areas: [1]
There is a fair amount of research funded by grants made by the IARPA ACE program. [11]
The ACE has collaborated with partners who compete in its forecasting tournaments. Their most notable partner is The Good Judgment Project from Philip E. Tetlock et al. [12] (winner of a 2013 ACE tournament) [7] ACE also partnered with the ARA to create the Aggregative Contingent Estimation System (ACES). [5]
Data from ACE is fed into another program, called Forecasting Science and Technology (ForeST), which partners with SciCast from George Mason University. [13]
Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and resolution itself. Usage can vary between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.
Prediction markets, also known as betting markets, information markets, decision markets, idea futures or event derivatives, are open markets that enable the prediction of specific outcomes using financial incentives. They are exchange-traded markets established for trading bets in the outcome of various events. The market prices can indicate what the crowd thinks the probability of the event is. A typical prediction market contract is set up to trade between 0 and 100%. The most common form of a prediction market is a binary option market, which will expire at the price of 0 or 100%. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.
The Disruptive Technology Office (DTO) was a funding agency within the United States Intelligence Community. It was previously known as the Advanced Research and Development Activity (ARDA). In December 2007, DTO was folded into the newly created IARPA.
Homeland Security Advanced Research Projects Agency (HSARPA) is a part of the Science and Technology Directorate at the United States Department of Homeland Security. Much like DARPA in the Department of Defense, HSARPA is tasked with advanced projects to advance the technology needed to protect the US. Some of the chief beneficiaries of HSARPA are the Customs and Border Protection, and the Office of Intelligence and Analysis.
Jason Gaverick Matheny is a United States national security expert serving as president and CEO of the RAND Corporation since July 2022. He was previously a senior appointee in the Biden administration from March 2021 to June 2022. He served as deputy assistant to the president for technology and national security, deputy director for national security in the White House Office of Science and Technology Policy and coordinator for technology and national security at the White House National Security Council.
The Intelligence Advanced Research Projects Activity (IARPA) is an organization within the Office of the Director of National Intelligence responsible for leading research to overcome difficult challenges relevant to the United States Intelligence Community. IARPA characterizes its mission as follows: "To envision and lead high-risk, high-payoff research that delivers innovative technology for future overwhelming intelligence advantage."
The wisdom of the crowd is the collective opinion of a diverse and independent group of individuals rather than that of a single expert. This process, while not new to the Information Age, has been pushed into the mainstream spotlight by social information sites such as Quora, Reddit, Stack Exchange, Wikipedia, Yahoo! Answers, and other web resources which rely on collective human knowledge. An explanation for this phenomenon is that there is idiosyncratic noise associated with each individual judgment, and taking the average over a large number of responses will go some way toward canceling the effect of this noise.
In software development, effort estimation is the process of predicting the most realistic amount of effort required to develop or maintain software based on incomplete, uncertain and noisy input. Effort estimates may be used as input to project plans, iteration plans, budgets, investment analyses, pricing processes and bidding rounds.
Philip E. Tetlock is a Canadian-American political science writer, and is currently the Annenberg University Professor at the University of Pennsylvania, where he is cross-appointed at the Wharton School and the School of Arts and Sciences. He was elected a Member of the American Philosophical Society in 2019.
The Good Judgment Project (GJP) is an organization dedicated to "harnessing the wisdom of the crowd to forecast world events". It was co-created by Philip E. Tetlock, decision scientist Barbara Mellers, and Don Moore, all professors at the University of Pennsylvania.
Lyle H. Ungar is a machine learning researcher and professor of Computer and Information Science at the University of Pennsylvania. He is also affiliated with the psychology department at the university.
SciCast is a collaborative platform for science and technology forecasting created by George Mason University with the help of a grant from the Intelligence Advanced Research Projects Activity (IARPA) as part of its ForeST program. SciCast is currently on hiatus, after losing its main IARPA funding. It was expected to re-open in the fall of 2015 with the support of a major Science & Technology sponsor, but this had not occurred by January 2016.
Expert Judgment (EJ) denotes a wide variety of techniques ranging from a single undocumented opinion, through preference surveys, to formal elicitation with external validation of expert probability assessments. Recent books are . In the nuclear safety area, Rasmussen formalized EJ by documenting all steps in the expert elicitation process for scientific review. This made visible wide spreads in expert assessments and teed up questions regarding the validation and synthesis of expert judgments. The nuclear safety community later took onboard expert judgment techniques underpinned by external validation . Empirical validation is the hallmark of science, and forms the centerpiece of the classical model of probabilistic forecasting . A European Network coordinates workshops. Application areas include nuclear safety, investment banking, volcanology, public health, ecology, engineering, climate change and aeronautics/aerospace. For a survey of applications through 2006 see and give exhortatory overviews. A recent large scale implementation by the World Health Organization is described in . A long running application at the Montserrat Volcano Observatory is described in . The classical model scores expert performance in terms of statistical accuracy and informativeness . These terms should not be confused with “accuracy and precision”. Accuracy “is a description of systematic errors” while precision “is a description of random errors”. In the classical model statistical accuracy is measured as the p-value or probability with which one would falsely reject the hypotheses that an expert's probability assessments were statistically accurate. A low value means it is very unlikely that the discrepancy between an expert's probability statements and observed outcomes should arise by chance. Informativeness is measured as Shannon relative information with respect to an analyst-supplied background measure. Shannon relative information is used because it is scale invariant, tail insensitive, slow, and familiar. Parenthetically, measures with physical dimensions, such as the standard deviation, or the width of prediction intervals, raise serious problems, as a change of units would affect some variables but not others. The product of statistical accuracy and informativeness for each expert is their combined score. With an optimal choice of a statistical accuracy threshold beneath which experts are unweighted, the combined score is a long run “strictly proper scoring rule”: an expert achieves his long run maximal expected score by and only by stating his true beliefs. The classical model derives Performance Weighted (PW) combinations. These are compared with Equally Weighted (EW) combinations, and recently with Harmonically Weighted (HW) combinations, as well as with individual expert assessments.
The Smartly-assembled Wiki-style Argument Marshalling (SWARM) Project is a research project, looking at how human reasoning can be improved. The project is based at the University of Melbourne, and is developing and testing a cloud-based platform and methods aimed at fundamental advancing in reasoning performance through crowdsourcing. The SWARM Project is part of the Crowdsourcing Evidence, Argumentation, Thinking and Evaluation (CREATE) Program managed by the Intelligence Advanced Research Projects Activity.
The IARPA Babel program developed speech recognition technology for noisy telephone conversations. The main goal of the program was to improve the performance of keyword search on languages with very little transcribed data, i.e. low-resource languages. Data from 26 languages was collected with certain languages being held-out as "surprise" languages to test the ability of the teams to rapidly build a system for a new language.
Applied Research Associates Inc. (ARA), is an engineering, management, and public sector consulting firm and a research and development company headquartered in Albuquerque, New Mexico, founded in 1979. As of 2018, its revenue was estimated at between $100 and $750 million by The Washington Post. As of 2011, it had approximately 1,600 employees.
Don Andrew Moore is an author, academic, and professor. He is the Lorraine Tyson Mitchell Chair I of Leadership and Communication at UC Berkeley's Haas School of Business where he teaches classes on leadership, negotiation, and decision making.
A superforecaster is a person who makes forecasts that can be shown by statistical means to have been consistently more accurate than the general public or experts. Superforecasters sometimes use modern analytical and statistical methodologies to augment estimates of base rates of events; research finds that such forecasters are typically more accurate than experts in the field who do not use analytical and statistical techniques. The term "superforecaster" is a trademark of Good Judgment Inc.
Oil and gas reserves denote discovered quantities of crude oil and natural gas that can be profitably produced/recovered from an approved development. Oil and gas reserves tied to approved operational plans filed on the day of reserves reporting are also sensitive to fluctuating global market pricing. The remaining resource estimates are likely sub-commercial and may still be under appraisal with the potential to be technically recoverable once commercially established. Natural gas is frequently associated with oil directly and gas reserves are commonly quoted in barrels of oil equivalent (BOE). Consequently, both oil and gas reserves, as well as resource estimates, follow the same reporting guidelines, and are referred to collectively hereinafter as oil & gas.
Belief aggregation, also called risk aggregation,opinion aggregation or probabilistic opinion pooling, is a process in which different probability distributions, produced by different experts, are combined to yield a single probability distribution.
The ACE program manager is Jason Matheny