Hypermind Prescience

Get reliable quantitative forecasts on geopolitics, economics, and public health

Forecasting can be tough, especially when there are too many variables for individual experts to consider, and too few structured data to train an AI.

But crowds of human forecasters excel at prediction complex problems.

Our technology has pioneered a way to gather and combine valuable information from a crowd of diverse forecasters and turn it into reliable insights so you can make informed decisions.

 

prescience forecast platform
Hypermind Prescience Platform

Future-proof your business by anticipating risks and opportunities

Smart decisions come from a clear vision of what is to come.
Precisely quantify what is often left to guesswork, and see further into the future.

  • How likely is drug A or B to be approved in clinical trials ?
  • How many cars will be sold in Q4 months ?
  • How many Covid-19 cases will we observe in 4 months ?
    (read about our infectious disease forecasting contest with John Hopkins)

Our forward thinking Prescience customers

IARPA logo
US Air Force logo
Johns Hopkins Center Health Security Hypermind
Logo Pfizer
logo EDF
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Prediction poll Prescience forecasting method

How it works: our prediction market and prediction poll

Prescience draws reliable predictions from a crowd of vetted forecasters by following a 4 step process :

Crowd forecasting pioneers since 2000

Our panel's accuracy on 500 000+ forecasts

Because reality aligns itself with our prediction market’s forecasts, Hypermind can reliably predict the probability of any event, from elections, to GDP, to sales volumes and more.

Hypermind prediction market calibration graph

537,640

probability forecasts

1185

possible answers

400

forecasting questions on geopolitics, business, and economics

Each data point in this graph answers the question:
« What is the proportion of events forecasted with probability p that actually occurred? »

Our evidence based prediction method

We designed our Prescience forecasting platform from a over a decade of scientific collaborations alongside :

This experience means we use the most advanced algorithms to optimize collective forecasts into accurate quantitative probability forecasts and reliable insights.

Future Africa 2022

The Future of Africa – 2022 predictions

What will happen in West Africa in 2022 ? Le Point Afrique and Hypermind have gathered predictions on the future of Africa and Côte d’Ivoire in particular via a forecasting contest with over 300 participants, and 1600 predictions.

Read More »

A prediction market is a competitive betting game designed to predict specific future events or quantities by tapping into the collective intelligence of a large group of participants. Prediction markets combine many diverging viewpoints, expressed as probability forecasts, into a single probability. This collective estimate changes in real time according to all the available information to human minds.

Individual forecasters place bets on outcomes and receive payouts based on their success. The best forecasters are given more weight in the final mix.

Crowd forecasting is usually done in one of two ways.

  1. The first method is called a “prediction market”. That’s essentially an online betting platform that lets people buy and sell predictions from each other. It looks and feels like a financial market, but instead of trading company stocks, participants trade predictions that end up being right or wrong. Shares of correct predictions will eventually be paid 100 points, while shares of wrong predictions will be worthless. The result is that the “market price” of a prediction measures its probability of coming true, according to many diverging opinions.
  2. The second method is called a “prediction poll”. It’s a contest where participants are competing to give the most accurate probabilities for future events. Each person shares their probability forecasts without a central marketplace. Then, smart algorithms consolidate and optimizes everyone’s guesswork into a reliable collective forecast.

The two methods yield similar results in terms of prediction accuracy, but prediction polls are easier for most people to participate in. So that’s the method we will use for the prediction contest to which you are invited to participate at the end of this course.

Businesses and governments have used prediction markets in various settings:

  • Intel, Hewlett Packard, Ford, Eli Lilly, and EDF (Electricité de France) have used prediction markets to anticipate KPIs, market shifts, sales volumes, a product’s chances of success, and delays in projects.
  • The US intelligence community used prediction markets in geopolitical forecasting to understand the likelihood of election results, wars and diplomatic events.
  • In the public health sector, the Johns Hopkins Center for Health Security has used crowd forecasting to anticipate the spread and severity of infectious diseases like dengue, malaria and Covid-19.

Prediction markets are most useful when forecasting short to medium term observable events (under 24 months), especially in the following cases:

When the past becomes irrelevant:

Traditional forecasting methods such as time series forecasting rely on the past to predict the future, the assumption being that the past can inform the future. 

But sometimes you’ll have little relevant or reliable data at your disposal to make useful projections, and hanging on to historical data may skew your forecasts. Predictions markets aggregate all the available relevant data using the wisdom of crowds.

When knowledge is decentralized:

Nobody knows everything, but everybody knows something. Usually, we solve hard problems by asking an expert: an engineer, a doctor, a lawyer. But sometimes when a problem is too complex,  when too many variables are involved for a single expert to handle, or when there is too little data to train an artificial intelligence individual expertise falls short.

Prediction markets (or prediction polls like our Prescience platform) help consolidate the informed guesses of the many based on all the available data.

When the situation is fluid:

Forecasts need to integrate new information continuously, you need real time forecasts to be able to react accordingly. 

Human forecasters excel at integrating new information because they spot things that AI would miss, information available on the ground but not yet in data bases.

Prediction markets (or prediction polls) offer 3 main advantages for companies and governments seeking a clearer view of the future:

  • They shine under uncertainty. They excel at accurate prediction where other methods fail because they do not rely on AI crunching structured data, but on the informed guesswork of a diversely minded crowd of humans. (hence “crowd forecasting”)

  • They offer precise predictions expressed as probability forecasts. Saying an event is “probable” means different things to different people. “Probable” could mean a 60% chance to one person, but 85% to another. Numbers make things crystal clear, so you can make an informed decision.

  • They are a sturdy and trustworthy forecasting method. By gathering information from many minds thanks to an objective aggregation process, prediction markets easily outperform biased or noisy individual judgments. Relying on a crowd helps reduce the risk of listening to the wrong person, instead trusting a decentralized network of independent points of view.

The first modern prediction market began in 1988 as an academic research project at the University of Iowa’s Tippie College of Business and offered forecasts on that year’s US presidential election. The World Wide Web soon enabled the launch of larger prediction markets targeted at the general public, sometimes bearing other names, like betting exchanges or idea futures.

Hypermind’s pioneering prediction market was first launched in 2000 under the name NewsFutures.

Over the years, this form of “crowd wisdom” has acquired an impressive track record of accurate forecasting in diverse fields ranging from sports and film to business, elections, geopolitics and even medicine.

Prediction Market forecasts are probabilistic. They answer the question: “What are the chances that this or that outcome will come true?” The best way to assess accuracy in the absolute is over many events, by comparing the probability estimations against observed event frequencies.

Here’s a comparison at every level of probability from 1% to 99%, over 5 years, on 400 questions with 1185 possible outcomes, on international topics ranged from elections, geopolitics, geo-economics, and business. Hypermind prediction market calibration graph

The figure allows us to answer the following question: “Of all the events predicted by Hypermind to be x% likely, what percentage actually occurred?” The closer to x% the answer; i.e., the closer the data points sit to the chart’s diagonal (bottom left to top right), the more accurate the market’s probabilities.

You can use prediction markets to forecast four types of questions:

  • binary: “will John Doe win the election ?” (yes/no)
  • discrete: “who will win the election ?”(John, Jane, Kane)
  • ordered: “what will John Doe’s share of the vote be ?” (more than 55% / 50 to 55% / 45 to 50% …)
  • linear: “what will John Doe’s share of the vote be ?” (market price = predicted vote share in %)

They have been used to predict events in various application domains like sports, elections, geopolitics, medicine, science and technology.

To learn more or schedule a demo. We’ll answer any question you have.