I wanted to do a brief year-end retrospective on what we’ve been focused on both for your sake and for ours. Sometimes we take for granted how much we’ve accomplished in a 12-month window. Taking a moment to pause and look back helps us appreciate that better.
Many effective altruist (EA) core values illustrate why they are enthusiastic to use crowd forecasting methods. EAs seek to tackle problems of global significance, placing an emphasis on not only doing good, but doing good effectively. "When decision-makers in government have to make high-stakes judgment calls, using rigorous forecasting techniques can improve our ability to predict the future and make better decisions."
A key practice of a good forecaster is doing post-mortems on your forecasts. Whether the result was good or bad, a quality post-mortem can help you identify what you did well or poorly and can improve on next time. See what Zach learned about his blindspots on a recent forecast about now-former UK Prime Minister, Liz Truss.
Cultivate Labs is excited to announce a partnership with The Bertelsmann
Stiftung, an independent foundation headquartered in Germany, and the
Washington, DC-based Bertelsmann Foundation, part of the Stiftung’s
Cultivate Labs and Pytho.io announced today the creation of a forecaster training curriculum for INFER
(INtegrated Forecasting and Estimates of Risk), a crowdsourced forecasting
program run by the Advanced Research Lab for Intelligence and Security (ARLIS)
at the University of Maryland.
For many years, Cultivate Forecasts supported two different forecasting interface modes: prediction markets and opinion pools (aka opinion surveys or probability surveys). In a prediction market, forecasters buy and sell shares of answer options using real or virtual/fantasy currency (ie. I spend $10 to buy shares of “Yes” in the market “Will candidate X win the election?”). In an opinion pool, forecasters assign a probability to each potential answer (ie. I for
As consumers learn to use these forecasts as part of their own analysis and decision-making, we've been thinking through how we can make sure they see a complete picture - not just the one represented by the graph visualization that tracks the consensus, but why that consensus may be wrong. We want the consumer to always question their assumptions and question the consensus.
Questioning the assumptions and probabilities of the consensus is a simple best practice of forecasting. Do I currently agree with the prevailing winds, or do I predict something different will occur? We've recently introduced the "contrarian sort..."
Many of our clients find that going through the process of making forecasts and "practicing" improves their "off-platform" decisions. That's why we're starting a new blog series about applying forecasting principles to other areas of life and work.
We’re excited to announce the launch of a beta version of a new forecasting capability on the Cultivate platform we’re calling “multi-time period questions.” This new type of forecasting input now gives us the ability to collect several points of input from the forecaster all at once.
For a long time we've had a rudimentary reminder system a user can set after they make a forecast. But now, we've introduced a more intelligent "nudging" system to ensure a larger percentage of forecasters are updating their forecasts on a regular basis.