We believe that news combined with forecasts of what's next can build trust and understanding.

 

Here's Why

 

College students in an experiment by Wharton Professors Barbara Mellers and Philip Tetlock that asked them to foreast on noncontroversial topics became became more humble, less polarized in their views of the world.

 

Seventeen research reports along with a meta-analysis of them have documented similar effects. Researchers asked participants to rate the accuracy of a single neutral (nonpolitical, non-COVID-19) headline. Afterward, participants became bettter at identifying unrelated false news. Both self-identified Republicans and Democrats improved.

 

Our 2019 experiment with a hybrid human/machine forecasting system in IARPA's Geopolitical Forecasting Challenge II typically resulted in successful predictions at lest 80% of the time. A crucial part of this was that our human forecasters told us the news sources and historical facts they are using to discern what the future holds.

 

Recent plus ongoing research at INFER and our partner Good Judgment, Inc. have revealed yet better ways to forecast by providing with an AI helper.At INFER, the helper is a specialized version of Anthropic. At Good Judgment, it is a specialized version of ChatGPT. At BestWorld, we will begin with the Good Judgment ChatGPT.

 

BestWorld also builds on forecasting research underway at INFER and our partner organization of Good Judgment. Both have found that their forecasting efforts have the side effect of building new friendships and communities while reducing polarization.

 

A crucial factor appears to be primarily forecasting on topics that have not been staked out by disinformation or political activism claims. This avoids getting into arguments on topics that can spark highly emotional effects.

 

Based upon the ten years so far of our team members' research, we plan to constantly improve and scale up BestWord's social media/news/forecasting collaborative system. Led by BestWorld's Dr. Dawna Coutant, we will ensure both transparency and best research ethics as we continue to improve BestWorld.

 

Our next step will be to combine forecasts and our participants' rationales to produce a news aggregation system. We believe that associating news sources with successful forecasts will build trust in our news aggregation system. This gets around the problem of fact checking services, which ask the reader to simply trust them. We will make use of current and upcoming automated news aggregation and summarization research at the Intelligence Advanced Research Projects Agency (IARPA) along with our current research using leading edge summarization research combined with translations of news stories in dozens of languages provided by Linguistic Systems, Inc.

 

We also will encourage politeness and remove toxic influences with early warnings by both our users and our AI + natural language processing system; a team of humans to constantly monitor all systems, and our journalists team. We look to the Good Judgment Open and INFER platforms as models for how to gently and compassionately moderate our system. Thank you to our friends there! We also are looking to David Brin's briefing to Facebook -- which Facebook requested, but then ignored! -- for ways to gently moderate our participants' posts.

 

Ultimately we envision something similar to the IEEE, the world’s largest professional society, which in addition to its massive online presence, hosts thousands of local dinner gatherings, and hundreds of conferences. By building in-person relationships, we could further counter the toxic tendencies of today’s news and social media platforms.

 

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