@don86326, your most recent post reminds me a bit of anthropogenic climate change.
Like others here, I’ve put some thought into prediction and forecasting, including with respect to stories [1] and multi-agent systems [2].
Perhaps, in addition to finding ways to enhance forums – both moderation and participation – with AI tools, some technologists might want to consider how to better equip change managers, those who help individuals and organizations to implement and manage changes.
In these regards, there are related topics including: evidence-based policy, organizational learning, and collective intelligence. How can people, at scale, learn from the past, from past policy efforts, implementations, and observations, to together make and enact better plans and policies?
Let us consider a case study: the history of recycling in the United States.
Looking back on it, obviously people should have started recycling sooner. What about their policy plans and implementations, across cities, to our modern recycling programs? Could past policies for the better have been implemented more efficiently?
I, for one, am excited about AI-enhanced forums for reasons including, but not limited to, that these could help communities to deliberate, to make better decisions with respect to their short-, medium- and long-term objectives, to create and enact better plans and policies, and to facilitate the emergence of collective intelligence.
P.S.: I should indicate, here, the the law of the instrument: “when you have a hammer, everything looks like a nail”. As a toolmaker, I might be biased towards finding solutions involving opportunities to better equip some group of people with new tools…
Bibliography
[1] Chaturvedi, Snigdha, Haoruo Peng, and Dan Roth. “Story comprehension for predicting what happens next.” In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , pp. 1603-1614. 2017. [PDF]
[2] Yuan, Ye, Xinshuo Weng, Yanglan Ou, and Kris M. Kitani. “AgentFormer: Agent-aware transformers for socio-temporal multi-agent forecasting.” In Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 9813-9823. 2021. [PDF]