A couple of important edge cases here. One is reacting swiftly to extremely high cost situations. Like an apparent nuke launch or an apparent AI foom. Quickness of reaction would be demanded yet not very helpful for producing the best outcome. Predictive knowledge has far, far more value - in support of avoiding the need for reaction at all. And that is what sensible people are advocating.
Second case is that signal detection dominates in some reaction situations. Warming is an example where clear signals abound. E.g., the oceans have clearly heated up, methane is rising, species and populations disappearing, fire and flood, temperature records. But we are doing nothing. The quick reaction/fast money faction is ignoring the signal and using every defense mechanism in the book to prevent anyone (not just themselves) from meaningfully responding to it.
Every viable proposal for AI alignment will need "better tools for anticipation, better tools for ceaseless monitoring and testing, better tools for determining and ranking risks, better tools for remediation of harm done, and better tools and techniques for redirecting technologies as they grow."
I think the reacting vs. predicting in a similar way I think the systems vs. goals. In a complex world, goals make more sense within a system context and are helpful only for short straightforward things.
Similarly predicting might make more sense within a reacting mindset so predictions can be funneled into a chain reaction of action.
But again from an evolutionary perspective, our brain always chooses the process that is less costly. In a way, prediction is a goal, and to be able to pivot when one needs to is a system.
The issue here is that your chosen examples (start ups, trading) generally have relatively low momentum, so it's feasible to be flexible in reaction to things and have your response in time for it to still matter.
This is not true for many other fields, or for large governments or organisations. The ability to react can't be open ended if you have thousands of moving parts; whatever caused the reaction would have long gone by the time you could have feasibly responded. What planning and prediction does is allow you to winnow down to a few higher probability scenarios, then put you in a situation to respond quickly when those scenarios apply; you don't have the luxury of waiting around to see what happens before starting to plan, and if you don't try to predict you can't plan meaningfully.
adapting to a dynamic landscape rather than just having some belief (however accurate)
Might I interest you in a similar idea from Cedric over @ commoncog: "When Action Beats Prediction"?
The idea here is that there's a place for forecasting and backwards induction but sometimes taking action generates or uncovers info and then tinker and iterate forward.
The guys who assemble China's Five Year Plans haven't missed a prediction in 50 years.
Being planners, they love to start planning long before anything happens, and they're drawing up a grand plan for the People Republic's first centenary, a generation hence, in 2049.
They predict that, by the centenary, they will be the richest society on earth with the lowest Gini coefficient on earth.
Agree overall that preparation to respond quickly is perhaps more important than trying to predict in several of the domains you outline. However I do not see this argument you make to be universal across all domains. There are domains like weather forecasting for example where we have gotten better at predictions (albeit in the shorter time horizons) and this has been very helpful TOGETHER with better preparation to save lives etc. In other domains (politics, stock markets, AI etc) predictions maybe useful but as one of several ways to "figure out" what's going on - and this is useful when forecasters make their assumptions explicit. You may have seen the results of the Extinction Forecasting Tournament (XPT) by Karger & Tetlock - some interesting insights there. So I would adjust your lead as "The (limited) case for using predictions smartly - together with better preparation"
The Case Against Prediction
A couple of important edge cases here. One is reacting swiftly to extremely high cost situations. Like an apparent nuke launch or an apparent AI foom. Quickness of reaction would be demanded yet not very helpful for producing the best outcome. Predictive knowledge has far, far more value - in support of avoiding the need for reaction at all. And that is what sensible people are advocating.
Second case is that signal detection dominates in some reaction situations. Warming is an example where clear signals abound. E.g., the oceans have clearly heated up, methane is rising, species and populations disappearing, fire and flood, temperature records. But we are doing nothing. The quick reaction/fast money faction is ignoring the signal and using every defense mechanism in the book to prevent anyone (not just themselves) from meaningfully responding to it.
Yes! Lots of resonance with Kevin Kelly's Pro-Actionary Principle (via Max More): https://kk.org/thetechnium/the-pro-actiona/
Every viable proposal for AI alignment will need "better tools for anticipation, better tools for ceaseless monitoring and testing, better tools for determining and ranking risks, better tools for remediation of harm done, and better tools and techniques for redirecting technologies as they grow."
I think the reacting vs. predicting in a similar way I think the systems vs. goals. In a complex world, goals make more sense within a system context and are helpful only for short straightforward things.
Similarly predicting might make more sense within a reacting mindset so predictions can be funneled into a chain reaction of action.
But again from an evolutionary perspective, our brain always chooses the process that is less costly. In a way, prediction is a goal, and to be able to pivot when one needs to is a system.
Useful for attempting to live a peaceful life, too!
The issue here is that your chosen examples (start ups, trading) generally have relatively low momentum, so it's feasible to be flexible in reaction to things and have your response in time for it to still matter.
This is not true for many other fields, or for large governments or organisations. The ability to react can't be open ended if you have thousands of moving parts; whatever caused the reaction would have long gone by the time you could have feasibly responded. What planning and prediction does is allow you to winnow down to a few higher probability scenarios, then put you in a situation to respond quickly when those scenarios apply; you don't have the luxury of waiting around to see what happens before starting to plan, and if you don't try to predict you can't plan meaningfully.
adapting to a dynamic landscape rather than just having some belief (however accurate)
Might I interest you in a similar idea from Cedric over @ commoncog: "When Action Beats Prediction"?
The idea here is that there's a place for forecasting and backwards induction but sometimes taking action generates or uncovers info and then tinker and iterate forward.
link: https://commoncog.com/when-action-beats-prediction/
You know who's good at predictions?
The guys who assemble China's Five Year Plans haven't missed a prediction in 50 years.
Being planners, they love to start planning long before anything happens, and they're drawing up a grand plan for the People Republic's first centenary, a generation hence, in 2049.
They predict that, by the centenary, they will be the richest society on earth with the lowest Gini coefficient on earth.
What's not to like?
Agree overall that preparation to respond quickly is perhaps more important than trying to predict in several of the domains you outline. However I do not see this argument you make to be universal across all domains. There are domains like weather forecasting for example where we have gotten better at predictions (albeit in the shorter time horizons) and this has been very helpful TOGETHER with better preparation to save lives etc. In other domains (politics, stock markets, AI etc) predictions maybe useful but as one of several ways to "figure out" what's going on - and this is useful when forecasters make their assumptions explicit. You may have seen the results of the Extinction Forecasting Tournament (XPT) by Karger & Tetlock - some interesting insights there. So I would adjust your lead as "The (limited) case for using predictions smartly - together with better preparation"