How Automation and Big Data Affect Forecasting

This is our fourth or fifth post on Superforecasting. I promise I'll stop.

Soon.
One of the gems in Abnormal Returns' Superforecasting link mania:

From Scientific American:

I’ve been hard on social science, even suggesting that “social science” is an oxymoron. I noted, however, that social science has enormous potential, especially when it combines “rigorous empiricism with a resistance to absolute answers.” 

The work of Philip Tetlock possesses these qualities, and it addresses a fundamental question: How predictable are social events? His early research, which assessed experts’ ability to foresee things like elections, economic collapses and wars, highlighted the difficulties of prediction. See, for example, how I cite him in a column on whether the public should defer to the judgment of scientific experts. 

Tetlock’s new book Superforecasting: The Art and Science of Prediction, co-written with journalist Dan Gardner, is much more upbeat. The book has already received raves from The Economist, Wall Street Journal, former Treasury Secretary Robert Rubin, psychologist Steven Pinker, Nobel laureate Daniel Kahneman and others.... 

...Horgan: Are you a believer in the power of Big Data to revolutionize the social sciences? Will social science ever be as precise and rigorous as physics? 

Tetlock: I'm not sure about "revolutionizing" social science, but Big Data will clearly make it possible to answer many categories of questions that were previously unanswerable. We now have massive databases on interpersonal relations (e.g. Facebook), search behavior (Google), consumer behavior (seemingly everywhere). Tangentially: Companies routinely do things to all of us that the human subjects review boards at universities would categorize as unconscionably unethical.  Either university review boards are ridiculously hypersensitive or Big Data firms are ridiculously insensitive. I think it is a mix. 

Horgan: Social theories and predictions can have an enormous impact on societies, as Marx’s impact on history demonstrates. Does this feedback factor contribute to the difficulty of social prediction? Is it possible to build models that take this factor into account?...MUCH MORE

We have a bit of a love/hate relationship with SciAm. For example, I still hold this against them:

"That the automobile has practically reached the limit of its development is suggested by the fact that during the past year no improvements of a radical nature have been introduced." 

-Scientific American, Jan. 2, 1909.

As for Tetlock, We dumped most of our links into Credit Suisse's Mauboussin: "Sharpening Your Forecasting Skills"