Book Bits: 12 November 2022

For Profit: A History of Corporations
William Magnuson
Review via The Economist
There is no mention of Elon Musk in William Magnuson’s magnificent history of corporations, which stretches from the societas publicanorum of ancient Rome, through Renaissance Florence, the Age of Discovery and the might of American industrial capitalism to Silicon Valley. Yet reading it makes clear why the pioneer of electric cars, private rockets—and now, via Twitter, controller of part of the public sphere—commands attention. For more than 2,000 years, corporations such as his have produced some of humankind’s greatest achievements. But usually the most dazzling overstep the mark, leaving a trail of debris and distrust behind them.

Epistemic Risk and the Demands of Rationality
Richard Pettigrew
Summary via publisher (Oxford U. Press)
How much does rationality constrain what we should believe on the basis of our evidence? According to this book, not very much. For most people and most bodies of evidence, there is a wide range of beliefs that rationality permits them to have in response to that evidence. The argument, which takes inspiration from William James’ ideas in ‘The Will to Believe’, proceeds from two premises. The first is a theory about the basis of epistemic rationality. It’s called epistemic utility theory, and it says that what it is epistemically rational for you to believe is what it would be rational for you to choose if you were given the chance to pick your beliefs and, when picking them, you were to care only about their epistemic value. So, to say which beliefs are permitted, we must say how to measure epistemic value, and which decision rule to use when picking your beliefs. The second premise is a claim about attitudes to epistemic risk, and it says that rationality permits many different such attitudes.

Microprediction: Building an Open AI Network
Peter Cotton
Summary via publisher (MIT Press)
The artificial intelligence (AI) revolution is leaving behind small businesses and organizations that cannot afford in-house teams of data scientists. In Microprediction, Peter Cotton examines the repeated quantitative tasks that drive business optimization from the perspectives of economics, statistics, decision making under uncertainty, and privacy concerns. He asks what things currently described as AI are not “microprediction,” whether microprediction is an individual or collective activity, and how we can produce and distribute high-quality microprediction at low cost. The world is missing a public utility, he concludes, while companies are missing an important strategic approach that would enable them to benefit—and also give back. In an engaging, colloquial style, Cotton argues that market-inspired “superminds” are likely to be very effective compared with other orchestration mechanisms in the domain of microprediction.

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