The Knowledge Machine and The Question of Motivation in Science
Michael Strevens (New York University)

September 15, 2021, 7:30pm - 9:30pm

This event is online

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We are happy to announce that Michael Strevens will give two online lectures on his new book The Knowledge Machine on 22nd Sept and 15th Sept at CASIP and Fudan PSI. In his book, Professor Strevens advances a new model to demonstrate how modern science departed from various kinds of intellectual practices (e.g., religion, and natural philosophy) and eventually became the powerful ‘knowledge machine’ that continually seek truth and knowledge.

The lectures are jointly organized by the Institute of Philosophy, Chinese Academy of Sciences (CASIP), and the Philosophy and Science of Intelligence Center (PSI), Fudan University. Everyone is welcome to attend. More information can be found below.


Fudan PSI Lecture: The Knowledge Machine and the question of motivation in science

Organizer: The Philosophy and Science of Intelligence Center (PSI), Fudan University

Why is science so powerful?

How does science work, and why is it so effective?

Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe?

Michael Strevens will give two online lectures on his new book The Knowledge Machine on 22nd Sept and 15th at CASIP and Fudan PSI, respectively. In his talks, Strevens will answer these challenging questions, showing the astonishing idea that modern science only came into birth when its early practitioners gave up a bunch of methods in reasoning. Accordingly, modern science, which is usually taken as the achievement of human rationality, is surprisingly rooted in something inhuman and irrational.


In 1962, Thomas Kuhn argued that working scientists are blind to alternative ways of thinking about their subject matter -- to different paradigms -- and that this blindness is, intriguingly, essential to science's success, providing a motivational framework that "forces scientists to investigate some part of nature in a detail and depth that would otherwise be unimaginable". The blindness makes scientists intellectually impoverished or even irrational in a certain sense, yet without it science would work far less well. I argue that Kuhn is right: science thrives on an irrational narrowness in scientists' mode of inquiry. Yet as even Kuhn's closest allies would now concede, the existence of totalizing paradigms from which scientists' thinking cannot under any normal circumstances escape is extremely doubtful. What plays the narrowing role and thus supplies the motivation to focus so intensely is, as I have argued in The Knowledge Machine, not a sequence of paradigms but rather the scientific method itself, with its emphasis on a highly specific but extremely powerful form of argument to the exclusion of all else. The method is restrictive to the point of unreasonableness, yet it motivates, indeed compels, scientists "to investigate some part of nature in a detail and depth" that accounts for the unprecedented success of modern science.


Michael Strevens was born and raised in New Zealand. He moved to the US in 1991 to undertake a PhD at Rutgers University; currently, he teaches philosophy of science at New York University. His academic work is principally concerned with the nature of science, covering topics such as scientific explanation, understanding, complex systems, probability of various sorts, causation, and the social structure of science; he also applies contemporary research in cognitive psychology to explain aspects of both philosophical and scientific thinking. In The Knowledge Machine, a trade book, he explains why science is so successful at creating knowledge and why it took so long for humans to figure out how to do it right.

Time: Wednesday, 15th September, 2021, 7:30 PM — 9:30 PM (UTC+8)

Online Platform: Zoom

Meeting ID: 489 550 5875

Passcode: 6666

Speaker: Michael Strevens (New York University)

Chair: Chuang Liu (Fudan University/CASIP)


1: Alex Rosenberg (Duke University)

2: Xiao Tan (Capital Normal University)

3: Malcolm Forster (Fudan University)

4: Xiang Huang (Fudan University)

Language: English


Tung-Ying Wu: [email protected]

Mingjun Zhang: [email protected]

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