Readings Newsletter
Become a Readings Member to make your shopping experience even easier.
Sign in or sign up for free!
You’re not far away from qualifying for FREE standard shipping within Australia
You’ve qualified for FREE standard shipping within Australia
The cart is loading…
A probing examination of the dynamic history of predictive methods and values in science and engineering that helps us better understand today's cultures of prediction.
A probing examination of the dynamic history of predictive methods and values in science and engineering that helps us better understand today's cultures of prediction.
The ability to make reliable predictions based on robust and replicable methods is a defining feature of the scientific endeavor, allowing engineers to determine whether a building will stand up or where a cannonball will strike. Cultures of Prediction, which bridges history and philosophy, uncovers the dynamic history of prediction in science and engineering over four centuries. Ann Johnson and Johannes Lenhard identify four different cultures, or modes, of prediction in the history of science and engineering- rational, empirical, iterative-numerical, and exploratory-iterative. They show how all four develop together and interact with one another while emphasizing that mathematization is not a single unitary process but one that has taken many forms.
The story is not one of the triumph of abstract mathematics or technology but of how different modes of prediction, complementary concepts of mathematization, and technology coevolved, building what the authors call "cultures of prediction." The first part of the book examines prediction from early modernity up to the computer age. The second part probes computer-related cultures of prediction, which focus on making things and testing their performance, often in computer simulations. This new orientation challenges basic tenets of the philosophy of science, in which scientific theories and models are predominantly seen as explanatory rather than predictive. It also influences the types of research projects that scientists and engineers undertake, as well as which ones receive support from funding agencies.
$9.00 standard shipping within Australia
FREE standard shipping within Australia for orders over $100.00
Express & International shipping calculated at checkout
A probing examination of the dynamic history of predictive methods and values in science and engineering that helps us better understand today's cultures of prediction.
A probing examination of the dynamic history of predictive methods and values in science and engineering that helps us better understand today's cultures of prediction.
The ability to make reliable predictions based on robust and replicable methods is a defining feature of the scientific endeavor, allowing engineers to determine whether a building will stand up or where a cannonball will strike. Cultures of Prediction, which bridges history and philosophy, uncovers the dynamic history of prediction in science and engineering over four centuries. Ann Johnson and Johannes Lenhard identify four different cultures, or modes, of prediction in the history of science and engineering- rational, empirical, iterative-numerical, and exploratory-iterative. They show how all four develop together and interact with one another while emphasizing that mathematization is not a single unitary process but one that has taken many forms.
The story is not one of the triumph of abstract mathematics or technology but of how different modes of prediction, complementary concepts of mathematization, and technology coevolved, building what the authors call "cultures of prediction." The first part of the book examines prediction from early modernity up to the computer age. The second part probes computer-related cultures of prediction, which focus on making things and testing their performance, often in computer simulations. This new orientation challenges basic tenets of the philosophy of science, in which scientific theories and models are predominantly seen as explanatory rather than predictive. It also influences the types of research projects that scientists and engineers undertake, as well as which ones receive support from funding agencies.