Scientific research and technological progress have gone hand in hand since the invention of the wheel. Without research, we lack the knowledge base to advance the state of the art, and without technological progress we lack the functional basis for further scientific research. In her new book The genesis of technical and scientific revolutions, Harvard University Professor of Technology and Public Policy, Venkatesh Narayanamurti, and Sandia National Laboratories Senior Scientist, Jeffrey Y. Tsao, examine the symbiotic relationship between these two concepts and how their interaction could be modulated to better suit the rapid pace of the 21st century meet engineering discovery.
Harvard University Press
Excerpt from THE GENERATION OF TECHNOLOGICAL REVOLUTIONS: RETHINKING THE NATURE AND CARE OF RESEARCH by VENKATESH NARAYANAMURTI AND JEFFREY Y. TSAO, published by Harvard University Press. Copyright © 2021 by the President and Fellows of Harvard College. Used with permission. All rights reserved.
The network is hierarchical: the nesting of questions and answers
The way in which scientific and technological knowledge is hierarchical results from the nesting of both scientific facts and explanations, as well as technological functions and the forms they fulfill, discussed in the last chapter.
Harvard University Press
In science, at the top of the hierarchy is facts – raw patterns in observed phenomena. These patterns can be thought of as questions: Why does a particular pattern occur? Why does the ball fall when you let go of a ball and the faster it falls the further it falls? Explanations for these rough patterns can be found one level down in the hierarchy and can be seen as answers to these questions: Galileo’s explanation of the observed distance-time pattern from the 16th century was that the velocities of falling spheres increase linearly with time . But that answer or explanation becomes a different question itself: Why do the velocities of falling spheres increase linearly over time? This question requires a deeper explanation, a deeper answer: Newton’s explanation was that gravity is a force, that uniform forces cause uniform acceleration, and that steady acceleration causes a linear increase in speed. Scientific understanding is always incomplete, of course, so there is always a point where we don’t have a deeper explanation. This in no way diminishes the validity of the existing explanations: Science looks for the immediate why, but does not insist on the ultimate why. General relativity explains Newton’s laws of gravitation, even if their own origin has yet to be explained.
In technology, at the top of the hierarchy are the functions people want. These functions represent problems that are solved by forms below them in the hierarchy. Shapes fulfill functions, but these shapes pose new problems that have to be solved on successively deeper levels. Moving from the problem-solving nomenclature to the equivalent question-and-answer nomenclature, we can say that the iPhone represents a technological question: How does an internet-enabled mobile phone with a software-programmable interactive display come about? A partial answer came in the form of capacitive, multi-touch surfaces, which open up considerable scope for user interaction when using multiple fingers at the same time. But the opacity of existing multi-touch surfaces itself became a question: How do we make multi-touch surfaces transparent so that the display is visible? The multi-touch display with a transparent surface provided an answer.
In other words, science and technology are both organized in hierarchies of question-and-answer pairs, with each question or answer having two “faces”. One side that points down in the hierarchy represents a question to an answer directly below in the hierarchy. The other side, that points up in the hierarchy, represents an answer to a question that is directly above it in the hierarchy lies. We emphasize that our presentation of questions as “above” answers and answers as “below” questions is arbitrary – it does not mean any relative importance or valence, but should simply correspond to common parlance. In science, an explanation is deeper and more “fundamental” than the fact it explains, especially when generalized to explanations of many other facts. The special theory of relativity is deeper in this sense than the constancy of c because it answers the question why c is constant; it also answers the question of how much energy is released during nuclear fission and fusion. In engineering, forms are deeper and more “fundamental” than the functions they fulfill, especially when they have been adapted to many other functions. The transparent multi-touch surface display is more fundamental than the iPhone as it not only helps answer the question of how to make the iPhone, but also how to make human-interactive displays in general. Rubber is more fundamental than a bicycle tire because it not only helps answer the question of how to make a bicycle tire, but it also helps answer the question of how to make myriad other types of tires.
The network is modular: ease of use and exploration
Closely related scientific questions and answers are organized in so-called scientific domains, which we refer to as scientific knowledge modules. Closely linked technological problems and solutions are organized in engineering components, which we refer to as technological knowledge modules.
Harvard University Press
Closely related scientific questions are often to be answered within a scientific knowledge domain or a scientific knowledge module that accesses several sub-domains that are nested within the larger domain. A question related to an electron transport phenomenon in a particular semiconductor structure lies within the broad field of semiconductor science, but the answer might require an integrated understanding of both the electron transport physics sub-area and the material science sub-area of the synthesized structure. The sub-question related to electron transport physics could require an integrated understanding of the subdomain of electrons in different types of structures (bulk materials, heterojunctions, nanostructures, coupled nanostructures) and the sub-subdomains of the interactions of electrons with phonons in these structures. The sub-question associated with the materials science of the synthesized structure might require an understanding of the sub-domains of substrates and epitaxy, thin films, or post-material fabrication. In other words, we can think of scientific knowledge domains as a modular hierarchy and their subdomains as sub-modules and sub-sub-modules.
Closely related technological problems are also often solved by key technological components or technological knowledge modules that may incorporate multiple sub-components that are nested within the larger components. An iPhone is itself a component made up of many sub-components, and each sub-component is similarly divided. We can think of the iPhone “problem” as a component that is “solved” by its sub-components – a case, a display, a circuit board, a camera, and input / output connectors. We can think of the “problem” of a printed circuit board as a sub-component that is “solved” by sub-components that contain integrated circuit chips with low power consumption. Conversely, an iPhone is also a component that is itself embedded in a hierarchy of usage functions. An iPhone could be used as a solution to the problem of “running” an SMS app; an SMS app could be used as a solution to the problem of sending a bulk SMS to a group of friends; the bulk text message could be used as a solution to the problem of organizing the group of friends for a protest in Times Square; and the protest in Times Square could be part of a solution to the problem of organizing a broader social movement for a human-desired social cause.
One could ask: Why is scientific and technological knowledge modular? They are modular because they are complex adaptive systems – systems that are carried by and adapted to their environment by complex internal changes – and practically all complex adaptive systems are modular (Simon, 1962). Complex adaptive systems both exploit their environments and explore their environments to improve that use. Modularity enables efficiency both in using existing knowledge of the environment and in exploring that environment to create new knowledge.
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