Tag: Eric Drexler

Eric Drexler on taking action in the face of limited knowledge

radical abundance

Science pursues answers to questions, but not always the questions that engineering must ask.

The founding father of nanotechnology, Eric Drexler, who aptly described the difference between science and engineering, comments on the central differences between how science and engineering approach solutions in a world of limited knowledge.

Drexler's explanation, found in his insightful book Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization, discusses how there is a certain amount of ignorance that pervades everything. How then, should we respond? Engineers apply a margin of safety.

Drexler writes:

When faced with imprecise knowledge, a scientist will be inclined to improve it, yet an engineer will routinely accept it. Might predictions be wrong by as much as 10 percent, and for poorly understood reasons? The reasons may pose a difficult scientific puzzle, yet an engineer might see no problem at all. Add a 50 percent margin of safety, and move on.

Safety margins are standard parts of design, and imprecise knowledge is but one of many reasons.

Engineers and scientists ask different questions:

… Accuracy can only be judged with respect to a purpose and engineers often can choose to ask questions for which models give good-enough answers.

The moral of the story: Beware of mistaking the precise knowledge that scientists naturally seek for the reliable knowledge that engineers actually need.

beware

Nature presents puzzles that thwart human understanding.

Some of this is necessary fallibility—some things we simply cannot understand or predict. Just because we want to understand something doesn't mean it's within our capacity to do so.

Other problems represent limited understanding and predictability — there are things we simply cannot do yet, for a variety of reasons.

… Predicting the weather, predicting the folding of membrane proteins, predicting how particular molecules will fit together to form a crystal— all of these problems are long-standing areas of research that have achieved substantial but only partial success. In each of these cases, the unpredictable objects of study result from a spontaneous process— evolution, crystallization, atmospheric dynamics— and none has the essential features of engineering design.

What leads to system-level predictability?

— Well-understood parts with predictable local interactions, whether predictability stems from calculation or testing
— Design margins and controlled system dynamics to limit the effects of imprecision and variable conditions
— Modular organization, to facilitate calculation and testing and to insulate subsystems from one another and the external

… When judging engineering concepts, beware of assuming that familiar concerns will cause problems in systems designed to avoid them.

Seeking Unique Answers vs. Seeking Multiple Options

Expanding the range of possibilities plays opposite roles in inquiry and design.

If elephantologists have three viable hypotheses about an animal’s ancestry, at least two hypotheses must be wrong. Discovering yet another possible line of descent creates more uncertainty, not less— now three must be wrong. In science, alternatives represent ignorance.

If automobile engineers have three viable designs for a car’s suspension, all three designs will presumably work. Finding yet another design reduces overall risk and increases the likelihood that at least one of the designs will be excellent. In engineering, alternatives represent options. Not knowing which scientific hypothesis is true isn’t at all like having a choice of engineering solutions. Once again, what may seem like similar questions in science and engineering are more nearly opposite.

Knowledge of options is sometimes mistaken for ignorance of facts.

Remarkably, in engineering, even scientific uncertainty can contribute to knowledge, because uncertainty about scientific facts can suggest engineering options.

Simple, Specific Theories vs. Complex, Flexible Designs

Engineers value what scientists don't: flexibility.

Science likewise has no use for a theory that can be adjusted to fit arbitrary data, because a theory that fits anything forbids nothing, which is to say that it makes no predictions at all. In developing designs, by contrast, engineers prize flexibility — a design that can be adjusted to fit more requirements can solve more problems. The components of the Saturn V vehicle fit together because the design of each component could be adjusted to fit its role.

In science, a theory should be easy to state and within reach of an individual’s understanding. In engineering, however, a fully detailed design might fill a truck if printed out on paper.

This is why engineers must sometimes design, analyze, and judge concepts while working with descriptions that take masses of detail for granted. A million parameters may be left unspecified, but these parameters represent adjustable engineering options, not scientific uncertainty; they represent, not a uselessly bloated and flexible theory, but a stage in a process that routinely culminates in a fully specified product.


Beware of judging designs as if they were theories in science. An esthetic that demands uniqueness and simplicity is simply misplaced.

Curiosity-Driven Investigation vs. Goal-Oriented Development

Organizational structure differs between scientific and engineering pursuits. The coordination of work isn't interchangeable.

In science, independent exploration by groups with diverse ideas leads to discovery, while in systems engineering, independent work would lead to nothing of use, because building a tightly integrated system requires tight coordination. Small, independent teams can design simple devices, but never a higher-order system like a passenger jet.

In inquiry, investigator-led, curiosity-driven research is essential and productive. If the goal is to engineer complex products, however, even the most brilliant independent work will reliably produce no results.

The moral of the story: Beware of approaching engineering as if it were science, because this mistake has opportunity costs that reduce the value of science itself.

In closing, Drexler comments on applying the engineering perspective.

Drawing on established knowledge to expand human capabilities, by contrast, requires an intellectual discipline that, in its fullest, high-level form, differs from science in almost every respect.

Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization is worth reading in its entirety.

The Difference Between Science And Engineering

radical abundance

Eric Drexler is often described as “the founding father of nanotechnology.”

His recent book, Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization, includes a fascinating explanation of the difference between science and engineering.

At first glance, scientific inquiry and engineering design can seem the same. One important distinction, however, results from the flow of information.

The essence of science is inquiry; the essence of engineering is design. Scientific inquiry expands the scope of human perception and understanding; engineering design expands the scope of human plans and results.

Inquiry and design are perfectly distinct as concepts, but often interwoven in practice, whether within a field, a research program, a development team, or a single creative mind. Meshing design with inquiry can be as vital as hand-eye coordination. Engineering new instruments enables inquiry, while scientific inquiry can enable design. Chemical engineers investigate chemical systems, testing combinations of reactants, temperature, pressure, and time in search of conditions that maximize product yield; they may undertake inquiries every day, yet in the end their experiments support engineering design and analysis. Conversely, experimental physicists undertake engineering when they develop machines like the Large Hadron Collider. With its tunnels, vacuum systems, superconducting magnets, and ten-thousand-ton particle detectors, this machine demanded engineering design on a grand scale, yet all as part of a program of scientific inquiry.

But the close, interweaving links between scientific inquiry and engineering design can obscure how deeply they differ.

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While interacting with the same physical world, the way you look at the problem — through the lens of design or inquiry — shapes what you see.

The Bottom-Up Structure of Scientific Inquiry

Scientific inquiry builds knowledge from bottom to top, from the ground of the physical world to the heights of well-tested theories, which is to say, to general, abstract models of how the world works. The resulting structure can be divided into three levels linked by two bridges.

At the ground level, we find physical things of interest to science, things like grasses and grazing herds on the African savannah, galaxies and gas clouds seen across cosmological time, and ordered electronic phases that emerge within a thousandth of a degree of absolute zero.

On the bridge to the level above, physical things become objects of study through human perception, extended by instruments like radio telescopes, magnetometers, and binoculars, yielding results to be recorded and shared, extending human knowledge. Observations bring information across the first bridge, from physical things to the realm of symbols and thought.

At this next level of information flow, scientists build concrete descriptions of what they observe. …

On the bridge to the top level of this sketch of science, concrete descriptions drive the evolution of theories, first by suggesting ideas about how the world works, and then by enabling tests of those ideas through an intellectual form of natural selection. As theories compete for attention and use, the winning traits include simplicity, breadth, and precision, as well as the breadth and precision of observational tests— and how well theory and data agree, of course.

Newtonian mechanics serves as the standard example. Its breadth embraces every mass, force, and motion, while its precision is mathematically exact. This breadth and precision are the source of both its power in practice and its failure as an ultimate theory. Newton’s Laws make precise predictions for motions at any speed, enabling precise observations to reveal their flaws.

Thus, in scientific inquiry, knowledge flows from bottom to top:

  • Through observation and study, physical systems shape concrete descriptions.
  • By suggeting ideas and then testing them, concrete descriptions shape scientific theories.

Here is a schematic structure of Scientific inquiry contrasted with the structure of engineering design.

The Antiparallel Structures of Scientific Inquiry

The Top-Down Structure of Engineering Design

In scientific inquiry information flows from matter to mind, but in engineering design information flows from mind to matter:

  • Inquiry extracts information through instruments; design applies information through tools.
  • Inquiry shapes its descriptions to fit the physical world; design shapes the physical world to fit its descriptions.

At this level, the contrasts are often as concrete as the difference between a microscope in an academic laboratory and a milling machine on a factory floor. At the higher more abstract levels of science and engineering, the differences are less concrete, yet at least as profound. Here, the contrasts are between designs and theories, intangible yet different products of the mind.

  • Scientists seek unique, correct theories, and if several theories seem plausible, all but one must be wrong, while engineers seek options for working designs, and if several options will work, success is assured.
  • Scientists seek theories that apply across the widest possible range (the Standard Model applies to everything), while engineers seek concepts well-suited to particular domains (liquid-cooled nozzles for engines in liquid-fueled rockets).
  • Scientists seek theories that make precise, hence brittle predictions (like Newton’s), while engineers seek designs that provide a robust margin of safety.
  • In science a single failed prediction can disprove a theory, no matter how many previous tests it has passed, while in engineering one successful design can validate a concept, no matter how many previous versions have failed.

The Strategy of Systems Engineering

With differences this stark, it may seem a surprise that scientific inquiry and engineering design are ever confused, yet to judge by both the popular and scientific press, clear understanding seems uncomfortably rare.*

The key to understanding engineering at the systems level— the architectural level— is to understand how abstract engineering choices can be grounded in concrete facts about the physical world. And a key to this, in turn, is to understand how engineers can design systems that are beyond their full comprehension.

Seeking Knowledge vs. Applying Knowledge

Because science and engineering face opposite directions, they ask different questions.

Scientific inquiry faces toward the unknown, and this shapes the structure of scientific thought; although scientists apply established knowledge, the purpose of science demands that they look beyond it.

Engineering design, by contrast, shuns the unknown. In their work, engineers seek established knowledge and apply it in hopes of avoiding surprises. In engineering, the fewer experiments, the better.

Inquiry and design call for different patterns of thought, patterns that can clash. In considering the science in the area around an engineering problem, a scientist may see endless unknowns and assume that scarce knowledge will preclude engineering, while an engineer considering the very same problem and body of knowledge may find ample knowledge to do the job.

Radical Abundance “offers a mind-expanding vision of a world hurtling toward an unexpected future.”