The Antiparallel Structures of Science and Engineering

Wednesday 24 February, 2010
By Abalinx

The Antiparallel Structures
of Science and Engineering

by Eric Drexler on June 22, 2009

Part of a diagram of contrasting information flows in scientific  inquiry and engineering design
The heart of the problem
(Full image below)

Science and engineering are inseparable fields, linked by a shared language of mass and energy, molecules and thermodynamics, physical systems and physical law. This shared language makes communication deceptively easy — easy, because scientists and engineers can see every detail in the same way; deceptive, because they see these details in different contexts, forming different patterns and presenting different problems. In a fundamental sense, science and engineering are antiparallel, facing in opposite directions. The resulting gaps in understanding can open a chasm wide enough to trip a manager, or to swallow a project.

As I discussed in a recent post, scientific inquiry and engineering design are often intimately interleaved (in projects, in activities, in creative minds), and to such an extent that (perilously!) they may seem the same. Here, I will focus on the differences that thread through a complex relationship.

A familiar pattern difficulties at the science/engineering interface often impedes corporate research, and I’m working with a former R&D manager to develop a presentation package that addresses this. In the broader technical community, however, similar difficulties impede progress in understanding what science can and can’t tell us about the future potential of technology, thereby impeding the development of reality-based policies. In both instances, the costs include delay, friction, waste, risk, and missed opportunity, and in both instances, understanding the structural basis of the problem can help to resolve it.

Antiparallel Structures

Both scientific inquiry and engineering design can be dissected into three levels: physical systems, concrete descriptions of physical systems, and general patterns that apply to an indefinitely large number of systems. Here’s a diagram that captures some key relationships:

A diagram of contrasting information flows in scientific inquiry  and engineering design

The information flows that link these levels are antiparallel: In scientific inquiry, physical systems shape their descriptions through measurement, and the results constrain and shape general, abstract models (theories) by testing them. In engineering design, by contrast, descriptions (specifications) shape physical systems through fabrication, and general, abstract models (system concepts) shape descriptions through design.

The universal and the particular

These information relationships differ in another dimension: the universal vs. the particular, equality vs. constraint, “For all x, a = b, c = d,…” vs. “There exists x such that ab, cd,…”. On one side is an ideal of generality and perfection that can only be approximated; on the other side is a target that is routinely achieved.

While science aims (ideally) to produce exact descriptions of all parameters of all members of a general class of physical systems, engineering aims to manufacture instances of a single kind of system, making choices to ensure that itsfunctional parameters will equal or exceed those specified by a design description.

Likewise, while science aims to formulate a single theory that exactly fits all parameters of every description, engineering aims to design at least one description of a system having functional parameters that equal or exceed those required by one of a potential multiplicity of system concepts.

In this connection, is a proliferation of possible ways of satisfying a constraint good, or bad? In science, finding more possibilities creates greater uncertainty; in engineering, finding more possibilities provides greater freedom of design. This is a basic question with opposite answers — and there are many more.

Pervasive consequences

Science and engineering share a language of physical systems and physical law, but they ask different questions, seek different knowledge, and serve different ends. The ramifications range from different views of the non-linear system dynamics to differences in working relationships and institutions. The consequences are pervasive and deep, familiar and surprising, and extend far beyond what I have sketched here.

It is hard to quantify or predict the value of modest improvements in mutual understanding among scientists, engineers, and research managers, but the potential value is surely enormous. Modest improvements in understanding and communication can speed progress, reduce risks, and occasionally uncover a transformative strategic opportunity.

I think that a creating a deeper and more widely shared understanding of the contrasting faces of science and engineering can help to produce those modest improvements.


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