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The Difference Between Science & Engineering

6 October 2013 by NCCMMS

Shane Parrish at Farnam Street Blog, one the most thoughtful and well presented blogs on the internet, finds a lot of great radical-abundance.jpginformation through significant effort, so the rest of us don't have to work so hard to get smart.

A July 5, 2013 post titled The Difference Between Science And Engineering continues to stick with me, so I thought it should be shared here.  Pulling from Eric Dressler's book  Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization, Parrish hits upon the essential difference between scientists and engineers, which I think is important to the overall topic of medical modeling and simulation.  

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.

Medical modeling and simulation falls into three primary areas of use and purpose:  training & education, analysis, and systems engineering.  It takes scientists (clinicians fall into this category) and engineers (developers of simulations fall into this category) to create models and simulations for healthcare.  Figuring out how to coalesce the motivations and perspectives of scientists and engineers is critical to the creation of more useful, more impactful, more effective healthcare models and simulations.

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