Engineering’s Relationship To Science

One of the things that I hoped to get across in my post about perspectives on mature engineering was the subtle idea that engineering’s relationship to science is not straightforward.

My first caveat is that I am not a language expert, but I do respect it as a potential deadly weapon. I do hope that it’s not too controversial to state that doing science is not the same as doing engineering. I’d like to further state that the difference, in some part, lies in the discretionary space that engineers have in both the design and operation of their creations. Science alone doesn’t care about our intentions, while engineering cares very much about our intentions.

A fellow alumni of the master’s program I’m in, Martin Downey, did his thesis on a fascinating topic: “Is There More To Engineering Than Applied Science?” in which he asks the question:

“Does the belief that engineering is an applied science help engineers understand their profession and its practice?”

Martin graciously let me quote his chapter 6 of his thesis here, on the application of heuristics, which are essentially rules-of-thumb that are used to make decisions under some amount of uncertainty and ambiguity. Which you can hopefully agree is at the core of engineering as a discipline, yes?

My own research aims to look deep into this discretionary space as well. Closing the gap between how we think work gets done and actually how it gets done is in my crosshairs. At the moment, my own thesis looks to explore (my proposal is still not yet approved, so I do not want to speak too soon) how Internet engineers attempt (in many cases, using heuristics) to make sense of complex and sometimes disorienting scenarios (like, during an outage with cascading failures that can sometimes defy the imagination) and work as a team to untangle those scenarios. So Downey’s thesis is pretty relevant to me. 🙂

Martin’s chapter is below…


The Engineering Method

Billy Vaughn Koen* describes a heuristic based system of reasoning used by engineers which marries the theoretical and practical aspects of engineering (Koen, 1985, 2003). Koen’s view takes a radically skeptical standpoint towards engineering knowledge (be it ‘scientific’ or otherwise) by which all knowledge is fallible — and is better considered as heuristic, or rule of thumb. Koen (1985, p.6) defines the engineer not in terms of the artefacts he produces, but rather as someone who applies the engineering method, which he describes as ‘the strategy for causing the best change in a poorly understood or uncertain situation within the available resources.’ (Koen, 1985, p. 5). Koen argues engineering consists of the application of heuristics, rather than ‘science’ and ‘reason’. A heuristic, by Koen’s definition is ‘anything that provides a plausible aid or direction in the solution of a problem, but is in the final analysis unjustified, incapable of justification, and fallible.’ (Koen, 1985, p. 16). Koen (1985) provides four characteristics that aid in identifying heuristics (p.17):

  • ‘A heuristic does not guarantee a solution
  • It may contradict other heuristics
  • It reduces the search time in solving a problem
  • Its acceptance depends on the immediate context instead of an absolute standard.’

He contends that the epistemology of engineering is entirely based on heuristics, which contrasts starkly the idea that it is simply the application of ‘hard science’:

Engineering has no hint of the absolute, the deterministic, the guaranteed, the true. Instead it fairly reeks of the uncertain, the provisional and the doubtful. The engineer instinctively recognizes this and calls his ad hoc method “doing the best you can with what you’ve got,” “finding a seat-of-the-pants solution,” or just “muddling through”. (Koen, 1985, p. 23).


State of the Art

Koen (1985) uses the term ‘sota’ (‘state of the art’) to denote a specific set of heuristics that are considered to be best practice, at a given time (p.23). The sota will change and evolve due to changes to the technological or social context, and the sota will vary depending on the field of engineering and by geo-political context. What is considered as sota in a rapidly industrializing nation such as China will be different from that in a developed western democracy.

It is impossible for engineering in any sense to be considered as ‘value-free’** due to the overriding influence of context, which sets it apart from ‘science’. Koen (1985) emphasizes the primacy of context in determining the response to an engineering problem, and the role of the engineer is to determine the response appropriate to the context. To the engineer there is no absolute solution, at the core of practice is selecting adequate solutions given the time and resources available. Koen proposes his Rule of Engineering:

Do what you think represents best practice at the time you must decide, and only this rule must be present (Koen, 1985, p. 42).

Koen characterizes engineering as something altogether different from ‘applied science’. Indeed he provides the following heuristic:

Heuristic: Apply science when appropriate (Koen, 1985, p. 65).

He highlights the tendency for ‘some authors […] with limited technical training’ to become mesmerized by the ‘extensive and productive use made of science by engineers’, and elevate the use of science from its status as just one of the many heuristics used by engineers. He states that ‘the thesis that engineering is applied science fails because scientific knowledge has not always been available and is not always available now, and because, even if available, is not always appropriate for use’ (Koen, 1985, p. 63).


The Best Solution

Koen’s position points towards a practical, pragmatic experience based epistemology — flexible and adaptable. Koen’s definition of ‘best’ is highly contingent something can be the best outcome within available resources without necessarily being any good, in a universal, objective sense. Koen gives the example of judging whether a Mustang or a Mercedes is the better car. Although, objectively the Mercedes may be the better car, the Mustang could be considered as the best solution to the given problem statement and its constraints (Koen, 1985, p. 10). Koen’s viewpoint takes ‘scientific knowledge’ as provisional, and judges it in terms of its utility in arriving at an engineering solution in the context of other available heuristics.

Koen’s discussion of how the engineer arrives at a ‘best’ solution involves trading off the utility characteristics which are to a large extent incommensurable and negotiable — engineering judgement prevails, and it is the ability to achieve a solution under constraint that lies at the heart of the engineering approach to problem solving:

Theoretically […] best for an engineer is the result of manipulating a model of society’s perceived reality, including additional subjective considerations known only to the engineer constructing the model. In essence, the engineer creates what he thinks an informed society would want based on his knowledge of what an uninformed society thinks it wants (Koen, 1985, p. 12).


Trade-Offs Under Constraint?

On the face of it, Koen’s approach to arriving at the best solution under constraint sounds rather similar to Erik Hollnagel’s ETTO Principle (Hollnagel, 2009), however any similarity is superficial as Koen and Hollnagel appear to hold very different philosophical positions. Hollnagel takes an abstract view that human action balances two commensurate criteria: being efficient or being thorough. Hollnagel proposes a principle where trade-offs are made between efficiency and thoroughness under conditions of limited time and resources, which he terms as ETTO (Efficiency Thoroughness Trade-Off) (Hollnagel, 2009, p. 16). He suggests that people ‘routinely make a choice between being effective and being thorough, since it is rarely possible to be both at the same time’ (Hollnagel, 2009, p. 15). Using the analogy of a set of scales, Hollnagel proposes that successful performance requires that efficiency and thoroughness are balanced. Excessive thoroughness leads to failure as actions are performed too late, or exhaust available resources, excessive efficiency leads to failure through taking action that is either inappropriate, or at the expense of safety — an excess of either will tip the scales towards failure (Hollnagel, 2009, p. 14).

Hollnagel (2009) defines the ETTO fallacy in administrative decision making as the situation where there is the expectation that people will be ‘efficient and thorough at the same time — or rather to be thorough when in hindsight it was wrong to be efficient’ (p.68). He redefines safety as the ‘ability to succeed under varying conditions’ (p.100), and proposes that making an efficiency-thoroughness trade-off is never wrong in itself. Although Hollnagel does state that ETTOs are ‘normal and necessary’, there is an undercurrent of scientific positivism running through his book. In essence the approximations that are used in ETTOs are in his view driven by time and resource pressures — uncertainty is a result of insufficient time and information. Putting time and resource considerations to one side, there is the inference that greater thoroughness would be an effective barrier to failure — the right answer is out there if we care to be thorough enough in our actions. This, superficially, is not unlike Reason’s discussion of ‘skill based violations’ (Reason, 2008, pp. 51-52). Indeed Hollnagel suggests (Hollnagel, 2009, pp. 141-142) that for a system to be efficient and resilient ETTOs must be balanced by TETOs (Thoroughness-Efficiency Trade-Off) — having thoroughness in the present allows for efficiency in the future.


There Are No Right Answers, Only Best Answers

The engineering method as defined by Koen (recall: ‘The strategy for causing the best change in a poorly understood or uncertain situation within the available resources’(Koen, 1985, p. 5)) superficially bears the hallmarks of an ETTO, however, Koen would argue that there is ‘no one right answer out there’, and that in effect ‘all is heuristic’ — science is essentially a succession of approximations (Koen, 2003). Hollnagel’s ETTO Principle, understood on a superficial level, is unhelpful in understanding how safety is generated in an engineering context. It relies on hindsight and outcome knowledge, and simply asks at each critical decision point (which in itself is only defined with hindsight) ‘where could the engineer have been more thorough’, on the basis that being more thorough would have brought them closer to the ‘right answer’. If you accept, as Koen would assert, that there is no ‘right answer’, only the ‘best’ answer, then any assessment of engineering accountability reduces to a discussion as to whether the engineer used a set of heuristics that were considered at the time (and place) of the decision to be ‘state of the art’, in the context of the constraints of the engineering problem faced. This ethical discussion goes beyond the agency of the individual engineer or engineering team insofar as the constraints imposed (time, materials, budget, weight…) mean that the best is not good enough. The ‘wisdom’ to know when a problem is over-constrained, and the power to change the constraints need to go hand-in-hand. This decision is confounded by the tendency for the most successful systems to be optimised at the boundary of failure — too conservative and failure will come from being uncompetitive (too heavy, too expensive, too late…); too ambitious and you may discover where the boundary between successful operation and functional failure lies.


And Why is All This Important…?

The view that engineering is based on the application of heuristics in face of uncertainty provides a useful framework in which engineers can consider risk and the limitations of the methods used to assess system safety. The appearance (illusion?) of scientific rigour can blind engineers to the limitations in the ability of engineering models and abstractions to represent real systems. Over- confidence or blind acceptance of the approaches to risk management leave the engineer open to censure for presenting society with the impression that the models used are somehow precise and comprehensive. Koen’s way of defining the Engineering Method promotes a modest epistemology — an acceptance of the fallibility of the methods used by engineers, and a healthy scepticism about what constitutes ‘scientifically proven fact’ can paradoxically enhance safety. A modest approach encourages us to err on the side of caution and think more critically about the weaknesses in our models of risk.

* Emeritus Professor of Mechanical Engineering at University of Texas at Austin.
** ’Value free’ in this context refers to ideal of the Scientific Method; remaining purely objective and without ‘contaminating’ scientific inquiry with value judgements.



Hollnagel, E. (2009). The ETTO principle: efficiency-thoroughness trade-off : why things that go right sometimes go wrong. Farnham, UK: Ashgate.

Koen, B. V. (1985). Definition of the engineering method. Washington, DC: American Society for Engineering Education.
Koen, B. V. (2003).
Discussion of the method: conducting the engineer’s approach to problem solving. New York, NY: Oxford University Press.

Reason, J. T. (2008). The human contribution: unsafe acts, accidents and heroic recoveries. Farnham, UK: Ashgate.