Hierarchical modelling in engineering and biology

In the 1979 Glenn Harris proposed an analytical hierarchy of models for estimating tactical force effectiveness for the US Army which was represented as a pyramid with four layers with a theatre/campaign simulation at the apex supported by mission level simulations below which was engagement model and engineering models of assets/equipment at the base.  The idea was adopted by the aerospace industry [see the graphic on the left] who place the complete aircraft on the apex supported by systems, sub-systems and components beneath in increasing numbers with the pyramid divided vertically in half to represent physical tests on one side and simulations on the other.  This represents the need to validate predictions from computational models with measurements in the real-world [see post on ‘Model validation‘ on September 18th, 2012]. These diagrams are schematic representations used by engineers to plan and organise the extensive programmes of modelling and physical testing undertaken during the design of new aircraft [see post on ‘Models as fables‘ on March 16th, 2016].  The objective of the MOTIVATE research project is to reduce quantity and increase the quality of the physical tests so that pyramid becomes lop-sided, i.e. the triangle representing the experiments and tests is a much thinner slice than the one representing the modelling and simulations [see post on ‘Brave New World‘ on January 10th, 2018].

At the same time, I am working with colleagues in toxicology on approaches to establishing credibility in predictive models for chemical risk assessment.  I have constructed an equivalent pyramid to represent the system hierarchy which is shown on the right in the graphic.  The challenge is the lack of measurement data in the top left of the pyramid, for both moral and legal reasons, which means that there is very limited real-world data available to confirm the predictions from computational models represented on the right of the pyramid.  In other words, my colleagues in toxicology, and computational biology in general, are where my collaborators in the aerospace industry would like to be while my collaborators in the aerospace want to be where the computational biologists find themselves already.  The challenge is that in both cases a paradigm shift is required from objectivism toward relativism;  since, in the absence of comprehensive real-world measurement data, validation or confirmation of predictions becomes a social process involving judgement about where the predictions lie on a continuum of usefulness.


Harris GL, Computer models, laboratory simulators, and test ranges: meeting the challenge of estimating tactical force effectiveness in the 1980’s, US Army Command and General Staff College, May 1979.

Trevisani DA & Sisti AF, Air Force hierarchy of models: a look inside the great pyramid, Proc. SPIE 4026, Enabling Technology for Simulation Science IV, 23 June 2000.

Patterson EA & Whelan MP, A framework to establish credibility of computational models in biology, Progress in Biophysics and Molecular Biology, 129:13-19, 2017.


Tyranny of quantification

There is a growing feeling that our use of metrics is doing more harm than good.  My title today is a mis-quote from Rebecca Solnit; she actually said ‘tyranny of the quantifiable‘ or perhaps it is combination of her quote and the title of a new book by Jerry Muller: ‘The Tyranny of Metrics‘ that was reviewed in the FT Weekend on 27/28 January 2018 by Tim Harford, who recently published a book called Messy that dealt with similar issues, amongst other things.

I wrote ‘growing feeling’ and then almost fell into the trap of attempting to quantify the feeling by providing you with some evidence; but, I stopped short of trying to assign any numbers to the feeling and its growth – that would have been illogical since the definition of a feeling is ‘an emotional state or reaction, an idea or belief, especially a vague or irrational one’.

Harford puts it slightly differently: that ‘many of us have a vague sense that metrics are leading us astray, stripping away context, devaluing subtle human judgment‘.  Advances in sensors and the ubiquity of computing power allows vast amounts of data to be acquired and processed into metrics that can be ranked and used to make and justify decisions.  Data and consequently, empiricism is king.  Rationalism has been cast out into the wilderness.  Like Muller, I am not suggesting that metrics are useless, but that they are only one tool in decision-making and that they need to used by those with relevent expertise and experience in order to avoid unexpected consequences.

To quote Muller: ‘measurement is not an alternative to judgement: measurement demands judgement – judgement about whether to measure, what to measure, how to evaluate the significance of what’s been measured, whether rewards and penalties will be attached to the results, and to whom to make the measurements available‘.


Lunch with the FT – Rebecca Solnit by Rana Foroohar in FT Weekend 10/11 February 2018

Desperate measures by Tim Harford in FT Weekend 27/28 February 2018

Muller JZ, The Tyranny of Metrics, Princeton NJ: Princeton University Press, 2018.

Image: http://maxpixel.freegreatpicture.com/Measurement-Stopwatch-Timer-Clock-Symbol-Icon-2624277