Tag Archives: decision-making

Reduction in usefulness of reductionism

decorative paintingA couple of months ago I wrote about a set of credibility factors for computational models [see ‘Credible predictions for regulatory decision-making‘ on December 9th, 2020] that we designed to inform interactions between researchers, model builders and decision-makers that will establish trust in the predictions from computational models [1].  This is important because computational modelling is becoming ubiquitous in the development of everything from automobiles and power stations to drugs and vaccines which inevitably leads to its use in supporting regulatory applications.  However, there is another motivation underpinning our work which is that the systems being modelled are becoming increasingly complex with the likelihood that they will exhibit emergent behaviour [see ‘Emergent properties‘ on September 16th, 2015] and this makes it increasingly unlikely that a reductionist approach to establishing model credibility will be successful [2].  The reductionist approach to science, which was pioneered by Descartes and Newton, has served science well for hundreds of years and is based on the concept that everything about a complex system can be understood by reducing it to the smallest constituent part.  It is the method of analysis that underpins almost everything you learn as an undergraduate engineer or physicist. However, reductionism loses its power when a system is more than the sum of its parts, i.e., when it exhibits emergent behaviour.  Our approach to establishing model credibility is more holistic than traditional methods.  This seems appropriate when modelling complex systems for which a complete knowledge of the relationships and patterns of behaviour may not be attainable, e.g., when unexpected or unexplainable emergent behaviour occurs [3].  The hegemony of reductionism in science made us nervous about writing about its short-comings four years ago when we first published our ideas about model credibility [2].  So, I was pleased to see a paper published last year [4] that identified five fundamental properties of biology that weaken the power of reductionism, namely (1) biological variation is widespread and persistent, (2) biological systems are relentlessly nonlinear, (3) biological systems contain redundancy, (4) biology consists of multiple systems interacting across different time and spatial scales, and (5) biological properties are emergent.  Many engineered systems possess all five of these fundamental properties – you just to need to look at them from the appropriate perspective, for example, through a microscope to see the variation in microstructure of a mass-produced part.  Hence, in the future, there will need to be an increasing emphasis on holistic approaches and systems thinking in both the education and practices of engineers as well as biologists.

For more on emergence in computational modelling see Manuel Delanda Philosophy and Simulation: The Emergence of Synthetic Reason, Continuum, London, 2011. And, for more systems thinking see Fritjof Capra and Luigi Luisi, The Systems View of Life: A Unifying Vision, Cambridge University Press, 2014.

References:

[1] Patterson EA, Whelan MP & Worth A, The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application, Computational Toxicology, 17: 100144, 2021.

[2] Patterson EA &Whelan MP, A framework to establish credibility of computational models in biology. Progress in biophysics and molecular biology, 129: 13-19, 2017.

[3] Patterson EA & Whelan MP, On the validation of variable fidelity multi-physics simulations, J. Sound & Vibration, 448:247-258, 2019.

[4] Pruett WA, Clemmer JS & Hester RL, Physiological Modeling and Simulation—Validation, Credibility, and Application. Annual Review of Biomedical Engineering, 22:185-206, 2020.

Where is AI on the hype curve?

I suspect that artificial intelligence is somewhere near the top of the ‘Hype Curve’ [see ‘Hype cycle’ on September 23rd, 2015].  At the beginning of the year, I read Max Tegmark’s book, ‘Life 3.0 – being a human in the age of artificial intelligence’ in which he discusses the prospects for artificial general intelligence and its likely impact on life for humans.  Artificial intelligence means non-biological intelligence and artificial general intelligence is the ability to accomplish any cognitive task at least as well as humans.  Predictions vary about when we might develop artificial general intelligence but developments in machine learning and robotics have energised people in both science and the arts.  Machine learning consists of algorithms that use training data to build a mathematical model and make predictions or decisions without being explicitly programmed for the task.  Three of the books that I read while on vacation last month featured or discussed artificial intelligence which stimulated my opening remark about its position on the hype curve.  Jeanette Winterson in her novel, ‘Frankissstein‘ foresees a world in which humanoid robots can be bought by mail order; while Ian McEwan in his novel, ‘Machines Like Me‘, goes back to the early 1980s and describes a world in which robots with a level of consciousness close to or equal to humans are just being introduced to the market the place.  However, John Kay and Mervyn King in their recently published book, ‘Radical Uncertainty – decision-making beyond numbers‘, suggest that artificial intelligence will only ever enhance rather replace human intelligence because it will not be able to handle non-stationary ill-defined problems, i.e. problems for which there no objectively correct solution and that change with time.  I think I am with Kay & King and that we will shortly slide down into the trough of the hype curve before we start to see the true potential of artificial general intelligence implemented in robots.

The picture shows our holiday bookshelf.