Tag Archives: science

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.

Psychological entropy increased by ineffectual leaders

Decorative image of a flowerYou might have wondered why I used ‘entropy’, and ‘psychological entropy’ in particular, as examples in my post on drowning in information a couple of weeks ago [‘We are drowning in information while starving for wisdom‘ on January 20th, 2021].  It was not random.  I spent some of the Christmas break catching up on my reading pile of interesting looking scientific papers and one on psychological entropy stimulated my thinking.  Psychological entropy is the concept that our brains are self-organising systems in a continual dialogue with the environment which leads to the emergence of a relatively small number of stable low-entropy states.  These states could be considered to be assemblies of neurons or patterns of thoughts, perhaps a mindset.  When we are presented with a new situation or problem to solve for which the current assembly or mindset is unsuitable then we start to generate new ideas by generating more and different assemblies of neurons in our brains.  Our responses become unpredictable as the level of entropy in our minds increases until we identify a new approach that deals effectively with the new situation and we add it to our list of available low-entropy stable states.  If the external environment is constantly changing then our brains are likely to be constantly churning through high entropy states which leads to anxiety and psychological stress.  Effective leaders can help us cope with changing environments by providing us with a narrative that our brains can use as a blueprint for developing the appropriate low-entropy state.  Raising psychological entropy by the right amount is conducive to creativity in the arts, science and leadership but too much leads to mental breakdown.

Sources:

Hirsh JB, Mar RA, Peterson JB. Psychological entropy: A framework for understanding uncertainty-related anxiety. Psychological review. 2012 Apr;119(2):304

Handscombe RD & Patterson EA, The Entropy Vector: connecting science and business, Singapore: World Scientific Press, 2004.

Going against the flow

Decorative photograph of a mountain riverLast week I wrote about research we have been carrying out over the last decade that is being applied to large scale structures in the aerospace industry (see ‘Slowly crossing the valley of death‘ on January 27th, 2021). I also work on very much smaller ‘structures’ that are only tens of nanometers in diameter, or about a billion times smaller than the test samples in last week’s post (see ‘Toxic nanoparticles?‘ on November 13th, 2013). The connection is the use of light to measure shape, deformation and motion; and then utilising the measurements to validate predictions from theoretical or computational models. About three years ago, we published research which demonstrated that the motion of very small particles (less than about 300 nanometres) at low concentrations (less than about a billion per millilitre) in a fluid was dominated by the molecules of the fluid rather than interactions between the particles (see Coglitore et al, 2017 and ‘Slow moving nanoparticles‘ on December 13th, 2017). This data confirmed results from earlier molecular dynamic simulations that contradicted predictions using the Stokes-Einstein equation, which was derived by Einstein in his PhD thesis for a ‘Stokes’ particle undergoing Brownian motion. The Stokes-Einstein equation works well for large particles but the physics of motion changes when the particles are very small and far apart so that Van der Waals forces and electrostatic forces play a dominant role, as we have shown in a more recent paper (see Giorgi et al, 2019).  This becomes relevant when evaluating nanoparticles as potential drug delivery systems or assessing the toxicological impact of nanoparticles.  We have shown recently that instruments based on dynamic scattering of light from nanoparticles are likely to be inaccurate because they are based on fitting measurement data to the Stokes-Einstein equation.  In a paper published last month, we found that asymmetric flow field flow fractionation (or AF4)  in combination with dynamic light scattering when used to detect the size of nanoparticles in suspension, tended to over-estimate the diameter of particles smaller than 60 nanometres at low concentrations by upto a factor of two (see Giorgi et al, 2021).  Someone commented recently that our work in this area was not highly cited but perhaps this is unsurprising when it undermines a current paradigm.  We have certainly learnt to handle rejection letters, to redouble our efforts to demonstrate the rigor in our research and to present conclusions in a manner that appears to build on existing knowledge rather than demolishing it.

Sources:

Coglitore, D., Edwardson, S.P., Macko, P., Patterson, E.A. and Whelan, M., 2017. Transition from fractional to classical Stokes–Einstein behaviour in simple fluids. Royal Society open science, 4(12), p.170507.

Giorgi, F., Coglitore, D., Curran, J.M., Gilliland, D., Macko, P., Whelan, M., Worth, A. and Patterson, E.A., 2019. The influence of inter-particle forces on diffusion at the nanoscale. Scientific reports, 9(1), pp.1-6.

Giorgi, F., Curran, J.M., Gilliland, D., La Spina, R., Whelan, M.P. & Patterson, E.A. 2021, Limitations of nanoparticles size characterization by asymmetric flow field-fractionation coupled with online dynamic light scattering, Chromatographia, doi.org/10/1007/s10337-020-03997-7.

Image is a photograph of a fast flowing mountain river taken in Yellowstone National Park during a roadtrip across the USA in 2006.

We are drowning in information while starving for wisdom

Decorative image: Lake Maggiore from AngeraThe title of this post is a quote from Edward O. Wilson’s book ‘Consilience: The Unity of Knowledge‘. For example, if you search for scientific papers about “Entropy” then you will probably find more than 3.5 million. An impossible quantity for an individual to read and even when you narrow the search to those about “psychological entropy”, which is a fairly niche topic, you will still find nearly 500 papers – a challenging reading list for most people.  The analysis of the trends embedded in scientific papers has become a research activity in its own right, see for example Basurto-Flores et al 2018 on papers about entropy; however, this type of analysis seems to generate yet more information rather than wisdom.  In this context, wisdom is associated with insight based on knowledge and experience; however the quality of the experiences is important as well as the processes of self-reflection (see Nicholas Weststrate’s PhD thesis).  There are no prizes for wisdom and we appoint and promote researchers based on their publication record; hence it is unsurprising that editors of journals are swamped by thousands of manuscripts submitted for publication with more than 2 million papers published every year.  The system is out of control driven by authors building a publication list longer than their competitors for jobs, promotion and grant funding and by publishers seeking larger profits from publishing more and bigger journals.  There are so many manuscripts submitted to journals that the quality of the reviewing and editing is declining leading to both false positive and false negatives, i.e. papers being published that contain little, if any, original content or lacking sufficient evidence to support their conclusions  and highly innovative papers being rejected because they are perceived to be wrong rather than simply deviating from the current paradigm. The drop in quality and rise in quantity of papers published makes keeping up with the scientific literature both expensive and inefficient in terms of time and energy, which slows down acquisition of knowledge and leaves less time for reflection and gaining experiences that are prerequisites for wisdom. So what incentives are there for a scientist or engineer to aspire to be wise given the lack of prizes and career rewards for wisdom?  In Chinese thought wisdom is perceived as expertise in the art of living, the ability to grasp what is happening, and to adjust to the imminent future (Simandan, 2018).  All of these attributes seem to be advantageous to a career based on solving problems but you need the sagacity to realise that the rewards are indirect and often intangible.

References:

Basurto-Flores, R., Guzmán-Vargas, L., Velasco, S., Medina, A. and Hernandez, A.C., 2018. On entropy research analysis: cross-disciplinary knowledge transfer. Scientometrics, 117(1), pp.123-139.

Simandan, D., 2018. Wisdom and foresight in Chinese thought: sensing the immediate future. Journal of Futures Studies, 22(3), pp.35-50.

Nicholas M Weststrate, The examined life: relations amoong life experience, self-reflection and wisdom, PhD Thesis, University of Toronto, 2017.

Edward O. Wilson, Consilience: the unity of knowledge, London, Little Brown and Company, 1998.