Tag Archives: emergent behaviour

Reduction in usefulness of reductionism

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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.

Do you believe in an afterlife?

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‘I believe that energy can’t be destroyed, it can only be changed from one form to another.  There’s more to life than we can conceive of.’ The quote is from the singer and songwriter, Corinne Bailey Rae’s answer to the question: do you believe in an afterlife? [see Inventory in the FT Magazine, October 26/27 2019].  However, the first part of her answer is the first law of thermodynamics while the second part resonates with Erwin Schrödinger’s view on life and consciousness [see ‘Digital hive mind‘ on November 30th, 2016]. The garden writer and broadcaster, Monty Don gave a similar answer to the same question: ‘Absolutely.  I believe that the energy lives on and is connected to place.  I do have this idea of re-joining all of my past dogs and family on a summer’s day, like a Stanley Spencer painting.’ [see Inventory in the FT Magazine, January 18/19 2020].  The boundary between energy and mass is blurry because matter is constructed from atoms and atoms from sub-atomic particles, such as electrons that can behave as particles or waves of energy [see ‘More uncertainty about matter and energy‘ on August 3rd 2016].  Hence, the concept that after death our body reverts to a cloud of energy as the complex molecules of our anatomy are broken down into elemental particles is completely consistent with modern physics.  However, I suspect Rae and Don were going further and suggesting that our consciousness lives on in some form. Perhaps through some kind of unified mind that Schrödinger thought might exist as a consequence of our individual minds networking together to create emergent behaviour.  Schrödinger found it utterly impossible to form an idea about how this might happen and it seems unlikely that an individual mind could ever do so; however, perhaps the more percipient amongst us occasionally gets a hint of the existence of something beyond our individual consciousness.

Reference: Erwin Schrodinger, What is life? with Mind and Matter and Autobiographical Sketches, Cambridge University Press, 1992.

Image: ‘Sunflower and dog worship’ by Stanley Spencer, 1937 @ https://www.bbc.co.uk/news/entertainment-arts-13789029

Destruction of society as a complex system?

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Sadly my vacation is finished [see ‘Relieving stress‘ on July 17th, 2019] and I have reconnected to the digital world, including the news media.  Despite the sensational headlines and plenty of rhetoric from politicians, nothing very much appears to have really changed in the world.  Yes, we have a new prime minister in the UK, who has a different agenda to the previous incumbent; however, the impact of actions by politicians on society and the economy seems rather limited unless the action represents a step change and is accompanied by appropriate resources.  In addition, the consequences of such changes are often different to those anticipated by our leaders.  Perhaps, this is because society is a global network with simple operating rules, some of which we know intuitively, and without a central control because governments exert only limited and local control.  It is well-known in the scientific community that large networks, without central control but with simple operating rules, usually exhibit self-organising and non-trivial emergent behaviour. The emergent behaviour of a complex system cannot be predicted from the behaviour of its constituent components or sub-systems, i.e., the whole is more than the sum of its parts.  The mathematical approach to describing such systems is to use non-linear dynamics with solutions lying in phase space.  Modelling complex systems is difficult and interpreting the predictions is challenging; so, it is not surprising that when the actions of government have an impact then the outcomes are often unexpected and unintended.  However, if global society can be considered as a complex system, then it would appear that its self-organising behaviour tends to blunt the effectiveness of many of the actions of government.  This seems be a fortuitous regulatory mechanism that helps maintain the status quo.   In addition, we tend to ignore phenomena whose complexity exceeds our powers of explanation, or we use over-simplified explanations [see ‘Is the world incomprehensible?‘ on March 15th, 2017 and Blind to complexity‘ on December 19th, 2018].  And, politicians are no exception to this tendency; so, they usually legislate based on simple ideology rather than rational consideration of the likely outcomes of change on the complex system we call society. And, this is probably a further regulatory mechanism.

However, all of this is evolving rapidly because a small number of tech companies have created a central control by grabbing the flow of data between us and they are using it to manipulate those simple operating rules.  This appears to be weakening the self-organising and emergent characteristics of society so that the system can be controlled more easily without the influence of its constituent parts, i.e. us.

For a more straightforward explanation listen to Carole Cadwalladr’s TED talk on ‘Facebook’s role in Brexit – and the threat to democracy‘ or if you have more time on your hands then watch the new documentary movie ‘The Great Hack‘.  My thanks to Gillian Tett in the FT last weekend who alerted me to the scale of the issue: ‘Data brokers: from poachers to gamekeepers?‘

 

Blind to complexity

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fruit fly nervous system Albert Cardona HHMI Janelia Research Campus Welcome Image Awards 2015When faced with complexity, we tend to seek order and simplicity.  Most of us respond negatively to the uncertainty associated with complex systems and their apparent unpredictability.  Complex systems can be characterised as large networks operating using simple rules but without central control which results in self-organising behaviour and non-trivial emergent behaviour.  Emergent behaviour is the behaviour of the system that is not apparent or expected from the behaviour of its constituent parts [see ‘Emergent properties‘ on September 16th, 2015].

The philosopher, William Wimsatt observed that we tend to ignore phenomena whose complexity exceeds our predictive capability and our detection apparatus.  This is problematic because we try to over-simplify our descriptions of complex systems.  Occam’s razor is often mis-interpreted to mean that simple explanations are better ones, whereas in reality ‘everything should be made as simple as possible, but not simpler’, (which is often attributed to Einstein).  This implies that our explanation and any mathematical model of a complex system, such as the nervous system in the image, will need to be complex.  In mathematical terms, this will probably mean a non-linear dynamic model with a solution in the form of a phase portrait.  ‘Non-linear’ because the response of the system not proportional to the stimulus inducing the response; ‘dynamic’ because the system changes with time; and a ‘phase portrait’ because the system can exist in many states, some stable and some unstable, dependent on its prior history; so, for instance for a pendulum, its phase portrait is a plot of all of its possible positions and velocities.

If all this sounds too hard, then you see why people shy away from using complex models to describe a complex system even when it is obvious that the system is complex and extremely unlikely to be adequately described by a linear model, such as for the nervous system in the image.

In other words, if we can’t see it and its too hard to think about it, then we pretend it’s not happening!

 

The thumbnail shows an image of a fruit-fly’s nervous system taken by Albert Cardona from HHMI Janelia Research Campus.  The image won a Wellcome Image Award in 2015.

William C. Wimsatt, Randomness and perceived randomness in evolutionary biology, Synthese, 43(2):287-329, 1980.

For more on this topic see: ‘Is the world comprehensible?‘ on March 15th, 2017.