Tag Archives: knowledge

Forecasts and chimpanzees throwing darts

During the coronavirus pandemic, politicians have taken to telling us that their decisions are based on the advice of their experts while the news media have bombarded us with predictions from experts.  Perhaps not unexpectedly, with the benefit of hindsight, many of these decisions and predictions appear to be have been ill-advised or inaccurate which is likely to lead to a loss of trust in both politicians and experts.  However, this is unsurprising and the reliability of experts, particularly those willing to make public pronouncements, is well-known to be dubious.  Professor Philip E. Tetlock of the University of Pennsylvania has assessed the accuracy of forecasts made by purported experts over two decades and found that they were little better than a chimpanzee throwing darts.  However, the more well-known experts seemed to be worse at forecasting [Tetlock & Gardner, 2016].  In other words, we should assign less credibility to those experts whose advice is more frequently sought by politicians or quoted in the media.  Tetlock’s research has found that the best forecasters are better at inductive reasoning, pattern detection, cognitive flexibility and open-mindedness [Mellers et al, 2015]. People with these attributes will tend not to express unambiguous opinions but instead will attempt to balance all factors in reaching a view that embraces many uncertainties.  Politicians and the media believe that we want to hear a simple message unadorned by the complications of describing reality; and, hence they avoid the best forecasters and prefer those that provide the clear but usually inaccurate message.  Perhaps that’s why engineers are rarely interviewed by the media or quoted in the press because they tend to be good at inductive reasoning, pattern detection, cognitive flexibility and are open-minded [see ‘Einstein and public engagement‘ on August 8th, 2018].  Of course, this was well-known to the Chinese philosopher, Lao Tzu who is reported to have said: ‘Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.’

References:

Mellers, B., Stone, E., Atanasov, P., Rohrbaugh, N., Metz, S.E., Ungar, L., Bishop, M.M., Horowitz, M., Merkle, E. and Tetlock, P., 2015. The psychology of intelligence analysis: Drivers of prediction accuracy in world politics. Journal of experimental psychology: applied, 21(1):1-14.

Tetlock, P.E. and Gardner, D., 2016. Superforecasting: The art and science of prediction. London: Penguin Random House.

Tacit hurdle to digital twins

Tacit knowledge is traditionally defined as knowledge that is not explicit or that is difficult to express or transfer from someone else.  This description of what it is not makes the definition itself tacit knowledge which is not very helpful.  Management guides resolve this by giving examples, such as aesthetic sense, or innovation and leadership skills which are elusive skills that are hard to explain [see ‘Innovation out of chaos‘ on June 29th 2016 and  ‘Clueless on leadership style‘ on June 14th, 2017].  In engineering, there are a series of skills that are hard to explain or teach, including creative problem-solving [see ‘Learning problem-solving skills‘  on October 24th, 2018], artful design [see ‘Skilled in ingenuity‘ on August 19th, 2015] and elegant modelling [see ‘Credibility is in the eye of the beholder‘ on April 20th, 2016].  In a university course we attempt to lay the foundations for this tacit engineering knowledge; however, much of it is gained in work through experience and becomes regarded by organisations as part of their intellectual assets – the core of their competitiveness and source of their sustainable technology advantage.  In our work on integrated nuclear digital environments, from which digital twins can be spawned, we would like to capture both explicit and tacit knowledge about complex systems throughout their life cycle which will extend beyond the working lives of their designers, builders and operators.  One of the potential advantages of digital twins is as a knowledge management system by duplicating the life of the physical system and thus allowing its safer and cheaper operation in the long-term as well as its eventual decommissioning.   However, besides the very nature of tacit knowledge that makes its capture difficult, we are finding that its perceived value as an intellectual asset renders stakeholders reluctant to discuss it with us; never mind consider how it might be preserved as part of a digital twin.  Research has shown that tacit knowledge sharing is influenced by environmental factors including national culture, leadership characteristics and social networks [Cai et al, 2020].  I suspect that all of these factors were present in the heyday of the UK civil nuclear power industry when it worked together to construct advanced and complex systems; however, it has not built a power station since 1995 and, at the moment, new power stations are cancelled more often than built, which has almost certainly depressed all of these factors.  So, perhaps we should not be surprised by the difficulties encountered in establishing an integrated nuclear digital environment despite its importance for the future of the industry.

Reference: Cai, Y., Song, Y., Xiao, X. and Shi, W., 2020. The Effect of Social Capital on Tacit Knowledge-Sharing Intention: The Mediating Role of Employee Vigor. SAGE Open, 10(3), p.2158244020945722.

Thinking in straight lines is unproductive

I suspect that none of us think in straight lines.  We have random ideas that we progressively arrange into some sort of order, or forget them.  The Nobel Laureate, Herbert Simon thought that three characteristics defined creative thinking: first, the willingness to accept vaguely defined problems and gradually structure them; second, a preoccupation with problems over a considerable period of time; and, third, extensive background knowledge. The first two characteristics seem strongly connected because you need to think about an ill-defined problem over a significant period of time in order to gradually provide a structure that will allow you to create possible solutions.    We need to have random thoughts in order to generate new structures and possible solutions that might work better than those we have already tried out; so, thinking in straight lines is unlikely to be productive and instead we need intentional mind-wandering [see ‘Ideas from a balanced mind‘ on August 24th, 2016].   More complex problems will require the assembling of more components in the structure and, hence are likely to require a larger number of neurons to assemble and to take longer, i.e. to require longer and deeper thought with many random excursions [see ‘Slow deep thoughts from planet-sized brain‘ on March 25th, 2020] .

In a university curriculum it is relatively easy to deliver extensive background knowledge and perhaps we can demonstrate techniques to students, such as sketching simple diagrams [see ‘Meta-knowledge: knowledge about knowledge‘ on June 19th, 2019], so that they can gradually define vaguely posed problems; however, it is difficult to persuade students to become preoccupied with a problem since many of them are impatient for answers.  I have always found it challenging to teach creative problem-solving to undergraduate students; and, the prospect of continuing limitations on face-to-face teaching has converted this challenge into a problem requiring a creative solution in its own right.

Source:

Simon HA, Discovery, invention, and development: human creative thinking, Proc. National Academy of Sciences, USA (Physical Sciences), 80:4569-71, 1983.

Walking and reading during a staycation

I am on vacation this week though, due to the restrictions on our movement imposed to prevent the spread of the coronavirus, it will a be staycation in our house.  We usually go to the Lake District at this time of year to walk and read; so, I might make another virtual expedition [see: ‘Virtual ascent of Moel Famau’ on April 8th, 2020], perhaps to climb Stickle Pike and Great Stickle this time.  I was asked recently about books I would recommend prospective science and engineering students to read in preparation for to going to university.  It is not the first time that I have been asked the question.  This time I thought I should respond via this blog since the disruption brought about by the pandemic probably means that many prospective students will have more time and less preparation prior to starting their university course.  So, here are six books that are all available as ebooks, and might be of interest to anyone who is staying home to counter the spread of coronavirus and has time to fill:

[1] It is hard to find good novels either written by an engineer or about engineering [see ‘Engineering novelist‘ on August 5th, 2015]; however, Nevil Shute’s novel ‘Trustee from the toolroom‘ [Penguin Books, 1960] satisfies all of these criteria.

I have more than 40 years experience of engineering science so I am not the best person to ask about books that will appeal to young people just starting their journey in the field; however two books that have been popular recently are: [2] ‘Storm in a teacup: the physics of everyday life‘ by Helen Czerski [Penguin Books, 2016] and [3] ‘Think like an engineer‘ by Guru Madhavan [One World Publications, 2016]

Regular readers of this blog might have spotted some of my favourite science books in the lists of sources at the end of posts. Perhaps my top three at the moment are:

[4] Max Tegmark, Our Mathematical Universe, Penguin Books Ltd, 2014. [see: ‘Converting wealth into knowledge and back to wealth‘ on January 6th, 2016; ‘Trees are made of air‘ on April 1st, 2015; ‘Is the Earth a closed system? Does it matter?‘ on December 10th, 2014 & ‘Tidal energy‘ on September 17th, 2014]

[5] Susan Greenfield, A Day in the Life of the Brain, London: Allen Lane, 2016 [see: ‘Digital hive mind‘ on November 30th, 2016; ‘Gone walking‘ on April 19th, 2017 & ‘Walking through exams‘ on May 17th, 2017].

[6] Carlo Rovelli, The Order of Time, Penguin, 2019 [see: ‘We inhabit time as fish inhabit water’ on July 24th, 2019 and ‘Only the name of the airport changes‘ on June 12th, 2019].

Of course, I should not omit the books that I ask students to read for my own first year module in thermodynamics:

Peter Atkins, A very short introduction to thermodynamics, Oxford: OUP, 2010.

Manuel Delanda ‘Philosophy and Simulation: The Emergence of Synthetic Reason‘, London: Continuum Int. Pub. Group, 2011 [see: ‘More violent storms‘ on March 1st, 2017; ‘Emergent properties‘ on September 16th, 2015 & ‘Emerging inequality‘ on March 5th, 2014].