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Critical Thinking for Engineers and Managers

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As technology speeds up we are all having to raise our game to cope with the onset of new and complex problems that inevitably need a fast fix. The old ways and methods certainly served us well, but they are increasingly putting us at risk, and it is now very often the case that:

‘Simple thinking and simple solutions no longer work’

So in my new role as a Visiting Professor at the University of Suffolk in Ipswich UK I am turning my mind to how we should educate and train our young engineers and budding managers for a future dominated by non-linearity and chaos born of increasing complexity (in the math sense).

In this slide set I have tabulated some of the many problems that we now face along with our own very human limitations. These are complemented by a series of suggestions and approaches that are proven, and about to be augmented by AI, sophisticated computer modelling, and decision support. If problems are simple, complicated and essentially linear, we generally have all the techniques and solutions to hand, but if they are complex and non-linear, we are almost always flying blind and have to apply new ways of thinking to achieve success.

We have no generalised solutions to non-linear problems, but occasionally we have point, or limited domain solutions, that are unique to a given situation. Unfortunately, classical mathematics is of limited use here and we have to resort to computational power.

This is the first in a library off presentations being prepared for the UoS

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Critical Thinking for Engineers and Managers

  1. 1. C r i t i c a l T h i n k i n g For Engineers & Managers P e t e r C o c h r a n e cochrane.org.uk ca-global.biz Beyond the linear and simple May 2017
  2. 2. o u r f o r m a l e d u c at i o n Is all about the known the well understood “What we can model and characterise forms the bedrock of our most basic understandings founded on the strongest of scientific principals - tried & tested results over millennia” It is the best formula our species has derived - and responsible for all human progress - all other routes lead to belief systems - witchcraft and general ignorance “Whilst it is quite acceptable for the mathematicians, physicists (et al) to declare that a problem cannot be solved - we ENGINEERS enjoy no such luxury - we always have to get an answer”
  3. 3. a u n i v e r s e o f t h e u n k n o w n How much does our formal education prepare us for this ? “As a young student coming into industry I thought I knew and understood a lot and I was well prepared to tackle anything” “I quickly realised that this was not the case and my real education was just about to start - and I am still a student” Where we are educated What we understand well Where we can solve problems A mostly linear & well behaved space What we know we know What we know we don’t know What we don’t know we don’t know What we don’t know What we know Unknown ??? =
  4. 4. N o n l i n e a r u n i v e r s e We have no generally applicable solutions Our mathematics is limited to ‘Order 5’ at best Our visualisation only extends to ~ 4 dimensions Our simultaneous variable tracking is generally <7 Our linear problem sets are mostly bounded and trivial Our non- linear problem sets mostly unbounded & complex Our simultaneous variable tracking capability is generally <7 Sheer computing power and modelling dominate Point solutions abound and we have to avoid misuse The non-linear domain demands new approaches/thinking
  5. 5. t h e e n g i n e e r i n g t o o l b o x Finding workable solutions can be really challenging - Does a solution already exist? - Has anyone else tackled the problem before? - Do scientific/engineering papers report similar problem sets - Apply basic analysis exploiting results of paper/people search - Apply advanced analysis/modelling on the basis of the previous step If none of the above works, then: - Ask - what might a reasonable solution set be? - Apply iteration starting from a best estimate/guess - Conduct detailed experiments to gather data points - Iterate toward a workable solution
  6. 6. d e c i s i o n T I M E Fail fast and adapt even faster In the face of incomplete data and/ or information - and facing a big d e c i s i o n d e a d l i n e - m a k e a decision and adapt rapidly to the outcome…ie best guess followed by iterations.. “ E v o l u t i o n f a v o u r s t h e m o s t adaptable and not the strongest or the smartest”
  7. 7. s i m p l e s y s t e m s Generally within the grasp of a single mind α Output ≈ Input (Energy) Directly related/predictable Can be conceived, designed and (sometimes) built by a single person
  8. 8. C O M P L i c at e d s y s t e m s Generally beyond the grasp of a single mind Output ≈ Input (Energy) Directly related/predictable Might be conceived by a single person, but (nearly always) designed and built by a team
  9. 9. C O M P L E X s y s t e m s Generally demands far more than human minds Output ≈ Input (Energy or State) Not directly related/predictable Individual components may be simple, linear, well behaved, conceived, designed and (sometimes) built by a single person, BUT the sum of the parts can and exhibit unexpected and unpredictable behaviours Medical conditions Teleco Networks Weather systems Financial systems Mobile networks Political systems Mobile networks Power grids Internet Conflict Warfare IoT BMOD Strokes Missiles Rockets Flooding Tsunamis Landslides Earthquakes Crime waves F1 racing cars Fighter aircraft ++++++
  10. 10. No top down design Impossible to specify Impossible to design Difficult to model Easy/Hard to realise Easy/Hard to control Easy/Hard to operate Unpredictable Evolves + Adaps Always Changing Unstable/Stable Emergent behaviours Beyond human grasp and abilities Established wisdoms do not apply Mathematics no longer works No general laws C O M P L E X i t y Governs the universe and all life Only ever Evolves/Grows/Builds from small to big simple to complex And NEVER the converse
  11. 11. s p r e a d M o d e Biological & man made commonality Virus Meme Desease Malware
  12. 12. AXIOM...still not understood by many... Taking an interest in every system known to mankind pays dividends in providing us with insights and challenging concepts and occasionally , really useful results... ..and we no longer design, deploy and operate our systems in isolation...we live in a world of natural and unnatural systems... evolved and designed... ...and the way they connect coexist and interact is important especially when life dependency and mission critical issues are at stake !
  13. 13. Only we design & optimise - evolution rests at good enough “We often appear use vastly complex solutions to achieve incredibly simple outcomes…whilst Mother Nature mostly seems to do the converse” B I G D i f f e r e n c e s “Mother Nature cannot do what we do, but then she is still confounding us”
  14. 14. Only goes for ‘good enough’ and optimises nothing She conceals her underlying complexity at every level of her constructs and activity... M o t h e r N a t u r e Her systems are resilient or they rapidly die out.. The over optimisation of companies and organisations in a fast evolving field leads to their certain demise
  15. 15. Defining ‘good enough’ is not always trivial and is generally the biggest challenge ! ~80% of the need satisfied by ~20% of the effort….and then often destroyed/ devalued by specification creep…. P e r f e c t i o n The enemy of good enough Just because we can add something extra does not mean we should….we have to think in terms of the value to be realised…
  16. 16. Analogue dominant Digital spreading fast Hybrid Analogue//Digital ubiquitous What we know advancing rapidly Our understanding mathematically limited Made by mankind we all die without them Made by machine we all die without them Our species survival depends upon good systems Our planets survival depends upon good systems Machine intelligence overtaking us in many areas Symbiosis necessary man machine partnerships Challenges formidable but interesting G e n e r a l i t i e s The technology/engineering status quo
  17. 17. Stored information growth 1986 I can’t draw a dot small enough!! . 1996 0.001ZB 2006 0.13ZB 2016 13ZB Impossible to read everything professional for >50 years ZetaByte = 1021 ExaB = 1018 PetaB = 1015 TeraB = 1012 Prognosis: IoT small data distributed storage may exceed the internet
  18. 18. >20M texts available <3M selected for analysis Argument abstracted pro - con No human resource can do this !
  19. 19. THE BOX In, out, or new ? You can think inside the box OR You can create an entirely new box ! You can think outside the box
  20. 20. DIVERSITY Mostly essential Capable people of differing ethnics groups; social, education industry experiences; with a wide range of discipline differences and diverse modes of thinking Proven AI engines, modelling and visualisation tools are useful augmentations to help/overcome the limitations of human thinking It is never too late to stop everything, abandon a given path and start again Search out and identify all those who may be able to help you and form a (real/virtual) team
  21. 21. STATUS QUO Breaking new ground “If it your problem were simple, someone would have solved it a long time ago” “Be prepared to consider and employ the radical, the new, and even the crazy”
  22. 22. PENUMBRA/Thoughts Your chance to ask questions and discuss cochrane.org.uk ca-global.biz For more presentations, papers and videos on this topic GOTO: www.cochrane.org.uk May 2017

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