Systems 1.0 What They Should Have Told You in Class

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At school, college and university we learn about ‘academic systems’ - and they can be fully classifies, analyzed and characterized - they always have solutions. When we graduate into industry …

At school, college and university we learn about ‘academic systems’ - and they can be fully classifies, analyzed and characterized - they always have solutions. When we graduate into industry systems of this kind are deemed trivial and dispatched quickly and we then face a raft of problems previously skirted or avoided altogether. In this presentation we set out a core of things to be aware of right from the beginning of any study of systems - be they organic, inorganic, living tissue or a man made.

Systems design, understanding and realization is not only important, it is vital to the progress and survival of our species, but severely limited by our bounded mathematical models, whilst being full of new and exciting challenges. Increasingly we are turning to our man made systems to help us unpick and unravel biology and the systems we have created and engineered. The Genome, Protein Stack, and communication between the two is one example and Artificial Intelligence is another.

This slide set is not so much the first chapter, more likely the first sentence, in our overall understanding of systems, and one that is generally missing from courses in the topic. And our biggest challenge; we don’t know how big this book is going to be, or indeed how many chapters there will be and their precise content and coverage! This is what makes the study of ‘Systems’ so exciting - the opportunity to discover, understand and contribute!

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  • 1. Systems 1.0cochrane.org.ukCOCHRANEa s s o c i a t e sca-global.orgPeter CochraneWednesday, 22 May 13
  • 2. DefinitionsWhat do we meanby a system ?Wednesday, 22 May 13
  • 3. “A group of interacting, interrelated,or interdependent elements forminga complex whole”Not entirely satisfactory...Wednesday, 22 May 13
  • 4. A functionally related group of elements, especially:- The human body regarded as a functional physiological unit- An organism as a whole, especially with regard to its vital processes or functions- A group of physiologically or anatomically complementary organs or parts- A group of interacting mechanical or electrical components- A network of structures and channels, as for communication, travel, or distribution- A network of related computer software, hardware, and data transmission devicesAn organized set of interrelated ideas or principles- A social, economic, or political organisational form- A naturally occurring group of objects or phenomena: the solar system.- A set of objects or phenomena grouped together for classification or analysis- A condition of harmonious, orderly interaction- An organized and coordinated method; a procedurePerhaps a bit more comprehensive...Wednesday, 22 May 13
  • 5. But what a lot of words,disjointed concepts andexamples to remember......might we do better ?Wednesday, 22 May 13
  • 6. ‘A system takes energy, matter,information, and transforms itsnature’Wednesday, 22 May 13
  • 7. Ergo; Systems are ‘essentially entropic’S = k log WWednesday, 22 May 13
  • 8. Note that we do not fully understand...Energy and MatterTime and Space...they may all justshrink down tospace......but until we get a GUTthis will remain uncertain...Wednesday, 22 May 13
  • 9. AXIOM...still not understood by many...Taking an interest in every system known tomankind pays dividends in providing us withinsights and challenging concepts andoccasionally , really useful results.....and we no longer design, deploy and operate oursystems in isolation...we live in a world of naturaland unnatural systems... evolved and designed......and the way they connect coexist and intereact isimportant especially when life dependency andmission critical issues are at stake !Wednesday, 22 May 13
  • 10. Some big differences betweenMan and Mother Nature...Only we designOnly we optimize We often appear usevastly complex solutions toachieve incredibly simpleoutcomes...Wednesday, 22 May 13
  • 11. Whilst Mother Nature...Only evolves systemsOnly goes for ‘good enough’ and optimizes nothingShe conceals her underlyingcomplexity at every level of herconstructs and activity...Wednesday, 22 May 13
  • 12. “Perfection is the enemy ofGood Enough”Defining ‘good enough’ is notalways trivial and is generallythe biggest challenge !Wednesday, 22 May 13
  • 13. Some broad brush system generalitiesAnalogue dominantDigital spreading fastHybrid Analogue//Digital ubiquitousWhat we know advancing rapidlyOur understanding mathematically limitedMade by mankind we all die without themMade by machine we all die without themOur species survival depends upon good systemsOur planets survival depends upon good systemsMachine intelligence overtaking us in many areasSymbiosis necessary man machine partnershipsChallenges formidable but interestingWednesday, 22 May 13
  • 14. What’s in THE ENVIRONMENT?s(t) h(t) o(t)Other systems of the same or differing type may be sharing the samespace or some part of it, and therefore there can be many obviousand hidden opportunities for aliasing....AirWaterEarthMachinesLifeformsFluidsSolidsChemicalsRadiationInformationWednesday, 22 May 13
  • 15. What’s in THE BOX ?s(t) h(t) o(t)ChemicalPhysicalInformation/Data ProcessingMathematicalNaturalUnnaturalBiologicalElectricalElectronicMechanicalComputational+++OpticalAcousticOrganicInorganicLife forms+++Wednesday, 22 May 13
  • 16. What does the output do?s(t) h(t) o(t)In the general case it impacts/changes theenvironment and the input and is often agrossly non-linear series of loopse(t)f(t)Wednesday, 22 May 13
  • 17. What’s in THE BOX ?s(t) h(t) o(t)What can we describe and defineOpticalAcoustic++++++Life formso(t) = h[s(t)] = h(s) for ease of notationo = a + bt + ct2 + dt3 et4 + ft5 is the largest polynomial wecan solve for very limitedand narrow range of casesIn the absence of a closed form solution we often reduced to using polynomial orsome other form of approximate descriptorWednesday, 22 May 13
  • 18. But many of our systems are of a much higher orderwith hundreds of feedback and feedforward loops...Wednesday, 22 May 13
  • 19. They also have hundreds of diverse inputs and outputsand cannot be fully flood, or combinatorially tested...Wednesday, 22 May 13
  • 20. SizeScaleComplexityConnectivitySophisticationConnectivityMTBFSpeedAgilityReliabilityTestabilityPredicabilityResponsivityCommon/General system traitsWednesday, 22 May 13
  • 21. SizeScaleComplexityConnectivitySophisticationConnectivityMTBFSpeedAgilityReliabilityTestabilityPredicabilityResponsivityCommon/General system traitsWednesday, 22 May 13
  • 22. SizeScaleComplexityConnectivitySophisticationConnectivityMTBFSpeedAgilityReliabilityTestabilityPredicabilityResponsivityCommon/General system traitsWednesday, 22 May 13
  • 23. SizeScaleComplexityConnectivitySophisticationConnectivityMTBFSpeedAgilityReliabilityTestabilityPredicabilityResponsivityOften difficultto definewithany greatprecisionCommon/General system traitsWednesday, 22 May 13
  • 24. SizeScaleComplexityConnectivitySophisticationConnectivityMTBFSpeedAgilityReliabilityTestabilityPredicabilityResponsivityCost MTTR LatencyPower Heat ResourcesOften difficultto definewithany greatprecisionCommon/General system traitsWednesday, 22 May 13
  • 25. Common/General system traitss(t) h(t) o(t)s1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)Wednesday, 22 May 13
  • 26. Common/General system traitss(t) h(t) o(t)s1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)SimpleSingularLinearComplexMulti - I/OLinearNon-LinearWednesday, 22 May 13
  • 27. All known, understood, well describedand characterized, bounded, and wellbehaved with causality preservedContained/bounded in/bysome known, or well defined,environment/conditionsSimple System - Key Features 1s(t) h(t) o(t)s(t) = Stimulush(t) = Operatoro(t) = Output }s(t) and o(t) originateand terminate withinthe environmentWednesday, 22 May 13
  • 28. All known, understood, well describedand characterized, bounded, and wellbehaved with causality preservedContained/bounded in/bysome known, or well defined,environment/conditionsComplex System - Key Features Is(t) = Stimulush(t) = Operatoro(t) = Output }s(t) and o(t) originateand terminate withinthe environments1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)Wednesday, 22 May 13
  • 29. All known, understood, well describedand characterized, bounded, and wellbehaved with causality preservedContained/bounded in/bysome known, or well defined,environment/conditionsComplex System - Key Features Is(t) = Stimulush(t) = Operatoro(t) = Output }s(t) and o(t) originateand terminate withinthe environments1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)XWednesday, 22 May 13
  • 30. All known, understood, well describedand characterized, bounded, and wellbehaved with causality preservedContained/bounded in/bysome known, or well defined,environment/conditionsComplex System - Key Features Is(t) = Stimulush(t) = Operatoro(t) = Output }s(t) and o(t) originateand terminate withinthe environments1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)X XMay be violated by designor implementation error ++Wednesday, 22 May 13
  • 31. All known, understood, well describedand characterized, bounded, and wellbehaved with causality preservedContained/bounded in/bysome known, or well defined,environment/conditionsComplex System - Key Features Is(t) = Stimulush(t) = Operatoro(t) = Output }s(t) and o(t) originateand terminate withinthe environments1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)X Any one or more or all of theseconditions may no longer trueX XMay be violated by designor implementation error ++Wednesday, 22 May 13
  • 32. Response matches needSymbiotic with the environmentPredictable, reliable, with a fast recovery timeUpgrades and changes not traumatic or riskyShocks are not terminal or unduly debilitatingReproducible, easy to deploy and maintain/repair/replaceSimple System - Key Features IIs(t) h(t) o(t)Wednesday, 22 May 13
  • 33. Response matches needSymbiotic with the environmentPredictable, reliable, with a fast recovery timeUpgrades and changes not traumatic or riskyShocks are not terminal or unduly debilitatingReproducible, easy to deploy and maintain/repair/replaceSimple System - Key Features IIs(t) h(t) o(t)Sometimes we cannot satisfy this wish list 100%Wednesday, 22 May 13
  • 34. Complex System - Key Features IIs1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)Response matches needSymbiotic with the environmentPredictable, reliable, with a fast recovery timeUpgrades and changes not traumatic or riskyShocks are not terminal or unduly debilitatingReproducible, easy to deploy and maintain/repair/replaceWednesday, 22 May 13
  • 35. Complex System - Key Features IIs1(t)s2(t)s3(t)si(t)o1(t)ok(t)o3(t)o2(t)hi(t)Response matches needSymbiotic with the environmentPredictable, reliable, with a fast recovery timeUpgrades and changes not traumatic or riskyShocks are not terminal or unduly debilitatingReproducible, easy to deploy and maintain/repair/replaceAlmost by definition we cannot satisfy this wish list 100%Wednesday, 22 May 13
  • 36. The nature of non-linearityLinear = Output scales with input in some way: y = ax + bNon - Linear = Output does not scale with input: e.g. y = a.exPredictableNon - Linear = I/O does not scale with: e.g. y = f1(x0)+ fs(x1)Seldom or never gives arepeatable output for allinput statesUn PredictableWednesday, 22 May 13
  • 37. The nature of non-linearityLinear = Output scales with input in some way: y = ax + bNon - Linear = Output does not scale with input: e.g. y = a.exPredictableNon - Linear = I/O does not scale with: e.g. y = f1(x0)+ fs(x1)Seldom or never gives arepeatable output for allinput statesUn PredictableHuh !!!Wednesday, 22 May 13
  • 38. How come ???Non - Linear = I/O does not scale with: e.g. y = f1(x0)+ fs(x1)Seldom or never gives arepeatable output for allinput statesUn PredictableMemory Dynamic/Stochastic non-linearities Input/Output uncertainties FeedbackVariabilityDelay Dynamic/Stochastic configurations Conditional uncertainties FeedforwardVariabilityDynamic non-linearitiesDynamic configurationsWednesday, 22 May 13
  • 39. ExamplesNon - Linear = I/O does not scale with: e.g. y = f1(x0)+ fs(x1)Seldom or never gives arepeatable output for allinput statesUn PredictableWeather Markets War PeopleWednesday, 22 May 13
  • 40. ExamplesNon - Linear = I/O does not scale with: e.g. y = f1(x0)+ fs(x1)Seldom or never gives arepeatable output for allinput statesUn PredictableNetworkTrafficLarge BioEntitiesChanceGamblingAtomicInteractionsWednesday, 22 May 13
  • 41. AXIOMS - For Networked // Aliased SystemsComplex Systems are never rendered simpler - without incurring errors/costs !Simple systems are mostly rendered complex - unless we are very lucky !Complex systems never get easier to characteriseSimple systems always get more difficult to characteriseSimple systems don’t make the complex simplerComplex systems always make the simple more complexWednesday, 22 May 13
  • 42. AXIOMS - For Networked // Aliased SystemsComplex Systems are never stronger than their weakest elementSystems are never simpler than their most complex elementsThere are lots of simple solutions tocomplex problems.......but they are always wrong !Wednesday, 22 May 13
  • 43. Should you discover sometime in the future, that anyof this is untrue, or does not hold.......then there is a Nobel Prize waiting for you !!!Wednesday, 22 May 13
  • 44. How can we be so sure ?Because the universe is governed by Entropy‘Gods Celestial Ratchet’OrWednesday, 22 May 13
  • 45. Huh ?That’s a story for another dayANDSystems 1.1Wednesday, 22 May 13
  • 46. AND NOWThis Weeks Assignment!Wednesday, 22 May 13
  • 47. Read everything youcan on Entropy andcome back.......prepared to discussand debateWednesday, 22 May 13
  • 48. thejourneyhasBegunWednesday, 22 May 13