Computational Modelling: Technological Futures
Computational Modelling:Technological Futures
E SUMMARY AND RECOMMENDATIONS7CHAPTER 1: WHY MODEL?13CHAPTER 2: MAKING AND USING MODELS25CHAPTER 3: MODELLING TECHNIQUES37CHAPTER 4: THE FUTURE OF MODELLING49CHAPTER 5: MODELLING IN PUBLIC POLICY57CHAPTER 6: MODELLING IN BUSINESS AND MANUFACTURING73CHAPTER 7: MODELLING CITIES AND INFRASTRUCTURE81CHAPTER 8: MODELLING IN FINANCE AND ECONOMICS89CHAPTER 9: MODELLING THE ENVIRONMENT99GLOSSARY111REFERENCES1151
To refer to this report, please use:Government Office for Science ‘ComputationalModelling: Technological Futures’ 20182
ForewordFOREWORDComputational modelling is essential to ourfuture productivity and competitiveness, forbusinesses of all sizes and across all sectors of theeconomy. Modelling can help drive performanceimprovement of products and services, achieveproductivity and efficiency gains, and create newinnovative smart products and services. From thedesign of jet engines to new drug development andmanufacture, digital design and modelling will becrucial to the UK’s future competitiveness.In high-value manufacturing, modelling supportsinnovation in product and process design, reducingthe need for physical prototypes and testing, andleading to more efficient processes and qualityproducts. In in the retail sector models areincreasingly being used to offer new services toenhance the consumer experience. In healthcare,modelling can be used to improve the effectivenessof treatments and diagnoses. Scientific models,based on the natural laws of physics, handle amassive amount of data to provide our dailyweather forecasts. These are just a handful of themany applications of computational modelling,and as this report illustrates, the power ofcomputational modelling, already significant, is setto grow dramatically.Rt Hon Greg Clark MPSecretary of State for Business,Energy and Industrial StrategyThe UK is world-leading in many areas of modellingacross our excellent research and industrialbase. This puts us in an enviable position to takeadvantage of the opportunities offered by advancesin modelling, and it is essential that we continueto support the advancement of the skills, researchand innovation needed for the UK to remainat the forefront of the development and use ofadvanced modelling.3
Foreword4
PrefacePREFACERapid growth in the availability of data andcomputing power and new methods for modellingcomplex systems are transforming our capability inmodelling. Working with a panel of experts frombusiness and academia, the Council for Science andTechnology has been looking at UK computationalmodelling capability and how it could be betterleveraged in both the public and private sector. Ouraim for this report is to demystify computationalmodelling, to demonstrate our capabilities, and toconsider steps which could be taken to fully exploitthese capabilities both now and into the future.Modelling can be used for a variety of differentpurposes, and the report starts with a discussionof some of these different purposes. It goes onto discuss the key steps in developing a goodmodel, and to provide a summary of the differenttechniques that are used. Together these 3 openingchapters provide a guide to how models can beused, but also how they should not be used.A key message is the importance of closeengagement between the customer and the modellerthroughout the modelling process, with clarity onuser needs essential to getting good modellingoutcomes. At the same time the importance ofmodel users’ understanding of the strengths andlimitations of a model cannot be understated.Improper use of a model or misinterpretation ofmodel outputs can come at a high cost, damagingtrust and credibility which is then hard to restore.increasingly embedded in the design and operation ofour public services, business processes and nationalinfrastructure, highlighting the importance of supportfor new skills, standards and collaborations to matchour increasing reliance on complex modelling.Chapters 5 to 9 look at modelling through thelenses of different public and private sectors:public policy; business and manufacturing; citiesand infrastructure; finance and economics; and theenvironment. We have been necessarily selectivehere, aiming to provide a flavour of the range ofuses and decisions where modelling can be applied.The sheer range of modelling applications meansit would not be possible for a short report to beexhaustive in its coverage.Computational modelling provides us witha powerful toolkit. This report contains 7recommendations which we believe would helpensure the UK is well placed to take full advantageof the opportunities offered by advances inmodelling capability, as well as ensure resilienceto potential vulnerabilities which increasing use ofmodelling exposes.Computational modelling has changed dramaticallyover the last decade, and Chapter 4 considers thefuture opportunities and challenges. Modelling,already ubiquitous, will become even more so,We are deeply grateful to the authors of thereport chapters; experts from academia andindustry from across the UK, whose collectiveknowledge, expertise and insight helped toshape the report’s recommendations. We wouldalso particularly like to thank Rowan Douglas,who provided the impetus for this reportwhile a member of the Council for Science andTechnology, and who has remained a strongadvocate subsequently as a member of the expertpanel guiding its development.Sir Mark WalportGovernment Chief Scientific Adviser andco-Chair of the Prime Minister’s Councilfor Science and TechnologyDervilla Mitchell CBEMember of the Prime Minister’s Councilfor Science and Technology and Arup UKMEARegion Chair.(April 2013 to September 2017).5
AcknowledgementsACKNOWLEDGEMENTSWe would like to thank the authors of the reportchapters, for their guidance and drafting of this report:Additional contributions from:Muffy Calder, Vice-Principal, University of GlasgowChristine McHugh, Associate Director for AirQuality and Environment, ArupClaire Craig, Director of Science Policy, RoyalSocietyMatthew West, Director of InformationJunction Ltd.Dave Culley, Senior Modeller, ImprobableRicky Taylor, Senior Economic Advisor,Department for Communities and LocalGovernmentRichard de Cani, Head of Planning for the UK,Middle East and Africa, ArupChristl Donnelly, Professor of StatisticalEpidemiology, Imperial College LondonRowan Douglas, CEO of Capital Science and PolicyPractice, Willis Towers WatsonBruce Edmonds, Director of the Centre for PolicyModelling, Manchester Metropolitan UniversityJonathon Gascoigne, Senior Risk Adviser for CapitalScience and Policy Practice, Willis Towers WatsonNigel Gilbert, Director of the Centre for Researchin Social Simulation, University of SurreyCaroline Hargrove, Technical Director, McLarenApplied TechnologiesDerwen Hinds, National Cyber Security CentreDavid C Lane, Professor of Business Informatics,Henley Business School, University of ReadingGiles Pavey, consultant data scientistDavid Robertson, Vice-Principal, University ofEdinburghBridget Rosewell, Senior adviser, Volterra PartnersSpencer Sherwin, Professor of Computational FluidMechanics, Imperial College LondonAlan Wilson, Chief Executive, The AlanTuring Institute6And the review secretariat and editor of the report:Amanda Charles, Review secretariat, GovernmentOffice for ScienceDr Mark Peplow, EditorSir Mark Walport, Chief Scientific Adviser to HMGovernment and co-Chair of the Prime Minister’sCouncil for Science and Technology(April 2013 to September 2017)Dervilla Mitchell CBE, Member of the PrimeMinister’s Council for Science and Technology andArup UKMEA Region Chair
Executive Summary and RecommendationsEXECUTIVE SUMMARY AND RECOMMENDATIONSIntroductionThis report is about modelling — specificallycomputational modelling, a fundamentalcapability of increasing importance. It helps usto extract value from data and ask questionsabout behaviours; and then use the answersto understand, design, manage and predict theworkings of complex systems and processes,including robotic and autonomous systems.Modelling is as old as known human civilisations,long used as a way to portray and understandthe world. Many of the earliest surviving humanartefacts are physical models, from toys to symbolicrepresentations placed in graves. Architects haveused models to market their designs to clientsfor many centuries, a notable example being themodel of St Paul’s Cathedral constructed forSir Christopher Wren.Humans are natural modellers — we carry modelsof our world in our minds. Our memories aresignificantly comprised of a mental model of theworld in which we live, and our personal history ofour experiences within that world. We navigate bymeans of maps: mental maps and the physical mapsthat we create.During the last half century, widespread accessto computers has transformed mapping. Oursmartphones present us with maps that help usto navigate and locate the transport systems andother services and products that we use on adaily basis. We use these mapping models withouteven considering that they are models, and weare increasingly dependent on the technology thatdelivers them.crowds to the workings of economic and businessservices and manufactured products. One ofthe new capacities of computational modellingis the ability to integrate models at differentscales and of different types, for example to linkhydro-meteorological models to maps of physicalinfrastructure to help decide where to placeflood defences.Analysis and explanation are just the starting pointfor the utility of models. Models enable us to makedecisions. They can help us to visualise, predict,optimise, regulate and control complex systems.The 2050 Energy Calculator is an example of amodel that enables non-specialists to easily visualisehow complex variations in the energy mix can helpus to meet 2050 carbon emissions targets and toexplore the effects of altering different policies thataffect carbon emissions. Non-specialists can thenbecome rapidly acquainted with the trade-offs inmanaging complex systems.In the built and engineered world, manufacturedproducts can be simulated as part of the designprocess before they are physically created, savingtime, money and resources. Buildings and theirinfrastructure can be modelled, and those modelscan be used not only to maximise the efficiencyand effectiveness of the design and build processes,but also to analyse and manage buildings and theirassociated infrastructure throughout their wholeworking lifespan. In the public sector, policiescan be tested before they are implemented,exposing potential unanticipated consequences andpreventing their occurrence.Computational models are essential to analyseand explain complex natural systems varying insize from the very small, such as the workings ofa bacterium, to the very large, such as planetaryweather and climate systems or the workings ofstars and galaxies. They are equally valuable for theanalysis and explanation of enormously complexhuman systems, varying from the behaviour of7
Executive Summary and RecommendationsModelling is a ubiquitous and powerful tool kitthat is rapidly evolving, and it is important thatpolicymakers have a good understanding of whatit can achieve and where the technology is going.Like any tool kit, though, it is important to knowwhich tool to apply to which problem, and to beconversant with the safety instructions. Models canenlighten or deceive, depending on the fit betweenthe tool and the application. Models are alwayssimplifications, and it is not easy to make the rightones. It is important also to recognise that thisrapid evolution in modelling does not mean that acomplex model is a better model. Indeed, in somecircumstances simple models may perform betterthan more complex models. Models should be nomore, and no less, complex than they need to be.recommendations that are aimed at government,the public and the private sectors to maximise thebeneficial impacts that technology can have in thedevelopment of government policy, on the deliveryof public services, and on economic growthand development.The majority of modelling is still undertaken usingspreadsheets enhanced by implementation insoftware, and this remains a valuable activity. Butmodelling is going through a revolution. This isdriven by factors that include a dramatic increasein the availability of data; and an equally dramaticincrease in the availability of computing power,coupled with the growth of cloud computing,which means that modellers do not need topossess their own compute infrastructure in orderto undertake some types of computationallyintensive modelling. Together, these factors enablemodelling to be a much more powerful tool thanit has hitherto. The same factors are also drivingthe development of machine learning and artificialintelligence, types of modelling that can predictaccurate outcomes from complex systems, thoughthose predictions may require alternative standardsof robustness and approaches to understanding.How to be an intelligent customer forcomputational modellingModelling technologies are like any othertechnology: they are neither intrinsically good norbad. Models can lead or mislead. Modelling can beapplied well or misapplied. This Blackett Review isone of a series of reports from the GovernmentOffice for Science that have 3 aims. The first ofthese is to demystify complex evolving technologiesfor the policymaking community and for thosewho are interested in how these emergingtechnologies are making important impacts onsociety. Secondly, they start to identify the potentialchecks and balances that are necessary to maximisethe beneficial effects of these technologies andminimise potential harms. Thirdly, they provide8For this report on modelling, the GovernmentOffice for Science has worked closely withthe Prime Minister’s Council for Science andTechnology in its preparation, drafting and delivery.The chapters have been written by experts incomputational modelling and its applications, in astyle that should be accessible to non-experts. Weare extremely grateful to these experts for theirthoughtful contributions.The first 3 chapters of the report provide an analysisof the reasons for modelling and an explanationof the processes of making and using models,followed by a description of modelling techniques.These chapters are aimed at those in the publicand private sectors who could benefit from the useof computational models. They provide a guide towhat models can and cannot do. Importantly, theyprovide a group of linked recommendations thatcan be summarised under the rubric ‘How to bean intelligent customer for models’.The first recommendation of the report is aninjunction to decision-makers:Recommendation 1: Decision-makers shouldconsider how analysis using models might beable to help in making difficult decisions.This is important because a lack of awareness ofthe potential of models to help with problemsolving means that they are underused. Whilemodels can be powerful assistants in decisionmaking, they can also be dangerous and misleadingif misused and misapplied.So it follows that decision-makers need to beintelligent and challenging customers for modellers— and that modellers themselves need to provideguidance on the appropriate use of models tomaximise the benefits and minimise the potentialharms of using poor or inappropriate models for
Executive Summary and Recommendationsmaking important decisions. Decision-makersshould understand that models may not resolveuncertainty in difficult decisions but may illustratehow large it might be and how it might comeabout.So the second recommendation is aimed athelping decision-makers to be expert customersand modellers to provide the appropriate modelsto their customers. Modellers need to be guidedby a clear articulation of the purposes of themodel’s analysis, and a model designed for onepurpose may not always be suitable for another.Policymakers need to be clear about the questionsthey want answered. Equally, models need to beappropriately quality assured and come with clearspecifications that set out when and how theywere created, how they have been verified andvalidated, what is their purpose, and what aretheir limitations.Recommendation 2: Decision-makers need tobe intelligent customers for models, and thosethat supply models should provide appropriateguidance to model users to support proper useand interpretation. This includes providing suitablemodel documentation detailing model purpose,assumptions, sensitivities, and limitations, andevidence of appropriate quality assurance.Those that use models should be well informedabout what type of model might help them, alongwith the strengths and limitations of the models,in order to maximise the effectiveness of theirapplication and avoid their misapplication.These recommendations are supported bychecklists within this report that set out the keyquestions that policymakers should ask aboutmodels. For example, what data are available andhow robust are they? What assumptions are beingmade? All models are simplifications and onlyas good as the assumptions and data that theyoperate upon. As assumptions and data can changeover time, care needs to be taken to track changesthat could alter the conclusion or action resultingfrom a model. All models should be regularlyreviewed while they remain in use.These checklists are related to and follow onfrom the important work and recommendationsby Sir Nicholas Macpherson’s review of qualityassurance of government analytical models andthe associated ‘Aqua Book: guidance on producingquality analysis for government’ of 2015. This wasone of the products of the work commissionedby government following the failure in 2012 of theInterCity West Coast franchise competition, wherethe dominant issue was a model that started life forone purpose but was poorly adapted for another.The future of modellingChapter 4 considers the future of modelling, andchapters 5 to 9 look at modelling through theframes of different public and private sectors.Modelling technologies are developing extremelyrapidly and there are some important drivers forthis. Some sectors have large markets that aredriving developments. One of these is gaming —computer gaming has swept the world — andthe attention of many people has switched fromgames in the real world to games played oncomputer screens and virtual reality headsets invirtual modelled worlds. Some of the companiesinvolved have realised the power of these modelsto tackle policymakers’ real-world problems, andare now modelling the real world as well as createdworlds. The UK company Improbable is one suchcompany, and has recently received 500 million ininvestment from Softbank.Other sectors are driving forward advancedmodelling, including high-performance engineeringand construction. The ability to simultaneouslysolve multiple differential equations means that itis possible to design and test complex componentsof cars, ships and planes. Individual componentsof a car or jet engine can be simulated, tested andoptimised before they are ever built, providinglarge cost, resource and efficiency gains. Theimportance of Formula One motor racing goesfar beyond the race track, as its requirements forever increasing gains in efficiency and speed areincreasingly applied to much more humble vehicles.Building information modelling has transformed theconstruction, monitoring and management of highlycomplex buildings and other physical infrastructure,leading to a business model paradigm shift fromconstruction to whole-life asset management.Critical software and hardware systems requirenew types of models, especially for cybersecurity,performance, and reliability. Numerous newtechniques have been developed over the past9
Executive Summary and Recommendationsfew decades, and more work is needed to makethe techniques more usable, more scalable andmor
Technology has been looking at UK computational modelling capability and how it could be better leveraged in both the public and private sector. Our aim for this report is to demystify computational modelling, to demonstrate our capabilities, and to consider steps which could be taken to f
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