Science has a mixed record when it comes to predicting the future. Engineersbuild bridges based on foreknowledge of the forces that they are likely toencounter – and their constructions tend to withstand the test of time.Predicting the future course of epidemics and building intervention to containthem are much more precarious. And yet simulation models produced in prestigiouscentres for mathematical biology have played a significant role informingcoronavirus policy in the United Kingdom and elsewhere. The predictiveuncertainties include the inherent variability of the pathogen, considerablevariation in host population immunity as well as the concern of this article,namely, the constantly adapting human judgements of those designing,implementing and experiencing the national response to an outbreak. Assumptionsabout how interventions are implemented and how people will react are, ofcourse, built into modelling scenarios – but these estimates depict behaviouralchange in fixed, stimulus-response terms. Real reactions to the complexrestrictions introduced to combat the virus unfold in scores of differentpathways – people comply, they resist, they learn, they grow weary, they changetheir minds, they seek exceptions and so on. Model building is intrinsicallyspeculative, and it is important that crisis management is not boxed in by itslatent simplifications. A more pluralistic evidence base needs to be drawn on,to understand how complex interventions operate within complex societies. On two occasions I have been asked, ‘Pray, Mr. Babbage, if you put into themachine wrong figures, will the right answers come out?’ . . . I am not ablerightly to apprehend the kind of confusion of ideas that could provoke such aquestion.– Charles Babbage, the Father of Computing Rnumber’. Modelling terminology has taken on emblematic significance. But whatexactly does it foretell? And how seriously should we take Box’s dolefulwarning?‘All models are wrong’. Not many methodological maxims have their own Wikipediaentry, but this striking aphorism, usually attributed to the British statisticianGeorge Box, takes on particular significance as science struggles to predict thetrajectory of the coronavirus outbreak. In the face of the pitiless challenge of theCOVID-19 pandemic, political leaders throughout the world have been quick tovouchsafe that they will ‘follow the science’. The science in question, mathematicalbiology, was perhaps not so well known to the public, but in the space of a fewmonths, the basic concepts and imagery associated with the modelling of infectiousdisease have become remarkably familiar. Not a day goes by without a bulletin fromthe Assemblée nationale or from the Bundestag or from Westminster about renewedefforts to ‘flatten the curve’ or about the latest value of the ‘The article tackles these questions in three sections. The first outlines the basicmethodology of infectious disease modelling. It explains the construction of thedisease transmission curves that carry the predictions and extrapolations ofepidemic models, in particular, the underlying statistical assumptions about howsocial intervention gain traction is scrutinised. The second section examines howmodern programme evaluation answers these same questions. There is a vivid contrastinsofar as social interventions are understood by evaluation as complex, adaptive,self-transforming systems, which go on to produce complex, varied and oftenunanticipated outcomes. The third section portrays the resultant clash. It providesdetailed examples from two prominent UK simulations of the considerable gap betweenmechanical assumptions of human behaviour portrayed in ‘model world’ and themercurial responses to the virus played out in the ‘real world’. There is a shortconclusion.Infectious disease models use a standard format for presenting predictions on thelikely course of the outbreak, as illustrated in How is disease progression modelled? The natural transmission curve in a majorepidemic takes shape as a disease moves through four groups – the susceptible, theinfected, the recovered and the deceased. Epidemiology makes use of a genericpattern, the bell shape, whereby the number of cases increases exponentially untilthe proportion of the susceptible has been sufficiently depleted (through recoveryor death) so that the growth rate then slows and the number of cases eventuallydrops so that the epidemic is no longer sustained . This noR0. This provides a measure of its initialtransmission potential, the average number of secondary cases generated by a singleinfected individual. In popular parlance, it is often understood as an indicator ofthe basic ‘aggression’ of the infection. It allows us to differentiate routineinfection from epidemic – the higher the R0, the fasterthe disease will accelerate and the more pressure will be exerted on medicalprovision . The overallstrategy, to use a splendidly dramatic summary of epidemic modelling by We will examine in due course how closely the ‘virtual copies’ match up to the realthing. But first we must complete the basic account of model building and describehow the second curve in These measures came thick and fast as the virus deepened its hold: hand hygieneadvice, surface cleaning, provision of protective equipment, recruitment andtraining of additional staff, building new facilities, the closure of shops,stadiums and schools, social distancing, isolation and lockdowns, working from home,testing and tracing and so on. All of these seek to block the circulation of thevirus. They are classic social interventions – instigated via extra spending orlegislation, but mostly they are transmitted as government ‘guidance’. Themodellers’ task here is to predict the behavioural changes that will follow thismedley of measures. How many people will comply with the guidance? What proportionof the population will be taken out of circulation by a particular measure? How willcrisis management change social interaction through space and time?x% of population will comply with measureA; y% will comply with measure B and so on. The modifiedtransmission dynamics are then loaded into revised equations, generating the second,‘protective-measures model’ as in Providing answers to these fundamental questions depends on anticipating of dozens ofmicro-manoeuvres. How many people, with what institutional support, willself-isolate, work from home, take up the furlough scheme, obey travel restrictions,abandon public transport, trade less, keep to the 2-metre rule, commit to homeschooling, modify family ties, establish social bubbles, reduce medicalconsultations and so on. No one knows the answers to these questions in advance ofpolicy implementation, but estimating the extent and effectiveness of such socialprocesses is precisely the task entrusted to the model builders. Various ‘changescenarios’ are pondered – All models are approximations. Assumptions, whether implied or clearlystated, are never exactly true. All models are wrong, but some models areuseful. So the question you need to ask is not ‘Is the model true?’ (itnever is) but ‘Is the model good enough for this particular application?’And this brings us back to George Box. Box was a pioneer of statistical modelbuilding and so his tongue was partly in cheek when he first used that enigmaticphrase in this form: ‘All models are wrong, but some are useful’ . Box’s eLet us follow this advice and examine in some detail the ‘particular application’embodied in the UK national policy response to the coronavirus crisis. It has becomea cliché to describe the virus and the response as ‘unprecedented’ and it isimportant to test this utterance. How vast is the challenge? What exactly are wetrying to model? One useful way of approaching these questions is to use the lens ofcomplexity theory. In doing so, we change disciplines and we change mind-sets,shifting from ‘modellers’ to ‘evaluators’. Programme evaluation has its own historyand its own methodology. Increasingly, it has come to appreciate that public policyis delivered by complex interventions thrust into complex environments.Magenta Book, formal UK Treasuryguidance  each and every illustration could be pursued in much more detail and (2) thereare scores of parallel examples within each category. Hopefully, enough is said tounlock the breadth of the challenge facing epidemic modelling. Can total systemchange be predicted? Is it possible to build a virtual copy of an open system, whichimports and exports influences to and from the wider environment? Is it possible tomodel unintended consequences? Is it possible to predict social behaviour whenpredictions may change behaviour and become self-fulfilling or self-denying? Is itpossible to anticipate the influence of a programme on a system that continuallytransforms of its own accord?The research community remains divided on answers to these questions. A firm ‘yes’would come from the modelling community, arguing that it is possible to make soundprobabilistic estimates of the subjectivities and transformations listed in Much of the UK policy on the epidemic has been informed by models provided by theImperial College COVID-19 Response Team. In this section, I scrutinise in detail thespecific assumptions that guided the construction of two influential simulations –one built before the epidemic had taken hold and one when ‘lockdowns’ had started tobe lifted. The first attempted to compare and predict the likely impact of a rangeof public health measures  in reducingCOVID-19 mortality and the demand for critical care . The secThis report was published by the Imperial College Team in March 2020. At face value, these results have clear action implications and immense policysignificance . One can see at a glance howthe curve may be flattened. But are its underlying estimates warranted or mereguesswork? As Box advises the basic suppositions in a model are sometimesimplied and sometimes clearly stated, but we can make some headway with a closereading of what is assumed in this particular simulation. The report begins witha detailed catalogue of epidemiological estimates built into the COVID-19transmission model and then moves on to provide rather scant descriptions of itssociological assumptions about how various NPIs would be implemented andheeded.R0), incubation periods,numbers of symptomatic and asymptomatic individuals, and so on are ascertainedfrom the initial Wuhan outbreak and from other epidemics. Estimates of diseaseprogression in terms of treatment and bed requirements, the number of casesrequiring hospitalisation, the number of patients and time needed in intensivecare units (ICUs), the number of eventuating deaths are also provided. Some ofthese metrics are attributed to ‘expert opinion’ and some to ‘personalcommunication’, for example: ‘We assume that 30% of cases requiringhospitalisation will require critical care  based on early COVID-19 cases in the UK, and Italy ’ ’ : 5. As cThe next batch of estimators is applied to the ‘non-pharmaceutical interventionscenarios’. A number of such action plans are modelled: case isolation,voluntary home quarantine, social distancing as applied to different groups,stopping mass gatherings and the closure of schools and universities. Recallthat the models were published in advance of significant government action . This is territory open to all theuncertainties of social complexity. But it is met with estimates than areconspicuously fewer and simpler than in their clinical equivalents and areclearly rooted in the authors’ own assumptions. For instance, the model for anintervention involving the closure of schools and universities assumes: ‘Theclosure of all schools, 25% of universities remain open. Household contact ratesfor student families increase by 50% during closure. Contacts in the communityincrease by 25% during closure’. The model for social distancing those over 70assumes: ‘Reduce contacts by 50% in the workplace, increase household contactsby 25% and reduce other contacts by 75%. Assume 75% compliance with thepolicy’.Both clinical and behavioural assumptions are then loaded into a model andoutcomes for a range of different interventions are simulated. R0). This provides a measure of its transmissionpotential, the average number of secondary cases generated by a single infectedindividual. As noted, it is the first ingredient of all models of diseaseprogression – the higher the R0, the faster thedisease will seed. Quite remarkably, there is no agreed figure for the COVID-19R0. Many studies have attempted to estimate thebasic reproduction number and two overviews of those investigations havecollated the various estimates. Its range is reported to vary from 1.5 to 6.49,with a mean of 4.2 in a study by R0 of 2.4 used in theImperial model.Let us begin with the ‘basic reproduction number’ different time periods, for differentcountries, using different baselines, but none of the information suggestsanything near a definitive, 30 per cent metric for UK hospitalised casesrequiring critical care provision. All that can be said with any authority turnsout to be a mere truism – the higher the assumed throughput, the more ominouswill be the predictions about pressures on critical care capacity, on itsability to cope and on eventual ICU death rates. The Imperial estimate is at thehighest end of the range, with obvious and dramatic consequences.Before we leave the matter, however, there is a much more instructive lesson tobe learned from current ICU research. Most mathematical models utilise fixedestimates, as illustrated above. But what is estimated, using a complexity lens,is a responsive, self-transforming process. In model world, it is perceived thatthere is some enduring context called ICU provision, which will come underarithmetic pressure as the number of cases requiring critical care multiply. Inthe real world, ICU services are subject to ‘continuous improvement’. Changemanagement is the norm. And crucially from this perspective, ICUs turn from a‘recipient of’ to a ‘response to’ the virus. They become active interventionsrather than passive incumbents.Phua et al.’s (2020) paper pulls together an extensive catalogue of how ICUpractitioners and administrators adapted to and overcame the initial surge incritical care needs. Recommendations are offered on how to improve access andtriage, infection protection in the unit, the donning and doffing of personalprotective equipment (PPE), the ventilation of units, the spacing of beds,logistical planning for equipment, consumables and pharmaceuticals, workforceload and augmentation, workforce communication and support, patient medication,collecting respiratory tract samples, post-ICU care and so on. The followingquotation on ‘intubation’ provides detail on how just one aspect of ICU clinicalresponse can be improved. Non-specialists will find the details obscure; thepoint is to demonstrate the inevitable and perpetual adaptation of the servicepredicted by complexity theory but lost in the models.Intubation of patients with COVID-19 also poses a risk of viraltransmission to healthcare workers, and intubation drills are crucial.The most skilled operator available should perform the task with fullpersonal protective equipment (PPE) and the necessary preparation fordifficult airways. The number of assistants should be limited to reduceexposure. Bagmask ventilation, which generates aerosols, should beminimised by prolonged pre-oxygenation; a viral filter can be placedbetween the exhalation valve and the mask. Rapid sequence induction withmuscle relaxants will reduce coughing. End-tidal carbon dioxidedetection and observation of chest rise should be used to confirmendotracheal tube placement. The use of closed suctioning systemspost-intubation will reduce aerosolisation. flexibly – ‘according to local need’.The same lesson about perpetual adaptation applies to the Imperial modelling ofthe public health measures that carried the first-wave response to the virus –hygiene, distancing, lockdowns, isolation and so on. Recall the scenarios forschool shutdown – ‘The closure of all schools, 25% of universities remain open.Household contact rates for student families increase by 50% during closure’.This sounds simple but is it simplistic? What is really involved? To get aproper measure, it is useful to consult government guidance in the The document then moves on to pages and pages of advice of how to implementclosure. These involve hundreds of separate recommendations on matters suchstaffing levels, their pay and protection, attendance expectations and parentalliaison, opening hours, curriculum, meals, exams, pooled provision and so onthat defy detailed description here. However, what becomes clearer in delvinginto closure-in-practice is its difference with closure-as-modelled. Thesimulation operates on the bald assumption: ‘the closure of all schools’. But inreality, most schools remained open – to the ‘children of critical workers’ andto ‘vulnerable children’.Decisions on who precisely qualified under these criteria and hence on thenumbers of residual attendees proved difficult to pin down. A list of eightcategories of key workers was provided: (1) Health and social care, (2)Education and childcare, (3) Key public services, (4) Local and nationalgovernment, (5) Food and other necessary goods, (6) Public safety and nationalsecurity, (7) Transport, (8) Utilities, communication and financial services.Understandably, these rather abstract categories were subject to continuousclarification – one of which, too ironic to ignore, was that ‘parliamentarians’are indeed critical workers. But as with all social programmes, decision makingis delegated down the implementation chain and so individual schools were leftto juggle decisions on whether one or both parents were covered by a requisitecategory and ultimately whether the child turning up at the school gate wasgenuinely in the ‘critical’ category. The attendance of ‘vulnerable children’ issubject to equally convoluted regulations, the consequences of which are omittedhere.The point of this example is to show that ‘school closure’ takes on myriad forms.It thus has a variable impact on changing rates of social interaction and, mostsignificantly, it will have complex, uneven and unanticipated effects on diseasecirculation. And this is only the beginning. Recall from Nature in May2020  across 11 European countries . The paper was published in-house as Imperial CollegeCOVID-19 Report 13  and thenMay2020 . Given ttRand death rates in a specific time period across 11 nations and to attribute thevarious changes to the national responses, separating out the respectivecontributions of the five NPIs put in place. Given the timing of this report,more real-time data on the progress of the disease were available, but the basicfindings above remain modelled – using a counterfactual comparison betweendeaths predicted under the five interventions and that estimated as if nointerventions had taken place. Achieving this comparison relies, as ever, on theassumptions inserted into the model, which brings us back relentlessly to Box’stest – are they good enough to support the empirical conclusions notedabove?The modellers’ task here is challenging – to map I begin this test by quoting two sociological assumptions built into the Flaxmanmodel:tR assumes thatchanges in the reproduction number are an immediate response tointerventions rather than gradual changes in behaviour and does notinclude importation or subnational variation’.‘Our parametric form of ‘We make the strong assumption that individual interventions have asimilar impact in different countries and that the efficacy of thoseinterventions remains constant over time’.These are strong assumptions indeed but woefully misleading, the problem beingthat they regard interventions, lockdowns in this instance, as fixed treatmentsin fixed applications having fixed effects. This contrasts starkly with basicassumption of modern evaluation research, namely, that exploring whether anintervention ‘works’ requires detailed knowledge of (1) its underlyingmechanisms, (2) its implementation, (3) its context and (4) its sustainability.In the remainder of this section, I analyse lockdown through these lenses.howinterventions work – to examine what a clinician might term the ‘mechanismof action’ or an evaluator might describe as the ‘programme theory’ oflockdown. In the present case, we begin with a relatively simple andperfectly plausible idea – lockdowns work by restricting social circulation,which in turn reduces person-to-person disease transmission. Alas, thisunchallengeable understanding of how lockdowns work raises our firstmethodological challenge because, of course, the other four interventions inthe Flaxman model rest on exactly the same mechanism – and so do otherinitiatives not included in the model such as border controls, overseastravel bans, post-test quarantine, sporting event biobubbles and so on.It is useful to start with an understanding of Lockdown, of course, differs from the others in the specifics of how itrestricts social circulation, basically by introducing forms of homeconfinement. In all cases (except Sweden), lockdown came last in theresponse armoury, with the significant consequence that the other fourmeasures implemented beforehand are retained through lockdown. Thus, in thereal world, lockdown is experienced as a composite of a range of ‘socialsegregation’ interventions. These measures interact – a population familiarwith and adapting to social distancing, multiple closures and event bans isthus already primed to ‘stay at home’. Schools, for instance, reported asignificant decline in attendance before the formal closure was implemented.It is no surprise then that the compound is more powerful than any of thecomponents but their interconnectivity, their mutuality makes thearithmetical assignment of their relative contributions highly problematic –a bit like looking for success in terms of team membership rather thanteamwork. None of these intersections come under consideration in modelworld where, to echo a previous phrase, interventions are regarded as fixedand separate treatments with fixed and separate effects.It is a commonplace of evaluation research that a programme title signifieslittle and that the ‘same’ intervention will be implemented in differentways, in different incarnations, with different outcomes. This possibilitybecomes a probability when that same intervention is implemented acrossdifferent national polities, cultures and institutions. Comparative researchrests on the assumption that we are comparing like with like and that is thechallenge that we now put to Flaxman’s 11-nation study. Remember its strongassumption that ‘individual interventions have a similar impact in differentcountries’.The report claims, ‘we have tried to create consistent definitions of allinterventions’, but the team relies on what might be termed the populardiscourse around the concept – the common-sense understanding issuing fromguidance materials and public comment thereupon. A table is appended to thereport with a perfunctory summary of the lockdown measures as applied ineach country . In the Belgian lockdown,‘Citizens are required to stay at home except for work and essentialjourneys. Going outdoors only with household members or 1 friend’. The UKlockdown is described as follows, ‘Gathering of more than 2 people not fromthe same household are banned and police enforceable’. The French lockdownrequires ‘Everybody to stay at home. Need a self-authorisation form to leavehome’. The Spanish lockdown is afforded two words ‘Nationwide lockdown’.These descriptions are sourced haphazardly – sometimes from official bodies but often frommedia accounts . Nothing herepersuades me that the nature of each lockdown has been explored thoroughly.Nothing ensures that the subsequent analysis goes on to compare like withlike.It would be too massive an undertaking for this article to chart theextensive differences in lockdown implementation in each country, but suchefforts exist including a monumental exercise ‘National responses to theCOVID-19 pandemic’ in Coming to the rules themselves, they cover movement restrictions on shopping,exercise, medical needs, travel to work, funerals, places of worship, socialservices, mental health provision, hospital and care home visits, housemoves, libraries, playgrounds, pubs, cinemas, theatres, restaurants,takeaway services, parks, playgrounds, campsites, public gatherings,construction sites, outdoor businesses and so on. Many items receivesupplementary clarification over time such as on the permissible size,purpose and location of ‘outdoor gatherings’. Lockdown, in short, is alengthy, intricate, evolving business, making a nonsense of the model’sbasic assumption that changes in behaviour are an ‘immediate response’ tointerventions.But we have yet to reach the major lacuna in the Flaxman assumptions, whichis that each of the 11 countries implemented these regulations in differentways – sometimes subtly and sometimes starkly. Take the initial policing oflockdown in just two countries as an example:Engage: Officersspeak to people and try to establish their ‘awareness and understanding ofthe situation’. (2) Explain: Officers ‘try to educatepeople’ about the coronavirus risks. (3) Encourage:Officers encourage people to ‘act reasonably’. (4) Enforce:Officers may ‘as a last resort, remove a person to the place where theylive, using reasonable force only if it is a necessary and proportionatemeans of ensuring compliance’.Enforcing the UK lockdown was widely seen by chief constables as ‘impossible’. The EngFrench policing of ‘confinement’ was much more robust, especially for regionsdesignated ‘Red Zones’. The number and types of venues allowed to remainingopen were significantly less than in the United Kingdom. Citizens had tocarry documentation certifying why they are outside, including personaldetails, reason, date, reason and precise timing. Tight distancerestrictions were imposed for travel that was permitted. Checkpoints wereinstalled across the country. Fines began at €38 but were upped to €135.Repeat offenders faced fines of €1500. In total, 100,000 police weredeployed to manage these obligations. They conducted approximately19 million checks throughout the nation’s 8-week lockdown .Extend this comparison to a further nine countries and there is little tosuggest that lockdowns are ‘similar’, less still that they have a commonimpact.This brings us to the next lacunae in Flaxman et al.’s model. Rather thanassuming that programmes work universally, the norm in contemporaryevaluation research is to suppose that they will vary in effectiveness. Thecrucial imperative is to investigate ‘what works for whom and in whatcircumstances’ . The imp‘This ignores one of the most important empirical findings of research oncomplex social networks during the last twenty years . . . social networksare highly heterogeneous in terms of degree distributions’ . Evenwibut not allEuropean countries will have major conurbations that share thesecharacteristics.In the case of lockdown measures that are modelled nationwide orcontinent-wide, this finding suggests that the research will not havesufficient levels of ‘granularity’ to recognise the high degree ofheterogeneity in the networks of transmission. The same problem, of course,applies to the policies themselves – implemented ‘across the board’ withoutsufficient attention to the hubs and connectors. If we start with theassumption that connectivity and transmission are deeply nuanced, then itbecomes clear that lockdown (contra the models) will have markedly differenteffectiveness within and between nations. Let us illustrate with a simpleexample that conveniently begins with the idea of hubs. In the UnitedKingdom, the epidemic was first identified in London. Its initial highexcess mortality can be traced to several factors: it imported infection bybeing the major hub for international travel; its mass transport systemdemands close physical contact; high-density living, working and shoppingdoes the same; and hosting major cultural and sporting events also has aherding effect. Note that this configuration pinpoints a particular locationin one country as a ‘connector’, but it is an explanation that has the powerto differentiate national epidemics. Some By the same token, there are countless other differences in socialconnectivity that will generate the heterogeneous spread of the disease andaccount for the inconsistent impact of the lockdown. A recent paper by Another vital concern of contemporary evaluation research lies with thesustainability of interventions. A key issue for projects in communityregeneration, international development, public health, crime reduction andmany others can be put as follows – what happens next? What happens whenfunding stops, when additional resources are withdrawn, when participantsare left to their own devices? This dynamic is, of course, of essentialinterest in virus reduction, where the very idea is to scale down lockdownmeasure at the point where disease transmission is under control and theimpact of the NPIs presumed to be sustainable. This policy premise involvesthe brave assumption that behavioural changes are sufficiently deep rootedfor their impact to endure. People will continue to follow guidance andendure some deprivation for a considerable period in order to enhance thepublic good. The Flaxman model shares this same conviction – ‘We make thestrong assumption that the efficacy of those interventions remains constantover time’.Is this remotely realistic? There is no intervention in the world that hasgenerated a uniform response from its recipients and we know from media ‘voxpop’ that reactions to the complex restrictions introduced to combat thevirus unfold in scores of different ways – some people comply, some resist,some learn, some volunteer support, some grow weary, some change theirminds, some seek exceptions and so on. The crucial question in respect ofsustainability is whether overall public trust in the management of theepidemic remained ‘constant over time’ and by summer 2020 pertinent evidencebegan to emerge.A report for the all individuals refrained from doing A, everyindividual as a member of the community would derive a certainadvantage. But now if all individuals less onecontinue refraining from doing A, the community loss is very slight,whereas the one individual doing A makes a personal gain far greaterthan the loss that he incurs as a member of the community.If One trigger of growing resistance to interventions is of particular relevanceto the lockdown. It is known as the ‘free rider problem’. In the present case, if one person ignores the lockdown, she or he gains fromthe collective effort, without having to make an individual contribution.The problem occurs when one becomes two and two becomes many. A sense ofinjustice amplifies if free riding becomes conspicuous and commonplace,generating division and leading the collective effort to crumble.Arguably, the activities of free riders had such an effect on UK public trustin the management of the epidemic. A longitudinal survey by Box asks us to pursue this question, ‘Is the model good enough for this particularapplication?’ And on this basis, I have made the case that two influential modelsare potentially misleading, often arbitrary and clearly self-affirming. Themechanical assumptions and fixed statistical estimates built into the Imperialsimulations completely fail to reflect the complexity of the societal response tothe COVID-19 interventions. What follows?Policy makers need to beware of the spurious precision and the seductive visualrepresentation of the transmission curves in epidemic modelling. They might harkback to advice from an inquiry on UK governmental actions during the 2009 influenzapandemic: ‘modellers are not court astrologers’ .Evidence on how to contain the virus must be drawn from a wider range of inquiries,pursuing a broader range of research methodologies, including process, programmetheory and case study evaluations. The evidence fragments used earlier in thisarticle are used critically but, marshalled together with other local, pragmaticresearch, they have a profoundly positive role in building more adequateexplanations of how interventions work.Methodological pluralism should be reflected in the composition of the UKgovernment’s SAGE. The first-wave response to the virus consists entirely of socialinterventions and yet modellers and epidemiologists rather than programme evaluatorsdominate the ranks of senior advisors. It would be wiser to ‘Box and Cox’, the seatsin methodological high command.Research progresses by eschewing certainty and by encouraging debate and competition.All research methods are fallible, and the science of COVID-19 would benefit fromgreater humility. The profound philosophical and sociological accounts of theprivileged status of scientific knowledge recognise that it owes that status through‘organised scepticism’  and closUK policy making has preferred evidence with a macro-focus, generating a stutteringstream of national restrictions based on overzealous extrapolations of shifts in aggregate, national data. Thereshould be more emphasis on the micro-circuits of transmission, seeking and targetingcontinuous quality improvement in the many subprocesses, logistics and agencies thatembody the everyday response to the virus.Administrative science has studied government decision making under conditions ofduress for many a year and maintains a thread running back to a paper by


This paper discusses corporate entanglement, impactfulness and responsibility in the Anthropocene, amidst events and conditions that ‘uncontain’ time. It takes its direction of travel from artist Brian Jungen’s ‘Cetology’ (2002), a whalebone sculpture made out of cut-up plastic garden chairs, which conjoins the times of earth and world history, as it hangs in the air of the art gallery, ‘as if’ exhibited in the natural history museum. The paper relates ‘Cetology’s’ engagement with natural history, time, and commodification to matters of corporate entanglement and responsibility within company law and governance. Problems with understanding the comparable imprint of large and multinational companies on matters, places, and communities are identified, after the domination of corporate legal frameworks over nature and the stability and perfection of economic incentives at law. The reading of this  situation is developed through theory and engagement with materialist and critical thinkers, who unite in their concern with distributed human–nature relations and the ‘concreteness’ that attends collisions in time, ruin and affect. Walter Benjamin and Theodor Adorno’s writings are central to this analysis, and strengthen the commentary on ‘natural history’ themes curated by Jungen. The paper contemplates how times’ uncontainment might invoke a change in expectation and method for the company law field, assigning contingency to the corporation and provoking a new mode of reflection about corporate entanglement and responsibility at law. This paper discusses corporate entanglement, impactfulness and responsibility in uncontained times. The latter term (uncontained times) expresses the interest of the author in thinking corporate law and governance for the world impacted, over generations, by industry, commerce and globalisation and for the epoch of the Anthropocene.companies at the centre of world history instead. The paper, concerned about this approach, explores the possibility that the histories coming to light might better ‘de-center’ the corporation and dramatise its interconnectedness, as a legal personality that intersects with a huge range of existents  and experiences that bear upon justice and equality. It explores the development of companies’ actions and impactfulness among ‘matters’,The paper’s focus on corporate entanglement and responsibility fits with this special issue’s effort to reassess legal ‘theories and frameworks that have prevailed within dominant accounts of law, rights, governance, sovereignty and the ordering of associative life’  communities in the context of climate change, oceans of plastic, pollution, commercial hunting and overfishing. The sculpture registers a dramatic change in what societies might monumentalize in the Anthropocene, as man-made materials, like polypropylene, take on existences and entanglements encounters with existences thrown into such a state of ruin, and onto the natural history register. He speaks about his determination to bring mortified and estranged natures ‘out of infinite distance’ and ‘into infinite closeness’, as objects of philosophical interpretation (ibid. p. 119). He cites  the work of his peer and fellow philosopher, Benjamin, as the turning point for realising this ambition, and for reading history on the surfaces of the surviving, as the ‘register of the trace’. We learn about progress, in this reading, not by trying to fit the alienated and discounted (or their ‘living on’) into ‘general structures’, but in ‘paradigms’ and ‘as interpretations of concrete history’, which reveal themselves on the surfaces of objects, in ruin and in transience (ibid. p. 119). Ruin invokes the ‘structure of inner-historical events’ (ibid. p. 117),Jungen’s work is interesting, for this paper and issue, for where it leads its visitors quizzically and contemplatively through an unexpected experience of plastic, natural-cultural entanglement, and through the intended and unintended consequences of commerce and modernity. Theodor Adorno in his 1932 (but posthumously published) lecture concerning the ‘idea of natural history’  also fasjuxtaposes commodity histories (price tags on) and the incalculable histories that lie before and after production . As allegorist, Jungen takes a break from ‘the affirmative power of linear time’, and the latent desire within companies and markets to ‘resolve tensions among priorities, loyalties and accountabilities’ . This negative extensiveness is juridically important, where it happens at intra-temporal and cumulative proportions, impacting a community’s experience of harms, if not the most basic physical processes of the planet. It relates, in fact, to intensively-scaled or ‘new’ harms, defined by philosopher Judith Lichtenberg  observe how migrations of force and affect attend the global networks of companies, including flows of labour, resources, and information across human, plant, mineral, animal bodies and environmental change processes Fortun . Impact mainly comprise disclosure laws, designed to encourage corporate reporting on non-economic performance and environmental, social and governance (ESG) factors ‘material’ to businesses;13Voluntary-styled corporate social responsibility (CSR) and corporate environmental responsibility (CER) programmes have rapidly advanced, in this normatively and materially fragmented context, as a practical means of ‘enlightening’ business actors about their impacts and associations, with near and distant communities . It is, rather, better understood as part and parcel of the idealistic and modern reflexive tradition, which has expressed itself powerfully within the economic sphere through the recognition and extension of separate corporate legal personality, as the ‘systemically privileged juridical “human” subject’  contractual metaphors and fiduciary duties, managerial norms and calculations . This dematerialised reading or ‘interpretation’ of the company matters where it makes tracing the social and environmental imprint of companies difficult, since much of the disclosure and veracity required continues to lie with managers (as guardians). It has tended, in practice, to also undermine common participation within the company and to limit managerial discretion to the shareholder primacy norm, even against pressure for change, where this offers a strong means for ‘clarifying expectations’ and for improving market discipline over directors .18Evidence of this history of expectation can be gleaned from classic Anglo-American company law and governance debates from the start of the twentieth century, which, as Lyman Johnson  argues, expectation, over (attention to) matter (which Johnson describes as ‘corporate deregulation’ in the CSR and CER context). Policy makers try to counter this unease as above, encouraging more material concerns and modes of reflection at companies (and investors). But this work is not easy, or altogether coherent, when expectation and industrial reason combine to make matter ‘amenable’ (to management), and the main mode of communicating with companies and their decision-makers in the deregulated sphere is still the market and demand—arenas built for immediacy, instrumentalism, and economic ambitions , and vulnerable to inequalities in voice and ‘reified’ wills  ‘It is as though nothing new was being imparted, as though the hero was just being subtly invited to recall something that he had forgotten’ , the ability of the non-human  to offer historical testimony, and an ironic shattering of traditions amidst otherness, ‘which is the obverse of the crisis and the renewal of mankind’ (ibid. p. 215). Benjamin offered an early (1925) systematisation of these ideas in his work about the mourning play, The Origin of German Tragic Drama , Benjamin documents occurrences that live on, more specifically, beyond the accommodations (and victories) of modern commercial systems and ideologies, including their value-structures.Benjamin developed these ideas—about the fullness of the world, oblivion amongst purportedly ‘rational’ actors, and the limits of human understanding, including Benjamin . This issecularly—that is, as not exclusively anthropogenic in their development (not relatable only to the concerns of humans). Benjamin saw in this living on proof of the fullness of the world, but also, the historical dynamization of intransient ideas and calculations . He used the insight to suggest that we might trace living history from the surfaces of ‘surviving’ objects, working from visual concreteness and bodies of affect that reach into the realm of experience, and demand translation as such. These remnants or bodies demand interpretation, as a matter of justice, where they undercut the usual antinomies between causality and morality, natural history and world history .Benjamin, like the present paper and issue, was keen in these works to move away from modern and idealistic conceptions of societal-nature relations, which categorised nature as pre-given and as the back-drop to human action . He emphasised, instead, the entangled and historical nature of being , and studied occurrences beset by ‘the past and the subsequent history’ of their essences Benjamin , p. 47. adding layers of meaning, writing over commodity histories with effects and experiences, to enact a ‘vertical or paradigmatic reading of the correspondences on a horizontal or syntagmatic chain of events’ , Benjamin developed a citation mode of ‘writing’ social history. It sought to document ruin, decay, and discontinuity, carving out dates for their ‘physiognomy’ Benjamin : N11. Ths’ Owens , p. 73. Benjamin , N1a3.This paper started with ‘Cetology’s’ illumination of ruin and the impacts that accumulate after commodities and industries in uncontained times. It examined corporate entanglements and responsibilities, which, also, intersect with large-scale environmental crises and legions of the impacted. Help with this task was teased from Benjamin and Adorno on the ‘idea of natural history’, and the deployment of ruin and allegory as a means of returning actors and systems to the ‘fullness’ of their pasts. The paper, in this final part, explores how shifting shapes of harm and injustice that are relatable to business actors might be sensed and interpreted after this art and text and their methods, within legal and responsibility systems now facing alterity, distress, and ruin. It contemplates how the company might be seen, secularised and made dutiful towards diffuse impacts, not through more management and governance (strategies critiqued in Part II) but after re-contextualising the company among matters and the ‘historical imagination’.The prospect sounds imaginative. It does accord, though, with growing amounts of legal and normative engagement with diffuse matters and the ‘age of consequences’ at law. Such engagement is evident, for instance, in the recent increases in climate litigation, which seeks to hold governments and private organisations responsible for climate change-related damage and/or threats of damage.23New York v Exxon Mobil , adding that ‘No reasonable investor during the period from 2013 to 2016 would make investment decisions based on speculative assumptions of costs that may be incurred 20+ or 30+ years in the future’ (ibid. at p. 34).But what of the corporation in this matter-bound context, and the opportunities that lie around it for legally and normatively ‘disrupting’ company law and the political economies of ‘business as usual’, including shareholder primacy and disembodied corporate governance? In Part II, the paper identified corporate law (and CSR and CER) as an area where rising matters are made ‘amenable’ to expectation, and must currently achieve economic status and recognition, or they might fall into the tide of ‘natural history’—of ‘things created by man, yet lost to him’ after ‘progress’ Adorno , p. 117.on Mobil ,24 part on Mobil .25 In thinvestors’ capacity for judgement and better nature is echoed across mainstream corporate governance and law. Relatable court judgements in the UK and beyond confirm ‘business judgement’ as a first-order forum for reflection on large-scale, inter-temporal risks and phenomena. Courts remain reluctant to assert obligations that could counteract or interfere with commercial expectations. They affirm moral rather than legal obligations at the international level to mitigate climate change risks at companies (in Re. Greenpeace Southeast Asia and Others 2019),R (People and Planet) v HM TreasuryNew York v ExxonMobilThis summons of but to reflect upon it and understand it’ , speak in this proposition not against certainty and expectation (as the judges and indeed shareholder primacy advocates fear), but to ‘the ruin of time, and a process of decay that is at the same time a process of crystallization’ . They might relate intergenerational losses after new forms, which try to ‘make explicit’ the historical nature of what has been forgotten or ‘naturalised’ , learning from the ‘surfaces’ of surviving objects, ties, and communities in geologic and montaged forms.cross-checks calculation, and eternal trust in the corporate form, with a new-found receptiveness to the build-up of matter and histories of harm. It brings mortified and estranged natures ‘out of infinite distance’ and ‘into infinite closeness’, toward interpretation and the study of ‘decay’ as well as ‘detail’ in transient forms.34Jungen, Benjamin and Kafka deploy the historical imagination and allegory—the ‘intuition of impermanence’‘de-centering’ the corporation from its default position of judgement and self-knowing, matter by matter, community by community. Before things reach the air of the natural history museum, perhaps.Part IV offers, with this, to clarify the paper’s interest in substituting the naturalness of the company, and management, with spontaneous concern for the affected and for existences that lie within the horizons of ruin and disorder, at law. Times’ uncontainment, in light of the crises the world is facing, could and should invoke a change in expectation for the company law field, assigning contingency to the corporation (and economics) and provoking a new mode of reflection about corporate entanglement and responsibility at law, and across domestic and international legal fields.within natural-cultural entanglement, as neither ‘bad guy’  nor ‘saviour’ . Companies become, instead, implicated in, and bearing responsibility for, the kind of ruin that the epoch of the Anthropocene is making concrete and unwise to (only) bound, divide up, calculate, and surpass. This modest end-point to the analysis captures the subtle radicalism of the artists and writers studied. It also, and finally, reveals what it means to the author to ‘de-center’ the corporation, and to release earthly existences from the usualness of submitting to corporate expectation, one gulp of fresh air at a time.This paper has talked about art, theory and the ‘idea of natural history’, as a means of thinking corporate entanglement and responsibility for the Anthropocene. The findings invite lawyers, policy makers and scholars to increase their level of attentiveness to corporate matters, and to attend the worlds, existents, environments, and autonomies impacted by business organisations over time. The primacy that the interlocutors  accord natural history is powerful, in this context, in its capacity to position corporate organisations


Any piece of information is a selection from a set of possibilities. In this paper, this set is called a “domain”. Digital information consists of number sequences, which are selections from a domain. At present, these number sequences are defined contextually in a very variable way, which impairs their comparability. Therefore, global uniformly defined “domain vectors” (DVs), with a structure containing a “Uniform Locator” (“UL”), referred to as “UL plus number sequence”, are proposed. The “UL” is an efficient global pointer to the uniform online definition of the subsequent number sequence. DVs are globally defined, identified, comparable, and searchable by criteria which users can define online. In medicine, for example, patients, doctors, and medical specialists can define DVs online and can, therefore, form global criteria which are important for certain diagnoses. This allows for the immediate generation of precise diagnostic specific statistics of “similar medical cases”, in order to discern the best therapy. The introduction of a compact DV data structure may substantially improve the digital representation of medical information. Information is typically represented in a very variable manner, such that its comparison is often made difficult or even impossible. This is a very important shortcoming in the case of medical information, with direct consequences for therapy. Therefore, one aim of this article is to recall the underlying theoretical and technical details of information. Digitally, information is a number sequence which is always a selection from a common ordered set of possibilities . In this paper, it is explained in detail that this domain crucially determines the digital representation of information and its comparability. Furthermore, it is also shown that the internet provides an efficient possibility for the long-term improvement of the current situation, through the online definition of adapted domains and of number sequences . These form the basis of a new type of language-independent medical information, which is globally comparable and searchable by means of user-defined criteria  which, therefore, makes it interesting and relevant to users. To construct the infrastructure for this approach, it is recommended that an attractive internet presence for the online definition of adapted domains by users is created.In terms of “information”, the exact and complete concept is meant here. This should not be exchanged with “information quantity”, which can be measured by counting bits, and which is only one property of information. There is a large amount of literature about information; however, imprecise and unclear concepts have been used for the definition of the exact term “information”. For the quantification of similarity and for the general comparison of information, a clear, precise, and natural approach is necessary. For this, it should be recalled that information is selection. It is well-known that any piece of digital information is a bit sequence and, therefore, a selection. Information, in general, as a result of any physical experiment, is also a selection . The apHere, “domain” denotes an ordered set of possibilities, which are common between the sender and receiver of the information. Information is always associated with a domain, which, in turn, is the domain of the information. The sender and receiver must both know the domain; for example, they must have a common vocabulary. Then, information is processed and transported digitally as a selection from the domain, as a number sequence. The domain of information crucially determines its digital representation. Therefore, information is fully defined by its domain and its selection from the domain ,3,4.It is important that (1) is a fundamental principle which is generally valid, even in elementary physics, and more research concerning this is recommended. For example, a common elementary charge and the derived common set (domain) of multiples of this elementary charge are preconditions for any electronic communication.Digital information consists of number sequences which are defined, by context, in a variable way. This can be improved by globally defining the domains of digital information (respective number sequences) in a uniform machine-readable way on the internet . Let “Uniform Locator” (“UL”) denote an efficient link to the online definition of the domain of the subsequent number sequence. Then, the data structure can be used to transport any globally defined digital information. This is called a “domain vector” (“DV”). The online definition of the domain is the global predefinition of information . As the Usually, information is implicitly regarded as a selection from a set of possibilities . However, the global and uniform definition of this set is not focused. For an extensive literature review in this paper, Google Scholar  was usedThe UL has a similar function to a link  , but allNumbers in the UL are self-elongating positive integers, starting with a half byte or byte, as shown in Figure 4 of ;The number sequence after the UL is completely defined in the online definition of the online address given by the UL. This is also a metric ;The binary format of the DV can be converted into a text-compatible form using, for example, the Base64 Data Encoding specification RFC 4648) . After t . After The DV is introduced in more detail in . Here, aIn general, information is only comparable if its domain is the same. Otherwise, the comparison and interpretation of information becomes imprecise or even impossible. Therefore, in this Section, important exemplary domains will be discussed. Then in , precondIn the case of language-based information, the domain is “language vocabulary” . There should be a common language, but, even in this case, the domain “language vocabulary” is not exactly the same for all speakers. This can cause misunderstandings. For example, as a comment on the weather, Alice may say “It is cold” when, at the same temperature, Bob might say “It is not cold”, because the word “cold”, as an element of the domain “language vocabulary” for Bob (who may wear warmer clothes) has another definition than for Alice. A further deep problem is caused by combinatorial complexity and redundancy. Multiple phrases are possible in the same situation. For example, in this situation, Alice may also say “I’m freezing”.Let us denote, by original information (“ORGINFO”), certain relevant original (language-independent) information that should be transported digitally as digital information (“DIGINFO”). In the case of typical language-based communication, ORGINFO is coded and transported by combinations of the domain “language vocabulary”. In the case of non-trivial ORGINFO, these combinations of words are long. As such, the coding (or representation) of ORGINFO by a free language is done in a non-reproducible way, and there is large variability in the resulting language-based digital representation, DIGINFO.For an illustration of the principle, we first start with the abovementioned simple weather commentary example, assuming that the original situation ORGINFO means “The temperature is 16 °C”, which caused Alice to say “It is cold”. Using “language vocabulary” as the domain, ORGINFO can be represented as DIGINFO in several ways, as Alice could also say “I’m freezing” or Bob could even say “It is not cold”. In every case, Alice and Bob think that they translated ORGINFO correctly into language, but the resultant DIGINFO is so imprecise that it can even look contradictory .Conversely, when searching for ORGINFO using the domain “language vocabulary”, several terms can be entered. The precise term “16 degrees Celsius” is too seldom used in conventional texts to be representative of ORGINFO. Moreover, similar situations are also interesting—for example, the precise term “15 degrees Celsius”, as a measurement result of temperature, in all languages. As a text search of all possibly interesting precise terms is not practicable, the term “temperature” can be used to represent the imprecise term “It is cold”, as shown in Completely different combinations of words or phrases (elements of the domain “language vocabulary”), as shown on the right side of To describe everything feasible using this domain, there is freedom in combining its elements (words and phrases). However, this leads to overlapping of meaning. The same thing can be described in several ways . Therefore, a text search of a certain sequence yields only a part of all locations with this meaning. As the number of possible sequences increases exponentially with the count of words in the sequence, the probability of finding a certain meaning with a single word sequence decreases exponentially with the number of words in it. Thus, if more than a few words are necessary to obtain a certain meaning, the probability of finding the most interesting locations with this meaning using a text search becomes very small. Therefore, text searches are practicable only for short sequences of words.However, in the case of professional communication , communicated information is usually nested and non-trivial. This means that a few words are not sufficient to describe a certain situation. An additional introduction is necessary, which is too long to be searchable using a text search. As searchability and comparability of non-trivial and nested information is important, a solution is necessary.For a precise comparison and search of ORGINFO, a solution that is less variable and more reproducible than using “language vocabulary” as the domain see  of DIGIN(a)Which  independent feature (parameter) is relevant within the chosen topic? If an appropriate quantification of this feature is available online, reuse it; otherwise, ask:(b)Which variants of the feature are possible? Quantify the feature, order its variants, and define a bijection to the numeric values of a parameter with the corresponding order.As it is impossible to bijectively represent “all information”  of reality digitally, the restriction to relevant features  is necessary. This is possible because ORGINFO is communicated within a certain topic—that is, it should only represent features which are relevant within the chosen topic. Thus, for the adaptation of the domain of ORGINFO to this topic, the following questions are (repeatedly) asked:For (a), relevant independent features are repeatedly searched. Every feature has variants which are selected (represented) by ORGINFO. If these are naturally ordered , this order is taken; otherwise, a useful order is introduced. If the resulting order is multidimensional, every dimension can be regarded as an independent feature with a one-dimensional order.After this, every resulting feature has a one-dimensional set of variants, such that every variant of every feature is bijectively represented  by a single number. Thus, the feature is quantified. If “N” denotes the count of all features, then the selection of the variants of all features is done digitally using N numbers . The conversion of ORGINFO to this digital representation DIGINFO is a bijection into an N-dimensional vector space  from the to the topic-adapted domain of ORGINFO. Due to this bijection, the domains of ORGINFO and DIGINFO can be treated as equivalent. This substantially simplifies our considerations in the case of adapted domains. Within the adapted domain, the relevant features of the original information are represented by numbers. Therefore, the definition of an adapted domain can be regarded as the definition of the number sequence, DIGINFO, which represents certain relevant features within the chosen topic. Adapted domains can be defined online  ≥ 0,F = 0 if and only if DV1 = DV2,F + F ≥ F, andF = F.A domain D with such a metric F is called a “metric space“ in the literature . A metriThe definability of the metric F provides clear preconditions (3). for the comparability of information. The digital representation of information (DIGINFO) is always represented by a finite count of numbers (N), which can be seen as a vector in an N-dimensional vector space. There are many possibilities to define the metric F on such a vector space; the Manhattan and Euclidean metrics are well-known examples . TherefoFor example, there are severe difficulties in the case of the domain “language vocabulary”. According to However, if an adapted domain is used, there is a bijection between the original information (ORGINFO) and its digital representation, DIGINFO  algorithms ; otherwiThere are already many databases which work with “locally defined adapted domains”. In particular, if they contain quantitative measurable data, there is often already a bijection between ORGINFO and DIGINFO. For the global comparability of information, however, a global definition of the domain is also important. Therefore, according to In the case of a text search, the domain is “language vocabulary”. As shown in Special ontologies have been developed to obtain a better adaptation to applications, such as in medicine  is necessary.In the case of a similarity search, a distance function is additionally necessary. The next Section explains this in detail.Similarity searches have been well analyzed in the literature ,20,21,22This is simply the metric F, which was introduced in This principle was used in our online prototype , which wFor the example in (a)Ask for relevant features within the chosen topic;(b)Quantify them, reusing already existing online definitions.Now, we have a theoretical basis for conducting similarity searches on original information (ORGINFO). To obtain a bijection with its digital representation DIGINFO, the first step is the definition of a topic-specific adapted domain of ORGINFO  is designed for interpretation by colleagues. It is insufficient, however, concerning precision, comparability, and searchability, as it suffers from the serious problems described in The lengths A, B, and C in Concerning privacy, it is important that the data of an adapted domain can also be exchanged after averaging, due to their uniform definition. Thus, meaningful medical data can be exchanged globally in an anonymized statistical form ,3.http://loinc.org”, this code can be used as a globally valid identifier of these data. This is done, for example, using the currently recommended FHIR standard [There exist well-known resources for standardized communication in medicine, such as ICD  , HL7 , in order to make relevant data in their area of expertise comparable and searchable, as shown in When comparing data about findings, diagnostics, and treatment, the users  are especially interested in the decision-relevant features; furthermore, they have the best ability to define and quantify such features, due to their knowledge of the subject. After quantification, similarity searching becomes possible. This user-defined similarity search provides an indicator for the comparability of medical information, considering the decision-relevant criteria and, therefore, should be a basal requirement.At present, however, users are essentially confined to text searches . This is better than nothing, but is hindered by the serious problems described in The possibility of similarity searching can be considered as an indicator of the comparability of medical information, as described in From a technical point of view, the FHIR format could also transport certain data for similarity searches  (1)not been consequently emphasized and technically utilized from the beginning? This is far-reaching, as adapted domains can be defined online for all possible applications . If it iWhy can users not  define adapted domains  online fDigital information consists of number sequences. Why have these, up to now, been defined in variable and complex ways by context? Why have globally defined, identified, and searchable information carriers (such as the domain vectors detailed above (2), up to now, not been introduced (as selections from an online defined and adapted domain), decades after the introduction of the internet?Why are global information searches still essentially restricted to text searching?Unsolved and complex interoperability problems have been discussed, but there has been no discussion about the online  definition of information. More than a decade after the publication of ,44 and lIt should be clear that such restrictions have enormous adverse effects . Furthermore, in other professional areas, precise global comparability and precise neutral searches for information would be very advantageous. As preparation for this, the introduction of domain vectors (2), as globally defined searchable information carriers, would be an important step.Should this basal task not receive support from responsible information scientists?The domain of information is crucial for the digital representation of original data. User-guided online definitions of adapted domains for typical medical situations  prepare medical information for similarity comparisons, considering decision-relevant criteria and features which are interesting for users.Therefore, the introduction of domain vectors DVs, see , as globFurthermore, DVs and their definitions can also be used for global interfaces between sub-programs. This allows for the global programming and optimization of modular designs.