MANAGEMENT AND DESIGN DISCUSSION MEETING

Systems theory and systems engineering

Prof. L. Finkelstein, MSc, FEng, FIEEProf. F. Land, BSc(Econ), FBCSProf. E.R. Carson, MSc, PhD, CEng, FIEEProf. J.H. Westcott, DSc(Eng), PhD, FEng, FIEE, FRS

Indexing terms: Design, Engineering administration and management, Management, Control systems

Abstract: The main features of systems theory arepresented in an overview by four members of theSystems Engineering Action Committee of theManagement and Design Division. The systemsapproach is one of the major intellectual develop-ments of the last half century, and comprises abody of concepts for the description, analysis anddesign of complex entities. It embraces the engin-eering of control, computing systems, biologicalorganisms, human organisation and economics.The generalised theory has the feature of holism,'the whole is greater than the sum of its parts'.Complex entities are decomposed into simplercomponents, handled separately, and then recom-bined in formal models, and as analogies betweendiverse systems that show similarity of structureand of dynamic behaviour.

Systems theoryProf. L. Finkelstein

1 Introduction

Systems theory and the systems approach are one of themajor intellectual developments of the last half century.The present brief overview cannot do more than to iden-tify the major features of the theory. The overview willanalyse present understanding rather than trace histori-cal development. It is written from an engineering view-point and in its constrained compass it cannot do fulljustice to divergent views.

2 The essence of systems theory

Systems theory is a body of concepts and methods for thedescription, analysis and design of complex entities

Paper 6068A (Ml, M3, M4, M5) received 18th January 1988These papers were first presented at a Discussion Meeting at SavoyPlace, on 24th April 1987, D.K. Hitchins was in the chairProfessors Finkelstein and Carson are with The City University,London ECIV OHB, United KingdomProfessor Land is with the London Business School, London NW14SA, United KingdomProfessor Westcott is with Imperial College, London SW7 2BT, UnitedKingdom

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leading to some important generalisations about suchentities.

A system is a set of related elements considered as aunity. Systems may be concrete or abstract.

The essence of systems theory is that when modelled inabstract formal language, systems of apparently diversekinds show significant and useful isomorphisms of struc-ture and behaviour.

3 Domains of application of systems theory

The classical domain in which systems theory is applic-able is that of the engineering of control, informationprocessing and computing systems all of which consist ofcomponent equipments functioning together as a whole.Similarly in biology organisms can be viewed as systemsof organs and processes forming a functional unit.Human organisations are again systems of interactingcomponents. Among other major areas to which systemsconcepts and methods are usefully applied are geographyand economics. These applications provide the systemsparadigm.

4 Holism

Holism is held to be an essential feature of systemstheory. This is often expressed in the phrase: 'The wholeis greater than the sum of its parts'.

Essentially holism in the sense of systems theorymeans that the modelling and analytical methods of thetheory enable all essential effects and interactions in asystem and those between a system and its environmentto be taken into account. Errors resulting from the ideal-isation and approximation involved in treating parts of asystem in isolation, or reducing consideration to someaspects are thus avoided.

Systems theory lays stress on the so termed emergentproperties of systems, that is those properties whichresult from the interaction of system components, proper-ties which are not those of the components themselves.

5 Decomposition

To master complexity systems theory approachesdescription, analysis and design of complex entities bydecomposing them into simpler components. Thesesimpler components descriptions, analyses and designsare separately handled and then recombined.

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6 Models

Systems theory is based on the employment of formalmodels. It extensively uses mathematical models todescribe the structure and behaviour of entities. In suchmathematical models considerable stress is laid on usingmethods of modelling which bring out isomorphismsamong apparently diverse systems.

Other models commonly used are graphical such asfor example, block diagrams, flow diagrams, bond graphsand the like.

An important feature of systems models is abstraction,in particular the description of entities in terms of theirfunction rather than in terms of their form.

7 Isomorphisms

7.1 FunctionAs was mentioned above, systems of diverse kinds exhibitimportant isomorphisms. It is well known that equationsgoverning the behaviour of electrical, mechanical, fluidmechanical and thermal systems are essentially identicalin form. Such systems are describable in terms of powerflows, and energy stores, converters and the like. Physio-logical and metabolic systems are similarly describable interms of flows, stores and so on. Such analogies are alsopossible in other forms of system.

7.2 StructureSome important interconnection structures occur widelyin diverse systems. It is essential to mention here feed-back and hierarchy.

7.3 BehaviourIsomorphisms of function and structure lead to iso-morphisms of behaviour of systems. In particular diversesystems show similarity of dynamic behaviour, such asresponses to disturbances, instability and so on.

8 Hard and soft systems

There exists a distinction between so called hard systems,those in which the components and their interactions canbe described adequately by mathematical models, and socalled soft systems which cannot be so described. Thelatter are predominantly human activity systems whichinvolve difficulties and uncertainties of conceptualisation,description and measurement, non uniformity and varia-bility of behaviour and so on. This limits the applicabilityof the kind of systems concepts and methods describedabove. It is precisely in these inherently complex softsystems, however, in which systems concepts are particu-larly attractive.

9 Systems design

Systems theory has evolved a systematic approach tosystem design. This is based on first defining the systemobjectives, proceeding to the generation of a candidatedesign, analysing and evaluating the candidate in termsof the objectives and then deciding either to implementthe candidate or to return to an earlier stage to generateanother candidate, systems design proceeds by decompo-sition of the task and then by the combination of partialdesigns into a whole.

10 Systems theory and systems engineering

Systems theory is the theoretical basis of systems engin-eering, the latter being defined as the design, implementa-

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tion and operation of complex systems. The keycomponents of systems theory applied in systemsengineering are a holistic approach, the decompositionof problems, the exploitation of analogies and the use ofmodels.

Information systems andcomplexityProf. F. Land

This paper sets out to illustrate some of the reasons whyour understanding of information systems is limited, andwhat underlies the inherent complexity of such systems.

An information system has no existence of its own. Itis always a subsystem of some larger system, often calledan organisation, or an enterprise. Organisations comprisepeople working to achieve certain goals, assisted by avariety of artefacts and constrained by rules and normsof behaviour. Information systems exist to support theactivities of the organisation, and themselves comprisepeople and artefacts [1]. Information systems, like organ-isations, are social systems which use technology to helpachieve goals. Peter Checkland calls such systems 'humanactivity systems' [2].

In any organisation there are two types of informationsystems:

(i) Designed systems: Systems that are formally speci-fied, rule-based and purposeful. Most designed informa-tion systems of interest are open systems, operatingthrough the interaction of individuals or groups assistedby the use of a variety of tools and instruments.

(ii) Undesigned systems: Systems that are informal,have no specification, may not be authorised and operatethrough informal and undefined interactions betweenindividuals and groups. They, too, are purposeful,although the purposes are often covert. Their operation,although informal, is often constrained by tacit rules ofbehaviour and through the action of norms. Undesignedsystems may also involve the use of tools and instru-ments.

Formal designed information systems operating in anorganisational (social) context have a tendency to decay(or evolve, according to the viewpoint) into informalsystems. As soon as a formal system has been implement-ed, social forces tend to alter the system by a process ofaugmentation and replacement. Often, such processes arenonauthorised and hence covert, but they may be theresult of properly authorised or semiauthorised actions.Formal systems in an organisational setting undergo akind of entropy, although, with present knowledge, wehave no way of predicting the rate of entropy. Thus,formal systems are fragile, and in their designed formhave only a short life. Informal systems, on the otherhand, are relatively robust, and attempts to replace aninformal system by a formal system can cause difficulties,manifested by such behaviour as 'resistance to change'[3].

The problem of 'knowing' or describing a desiredinformation system is constrained by some of the follow-ing factors:

(a) The analyst's bounded rationality. The problem iscompounded because the analyst has to interact withpotential users and sponsors of the system under review,where each respondent may operate with different con-ceptions and viewpoints.

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(b) The problem of knowledge acquisition. Can theanalyst distinguish between 'espousal' and 'action' interms of the Argyris [4] theory of single-loop anddouble-loop learning.

(c) The political nature of social systems. Individualsand groups operate in a context of conflict and compro-mise, of coalitions and adverserial groupings which areconstantly shifting, of negotiation, bargaining and gameplaying.

(d) The problem that organisations comprise individ-uals and groups which only partly share goals andvalues. The analyst faces a multi-objective world, whereonly some objectives are clearly articulated, and manyare tacit, where, even if objectives are common, the valuesattached to the objectives may differ widely.

(e) The problem of an imperfect, 'dirty', often unpre-dictable, environment. All social systems have to contendwith unpredictable behaviour. Hence the popularity ofconcepts like Murphy's law.

(/) The difficulty of predicting the future in which thesystem under review will be expected to operate at a highlevel of effectiveness. Over the lifetime of a system,changes, often unexpected, will occur; e.g. changes in atti-tudes and values, changes in organisation and personnel.A system may be expected to survive a number of yearsto earn an adequate return on the capital invested indeveloping it. However, forecasting horizons tend to beshort.

(g) The tools used to analyse and understand require-ments are inherently incapable of providing more than adubious model of the real world and its needs. The classicmethods of interview, observation and survey providepartial models of the real world, and the more recentlydeveloped techniques such as portfolio analysis or cogni-tive mapping have their own problems.

This list of problems is not exhaustive. But it doesillustrate that understanding and designing effectiveinformation systems is an area which is difficult. In thepast, a great deal of attention has been paid to solvingtechnical problems. It is clear that, in the future, muchmore attention has to be paid to the systemic problemsinherent in coping with social systems.

References

1 LAND, F.: 'Is an information theory enough', Comput. J., 1985, 28,(2)

2 CHECKLAND, P.: 'Systems thinking, systems practice' (John Wiley& Son, Chichester, 1981)

3 KEEN, P.G.W.: 'Information systems and organisational change',Commun. ACM, 1981, 24, (1)

4 ARGYRIS, C, and SCHON, D.: Theory in practice' (Jossey-Bass,San Francisco, 1974)

Physiological and medicalsystemsProf. E.R. Carson

1 The complex physiological system

For the systems engineer, the human organism canprovide an important source of analogy. In systemsterms, it can be regarded as a complex physico-chemicalsystem with highly developed adaptive control systems.Some measure of this complexity is evidenced by thehighly simplified control system view depicted in Fig. A.

The components of sensors, nervous transmission, brainand effectors are clearly portrayed.

The brain is the overall organiser, filtering incominginformation, comparing input sensory patterns with thosealready stored, making decisions of varying consequencein time, memorising and controlling nervous outputs.The operational controllers govern the actuators of thephysical systems, controlling variables in such operationsas muscle contraction and chemical activation. The actu-ators operate on the plant, such as the skeleton andblood circulatory system.

Innumerable feedback loops are present, feeding infor-mation back to all levels of control through a variety ofsensing mechanisms. For example, blood pressure ismonitored by many pressure receptors in the arterieswhich send signals back to the cardiac centre, controllingvariables such as heart rate and ventricular strokevolume. The whole system is subjected to constraints anddisturbances, which may limit its performance or cause itto re-evaluate its decisions [A].

Much of the decision-making and control action ishighly decentralised, such that there is, for example,decision-making within the individual cell determiningwhich chemical product should be produced in condi-tions in which alternative chemical pathways are avail-able. The controllers exhibit positive as well as negativefeedback action, with copious examples of derivative andintegral as well as proportional action.

2 Model-based approaches

To gain insight into the full richness of the complexitywhich physiological systems display, model-based studiesare required, studies which can thereby yield lessons forsystems theory. Traditionally, this has involved, forexample, the use of mathematical representations ofphysiological dynamics and their regulation and control.Other modelling modalities, both quantitative and quali-tative should be considered also, however, modalitiesembracing the mathematical, statistical, logical andgraphical [B]. By such means, a clearer understanding ofthe structure and functioning of the human organism canbe achieved. These model-based approaches are,however, useful, not only in the context of gaining insightand providing explanation, but also, as indicated by thefollowing, in the context of assisting the clinician inpatient management.

3 System failure

The nature of pathological states arising from the failureof physiological control is illustrated by considering thesimplified picture of the interaction of glucose and insulindynamics shown in Fig. B. In the normal physiologicalcondition, an elevated concentration of plasma glucose isrecognised by the pancreas which results in insulin beingsecreted. The net chemical effect of this secretion is areduction in the blood glucose level towards its normalvalue, an example of classical negative feedback control.In type I diabetes the pancreas no longer responds to anelevated glucose with an appropriate insulin response,and hence the regulatory feedback loop is broken. Exter-nal control has to be introduced typically in the form oftwice daily insulin injections. This replacement of thefailed, endogenous control mechanism by an externalcontrol loop constitutes the classical control system viewof medical treatment.

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4 The medical system

This control system view of patient care can be regardedas a special case of the management of a complex system.Within the human organism, as indicated above, a hier-archy of functioning control mechanisms operates, over a

spectrum from neuro-endocrine surveillance mechanismswith rapid time constants to the slow processes ofchronic adaptation. Given the complexity of such pro-cesses, the clinician often does not have time to learn thefull dynamic characteristics of the patient before treat-ment, but must act as soon as possible to minimise the

raw materialsdisturbancesphysicalmental etc.

constraintssurvivalphysicalsocialmoral etc.

goal networks

decisionmaking

operationalcontrollerspattern

recognition muscles(voluntary andinvoluntary)

musclecontrolcentres

blood-circulatorysystem

cardiac centrevasomotor

centregaseous -exchangesystem

respiratorycentre

bloodconstitution

metabolicsystemsauditory

balancechemicalactivators digestive

systeminternalfeedback

internal sensors

from actuators

position touch / balance

Fig. A Complexity of the human organism viewed as a physico-chemical system [modified from Reference A)

insulinglucose

dynamics

insulindynamics

1 bloodI glucoseI concentrationI

i _ I 1 \

I Iexternal control

Fig. B Glucose/insulin feedback control system indicating the need forexternal clinical control when the endogenous control mechanism fails tofunction effectively

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risk of patient discomfort or danger. This parallels thegeneral situation in which the nature and behaviour of acomplex process may not be understood, and thecontroller/manager is simply instructed to maintainstates within certain limits. In both cases, experience willmodify the controller according to overall objectives, bymodifying both the structure and the local objectives ofthe controller [C].

This external control action, carried out in order thatthe complex pattern of system dynamics can be returnedtowards the normal state, involves both feedback andfeedforward operations. Feedforward control by the clini-cian involves the taking of action at the current time toproduce a predicted, desired state; and equally to preventa predicted, undesired state. In response to a disturbanceacting on the physiological system, the clinician, acting as

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a controller, responds in such a way as to eliminate theeffect of that disturbance on the physiological system.Such feedforward control requires the use of a model ofthe patient's physiological system to assess the effects ofboth disturbance and control on the physiologicalsystem. This feedforward control can then be seen asbeing embedded within the classical feedback controlmodel of Fig. B.

The way in which data from the patient need to beprocessed and interpreted in context, to yield informationfor the physician for diagnosis, decision-making andcontrol, is shown schematically in Fig. C. The component

1

information

data interpretation

1 1data processing

tdata acquisition

1L , r

physician

patient

r1Ii

L

therapy

Fig. C Patient/physician feedback loop illustrating the acquisition,processing and interpretation of clinical data to provide information forclinical decision-making

of data interpretation is the one where model-basedapproaches are widely applied, whether the models arethose of the underlying physiological dynamics or therule-based models more conventionally found withinexpert systems.

5 References

A JANES, F.R., and CARSON, E.R.: 'Modelling biological systems',Electron. & Power, 1971,17, pp. 110-116

B FLOOD, R.L., and CARSON, E.R.: 'Dealing with complexity: anintroduction to the theory and application of systems science'(Plenum, 1988)

C CARSON, E.R., and FINKELSTEIN, L.: 'Model-based control inmedical treatment: a complex system paradigm', in TRAPPL, R.,PASK, G., and RICCIARDI, A. (Eds.): 'Progress in cybernetics andsystems research, vol. IX' (Hemisphere Publishing Corporation,Washington DC, 1982), pp. 29-37

Systems engineering:Economic systemsProf. J.H. Westcott

Systems theory now encompasses a well structured set oftheoretical disciplines providing insight into the dynamicresponse of a wide range of complex systems.

A national economy is just such a dynamic system, soone could hope that the application of control systemtheory would be relevant. However, the differences frommechanical systems are of such a magnitude that it is notimmediately obvious that it will lend itself successfully tothis treatment.

If it does apply, then there exists a realistic possibilitythat the manner in which the economy is controlled

could be improved. This remark has only to be said inthe presence of businessmen to cause signs of exasper-ation. To many of them, the way in which the economy iscurrently controlled is totally baffling. The more sobecause the problems of control do not seem all that dis-similar to those with which they are familiar in running abusiness, albeit on a different scale of magnitude. In amanufacturing company, for example, there are specialistfunctions that are undertaken by qualified experts whocan be expected to fight their corner, but the whole oper-ation is brought into balance through the function of theboard of directors. The balance may be precarious, it iscertainly very sensitive to all manner of influences, manyof them unpredictable and unforeseen; there tends to be astate of constant mild panic, but the balance prevails aslong as all are aware of what is required and get on withit. In companies there is a broad consensus on the subjectacquired over a long experience of what needs to bedone.

Things appear to be quite contrary when it comes tocontrol of the economy. There is no consensus on howthe economy works, even less on what action should betaken in given circumstances. There are fashions ofthought that seem to come and go, but with little chanceof quantification. There are economic theories in abun-dance, but no easy route to verification. So one mightwell ask why would a respectable engineer wish to haveanything to do with it?

For two reasons outstandingly: (1) the matter is ofsuch national importance to all of us, but particularly tothe business community; (2) there ought to be a morerational way of doing things, so why not try and find one.

1 Models of the economic system

Standard economic theory classifies the activities in anational economy in terms of three major, but ratherabstract, markets, namely the goods market, the moneymarket and the labour market. Fig. 1 is a simplified

|-demand side-

-moneymarket

interest rates

goodsmarket

govermentspending |

consumption

supplyside ""labour —»•market

unemployment

Fig. 1balance of payments

Simplified model of a national economy

diagram for a national economy model showing each ofthese markets. The three markets are in constant inter-action to achieve a state of equilibrium among them-selves. The goods market, interacting with the other twomarkets, determines the behaviour of such aggregatedeconomic activities as consumption, investment, importsand exports. The money market determines variablessuch as interest rates, credit and money stock. These vari-ables, in turn, affect, and are affected by, decisions in the

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goods market. Thus, the goods and money marketsjointly determine the demand for goods and services,usually referred to as the demand side of the economy.The supply side of the economy is the third market, thelabour market. This determines the supply of goods andservices in the economy, and, in so doing, determinesprices, wages and employment. Equilibrium is achievedwhen the supply of goods and services matches thedemand for goods and services. This seeking of an equi-librium is the one consistent underlying assumptionadopted in all models of the economy. The inputs to themodel are the policy instruments (or control variables),which a government is free to manipulate, such as its ownspending, raising of taxes and 'fixing' of interest rates.Also, inputs are the exogenous variables which areoutside the influence of the national economy, such asworld trade and world price variables.

As there is no consensus on how the economy works,this leads to rival models for representing it. At oneextreme are monetarist models which emphasise theimportance of the money stock, at the other the adher-ents of Keynes, with their emphasis on the volume of

demand. These two types of model are divided in theirexplanation of both short-run and long-run effects in theeconomy.

A government, of whatever flavour, has a difficult taskin achieving its desired economic aims. The economiclevers that are available to it are very indirectly con-nected to its principal targets, such as unemployment,inflation and growth. However, the necessity that it seesto regulate the economic system, poses questions that canbe answered by judicious use of the techniques of optimalcontrol theory. Optimal control is an application of themethod of variations which was so successfully applied indetermining the laws of mechanics and electromagnetism.

The use of optimal control is not necessarily restrictedonly to providing the indication of necessary actions tobe taken by governments. An equally important use ofoptimal control is in gaining further insight into themodel and the economic system. Optimal control maygenerate variable values due to the nonlinear character ofthe equations into regions where the performance of themodel is untested. This can highlight deficiencies in therelationships in the model which can then be amended.

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