IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICS, VOL. ssC-6, NO. 4, OCTOBER 1970

Systems Analysis as a Tool for Urban PlanningJAY W. FORRESTER, FELLOW, IEEE

Abstract-New ways are becoming available for analyzing oursocial systems. These permit the design of revised policies to improvethe behavior of the systems within which we live. Industrial dynamicsrelates system structure to behavior. Industrial dynamics belongs tothe same general subject area as feedback systems, servomechanismstheory, and cybernetics. Industrial dynamics is the study of how thefeedback loop structure of a system produces the dynamic behavior ofthat system. In managerial terms industrial dynamics makes possiblethe structuring of the components and policies of a system to showhow the resulting dynamic behavior is produced. In terms of socialsystems it deals with the forces that arise within a system to causechanges through time. The structure of an urban area has beenorganized into a system model to show the life cycle dynamics ofurban growth and decay. The results suggest that most of today'spopular urban policies lie between neutral and detrimental in theireffectiveness. Quite different policies are suggested when one comesto an understanding of why urban areas evolve as they do.The city shows the general characteristics of a complex system.

Complex systems are counterintuitive; they resist policy changes;they contain influence points from which changes can be made toradiate; they tend to counteract programs aimed at alleviatingsymptoms; their short-term response to a policy change is often inthe opposite direction from the long-term response; they containdynamic mechanisms that produce the observed undesirable behavior;and they generate pressures characteristic of each stable mode inwhich they might operate.

NEW WAYS are becoming available for analyzing oursocial systems. These permit the design of revised

policies to improve the behavior of the systems withinwhich we live. Many of the ideas discussed here are treatedmore fully in [1] which shows the city as an interactingsystem of industry, housing, and people. The book presentsa theory, in the form of a computer model, that interrelatesthe components of a city. It shows how the interactingprocesses produce urban growth and cause growth to giveway to stagnation. Various changes in policies are examinedwith the laboratory model to show their effect on an urbanarea. A number of presently popular proposals are tested:a job training program, job creation by bussing to suburbanindustries or by the government as employer of last resort,financial subsidies to the city, and low-cost housing pro-grams. These are all shown to lie between neutral and detri-mental in their effect on a depressed urban area. Theevolution of an urban area from growth into stagnationcreates a condition of excess housing. Housing is excesscompared to the population and compared to the avail-ability of income earning opportunities. To reestablish ahealthy economic balance and a continuous process ofinternal renewal, it appears necessary to reduce the in-

Manuscript received January 5, 1970. An earlier version of thispaper was presented at the Symposium on the Engineer an(d theCity, sponsored by the National Academy of Engineering, Washing-ton, D.C., October 22-23, 1969.The author is with the Massachusetts Institute of Technology,

Cambridge, Mass. 02139.

herent excess housing of depressed areas and to encouragethe conversion of part of the land to industrial use. By sodoing, a large enough wage and salary stream can be broughtfrom the outside economy to make the area self sustaining.As you can see, these results are controversial. If they

are right, it shows that most of the traditional steps takento alleviate the conditions of our cities may actually bemaking matters worse. This book [1] first appeared thislast May; it is already in the second printing. Although ithas so far received little public notice in this country, ithas become the center of a political tempest in Canada.North of the border, newspaper headlines, editorials, andradio and television panel discussions are debating its merits.

It is based on methods for studying complex systems thatform a bridge between engineering and the social sciences.Although we will present here some of those results, myprincipal emphasis will be on the importance of the methodsto all social systems.

Over a decade ago at M.I.T., we began to examine thedynamic characteristics of managerial systems. The fieldknown as industrial dynamics resulted [2]. Industrialdynamics belongs to the same general subject area asfeedback systems, servomechanisms theory, and cyber-netics. Industrial dynamics is the study of how the feed-back loop structure of a system produces the dynamicbehavior of that system. In managerial terms industrialdynamics makes possible the structuring of the com-ponents and policies of a system to show how the resultingdynamic behavior is produced. In terms of social systemsit deals with the forces that arise within a system to causechanges through time.A design study of a social system seeks changes in

structure and policies that will improve the behavior of thesystem. Some people recoil at the thought of designingsocial systems. They feel that designing a society is immoral.But we have no choice about living in a system that hasbeen designed. The laws, tax policies, and traditions of asociety constitute the design of a social system. Our avail-able choice is only between different designis. If we lamentthe functioning of our cities, or the persistence of inflation,or the changes in our environment, we mean that weprefer a social system of a different design.The design process is first to observe the behavior modes

of a system to identify the symptoms of trouble. Second,the system is searched for the feedback structures thatmight produce the observed behavior. Third, the level andrate variables making up that structure are identified andexplicitly described in the equations of a computer simula-tion model. Fourth, the computer model is then used tosimulate in the laboratory the dynamic behavior implicitin the identified structure. Fifth, the structure is modifieduntil components of the structure and the resulting behavior

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agree with the observed conditions in the actual system.Sixth, modified policies can then be introduced into thesimulation model in search of usable and acceptable policiesthat give improved behavior.

This design process brings the essential substance of asocial system into the laboratory where the system can bestudied. Laboratory representation of a social system canbe far more effective than most people would expect. Any-thing that can be stated or described about a social systemcan be represented in such a laboratory model. The majordifficulty is the rarity of skilled professional talent. Thereare very few men with a knowledge of the proper guidingprinciples and with experience in perceiving the pertinentfeedback structure of complex poorly defined systems.Whatever one may say about the shortcomings of theprocess, there is no comparably effective substitute.

Surprising discoveries come from this combination oftheory and laboratory experimentation. We observe thatrelatively simrple structures produce much of the complexbehavior of real-life systems. We find that people's skillsin perception are very different from those commonlysupposed. It is often asserted in the social sciences thatpeople are unreliable in analyzing their own actions, yetwe find time and again that the policies and practices thatpeople know they are following are the ones that interactto produce the most troublesome consequences. Conversely,it can be clearly demonstrated that the vaunted powers ofjudgment and intuition usually deceive the person who triesto guess the time-varying consequences that follow evenfrom a completely known system structure. We find thatthe modes of behavior which are most conspicuous inmanagerial, urban, and economic systems are produced bynonlinearities within those systems. The linearized modelswhich have been used in much of engineering and the socialsciences cannot even approximate the important modes ofbehavior in our social systems. The most visible andtroublesome modes are manifestations of nonlinear inter-actions. We find it relatively straightforward to include theso-called intangible factors relating to psychologicalvariables, attitudes, and human reactions. Again, if theinfluences cart be discussed and described, they can beinserted in the policy structure of a model. Any person whodiscusses why people act the way they do, or explains apast decision, or anticipates a future action is relating thesurrounding circumstances to the corresponding humanresponse. Any such discussion is a description of decision-making policy. Any such policy statement can be put intoa system model.A body of dynamic theory and principles of structure is

emerging that allows us to organize, and understand complexsystems [3J. For example, the feedback loop becomes thebasic building block of systems. Within the feedback loopthere are two and only two kinds of variables. One is thelevel variable produced by integration; the other is thepolicy statement or rate variable which governs the changesin a system. The level variables are changed only by therates of flow. The rate variables depend only on the levels.Any path through a system network encounters alternating

Fig. 1. Urban structure.

level and rate variables. These and many other principlesof structure are universal in the entire sweep of systemsthat change through time. Furthermore, the structure of asystem determines its possible modes of behavior. Identicalstructures recur as one moves between apparently dis-similar fields. These identical structures behave in identicalways wherever they are found.The same principles of structure and the same relation-

ships between structure and behavior apply to a simpleswinging pendulum, a chemical plant, the processes ofmanagement, internal medicine, economics, power politics,and psychiatry. A universal approach to time-varyingsystems is emerging which seems capable of dealing withsystems of any complexity. We observe that students, asthey master the principles and practice of dynamic analysis,develop a remarkable mobility between fields of endeavor.The same person can clarify the dynamics of how a tran-sistor functions, organize the processes of a public healthepidemic, design new management policies to avoid stag-nation in product growth, discover the sensitive factors inecological change, and show how government policies affectthe growth and decline of a city.Some diagrams showing urban behavior will illustrate

these ideas. Fig. 1 shows the central structure of an urbanarea. The nine rectangles represent the selected levelvariables. The 22 valve symbols represent the rates of flowthat cause the nine system levels to change. Engineersoften refer to these level variables as the state variables ofa system. The distinction between level and rate variablesis also familiar to anyone who examines financial statements.Balance sheet variables are always separated from variableson the profit and loss statement. They are separate becausethey are conceptually quite different. The balance sheetvariables are system levels. They are created by accumulat-ing financial flows. The profit and loss variables are systemrates. This sharp distinction is found in all systems.

In this figure one can begin to detect the reasons forurban decline. The age of a building tends to determine thecharacter of its occupants. New commercial buildings areconstructed through the rate of flow NEC in the upper leftcorner of Fig. 1. A new commercial building is occupied bya healthy successful commercial organization that uses

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IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICS, OCTOBER 1970

NE MB DI ,i,,,# _ - o' A3-~

*~~~~-,,, 'I______f

PHPH / / %

//I/

MP

~~ ~ ~ ~ ~ ~

Fig. 2. Information links to UA rate.

relatively more managers and skilled workers than those

who are unskilled. As the building ages first into the maturebusiness category and then into the declining industrycategory, it tends to be occupied by a progressively lesssuccessful enterprise with lower employment skills. In addi-tion to the changing employment mix as the industrialbuilding ages, there is a tendency for total employment per

unit of floor space to decline. This means that as industrialbuildings age the total employment declines and also theaverage wage paid declines. On the other hand, as residentialbuildings age, there is a tendency for occupancy to increaseas well as to shift to a lower economic category of popula-tion. As housing in Fig. 1 moves from premium housingand worker housing into the older underemployed housingcategory, the housing is acceptable only to the lower in-come population. But the income of this group is not suffi-cient to use the buildings at the original population density.Lower incomes mean that the floor space must be usedmore intensively. As the building ages it attracts a lowerincome occupant and the building houses a higher popula-tion density. One perceives then a condition where theaging of buildings in an urban area simultaneously reducesthe opportunities for employment and increases the popula-tion. The average income and standard of living decline.

Fig. 2 shows the same nine system levels and one of the22 flow rates. The dotted lines are the information linkagesfrom the system levels to control the one flow rate, here thearrival of underemployed population into the urban area.

The various levels of the system combine to create a com-

posite attractiveness which determines the inflow rate tothe area. If the area is more attractive than those fromwhich people might come, a net inward population flowoccurs. If the area is less attractive, an outward flowdominates. Five components of attractiveness are shownin Fig. 2. In the upper right corner UJM relates the popula-tion to the available jobs and represents the income-earning attractiveness of the area. The circle UAMMgenerates the attractiveness created by upward economic

mobility. In other words, an area with high upwardeconomic mobility is more attractive than one offering nohope of advancement. The circle UHM relates the under-employed population to the available housing. The areabecomes more attractive as housing becomes more available.UHPM represents the attractiveness of a low-cost housingprogram if such exists. And in the lower right corner PEMis the influence on attractiveness of the public expenditureper capita. As per capita expenditure arises, it meansbetter public services, better schools, and higher welfarebudgets.The concept of attractiveness is fundamental to the

population flows. Of all the characteristics of an area thatmake it attractive, these five and many more combine toinfluence migration. An attractive area draws people. Butalmost every component of attractiveness is driven downby an increase in population. If there is an excess of housing,the area is attractive, but a rising population crowds thehousing. If there is an excess of jobs, the area is attractive,but the incoming flow of people fills those jobs. In otherwords, migration continues until the attractiveness of thearea falls and becomes equal to all other places fromwhich people might come.An important idea follows from examining these com-

ponents of attractiveness. In a condition of populationequilibrium, all areas must be equally attractive to anygiven population class; otherwise net migration wouldoccur. If one component of attractiveness is increased inan area, other components must necessarily fall to establisha new equilibrium. Compensating changes in the com-ponents of attractiveness explain many past failures inour cities wherein we attempt to improve one aspect of thecity only to discover that other aspects have become worse.

In making a laboratory model of a social system oneshould not attempt straightaway to solve a problem.Instead one should generate a model which will create thetrouble symptoms. Only if one fully understands the pro-cesses whereby difficulties are created can he hope to correctthe causes. This means that we want a model of an urbanarea which can start with empty land, grow a city, and showthe processes whereby economic health falters into stag-nation and decay.As another guide to modeling, one should start, not by

building a model of a particular situation, but by modelingthe general class of systems under study. This may seemsurprising, but the general model is simpler and initiallymore informative than a model of a special case. Here wewish to model the general process of urban growth andstagnation. It should be a model which, with proper changesin parameters, is good for New York, Calcutta, a gold rushcamp, or West Berlin. These all seem to have very differentcharacteristics but they have certain elements in commonwhich describe their urban processes. There are fewerconcepts which are common to all than are to be found inany one. The general model can strip away the multitudeof detail which confuses any one special situation. Thegeneral model identifies the central processes and is astatement of the theory for the entire class of systems.

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FORRESTER: SYSTEMS ANALYSIS IN URBAN PLANNING

STD.Labor

Worker housing

.Underemployed

Co W

.'2Z4neremplyed housing'~~~~Declining 6

X 3* _^ndustry

Managers.D:1. a:tu ..........

0.~~~~~~Premium

9 :h.enterprise

Years

Fig. 3. Growth and stagnation.

Iz~ ~ ~

.....I.....Underemployed.Under- Land to Job ratio

:employed fraction

.:to housing occupied C

/~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- 22w, '. t e A. | .. .. ..... .. . .. .

.... .

6t qav

'Tax ratio

needed

Years

Fig. 4. Compensating changes in housing and employment.

;LCHPC=.05

Underemployed

Underemployed housing

~LaborJ

- *Monagers

- XA,MaMature business

Years

Fig. 5. Decline of u.ban area caused by low-cost housing construc-

tion each year for 2.5 percent of underemployed.

Fig. 3 shows the behavior of the laboratory model of anurban area. It presents the nine system level variablesover 250 years. The first 100 years is a period of exponentialgrowth; however, when the land area becomes filled,growth ceases, and the aging process begins. At year 100,near the end of the growth phase, the labor population isalmost double the underemployed population. This is ahealthy mix which is well matched to the job distributionin the area and which gives a high upward economicmobility to the underemployed population. But by year150, the labor population has fallen and the underemployedpopulation has risen until these two groups are almost equal.Business activity has declined and the area has taken onthe characteristics of a depressed city. This has occurredbecause of the way that the industry, housing, and popula-tions in Fig. 1 have interacted with each other.

Fig. 4 shows other variables during the same 250 years.Notice especially the UR and UAR. During most of thefirst 100 years of growth these two ratios were almostconstant. The UHR was high (above the center of thefigure) meaning that the population is large compared tothe housing. In other words, during the first 100 yearsthere was a housing shortage for the underemployedpopulation. On the other hand, the UR was low, meaningthat the population was below the job opportunities, jobswere readily available, economic opportunity was good,and upward economic mobility was high. During this earlyperiod of growth and high economic activity, the under-employed population was being effectively adjusted inrelation to other activity by balancing good economicopportunity against a housing shortage.But between 90 and 140 years, notice the sharp reversal

of the curves for the UR and UHR. Within this 50-yearspan, the underemployed have increased while availablejobs decreased; the result is a precipitous rise in unemploy-ment. But in this same period, the housing that is agingand becoming available to the underemployed is risingeven more rapidly than the underemployed population.Jobs have become scarce while housing has become surplus.The model is behaving the way our cities do.Many people seem not to realize that the depressed areas

of our cities are areas of excess housing. The economy ofthe area is not able to maintain all of the available housing.Because of low incomes, people crowd into some dwellingunits while other buildings are abandoned, stand idle, anddecay.

Recall the earlier comments about compensating move-ments in the components of attractiveness. Here, as housingbecomes more available, jobs become more scarce. Thestagnating urban area has become a social trap. Excesshousing beckons people and causes inward migration untilthe rising population drives down the standard of living farenough to stop the population inflow. Anything whichtends to raise the standard of living is defeated by a riseof population into the empty housing.

Fig. 5 shows 50 years beginning with the conditions foundat the end of Fig. 3. At time 0, a low-cost housing programis introduced which each year builds low-cost housing for

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262 ~~~~~~~~~~~~IEEETRANSACTIONS ON SYSTEMS SCIE-NCE AND CYBERNETICS, OCTOBER 1970

2.5 percent of the underemployed population. Observe whathappens. Underemployed housing, which is being activelyconstructed, rises 45 percent, but premium housing falls35 per cent, and worker housing falls 30 percent. New enter-prise declines 50 percent and mature business declines45' percent, all in the 50-year period. Economic conditionsbecome sufficiently worse that eveni the underemployedpopulation, although it rises initially, eventually falls toslightly less than its beginning value. These changes are aresult of the low-cost housing program.

In Fig. 6, the corresponding UR has risen 30 percent,indicating substantially higher unemployment, while theUHR ratio has fallen 30 percent indicating a still higherexcess of housing. Again, the two components of attractive-ness compensate for one another with better housing and a

falling standard of living. In the long run, the low-costhousing program has not served the interests of the low-income residents. Instead, it has intensified the socialtrapping characteristic of the area. Over the period, thetax levies rise 35 percent. The area has become worse fromalmost all viewpoints.

In this same manner, job training programs, job creationprograms, and financial subsidies were examined. All liebetween ineffective and harmful. The low-cost housingprogram was the most powverful in depressing the conditionof a stagnant urban area.The depressed areas of our cities seem to be characterized

by excess housing compared to jobs and by excessiveconcentration of low-income population. These conditions,created by aging industrial and dwelling buildings, interactto drive out the upper-income population and businessactivity,. and to reduce the tax base. Once the decline starts,it tends to accelerate. Unless one can devise urban manage-ment policies that produce continuous renewal, difficultiesare inherent.

Fig. 7 shows an urban condition that begins witb stag-nation and then changes toward revival. Here, 5 percentof the slum housing is removed each year, and the incentivesfor new enterprise construction are increased somewhat.The result is a cascading of mutual interactions which raisethe economic activity of the area, increase upward economicmobility for the underemployed population, and shift thepopulation internally from the underemployed to the laborclass. This is done without driving the existing low-incomepopulation out of the area. Underemployed housing isreduced. Initially this reduction comes largely from theempty housing. The resulting housing shortage restrainsthe population inflow which would otherwise defeat therevival of the area.

Fig. 8 shows the same 50-year span as in the precedingfigure. Here again, employment and housing move inopposite directions. The UR falls which means more jobsand lower unemployment. On the other hand, the UHRrises which means a tighter housing situation. If theeconomi-c circumstances are to be im-proved, we must

7 ~ ~ ~ nderemployed

~~~~~~~jobratio

-~-- - - -- - - - -- - - - --

.--`Underemployed ...............

to housing ratio ......:"Tax ratio needed7

71 V~~ ~ ~ ~ ~

Years

Fig. 6. Rising unemployment and falling occupancy of housing.

?55

SHDR=.05NECN =07 Labor

iWorker housing

UneremployeUnderem played ---

~housing 4Mature business:

55~~ ~~~~~~~~~~TV T

~~~~~~~Years

Fig. 7. Revival caused by removing 5 percent of underemployedhousing each year and encouraging business construction to

generate jobs.

55~~~~~

Underemployed:

to job ratio

... ~~~Underemployedto housing ratio'

~jTax ratio

:needed

Years

Fig. 8. Falling unemployment and rise in housing occupancy.

accept some compensating change in other components ofattractiveness. Here it is the increased tightness of housingwhich allows job opportunities to increase faster than

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FORRESTER: SYSTEMS A:NALYSIS IN URBAN PLANNING

population until a good economic balance is reached. Istress economic revival as the first stage of rebuilding adepressed area because it appears that an economic basemust precede social and cultural development.

It is simply not possible to increase all of the attractive-ness components of an area simultaneously. Attractivenessis here defined in a very broad sense. For example, legalrestrictions, like an immigration barrier into a country,can produce enough unattractiveness to inward migrationso that other components might be maintained at a highlevel. But wherever one component of attractiveness ishigh, others will be found that are low.

Engineers, especially, should consider the compensatingchanges that will occur in the attractiveness components ofan area, because engineers tend to deal with economicconsiderations and technology. Economic and technical fac-tors are more concrete than the intangible quality of lifevariables. The economic and technical aspects of a city arethe ones we most easily see how to improve. Our techno-logical society tends, therefore, to observe, react to, andimprove the economic and technical aspects of a city. Suchimprovements increase the technical and economic com-ponents of urban attractiveness. But, as a result, populationdensity rises until the urban area once again reaches attrac-tiveness equilibrium with its environment. The burden offorced reduction in other components of attractivenessfalls on the quality of life variables: crowding, pollution,and psychological stress. These less tangible variables havebeen weak, hard to measure, and have been defenselessagainst the persuasiveness and the certainty of improve-ment shown by the technical and economic considerations.But we are entering a time when a reversal will occurbetween the formerly weak and strong variables. For asubstantial fraction of our population, the standard ofliving is already high enough so that more gain in theeconomic and technical areas will come at too high a pricein the quality-of-life components of our environment. Theengineer, if he continues to serve society, must balance agreater number of social needs against one another. At onetime his task was simply to balance financial cost againsteconomic performance of his technology. Now the productand also the medium of payment are both expanding.Social value and quality of life become part of the product.Psychological stress, ugliness, and crowding become partof the cost. Engineers who fail to recognize this broadenedrole will be vilified and castigated by a society which per-ceives them as narrow and insensitive to the demands ofthe times.When a system misbehaves, we should ask ourselves

what policies within that system cause the undesirablecharacteristics. If we examine the laws under which a cityoperates, we see a structure of regulations which couldhardly be better designed to create stagnation and decline.The aging and decay of buildings is central to the urbandecline process; yet we see throughout our tax laws andregulations numerous incentives to keep old buildings inplace. As the value of a building decreases, so do theassessed taxes. The reduced expense makes it possible to

retain the old building longer. For income tax purposesunder some circumstances the value of a building can bedepreciated several times. This produces incentives to keepan old building in place. Here is not the place for detail,but it seems clear that a different set of tax laws and cityregulations could be devised to produce the individualincentives necessary for continuous renewal. As an example,I recently saw a suggestion that each building have amandatory trust fund into which the owner must pay alevy each year. At any time, whoever owns the building candraw out the money in the trust fund if he demolishes thebuilding and clears the land. This, you see, would createan earlier incentive for replacement. Property tax leviesand income tax accounting could both be changed toproduce pressures in the same direction.Our studies of managerial, urban, and other social systems

have uncovered many general characteristics of complexsystems to which we must be alert if we are to avoidcontinuing to create detrimental modes of behavior.

First, complex systems are counterintuitive. They behavein ways that are opposite to what most people expect. Theyare counterintuitive because our experience and intuitionhave been developed almost entirely from contact withsimple systems. But in many ways, simple systems behaveexactly in the opposite way from complex systems. There-fore, our experience misleads us into drawing the wrongconclusions about complex social systems.

Second, complex systems are strongly resistant to mostpolicy changes. A new policy tends to warp the system sothat slightly changed levels present new information to thepolicy points in the system. The new information, asprocessed through the new policies, tends to give the oldresults. There are inherent reasons within complex systemswhy so many of our attempts at correcting a city, acompany, or an economy are destined to fail.But third, the converse is also true. There are points in

systems from which favorable influence will radiate. Oftenthese points are difficult to perceive. Often the actionrequired is the opposite to that which might be expected.But when these points are found, they tend to radiate newinformation streams in such a way that the new circum-stances, when processed through the old attitudes andpolicies, produce a new result.

Fourth, complex systems tend to counteract most activeprograms aimed at alleviating symptoms. For example,[1, ch. 4] shows how a job training program can increasethe number of underemployed in a city. When outsideaction tries to alter the condition of a system, the systemrelaxes its own internal processes aimed at the same resultand throws the burden even more onto the outside forcewhich is attempting to produce a correction. The internalneed for action is reduced and the external supplier ofaction must work even harder.

Fifth, in complex systems the short-term response to apolicy change is apt to be in the opposite direction from thelong-term effect. This is especially treacherous. A policychange which improves matters in the short run lays afoundation for degradation in the long run. The short

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IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERN-ETICS, OCTOBER 1970

tenure of men in political office favors decisions whichproduce results quickly. These are often the very actionsthat e-ventually drive the system to ever-worsening per-formance. Short- versus long-run reversal processes are allaround us. If an agricultural country is to industrialize, itmust accumulate railroads, factories, and steel mills. Thiscapital accumulation can only be done by forgoing con-sumption and reducing the standard of living first in orderthat the standard of living may rise at a later time. If acompany faces declining earnings because its products areobsolete, it must invest more heavily in product researchand incur even deeper short-term losses if it is to recoverin the more distant future to a profitable product stream.A student forgoes short-term earning opportunities byattending college in order to increase his longer term earningcapability. This reversal between the short run and thelong run occurs repeatedly.

Sixth, a system contains internal dynamic mechanismsthat produce the observed undesirable behavior. If weignore the fundamental causes and simply try to over-whelm the symptoms, we pit two great sets of forces againstone another. In general, our social systems have evolvedto a very stable configurationi. If the system is troublesome,we should expect that the causes of the trouble are deeplyembedded. The causes will outlast our persistence in over-whelming the symptoms. Furthermore, the internal pres-sures usually rise to counteract a corrective force from theoutside. We can expend all our energy to no avail in tryingto compensate for the troubles unless we discover the basiccauses and redesign the system so that it spontaneouslymoves to a new mode of behavior.And, as the last of these characteristics of complex

systems. we must recognize that a certain ensemble ofconditions goes with each possible mode of a system. Morespecifically, each mode of a system is accompanied by aset of pressures characteristic of that mode. We can notsustain a particular mode unless wie are willing to acceptthe corresponding pressures. For example, contrast thedepressed mode of a city in Figs. 5 and 6 with the revivedmode in Figs. 7 and 8. The depressed mode is one charac-terized by the pressures that come from decaying buildings,low incomes, and social disorientation. But the revivedmode also contains pressures. The revived Inode is sus-tained by the housing shortage and the legal and taxpressures that generate a steady demolition and replace-ment of old buildings. But everyone in the system will wantto alleviate the pressures. Active industry will -want moreemployees; residents will waant more floor space; and out-siders will want housing so they can move to the attractivejob opportunities. Rents will be high. These pressures areeasy to relieve by increasing the fraction of the land areaperinissible for housing, by keeping old buildings in placelonger, and by allowing taller apartment buildings. But suchmoves will start the area back toward the depressed mode.We must decide the kind of system we want with knowledgeof and acceptance of the accompanying pressures. Instead,much of our social legislation of the last several decades hasconsisted of trying to relieve one set of pressures after

another. The result is a system mode characterized byinflexibility, conformity, crowding, frustration, supremacyof the organization over the individual, and a choking of theenvironment. And the resulting pressures, acting throughthe counterintuitive and short- versus long-term reversalcharacteristics of complex systems, may well move usfurther in the same direction.We are suggesting that the time is approaching when we

can design social systems to obtain far better behavior.Different policies could change our urban areas from oneswhich are designed to deteriorate into ones which aredesigned for self renewal. One can foresee a time when wewill understand far better the relationships between mon-etary policy, interest rates, unemployment, and foreignexchange. Already such studies have thrown new light onthe processes of corporate growth, on the reasons for productstagnation and loss of market share, and on the growthand decline of cities.But to design new policies for social systems requires a

level of skill which is rare. The kind of system modelingand policy design which we have been describing requiresa professional training at least as extensive as that in anyof the established professions. The proper training requirestheory, laboratory, case studies, apprenticeship, andpracticing experience.But in the area of designing the dynamic behavior of

social systems, there are as yet no adequate professionalschools. The educational materials are still in the develop-ment stage. The few who show skill in this area have learnedby apprenticeship and by trial and error.

Those interested in the long-run improvement of societycan make the greatest contribution by encouraging researchand educational programs aimed at developing a high levelof talent. Again the long run competes with the short run.Creating educationial materials and teachers will at firstabsorb money and talent which in the short run mightinstead be devoted to solving particular present social prob-lems. Unless a proper balance is maintained, with substan-tial energy devoted to establishing an educational capabilityfor enlarging the future pool of skills in social systemdesign, the time when we can master our own systems willbe further delayed.

NOMENCLATURE

Definitionrs For City Growth, Stagnation, and Revival ModelAMM attractiveness-for-migration factor (dimension-

less)AMMP attractiveness - for - migration multiplier per -

ceived (dimensionless)DID declining-industry demolition (productive units

per year)LA labor arrivals (men per year)LB labor births (men per year)LD labor deaths (men per year)LTM labor to manager (men per year)LTU labor to underemployed (men per year)MA manager arrivals (men per year)

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IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICS, VOL. ssc-6, NO. 4, OCTOBER 1970

MBD mature-business decline (productive units peryear)

MPB managerial-professional births (men per year)NEC new-enterprise construction (productive units

per year)NED new-enterprise decline (productive units per

year)PEM public-expenditure multiplier (dimensionless)PHC premium-housing construction (housing units

per year)PHO premium-housing obsoleseence (housing units

per year)SHD slum-housing demolition (housing units per

year)TC tax collections (dollars per year)TPCR tax per capita ratio (dimensionless)UA underemployed arrivals (men per year)UAMM underemployed-arrivals-mobility multiplier (di-

mensionless)UB underemployed births (men per year)UD underemployed departures (men per year)UHM underemployed/housing multiplier (dimension-

less)

UHPM underemployed-housing-program multiplier (di-mensionless)

UHPR underemployed-housing-program rate (housingper year per man)

UHR underemployed/housing ratio (dimensionless)UJ underemployed jobs (men)UJM underemployed/job multiplier (dimensionless)UM underemployed mobility (fraction per year)UR underemployed/job ratio (dimensionless)UTL underemployed to labor (men per year)WHC worker-housing construetion (housing units per

year)WHO worker-housing obsolescence (housing units per

year).

REFERENCES

[1] J. W. Forrester, Urban Dynamics. Cambridge, Mass.: M.I.T.Press, 1969.

[2] --, Industrial Dynamics. Cambridge, Mass.: M.I.T. Press,1961.

[3] , Principles of Systems. 238 Main St., Cambridge, Mass.:Wright-Allen Press, 1968.

A Case Study of Citizen Complaints as Social IndicatorsEZRA S. KRENDEL, FELLOW, IEEE

Abstract-The purpose is to illustrate the applicability of theapproach and the techniques of systems engineering to certain urbanproblems. Systems engineering can be an effective tool in the designand operation of organizations to accomplish such urban activitiesas police scheduling, waste disposal, river purification, fire houselocation, etc. The relatively unploughed ground of applying systemsengineering to the quality of urban life is addressed here. The quality ofurban life, an elusive but intuitively satisfying concept, is operationallyuseful to the extent that a city can identify and mor-e toulard achiev-ing the goals of its citizenry. Social indicators measure the extent towhich these goals have been achieved.

For such indicators to be usable on line inputs for determiningchanges in urban subsystems, they must respond rapidly and sen-sitively to the citizenry's changing perception of the gap betweengoals and actual achievements. Indicators aggregated over longintervals of time, large physical areas, or population groups tend tobe sluggish and historical. It is shown how unsolicited complaintsand comments from the citizenry may help to define such opera-tionally useful social indicators. A conceptual framework emphasizingadaptive urban subsystems is presented, and data are used to illustratethe feasibility of the approach.

Manuscript received February 18, 1970; revised May 25, 1970, andJune 20, 1970.The author is with the Department of Operations Research and

Statistics, University of Pennsylvania, Philadelphia, Pa.

I. SYSTEMS ENGINEERING CONTEXT

ACITY IS an assembly of diverse physical, economic,administrative, and social subsystems. Some exist on

a parallel level with one another; others are parts of ahierarchy. These subsystems, acting in concert, generatethe characteristics of the city which are sensed by theresidents as the quality of life in the city. This concept isfuzzy and difficult to quantify, and it is generally inferredfrom a series of plausible indices about such aspects ofurban life as health, social mobility, income, physical en-vironment, etc. [1]. Such indices as infant mortality,narcotics addiction, unemployment rate, air pollutionmeasures, etc. have been used [2] to compare the qualityof life in Washington, D.C., with 16 other major Americancities. These comparisons provide valuable statistical bench-marks for assessing planned or unplanned social change.The drawbacks in using selected quality of life indicators,however plausible they may appear, are that 1) their choiceis influenced by the values of the selector and 2) theseindicators are generally broadly based aggregations overpeople, space, or time; Consequently, they are highly

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