I A T A F I

Bimodal System DynamicA Technology Assessmentand Forecasting Approach

PETER KELLER and URS LEDERGERBER

ABSTRACT

Technology assessment and science forecasting are based on the long-term forecasting of importantprocesses within complex systems. The Bimodal System Model was developed for their modeling. The systemdynamics and the system itself are based on the combined action of two forces: the evolutive intrinsic dynamicsand the decisionistic formation. Evolutively intrinsic dynamic forces emerge from two basic principles: assimila-tion and comprehension (the basis of any individual endeavor) and exchange and interchange (the heart ofany communication and interaction between individual people, groups, associations). These forces are solelyinduced by individual optimization of benefits related to material goods and ideas. From the point of view oftheir emergence they are heterogeneous and chaotic and are neither globally nor centrally planned. Theireffect in a system occurs a million-fold, however uncoordinated. Intrinsic forces are insensitive to other effects(e.g., decisionistic or formative) due to their million-fold, heterogenous origin. Decisionistic formative forcesdeploy their effect in a system when a community is conscious of being a subject and as such is capable ofexpressing and translating its coordinated will (decision). The decisionistically formative forces can producea consistent effect when they are aligned to the evolutive intrinsic forces and utilize their dynamics in arequired manner. Therefore, process-oriented priorities must be construed in the course of consensus formation,determination of aims and headroom evaluation, which would allow a concerted and sustained application ofthe available forces. 1998 Elsevier Science Inc.

IntroductionThe ultimate aim of technology assessment is the optimization of the long-term

effects of technology on people, society, and the environment. This requires the early

PETER KELLER studies architecture and urban and regional planning (post-graduate) at the SwissFederal Institute of Technology (ETH) in Zurich, Switzerland.

URS LEDERGERBER studies economics and management accounting in Bremen, Germany, andZurich, Switzerland.

Address correspondence to P. Keller, ETH Honggerberg (HIL), IVT ETH, CH-8093 Zurich, Switzerland.

Technological Forecasting and Social Change 58, 47–52 (1998) 1998 Elsevier Science Inc. All rights reserved. 0040-1625/98/$19.00655 Avenue of the Americas, New York, NY 10010 PII S0040-1625(97)00054-1

48 P. KELLER AND U. LEDERGERBER

recognition of the probability of a desired development and the risks of an undesiredone, so that timely measures may be taken either to take advantage of them or to avoidthem. The consequences of technologies are essentially a function of the nature andextent in which they are used, rather than of the technology itself. (By nature andextent of use we mean the concrete application of a technology by a specific user fora specific purpose.) The central object of research of technology assessment is thereforethe long-term forecasting of the nature and extent of the use of a given technology byand in a specific society. Such forecasting is extremely difficult, because human behaviorcannot ultimately be predicted with certainty.

Basic Concept of the ApproachThe Bimodal System Dynamic approach we are introducing views the dynamic of

technology application as the product of two principal forces: evolutional momentumand decision-based formation. These two forces have completely different origins andmechanisms. The available possibilities to predict and to control them are thereforeequally different.

Evolutional Momentum

ORIGIN

The basic aim of the use of a technology ultimately lies in the endeavor to optimizethe benefit of its use by each individual user. An individual or a household, as well asa business enterprise or administrative authority, use a given technology in order toachieve more at a lower expense. In this context, the endeavor to optimize is not limitedto the acquisition and exchange of material goods. On the contrary, it appropriates allmanner of material and nonmaterial things and is thus also directed at the exchangeof knowledge, information, or cultural phenomena.

The nature and extent of use of a technology in a society is therefore, globallyspeaking, the result of countless individual and uncoordinated optimization processesresponding to different individual goals.

The history of technology indicates that the development, application, and spreadof many technologies are to a large extent determined by evolutional momentum forces.A case in point is the automobile, which was and continues to be primarily used anddeveloped to satisfy individual transportation needs for work and leisure. The car givesthe individual user greater freedom in choosing his or her place of residence and work,as well as the location of consumer and leisure activities.

MECHANISM

By virtue of its orgin, the effect of evolutional momentum forces is highly heteroge-neous and chaotic. It is neither centrally planned nor driven, but is caused exclusivelyby the benefit optimization on the part of individuals or social groups. Though thisforce is uncoordinated, it can have a great impact when it arises millions of times.

GrowthThe endeavor to optimize individual benefit is not a one-time action, but rather a

continuous effort on the part of every user. The user therefore increasingly tends toappropriate material and nonmaterial goods and to connect with as many partners aspossible. Consequently, he or she attempts to extend his/her range of action and sphereof influence. This leads on the one hand to the growth of the system in question and

BIMODAL SYSTEM DYNAMIC 49

on the other to mobility and communication as means of the virtual joining togetherwith potential but otherwise unreachable partners.

The example of the automobile clearly shows the tendency for growth inherent inevolutional momentum: the car allows the individual driver to extend his or her individualrange of action to satisfy needs particular to various areas of life. This had made possiblea lifestyle largely independent of spatial limitations. This tendency is even more markedin the area of information technology (laptop) and telecommunications (cellular orportable phone), as well as the combination of both of these technologies, telematics.

The tendency for growth inherent in evolutional momentum can also be observedon a higher aggregate level: the availability of an individual means of transport for largesections of the population is an essential precondition for the unchecked growth ofurban conurbations, as well as for tourism. On the other hand, developments in telematicsare important preconditions for economic globalization.

GlobalizationTendencies toward growth, communication, and mobility are essentially not bound

by political-administrative boundaries such as state or administrative frontiers. Conse-quently, evolutional momentum forces display a marked tendency toward globalization.

StabilityThe impact of forces produced by individual benefit optimization are on the whole

highly unresponsive and resistant to outside influences, because their origins are million-fold and heterogeneous. They constitute a resistant mainstream with a strong momentumof its own. This resistance to external influences is evolutionary in character: the momen-tum renews itself continuously. It stabilizes itself precisely because of its heteroge-neous origin.

The example of the automobile shows the great stability of evolutional momentumin relation to external control influences: neither economic recession nor political guid-ance policies have hitherto been able to significantly slow down the use and spread ofthis technology. Since evolutional momentum is the product of million-fold, but highlyvaried individual use optimizations, all influences and measures affect for the most partonly a small minority of car drivers.

PREDICTABILITY

The use of a given technology occurs on an individual basis, making it impossibleto predict the concrete nature and extent of use by an individual. But evolutionalmomentum in its totality lends itself rather easily to prediction. The reason for this isits great stability and resistance to external influences. The probability of occurrenceis thus very high.

Thus each car driver determines largely independently why and where he or shedrives. A traffic event caused by millions of drivers is consequently unplanned andcannot be forecasted on an individual basis. But for technology assessment individualcar trips are not decisive; what is decisive is the million-fold and simultaneous occurrenceof individual car trips. At this level of aggregation, it is quite possible to make forecastsregarding the nature and extent of use of this technology, as well as its impact on societyand the environment.

CONTROLLABILITY

The great degree of stability and resistance to outside influences are also at theroot of a particular problem: evolutional momentum does not generally lend itself tocontrol, or if so only at a great expense.

50 P. KELLER AND U. LEDERGERBER

Decision-based Formation

ORIGIN

Decision-based formation arises from a community which consciously sees itselfas a subject and is capable of an expression of will (decision) and its realization.

There are two causes that give rise to such a decision and the action that follows it:

• To avoid developments that on ethical, moral, or cultural grounds are consideredto be problematic, harmful, or unjust (e.g., environmental damage and re-gional disparities).

• To reach certain goals (e.g., sustainable development, public welfare).

In this view decision-based formation can be understood as a corrective forceresponding to evolutional momentum. Its goals are primarily in the interests of thecommunity and are consequently often contrary to those of the individual.

MECHANISM

Decision-based formation therefore derives from public communities with sover-eign authority, that is to say from state institutions. It is based on collective decisionsfounded on political balancing of interests and consensus building.

The example of the car is also illustrative of the mechanism that governs decision-based formation. The widespread use of the car has without a doubt also had a negativeimpact on society and the environment. Measures to control and limit car traffic arenecessary in order to guard against great and potentially irreversible damage. We maycall to mind traffic restrictions (speed and weight limits), control taxes (traffic congestiontaxes and emission-based taxes, etc.), as well as technical measures (catalytic converters,noise isolation walls, etc.), as well as the promotion of public transport.

Nation-State LimitationSince the decision-based formation actors are states, their actions are essentially

restricted to formal and legally defined jurisdictions within the particular national ter-ritory.

ReactivityAs a corrective force responding to evolutional momentum, decision-based forma-

tion can in principle work predictively and proactively or retrospectively and reactively.The first case represents both the ideal and legitimization of technology assessment.The second case is closer to reality and represents its inefficacy.

InstabilityDecision-based formation is based on a goal-oriented consensus. However, the

political balancing of interests increases the difficulties of reaching a consensus which,once reached, will tend to be unstable: decisions that have been taken can be questionedand reversed, especially over the long term.

Weak EffectGiven the great stability of evolutional momentum, decision-based formation can

as a rule only have lasting effectiveness when it does not run in a direction completelycontrary to evolutional momentum. Major interventions, especially those that runcounter to evolutional momentum, tend to succumb in the long term to its great andpersistent force.

For this reason, utopia and ideology-driven decision-based formation can have adecisive influence on development in the short or medium term (e.g., technological

BIMODAL SYSTEM DYNAMIC 51

scepticism), but in the long term they will exhaust themselves against the force ofevolutional momentum.

PREDICTABILITY

Decision-based formation forces easily lend themselves to observation at theirinception, and its immediate effects can be controlled relatively well. But decision-based formation forces are not easy to forecast because they are preceded by an open-ended political process of balancing of interests and consensus building. Consensusbuilding is fundamentally, and especially in today’s general climate of a pluralism ofvalues, a largely unpredictable process.

CONTROLLABILITY

It is, however, possible to control decision-based formation forces. This presupposesthe effective participation in the corresponding process of balancing of interests andconsensus building.

Combined Effect of Evolutional Momentum and Decision-based FormationIn the bimodal system dynamic approach the development of technology use is

largely determined by evolutional momentum forces. These forces are constant, effectiveeverywhere and at all times, and very dynamic. They respond to any structural interven-tions with flexibility, by adapting to new conditions and developing their own momentum(or dynamic).

Decision-based formation forces, in contrast, are not so much interactive as reactivein responding to superior evolutional momentum forces. They assume a permanentdefense posture and are always liable to be washed over as soon as the consensus wanes.

The closer a given technological application is to the underlying tendency of evolu-tional mentum—namely, individual benefit optimization—and the smaller the share ofdecision-based formation relative to evolutional momentum, the easier, faster, and moreprobable the spread of this application and of its effects, both positive and negative.

Conversely, the greater the share of decision-based formation relative to evolutionalmomentum (i.e., the further removed a technological application is from the underlyingtendency of evolutional momentum), the harder, slower, and more improbable its spread.Such an application of technology along with its desired effects such as human, social,and environmental tolerability (sustainable development) may not be realizable at all,or if so only at a great political expense (i.e., collective balancing of interests anddecision and consensus building).

The automobile example suggests itself once again. In Singapore, cars can onlycirculate in the city if they are completely full. This regulation is an expression ofdecision-based formation. But it runs counter to the underlying tendency of evolutionalmomentum, i.e., individual benefit optimization. Car drivers who wish to drive in thecity even though they are alone, simply pick up a couple of youths at the city boundaryin exchange for a sum of money. In this case, decision-based formation has formallyprevailed. But in reality the formulated goal, namely the reduction of individual mobility,has not been achieved. However, both the youths and the car drivers have in their ownway acted to obtain optimal individual gain.

The use and wide application of the car as an individual means of transport takesplace by itself. Compared with this, the development and maintenance of efficient publictransport systems requires expending considerable public resources, as well as extensiveand long-lasting political consensus.

52 P. KELLER AND U. LEDERGERBER

ConclusionsThe present bimodal system dynamic approach has wide applicability in technology

assessment and forecasting, as well as in technology design and technology policy.

SIGNIFICANCE OF BIMODAL SYSTEM DYNAMICAS A TECHNOLOGY ASSESSMENT APPROACH

The present approach indicates that it is relatively easy to predict the effect ofevolutional momentum on the nature and extent of use of a specific technology. Con-versely, the direction and intensity of decision-based formation are important unknownfactors. The methodological conclusion to be drawn from this is that current deterministicforecasting methods are suited for predictions relative to evolutional momentum forces.Conversely, forecasts of decision-based formations are at best hypotheses (scenarios)regarding their conceivable development.

Thus, as a technology assessment approach, the bimodal system dynamic proposesthe following four-step procedure:

• Describe the possible applications of a technology, irrespective of desires andfears.

• Describe the spread and impact of individual applications under the ideal assump-tion that only the evolutional momentum and not the decision-based formationhave an impact.

• Make hypotheses regarding the impact of decision-based formation measures(alternative scenarios).

• Determine the probable spread and impact of individual applications by meansof hypotheses regarding different decision-based formation scenarios.

SIGNIFICANCE OF BIMODAL SYSTEM DYNAMIC AS ATECHNOLOGY DESIGN AND TECHNOLOGY POLICY

To optimize the social and ecological impact of technology through bimodal systemdynamic, two conditions must be met:

• The forces of decision-based formation must be optimally adjusted to the forcesunderlying the evolutional momentum; in other words, this momentum must beemployed in the desired way.

• The underlying tendencies of evolutional momentum that deviate from desiredgoals (growth, etc.) must be constantly guarded against or weakened. Given theirlimited power and efficacy, it is imperative to employ the forces of decision-based formation in an intelligent and economical manner.

To illustrate, a decision-based formation strategy that meets these conditions seeksto make the individual benefit of desired technology applications surpass that of unde-sired ones. Such a mode of operation would doubtlessly be more efficient and effectivethan a strategy that sought to put a stop to undesired uses through coercive measures.

The bimodal system dynamic approach indicates that demand-oriented and selectiveinterventions in the development of technology use are generally speaking better suitedthan undifferentiated global regulation to bring about a use of technology that is tolerablein human, social, and environmental terms.

Received 6 March 1997; accepted 16 April 1997