The acceleration of scientific and technological progress amplifies several sources of uncertainty. This greatly complicates decision making on innovation projects and project portfolios. In practice, to ignore risk means to deny t h e n a t u r e of t h e m a t t e r . For t h i s reason, problems of uncertainty and risk a r e taken up in most publications on decision making. No general recommendations can y e t be given on how best t o include t h e prospect of risk in decision making on innovation projects, although t h e r e i s a need for s u c h a methodology. In t h i s study we c a n consider only c e r t a i n theoretical aspects of t h e problem and possible approaches within t h e framework of o u r decision support s y s t e m for t h e lighting industry of [.he German Democratic Republic.
Taking into consideration all complicating aspects (risk, multiple objec- tives, t i m e variability of t h e preferences, e t c . ) independently or sequentially leads t o unsatisfactory or, a t least, theoretically insufficient results. Models t h a t allow one t o handle all these i m p o r t a n t aspects simultaneously a r e lack- ing. New approaches have been developed recently t o expand decision mak- ing involving multiple objectives t o a multicriterion concept of risk (Colson and Zeleny 1980).
Although t h e r e a r e differences between risk a n d u n c e r t a i n t y , t h e s e t e r m s a r e often s e e n a s identical (Salazar a n d Sen 1968). In our view, u n c e r - tainty denotes ambiguity. We distinguish between uncertainty t h a t c a n b e grasped by probability t h e o r y and uncertainty for which probability t h e o r y c a n n o t be applied (Fedorenko 1975, p. 376). F u r t h e r m o r e , t h e r e is u n c e r t a i n t y resulting from t h e n a t u r e of a process (situation) a n d u n c e r t a i n t y due t o incomplete a n d / o r i n a c c u r a t e information. In practice t h e s e differences become blurred: all types of uncertainty complicate decision making in a like m a n n e r .
We have to find t h e type of uncertainty t h a t best characterizes o u r prob- l e m in order t o progress i n our investigations. Generally speaking, t h e r e a r e unique choice situations and repetitive choice situations. The decision t o adopt or r e j e c t a n innovation project is either unique o r repetitive, depending on t h e class of innovations prevailing. For example, decisions concerning marginal i m p r o v e m e n t innovations have many repetitive f e a t u r e s , whereas basic innovations a r e always uniq-ue, a s a r e decisions to adopt or r e j e c t proj- e c t s of t h i s type. In o u r study we deal mainly with average and i m p o r t a n t i m p r o v e m e n t innovations (a precise definition of t h e s e t e r m s is given by Hau- stein a n d Maier 1980). Therelore, a n approximate probabilistic t r e a t m e n t of u n c e r t a i n t y s e e m s possible.
The i n t e r p r e t a t i o n of u n c e r t a i n t y and risk depends also upon t h e level of t h e m a n a g e m e n t hierarchy under consideration. On t h e level of society as a whole, risk is often associated with u n c e r t a i n and undesirable consequences of the application of modern technologies (Slovic e t al. 1977a. b, Pat6 1979).
Investigations c o n c e n t r a t e on psychological questions of perception a n d societal acceptance of undesirable side effects. While we do not deny t h e i m p o r t a n c e of t h e s e questions, we s t r e s s t h e influence of uncertain expecta- tions and possible f u t u r e events on decisions t h a t m u s t be m a d e today con- cerning innovation projects.
In every business situation, a qualitative risk analysis is absolutely necessary. Quantitative risk analyses a r e valid only for specific conditions and under c e r t a i n assumptions a n d c a n n o t be generalized in m o s t cases.
Undoubtedly t h i s fact greatly complicates t h e integration of risk in t h e decision-making process within t h e planned economy. I t has n o t y e t been d e t e r m i n e d whether decision situations c a n be classified with regard t o risk, so t h a t g e n e r a l approaches c a n be recommended for c e r t a i n classes.
Let u s s u m m a r i z e t h e m o s t i m p o r t a n t issues (BBcskai e t al. 1976, Zell- m e r 1980). Risk is t h e possibility t h a t a decision will lead t o consequences t h a t differ too rnuch from those expected or planned. This definition r e l a t e s risk to objectives derived from societal needs. Risk implies interdependence between:
t h e objectives of economic development;
t h e anticipated objectives and t h e actual r e s u l t s t h a t can be accepted by society;
t h e expected positive consequences;
t h e expected negative consequences, should actual results differ too much from those anticipated.
By quantifying these factors in an appropriate manner, we obtain the so- called risk coefficient, as i t was introduced by Bacskai e t al. (1976) and developed f u r t h e r by Zellmer (1980).
A variety of risk factors can lead to a discrepancy between actual and anticipated results. Most of t h e classifications of these factors reflect pecu- liarities of a certain field of investigation. For decisions on innovation proj- ects, we s e e t h e following factors as most important:
I . The potential areas of application of a particular innovation are only roughly predictable ( b u t accuracy increases with time). This is even more t r u e of t h e m a r k e t share of an innovation in a specific application. Difficulties in forecasting t h e m a r k e t s h a r e a r e caused, above all, by competing innovations. The m a r k e t s h a r e of a particu- lar innovation is determined by t h e development of prices (espe- cially t h e price of energy: Doblin 1982), by existing capacities, by t h e present economic mechanism and its main directions of development, by t h e present s t a t e of t h e economy, e t c . That is why i t is sometimes difficult for innovations t o realize t h e high expecta- tions of top management.
2. To realize its scientific, technological, arld economic potential, t h e innovation process presumes the availability of certain resources, machines, and equipment.
3. Governmental economic measures have a major impact on innova- tions, especially in countries with centrally planned economies.
4. International development of prices and costs h a s caused a deeply felt shift in t h e orientation of t h e economy a s a whole, with pro- found consequences for all innovation projects under consideration.
All of t h e factors listed above act together. It is only partly possible t o separate t h e m analytically in each application. Other i m p o r t a n t factors influencing risk are described by Martino (1972), Bacskai e t al. (1976), and others, who express alternative views on these problems.
Our practical experience with managers and theoretical investigations ( B ~ c s k a i e t d . 1976) confirm t h e view t h a t i t 1s t h e economic mechanism t h a t has the greatest impact on t h e formation of risk. The behavior of managers is determined t o a high degree by sanctions when risky decisions fail a n d by financial and other rewards when they succeed. Stimulation h a s been predominantly negative in nature. This h a s led most decision makers t o shy away from dynamic development with high potential gains for their e n t e r - prise and for society as a whole in favor of contemplative, riskless behavior characterized by leisure, stability, and t h e absence of conflicts. This behavior is also fostered by the fact t h a t risk-prone decision makers lack juridical pro- tection and have insufficient reserve funds. In addition, economic conditions during t h e 1970s and early 1980s (scarcity of raw materials a n d energy sup- plies) have increased restrictions on decision makers' latitude. Governmen- t a l agencies a r e being forced to allocate certain kinds of resources centrally.
Much h a s been done both i n theoretical work and in practice to create a n economic mechanism for stimulating decision makers to make decisions involving an admissible degree of risk. We consider risk t o be socially admis- sible if i t does not entail possible consequences t h a t could n o t be accepted by society even when the most i m p o r t a n t influencing factors are unfavorable.
Many of t h e aids to decision making developed by modern decision theory t o quantify risk have been applied successfully t o various problems in different branches of industry (e.g. Tsuji 1980 and references cited therein).
We want to warn against uncritical applications of these methods. First of all, t h e assumptions upon which t h e methods a r e based m u s t be examined.
All of t h e methods imply a certain m a n n e r or style of viewing a decision prob- l e m or place certain aspects of t h e problem into t h e c e n t e r of t h e analysis (Salazar and Sen 1968, BAcskai e t d. 1976, Pate 1979, Chapman 1979, Tsuji 1980). In applying methods based on decision theory i t is of critical impor- tance to identify all events relevant t o a risky decision situation and to deter- mine their interdependence. In order t o determine a t least t h e most impor- t a n t of these, t h e judgment of experts is needed. The quality of t h e whole analysis depends upon t h e competence of these experts (Pate 1979).
There a r e only a few methods t h a t do not employ event probability esti- m a t e s (behavioral factors producing biases in probability estimates were summarized in Section 2.3.3). Estimates of the probabilities of events pro- vide information that, a s yet cannot be obtained in other ways.
The application of decision t r e e s is one of t h e methods suggested by pro- ponents of decision theory for supporting risky decisions. (For details of t h i s and alternative approaches and combinations of methods we refer t o t h e literature, e.g. Belyaev (1977), Keefer (1978), Pat6 (1979), Chapman (1979), and Tsuji (1980).) A critical assessment of decision t r e e s was made by Lari- chev (1979). Decision trees a r e a n important element in Cazalet's (1981) integrated system of models. We shall discuss quantitative risk assessment based on decision trees and i t s integration into our approach after we have introduced t h e general outline of the decision support system for which we are aiming (Section 4.4).
Having reviewed existing models and concepts for decision support and their relevance to innovation m a n a g e m e n t , we shall now use t h e m for developing a decision support system for t h e lighting industry. For this pur- pose we need a clear understanding of t h e industry and its requirements for development.