FIGURE 1
Jeffrey A. Weber
Pennsylvania Senate Policy Development and Research Office
Uncertainty is both the tormentor and motivator of life. Our fears, concerns, expectations, and inquisitiveness are fostered by uncertainty over what is occurring and what may occur. Innovation, dissent, the unknown, all provide a combination of excitement and fear because of uncertainty.
Human actions have been and are being shaped by uncertainty. Because of uncertainty it is necessary to make decisions. If there was no uncertainty, then we would never have to make a decision because we would be omniscient; we would always know exactly what course of action to take and exactly when, where, why and how to take it. Moreover, it can be argued that there is not one aspect of our life which uncertainty does not affect. It is one of the foundation stones of existence.
Over the years, the research on uncertainty has been voluminous but at the sametime, limited in scope. Uncertainty is encountered frequently in: decision making (Barnard, 1940; Simon, 1947, 1973; Quade, 1975; Madansky, 1979; Einhorn, et. al., 1985; Aiginger, 1987; Eiselt, et.al., 1990; Nutt, 1990; Drazen, et. al., 1991; and Gosh and Cain, 1993); organizational open systems theory (March and Simon, 1958; Katz and Kahn, 1967; Emery and Trist, 1965; Milliken, 1987); psychology (James, 1890; Jamis and Mann, 1977; and Einhorn and Hograth, 1985); economics (von Mises, 1948; Keynes, 1935; Fishburn, 1964; Cyert and DeGroot, 1987; Vickers, 1994; and Hamouda and Rowley, 1996); artificial intelligence (Kyburg, 1987, 1994, 1996; and Horvitz, et. al., 1995); the medical sciences (Shortliffe, et. al., 1997); and, quantum physics (Heisenberg, 1927; and Werner, 1996). The current understanding of uncertainty is discipline-specific and has an epistemological basis, for each academic discipline has focused on uncertainty as it relates to a specific problem within a specific field.
Business administration, public administration, organizational theory, and sociology, tend to focus on decision making under uncertainty. Here, uncertainty is viewed as: (1)"a lack of knowledge of the situation" (Barnard, 1940; Simon, 1947, 1960, 1965, 1977, 1980; Allison, 1971; and Nutt, 1990); (2) a bias that interferes with rational decision making and needs to be controlled (Art, 1967; Aiginger, 1987; Baird, 1989; Nutt, 1990; and Hey, 1993); and, (3) a psychological perception that creates fear or indecision that is best reduced through heuristic techniques or procedures (Bachrach and Baratz, 1963; Gore, 1964; Schroeder and Suefield, 1971; Agor, 1986; Simon, 1987; McCalla, 1992; Nigel, 1993; Covey et. al., 1994).
Economics, statistics, forecasting, medicine, astronomy, quantum physics, and the science of artificial intelligence see uncertainty as: (1) a factor within Bayeasian probabilistic models (Churchman, 1963; Quade, 1964 and 1975; Asher, 1978; Madansky, 1979; and Bronner, 1993; Horvitz, et. al., 1995; and Kyburg, 1987, 1994 and 1996); and, (2) as an attribute of complexity that at first appears unpredictable, but with correct analysis and modeling, is predictable (Bullard and Butler, 1993; Kellert, 1993; and Keil, 1995).
Psychology and the behavioral sciences present uncertainty as: (1) a mental perception that can be reduced (Edwards, 1954; Becker and McClintock, 1967; Baron, 1994); and, (2) as a factor which influences communication (Koller, 1993).
In all the previously mentioned areas, the concept uncertainty is epistemologically biased, in that uncertainty is viewed as an attribute of how we know what we know. This epistemological bias has led to the development of four branches of uncertainty literature based on an actor’s (individual, group, or organization): (1) ability to gather and process information; (2) ability to predict consequences of actions; (3) use of intuition; or (4) perception of the environment (Weber, 1997: 455).
In addition, in each case, uncertainty is viewed as a factor within the context of a much larger picture. Thus, uncertainty is conceptualized as a factor in a Bayesian model; an heuristic technique; or, as a non-descript feeling or emotion. In these instances, uncertainty is implied to have an "obvious" meaning which requires no explicit definition, (i.e., lack of information or knowledge, psychological perception, etc.). The overall focus of the uncertainty literature is how to make sure one is sure, or at least make oneself feel sure.
Despite all the previous research and study of uncertainty, and despite the life-encompassing aspects of uncertainty, there does not exist a general theory. For instance Weber (1997) conducted an initial literature review of uncertainty which included western philosophy, public administration, business administration and organizational theory literature. This review demonstrated that that there is a gap in the uncertainty literature: the absence of a general theory of uncertainty.
Recently, Kyburg (1997) and Smith (1997) also concluded that there was no general theory of uncertainty, and recognized the need to build such a theory. Kyburg’s and Smith’s research was conducted independently of each other and of Weber (1997); therefore, three independent reviews of the uncertainty literature arrived at the same conclusion.
Without a general theory of uncertainty, its study will remain fragmented and disjointed as disciplines struggle to incorporate it. Because there is no general theory, there is a lack of consensus on uncertainty’s definition. hence, how does one know whether one is measuring or controlling for uncertainty? In essence, numerous bodies of knowledge have been built through the efforts of multiple disciplines, but none of them have faced the challenge of developing a thorough, conceptual definition of uncertainty.
Critical questions which a general theory of uncertainty would answer are: (1) What is uncertainty? and (2) What is uncertainty’s relationship with existence and humanity? There is no consensus on these questions, because there is no general theory. It is interesting to note that we study the influence of uncertainty and seek to reduce or avoid it, but we have not bothered to determine what it is. How do you know you are measuring uncertainty or controlling it, when you do not even know what it is? In essence, an entire edifice of literature has been built through the efforts of multiple disciplines, but none of them have ever bothered to construct a foundation.
A general theory of uncertainty is necessary. Uncertainty is one of the primary aspects of our existence. Specifically, within public administration a theory of uncertainty is important due to public administration’s underpinnings in decision making theory (Simon, 1947). By not developing a theory of uncertainty, we in the field of public administration have limited our ability to do and to study: planning, strategic management the making and managing of decisions, organizing, and public budgeting and financial management.
The purpose of this paper is to present a theoretical vision of uncertainty, which may serve as a way of guiding the building of a general theory of uncertainty. By theoretical vision I mean a broad general outline of the major constructs, which would comprise a theory. Before presenting the theoretical vision of uncertainty, the next section will briefly review the role of theory in order to provide the context.
Since the object of this paper is to present a theoretical vision of uncertainty, it is prudent that an understanding of what a theory is and its role be explained at the start, because how one views theory and its role is one of the first assumptions in theory building (Weick, 1989 and 1995).
The purpose of theory is to "organize (parsimoniously) and communicate (clearly)" an explanation of a complex phenomena (Bacharach, 1989:496). Epistemologically, a theory enables us to bring the components of a complex phenomenon together in one understandable whole, which then enables us to relate the complex phenomena to the rest of the body of knowledge.
For the purpose of this paper, a theory is:
a coherent narrative composed of assumptions, abstract reasoning, and speculation which describes and explains an observed or experienced phenomena’s constructs, their interrelationships, and their boundaries (Weick, 1995:388-389; Gioia and Pitre, 1989:587; and Bacharach, 1989:496).
Theories have been grouped into three types: (1) Grand Theory; (2) Middle Range Theory; and (3) Substantive Theory (Merriam, 1988). Grand theory attempts "to explain large categories of phenomena" (Creswell, 1994:83). It attempts to explains the interaction of phenomena on a broad scale and is not discipline specific. It consists of "open" concepts, which are universal (Osigweh, 1989:50). Examples of grand theory are: the nature of good and evil, the nature of justice, or a theory of education.
Middle-range theory is derived from a grand theory being applied to a particular academic discipline. The boundaries of middle-range theories are more defined and specified (Creswell, 1994:83). Middle range theories consist of constructs, which are defined as "mental configurations" which "though not observational either directly or indirectly, may be applied or even defined on the basis of the observables" (Kaplan, 1964: 55; and Bacharach, 1989: 500). Furthermore, the interrelationships between constructs in middle range theories are defined through propositions (Bacharach, 1989: 500). Quantitative research methods are more easily applied because the unit of analysis is limited in time and space (Merriam, 1988). Examples of middle-range theories are: theory of bureaucracy, socio-tech systems design theory, Lindblom’s incrementalism, and game theory.
Substantive theory is "restricted to a particular setting, group, time, population, or problem" (Creswell, 1994:83). Substantantive theories consist of variables, which are "an observable entity" which "may be viewed as an operational configuration derived from a construct" (Bacharach, 1989: 500). Hypotheses define the relationships between the variables. Substantive theories lend themselves to direct observation and measurement
Furthermore, though the theory building literature does not specify it, there can be seen in the literature two type of theories: general theory and special theory. General theory encompasses the most basic and essential components and their relationships, which explain the whole of the phenomena and is widely applicable to a range of issues. A special theory is narrow in context and focuses on explaining a specific issue within a phenomena.
Exactly what should be worthy to be called a theory is a subject of debate in the academic community. The Academy Management Review in 1989 and Administration Science Quarterly in 1995 have each published forums on theory building and "What is or is not good theory." Some have argued that theory is much maligned and misused and this has resulted in practically any speculation being called a theory (Sutton and Staw, 1995). Sutton and Staw (1995) argues that the this has led to a weakening of what constitutes theory and thus a lack of direction and a disorganization of the body of knowledge. Therefore, theory must be clearly defined, just as "a collection of words does not comprise a sentence" a collection of thoughts, speculations, assumptions, and data do not comprise a theory (Bacharach, 1989:496; and Sutton and Staw, 1995).
Seeming to contradict the above view point is the belief that we are afraid to use the word theory (Weick, 1989 and 1995). That academic false modesty has led to going to any extreme possible to state that a theory is not being put forth (Weick, 1995:387). There is a failure to put forth a theory because, it is not a grand theory, or it does not fit some particular definition of a "good theory." (Weick, 1995:386). Furthermore, there is the fear that if you state you are putting forth a theory it will leave you open to merciless criticisms from those that would argue the theory is not deep enough in its description of its constructs and their relationship; or that the theory is not broad enough to be generalizable and to cover the entire subject (DiMaggio, 1995). Indeed, if one was to take Bachrach’s (1989) criteria for the evaluation of organizational theory, it is possible that there is not a theory around that does not fail in at least one of his criteria.
Karl Weick (1995) solves the above dilemma by pointing out that "theory is a continuum." He holds forth, similar to Plato’s allegory of the cave, that theories are approximations of the phenomena which begin as "nonlinear visions" and are converted into detailed constructs and propositions. As theories move from visions to detailed constructs and propositions they lose some of their accuracy and become more of an approximation, but they become increasingly useful to the discipline. Not everything is a theory, but it is "difficult to judge" what is a theory and what is not a theory, because "theory work can take a variety of forms" and it is work in-progress (Weick, 1995: 387). Therefore, to recognize what is theory it is necessary to look at the process in which the theory is being developed (Weick, 1995:387).
The process of theorizing consists of the following activities (though not in a particular order): abstracting, generalizing, relating, selecting, explaining, synthesizing, and idealizing (Weick, 1995:389). To know if what one is putting forth is a theory, you have to put it in "context of what came before and what comes next" (Weick, 1995:389). If you move from part of the process to another part of the process, then there is the process of theorizing and it is or is becoming a theory (Weick, 1995:389). "Few things are full-fledged theories" but, there are theories that are involved in the process and are somewhere along the continuum.
A theory should add to the body of knowledge, not just rewrite existing knowledge (Whetten, 1989:491). Furthermore, a theory should be useful (Bacharach, 1989). The usefulness of a theory is tied directly to its ability to guide future research and its practicalness within a discipline (Whetten, 1989 and Bacharach, 1989). Research is guided by theory, because a theory helps to establishes the boundaries and the context for future research.
One of the first steps in building a theory is a vision of the theory based on initial research into the phenomenon one is seeking to explain. Karl Weick (1995) describes how in theory building one speculates and abstracts continually through the process. Using Weick’s theorizing process, instead of leaving the abstraction, speculation, and thought process as an implicit, this paper opts to explicitly lay it out as a theoretical vision. As previously stated a theoretical vision, for the purpose of this paper is an initial explanation of a phenomenon which consists of temporal and spatial boundaries as well as the constructs involved. This theoretical vision of uncertainty is based on literature reviews previously conducted (Weber, 1997).
This theoretical vision consists of a two part theoretical boundary and four initial constructs: temporal uncertainty, physiological uncertainty, psychological uncertainty, and sociological uncertainty. This section will explain the theoretical vision by taking describing each part of the vision and than bringing it all together at the end.
Theoretical Boundaries
FIGURE 1
This theoretical vision of uncertainty has a metaphysical and epistemological boundary that consists of a knowledge boundary and an actor boundary. The knowledge boundary consists of all knowledge that has occurred since the beginning of time (Aristotle, Metaphysics). It is independent of a single actor because it is the sum total of all events that have ever occurred in existence. The actor boundary is the ability of an actor (individual or group) to know and to act. The knowledge boundary establishes the metaphysical boundary, while the actor boundary is the epistemological boundary.
Having framed this theoretical vision of uncertainty with a knowledge and actor boundary, this section will briefly outline four constructs: (1) temporal; (2) physiological; (3) psychological; and (4) sociological.
Temporal Uncertainty
The first construct to be developed is temporal uncertainty, which refers to the influence of time. Figure 2 graphically depicts temporal uncertainty as it is developed in this section.
FIGURE 2
As previously stated, knowledge is the totality of all events which have occurred since the beginning of existence and it is represented by the knowledge boundary. The actor boundary operates within the knowledge boundary. Temporal uncertainty is the influence of time on knowledge. In the figure above, temporal uncertainty is depicted as an arrow, which starts at the beginning of time and goes through the present into the future. The past is all that has occur prior to the present. The present is a precise point of time when events are actually occurring. The future is everything beyond the present. Along existence’s time line knowledge can be divided into the known, the unknown, and the unknowable (Aristotle, Metaphysics, 982a. 30-b3). The known is all the data and information that we (humankind) have recorded, and which is presently retrievable. The known is represented by a curve, because the further one goes into the past the more information is lost or become unknown. At the present events are occurring and there and not enough time has passed to analysis and integrate that information. There is a portion of the known, where events have been analyzed, there are still witnesses to the events, and the recorded information still exists.
The unknown is all the data and information that exists, but has not been recorded or was recorded, but the record, over time, has been lost. The unknown has the attribute of existing, but just is not known or understood by the decision-maker and therefore could possibly become known. The known and the unknown exists in the present and the past. The unknowable is the data and information, which does not yet exist, and, therefore is not accessible to be recorded. The unknowable exists in the future. The unknowable is that which will never be known because it does not exist. The unknowable can be guessed but, because life consists of so many variables, the guess is never certain.
The actor boundary operates within the known portion of knowledge, because each actor, whether individual or group, possesses knowledge which makes up a portion of the known. Beyond the present the actor boundary fades into the future, because of guesses and speculations about future events. Just because the actor boundary fades into the future, does not make the future known, it is still unknowable, but the actor has guessed or speculated about what may occur.
It is possible for the actor to search the past and the present to accumulate and to integrate data and information. Theoretically, if the decision-maker’s search, filter, and integration mechanisms and abilities were good enough it would be possible to have knowledge of the exact events of the past and present. Currently, the mechanisms and abilities do not exist, and it is only possible for someone to obtain partial data and information.
Though the events have taken place in a certain manner, the discerning of those events is determined by the manner in which they were recorded. Events may occur, but depending how the actor processes the event, the reaction of the actor may occur after the event has taken place. An example would be the Federal Reserve reacting to changes in the unemployment and consumer price index. Unemployment figures and the consumer price index are events that have occurred in the past and may show a problem in the nation’s economy that did exist at the time, but does not necessarily exist at the present. Therefore, the Federal Reserve may be trying to solve a problem that does not exist anymore.
The above is an example of the affect of time on knowledge, creates uncertainty because of the time it takes an actor to process an event. Furthermore, the ability to guess future events (the unknowable) is a function of time. In the future no events have occurred to be processed, therefore knowledge of possible future events can occur in two ways: (1) using the known and projecting it into the future; and (2) using intuition or spiritual revelation.
Projecting the known into the future is a function of statistical probability, logic, and imagination. The more variables constituting an event the more difficult it is to guess future consequences; the further one projects into the future, the more variables may interact. All that one can have is a guess, and "the ability to guess the future is extremely limited" (Wildavsky, 1978:333). One can guess with "some accuracy" (probability) a limited number of events in the near future. By "some accuracy" we mean that the guess one makes about a future event matches will match how the actual event will occur. The "near future" means twelve months or less (Wildavsky, 1978).
Use of statistical predictive models is combined with logic and imagination. Through inductive and deductive reasoning one could project into the future the most logical occurrence that could take place. Moreover, the imagination provides a unique mechanism for projecting into the future because it creates a vision of what may be possible.
The primary limitation of a guess is that the guess may influence future behavior and thereby determine how the future will occur (Barnard, 1940: 12). This limitation appears to be lost on those who place so much faith in trend projections and forecasting. Generally, it most be remembered that the future has not yet occurred and, therefore, there is no data or information yet to be searched and to be gathered.
Spiritual revelation differs from above in that it does not relay on the past. Instead spiritual revelation relays on the actor connecting with a source that is operating simultaneously in the past, present, and future. Because the spiritual source is operating simultaneously in all three time periods, it is not effected by time and therefore has complete knowledge. Therefore, those that claim spiritual revelation are using information given from a source outside of the actor’s existence.
Prophecies of future events abound throughout human history and comprise a portion of most of the world’s religions. Sometimes belief in the validity of a prophecy becomes the driving force for an actor’s activities and thus leads to the fulfillment of the prophecy (in other words a self-fulfilling prophecy).
Thus far in this explanation of a theoretical vision of uncertainty we have described two theoretical boundaries: knowledge and actor. This section has provided a brief explanation of the initial construct temporal uncertainty. The next three constructs (physiological uncertainty, psychological uncertainty, and sociological uncertainty) all operate within the actor boundary and deal with the ability of the actor to know and act. Before addressing the next three constructs it is necessary to establish how an actor knows. The next section will explain how an actor accumulates knowledge.
How an Actor Accumulates Knowledge
An actor’s ability to know is composed of three parts: searching mechanisms; filter mechanisms; and compiling and integration mechanisms (Barnard, 1938: 27; Pasmore, 1989: 14) (see Figure 3). In addition, the extent that each of these mechanisms can gather and process data is dependent on the physical capabilities of the human organism(s) and on its (their) ability to use or to create technology and processes.
FIGURE 3
Searching Mechanisms
Searching for data is a constant and directed activity. The five senses are bringing in data constantly and data is being processed by the brain. Other data is brought in because it is searched for either purposively or by accident. The purposeful search for data implies a desire to get it for some end. The desire which produces the purposeful search is an area of overlap with the psychological component of uncertainty. (The psychological component is discussed in the next section). Moreover, how a decision-maker searches for data determines the type and amount of data gained (Barnard, 1938; March et. al., 1986; and Nutt, 1989). The search itself is an initial filtering of data because it is directed in a certain area and, therefore, may capture only a select amount of data or information (Nutt, 1989 and Cook, 1993).
Filtering Mechanisms
After the initial search, data travels through filtering mechanisms in which some may be discarded as irrelevant (Pasmore, 1989). An actor’s filtering mechanisms can range from an individual mentally discarding data as irrelevant to an organization’s information management processes which will perform the same task.
Compiling and Integration Mechanisms
After the incoming data has been filtered it goes through compiling mechanisms that sort and categorize. In order to sort and categorize the data an interpretation is Finally, the data is integrated with existing data and turned into information and placed in context with existing knowledge. The sum of all integrated data equals the total knowledge of the actor. The total knowledge of all actors since the beginning of time equals the known within the knowledge boundary.
Now how an actor accumulates knowledge has been explained, the next three constructs occur within the actor boundary and influence each other and in turn influence how the actor accumulates knowledge and acts upon it. The next section will develop physiological uncertainty.
Physiological Uncertainty
The second construct within this theoretical vision of uncertainty is physiological uncertainty which is found within the actor boundary (see figure 4).
FIGURE 4
The physiological construct of uncertainty consists of the physical function of individual human organism influence on the actor’s ability to know and act. Physiological uncertainty focuses specifically on the five senses, the ability of the individual to use them, and the influence it has on the actor’s ability to know and act. The five senses are: seeing, hearing, tasting, smelling, and feeling. Each of these five senses contribute to how much data we as an individual bring in and how we integrate that data. In other words, a person’s inability to see, will limit what type of data is being gathered and what will ultimately be processed by the individual’s brain.
Uncertainty is affected by the reliability of the biological capability of search, filtering, compiling and integrating mechanisms. If the mechanisms have a biological impairment (i.e., inability to see or hear), then there is an increase in uncertainty. For instance, the ability to search may be misdirected, and the wrong data are gathered. in addition there is the possibility of the search mechanism creating data which does not exist. Moreover, the filter mechanisms may filter too much or not enough. Finally, data which are irrelevant may be accepted as relevant, or relevant data could possibly be discarded.
Thus, physiological uncertainty is how the searching, filtering, and compiling and integrating mechanisms operate based on their respective biological characteristics. The biological operation of the mechanisms is just one side, the other side is found in the continual interpretation of data as it is gathered and processed. The interpretation of data is the realm of psychological uncertainty. The next section will develop the construct psychological uncertainty.
Psychological Uncertainty
The psychological uncertainty construct also operates within the actor boundary (see figure 5). Overall, psychological uncertainty is the influence of the actor’s perceptions and reasoning on the searching, filtering, and compiling and integrating mechanisms.
FIGURE 5
Psychological uncertainty consists of how the actor perceives and reasons, that is the actor’s intelligence and mental attributes which enhance or detract from it. Moreover, the actor’s intelligence poses limitations and boundaries to the acquisition of knowledge (Barnard, 1938 and 1940; and Simon, 1947 and 1976). Intelligence is influenced by the actor’s: biological capabilities (in terms of the functioning of the brain); existing integrated knowledge; and, understanding (Edwards, 1954 and Einhorn, 1981). This is true not only when the decision-maker is an individual, but also when it is a group or organization (March and Simon, 1958; Madansky and Olsen, 1979; and Hall, 1991).
The brain’s abilities limit the decision-maker in the pursuit and in interpretation of data and information. Knowledge of the functioning of the brain reveals that it consists of three distinct regions: hind, mid, and forebrain. The hind brain primarily transmits signals between the brain and the spinal cord, and controls automatic functions. The mid-brain serves as a connection between the hind and forebrain.
The fore brain consists of the two cerebral hemispheres which are further divided into four sections: frontal, parietal, temporal, and occipital lobes. Each of these lobes deals with motor and sensory areas of the body. Another major functional area of the brain that has been identified is the association cortex, which appears to control the functioning of higher level activities.
Damage to any of the parts of the brain or various chemical imbalances in the body can alter it functioning which influences how the actor reasons or perceives things. The actor’s perceptions and reasoning ability influences where the actor searches for data, and how it is processed. Perceptions of fear or distrust may cause an actor to discount certain data and distort the data that is gathered and in turn provide a perception of uncertainty (McCalla, 1992). Similarly, feelings of confidence can cause an actor to only accept data which reinforces current knowledge and thus produce perceptions of absolute certainty (McCalla, 1992).
Groups and organizations have the advantage of multiple individuals, thus, weakness in one individual may be offset by the abilities of another. The degree to which this compensation occurs depends on the formal and informal structures and processes established within the group or organization (March and Simon, 1958 and Pasmore, 1988). Groups and organizations, though, are still prone to psychological perceptions of uncertainty (Allison, 1967; Art, 1976; and McCalla, 1992).
So far this theoretical vision has developed a knowledge boundary, an actor boundary and three constructs: temporal uncertainty; physiological uncertainty; and psychological uncertainty. The next section will develop the fourth construct - sociological uncertainty.
Sociological Uncertainty
` Sociological uncertainty involves the interaction of individuals within groups or organizations and the whole social unit with the actor (Barnard, 1938: 41-42). In addition, social interactions, are both non-deliberate (Barnard, 1938: 42) and deliberate (Etzioni, 1968: 94-125). (see figure 6).
FIGURE 6
Non-Deliberate Interaction
Non-deliberate interactions consist of social norms and mores which subtly influence, direct, or limit the decision maker’s searching and filtering mechanisms. For instance, established beliefs and norms have long been viewed as unconscious guides in individual and group action. Thus, an actor may be unconsciously directed by beliefs and norms to focus on certain data, and some will be overlooked.
How data are filtered and compiled also will be influenced by the customs and norms of the decision maker. Actors can approach data from different perspectives that have been established by customs and norms. These perspectives serve as guides for the piecing together of the data, and explains why different actors, with different social backgrounds, can arrive at different conclusions after examining the same data and information.
Deliberate Interactions
The deliberate interactions consists of: laws, regulations, rules, and procedures formally established to direct and to control the actor’s action and may affect the actor’s searching mechanisms by preventing access to possibly relevant data. Furthermore, an actor may decide that various data are irrelevant, because of the existence of interactions which make the data unusable.
For example, a deliberate social interaction affecting a decision-maker’s search and filtering mechanisms would be laws and regulations concerning the conduct of medical research. Another would be the laws and regulations used to establish the conduct of research in space. The influence of laws and regulations on bureaucratic decision-makers has been well researched (Gore, 1964; Downs, 1967; Art, 1968; Clark and Shrode, 1979; Rourke, 1984; Peters, 1989; and Meier, 1993).
Consequently, the deliberate and non-deliberate social interactions not only influence the actor’s searching, filtering, and compiling and integrating mechanisms, but also affect how data are recorded for future use. Social norms, customs, laws and regulations direct what information will be preserved and saved and what information will be discarded, and thereby affecting what is possible to know and what will become unknown. Thus, the actor’s search for data and information maybe limited and directed already from the very start.
Figure 7 shows all the boundaries and constructs just described brought together to show the whole theoretical vision of uncertainty.
FIGURE 7
The theoretical vision of uncertainty just presented consisted of a knowledge boundary and an actor boundary. The knowledge boundary represents all events that have ever occurred since the beginning of existence. The actor boundary operates within the knowledge boundary and represents how an actor accumulates knowledge and acts.
This theoretical vision also consisted of four constructs: temporal uncertainty, physiological uncertainty; psychological uncertainty; and sociological uncertainty. Temporal uncertainty was developed as a time line that begins at the start of existence, goes through the present, and into the future. Knowledge, defined as all that was divided into the known, unknown, and the unknowable. The actor, acting only in the present, is able to search the known and occasionally into the unknown of the past and present, and because time distorts the data, there is uncertainty. Actor’s also use data gathered to try to predict the future, but because the data gathered is distorted by time, predictions are very limited and highly uncertain.
The physiological and psychological constructs, moreover, decrease the actor’s ability to be certain because of the limitations the constructs place on the actor’s searching, filtering, compiling, and integrating mechanisms. The sociological uncertainty construct also decreases the ability of the actor to be certain because social customs, norms, laws, regulations, rules, and procedures act as filters that influence the decision-maker and potential knowledge.
Public Administration is a discipline closely tied to decision making theory (Simon, 1947, 1973, and 1997). Furthermore, some primary areas of public administration: public budgeting and financial management, strategic management, regional and urban planning, etc., all involve uncertainty. Yet, as a discipline, public administration has not addressed uncertainty except as a factor that accounts for a lack of knowledge, as an inability to predict future conditions, and as a psychological perception.
A general theory of uncertainty will enable public administration to consider simultaneously the multiple aspects of uncertainty. Furthermore, a general theory of uncertainty will provide a different perspective for reevaluating current theories in public administration. One example, a general theory of uncertainty will provide a different perspective is in how it answers such questions as: Is uncertainty measurable?; Is uncertainty controllable?; and What actions are possible to do under uncertainty? The answers to the preceding questions will help to shape and determine a myriad of issues within public administration.
If uncertainty is measurable and controllable, then forecasting and information management systems serve a high value in reducing uncertainty and in producing a stable environment for organizations. If uncertainty is not measurable and controllable, then forecasting and prediction have limited value and need to be viewed and understood in such context. In short, how we view and understand uncertainty will determine how we make decisions.