Research of systems' uncertainty.

Kremljak, Zvonko

1. INTRODUCTION

State uncertainty is defined as the inability of an individual to understand and foresee possible changes of elements and their relationships in outside environment. State uncertainty depends on the complexity of the system. The more it is complex the higher, due to bonded rationality and basic inability to predict future, is the understood uncertainty.

Effect uncertainty is defined as the inability of an individual to predict the effect of change in outside environment on the organisational system. Decision making individual knows the state of the environment but does not know the cause-effect relationship between certain environmental changes and influences these changes will have on the system in question.

Uncertainty is the essential characteristics in which every system works. The importance of uncertainty for operations of organisational systems is proven by empirical research which examined the importance of uncertainty in business systems. Schulz (2001) showed that unreliability influences the transfer of knowledge in organisational systems. Unreliability lessens the synergies between marketing, development and production. High unreliability negatively influences the process of new product development.

2. DEFINITION OF SYSTEMS

Response uncertainty is defined as the inability to foresee the consequences of the response. This is a case when the decision making individual knows the response must be made but cannot know what kind of consequences his decisions will bring. In Fig. 1 the division of systems with the help of Venn diagram is shown.

Formal systems are abstract systems which are examined especially from theoretical point of view. Their elements are abstract notions like numbers, symbols, letters, and points. The relationship between these elements is defined by a system of rules which are called axioms. Natural systems are systems which were created independently of human will or wishes; their behaviour, development and growth abide certain natural laws. Here belong biological and ecological systems. Social system is comprised of groups of people which associate with the aim to reach certain goals. Social systems are defined by patterns of behaviour which change dynamically.

The intersection between formal systems and natural systems represent technical systems. These are systems which were designed by people which themselves are not a part of the system. Characteristic of technical systems is that they are based on the same laws as the natural systems, or that natural laws are used for achieving functionality and quality. Technical systems are defined by their functionality, quality of operations and adaptability. Clear boundaries of the system, well defined elements and relationships between them enable traditionalistic treatment of technical systems. It is possible to explain the workings of a technical system by taking it apart to its constituent components, where every element has stable incoming and outgoing characteristics (Ljung 1987).

According to the definition criteria technical systems are deterministic, which means that to inputs in technical system belong precisely specified outputs. This means that operations of technical system must be definite; there must be no doubt about the realisation of foreseen function at a specified quality. Technical systems are not completely deterministic but it is clear that uncertainty in operations of technical system clearly limits its functionality and quality. Uncertainty of technical system is usually defined with probability levels, where we presume that the probability levels are objectively definable.

Organisational systems represent an intersection between technical and social systems. Their characteristics are that technical systems are strategically important resources in the system, but the operations of the entire system are not subject to natural laws. Operations of organisational system can be described with certain patterns and principles; these can be changed, because people on the basis of interpretation of patterns and principles form their own decisions and behaviour, which leads to establishment of new patterns and principles. Organisational systems comprise of business systems, production systems, defence systems, logistics systems. Their characteristics are openness, complexity and stochasticity. Activities between elements create patterns of behaviour of the whole system which cannot be understood or foreseen on the basis of knowledge about characteristics of individual elements. Relationships between elements are difficult to define, for elements can change their behaviour according to the information about operations of other elements. This means that it is impossible to control all the aspects of operations of organisational system and estimate the consequences of all available options. Organisational system is not entirely stochastic.

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3. UNCERTAINTY IN ORGANISATIONAL SYSTEMS

Organisational systems are result of human planning and management. Planning and management in organisational systems can be illustrated with decision making process. This process is subject to uncertainty as the basic characteristics of organisational system.

Dealing with uncertainty in organisational systems can be started by traditional examination of differences between certainty, risk and uncertainty. Certainty in organisational system can be mentioned, when a competent individual reaches decisions connected with operations of organisational system, where he has all the knowledge about possible states in the future and these states are completely independent of activities this kind of system is performing. This kind of organisational system is adaptable, for it is possible to prepare for all eventual states.

The concept of risk in organisational system means that it is possible to objectively define the levels of probability of a state or event. It means that a competent individual knows all possible states in the future and probability that these states will be realised.

Reality in organisational system is usually difficult to describe with the concepts of certainty and risk. The concept of uncertainty is much more suitable for describing the state in organisational systems (Laviolette & Seaman 1994).

Parametric uncertainty represents a type of uncertainty which can be mathematically mastered. Let U(a; c; [pi]) be a function, which encompasses activities (a) and expected results (c). Let {a} be a group of possible activities, which can be performed, a = (1, A). Let {s} be a group of possible states s = (1, ..., S) and {[c.sub.as]} a group of results which come from reciprocal operations between activities and states. Let {[pi]} be a group of subjective probabilities connected to the results and states. Parametric uncertainty can now be clearly defined. It means that the person who makes decision has a full spectrum of activities that can be performed, knows all possible states, and knows all the possible results of all activities. Therefore, he has complete knowledge regarding the structure of the decision making problem {a}, {s}, {[c.sub.as]}. Uncertainty concerns only subjective parameters of possibility {[pi]}.

Structural uncertainty means that the decision maker has incomplete knowledge on the structure of the problem. Or, put another way, he has incomplete knowledge about the three, {a}, {s}, {[c.sub.as]}. For structural uncertainty is characteristic that it is impossible to possess knowledge about all possible consequences. It is important to point out that structural uncertainty means that states in the future are not independent of activities. This means that an individual, who is making decisions, can change his opinion on possible future states according to the information regarding consequences of the activities. Future states are therefore a construct with dynamic characteristics.

Structural uncertainty is not the only name in scientific literature. Kouvelis (1999) talks about radical uncertainty when pointing out that in organisational systems it is impossible to know in advance which knowledge will be developed and which combinations of diffused knowledge will be important in certain circumstances. Unforeseeable and radical uncertainty draws attention to important factors of uncertainty in organisational environment, for example complexity, cognitive limitations of an individual and basic human inability to predict future. The concept of bounded rationality is based on a premise that the complexity of organisational systems causes uncertainty which cannot be managed by individuals; they can only adapt to it. The reality in organisational systems is so complex that a person is limited in processing information. Bounded rationality thus means that a person can never have so much knowledge to transfer structural uncertainty objectively into parametric uncertainty. If it were possible to reduce reality to parametric uncertainty it would be so complex that it would be impossible to examine in its entirety. Bounded rationality highlights cognitive limitations of an individual to process large numbers of activities, alternatives and consequences.

A factor which most prominently presents uncertainty of organisational systems is a human inability to predict future. Future can be treated only as a part of the present. This means that when dealing with organisational systems one cannot have objective knowledge about the future. Structural uncertainty is the result of complexity and openness of organisational system and the inability of people to understand future.

Additional point of view regarding dealing with structural uncertainty represents the fact that a part of uncertainty comes from the environment of organisational system. Scientific literature established a term environmental uncertainty. Organisational systems are open and their survival depends on events in environment (Jauch & Kraft 1986). Milliken (1987) warns about specific inconsistencies in understanding the term environmental uncertainty. It must be distinguished between 'objective' and understood environmental uncertainty. The first describes the state of the environment. The second describes the understanding and experiences of an individual, who must make decisions and is trying to understand the state of environment. The concept of comprehended uncertainty means that environmental uncertainty is not an objective phenomenon but a result of human understanding of the environment. Milliken divides environmental uncertainty into state uncertainty, effect uncertainty and response uncertainty.

4. CONCLUSION

Extensive conceptual discussions and empirical research prove that understanding the concept of uncertainty is the key to understanding the operations of organisational system. The operations of every organisational system are subject to circumstances of uncertainty, therefore, the task of decision making individuals is to design such organisational systems, which will be able to successfully adapt to uncertain conditions. In spite of the knowledge about the importance of organisational system management in the circumstances of uncertainty, there are surprisingly few approaches and techniques which support the decision making process in uncertain circumstances. The main contribution of our work is in the development of new heuristic tools which support decision making process. The challenge for the future is in upgrade of mathematical procedures--informatisation.

5. REFERENCES

Jauch, L. R. & Kraft, K. L. (1986). Strategic management of uncertainty. Academy of Management Review, Vol. 11, No. 4, 777-790

Kouvelis, P. (1999). Global Sourcing Strategies under Exchange Rate Uncertainty. In: Quantitative Models for Supply Chain Management, Tayur, S.; Ganeshan, R. & Magazine, M., (Ed.), 625-667, Kluwer Academic Publishers, Boston

Laviolette, M. & Seaman, J. W. (1994). The efficacy of fuzzy representations of uncertainty. IEEE Transactions on Fuzzy Systems, Vol. 2, No. 1, 4-15

Ljung, L. (1987). System identification, Prentice-Hall, New Jersey

Milliken, F. J. (1987). Three types of perceived uncertainty about the environment: state effect, and response uncertainty. Academy of Management Review, Vol. 12, No. 1,133-144

Schulz, M. (2001). The uncertain relevance of newness: organizational learning and knowledge flows. Academy of Management Journal, Vol. 44, No. 4, 661-681