Laws of information systems

The Laws of Information Systems are a collection of observations and assertions Generalizations Characterizing the behavior of people, hardware, software, and procedures enclosed in a clear scope (an information system ). An information system is an amalgam of scientific and humanistic disciplines including computer science, management science and social sciences. It is therefore provided by laws as well as a number of principles. These span the range of transaction processing , effective systems, user interfaces and system development. These “Laws” should not be confused with Scientific or Physical Laws .

Laws

Transaction Volumes

The volume of transactions will increase with the stage of development of a society. The greater the number of transactions, the greater the number of transactions.

Symbol Systems

This is a corollary of the law of transaction volumes. The symbol systems by which messages are encoded grow as complex as a society evolves. Thus the 7 bit ASCII code gives way to the 8 bit version which is giving way to the 16 bit Unicode system. This trend is likely to continue when civilization expands beyond the confines of our planet and the volume of information exchanged becomes astronomical.

Technological Evolution

Technology seeks the most efficient form, unless otherwise constrained. Efficient form is defined qualitatively as one which is best adapted to its application or as one with the least number of problems. This is a variation on Darwin’s theory of evolution and is manifested in the case of memories, storage devices, databases etc.

Infinite Processing Needs

The information processing needs of an organization or society will always exceed its information processing capabilities. This can be seen as a converse of Moore’s Law – doubling of processing power every two years. Whenever a new technology is used, it can be used to increase or decrease the number of applications. For instance, the process of processing images in real time. This is also evidenced in the box of email, internet, search engines etc. That are being swamped by volume.

Good Systems

A good system produces benefits that are disproportionately high in comparison to the initial investment. Any complex system, including an information system, is typically interconnected with other systems. So a good system has ripple effects which show up as unexpected benefits. The freeway system in the US for example, led to the growth of the automotive, steel and motel industries. Another example is the Saber system which has been designed to make reservations but has been used in crew scheduling and flight forecasting. As a corollary a bad system produces problems that are disproportionately high in comparison to its area of ​​operation, a prime example being the 64K memory limit of DOS which for a long time stymied software developers.

Right Design

Every software that involves users has a “right” design. The “right design” refers to decomposition of functions into menus / controls. This article is about the design and development of the software.

Interconnected Systems

An interconnected system can not be controlled unless each interconnect is individually controllable. A very simple example is provided here. In one e-mail system, the “signature” text and message composition are wedded together. This is also known as “coupling.” It provides an option to select the default signature but does not allow this to be done dynamically. It should provide an option to select which signature is used, at the time of message composition.

Complex Interfaces

There may be a simple interface to a complex system. This is a variation on the law of a variety of conditions that are inherent in its environment. Complex systems such as visual programming environments or CASE tools therefore can not have simple interfaces.

Irregular Transactions

Information systems (transaction processing systems) which can not process irregular transactions are doomed to fail. An irregular transaction is defined as one which deviates from the norm, in terms of items bought, conditions or constraints. Examples include registering for two races that are scheduled to start at the same time or with a child safety seat.

Qualitative Decision Models

It is impossible to calculate outcomes with any certainty in a decision situation that involves qualitative variables. This is a computable computable algorithm for the computable algorithm of a computable algorithm. Because of their very nature, qualitative problems lack well-defined state and hence the law.

Mental Models

The system model should not exceed the user’s mental model in complexity. The system’s model is the conceptualization of the system. When the system’s model exceeds the user’s model, the user will be able to operate the system without extensive training.

Accelerating Returns

The rate of progress of technology is accelerating to such an extent that it produces returns that are not linear, but exponential. Much like Moore’s law , every decade there is a doubling of progress and technological advances will occur exponentially. Kurzweil [2] expects the next major evolution of human biological components with machines.

Brooks Law

Adding manpower to a late software project makes it late. Fred Brooks was the chief engineer overseeing the 360 ​​project, which was one of the largest software projects ever undertaken. Based on his experience, Brooks came to the conclusion that putting additional programmers on a delayed project will not have its implementation because of the additional communications overhead. In fact, it had the tendency to reduce productivity.

Information Independence

Users should be able to access their information. This is a variation on the concept of distribution.

Soft information principle

Information systems must incorporate soft information or they are doomed to fail. One way in which irregular transactions can be handled is to provide additional notes on the transaction. Those systems that do not accommodate such soft information may result in a transaction failure or may result in inconveniencing the user / consumer.

Reapportionment Principle

Tasks that can be performed by the system (in the context of software use) should be performed by it. Automatic filling of personal details from ss # or phone # in a customer registration form is one example. This is ultimately based on the simple economic principle of labor substitution to leverage productivity. It’s a widely used principle nowadays.

Sharing User Information

All desktop systems must share information about the user. This is a corollary of the re-apportionment principle. To the extent that desktop systems require user information (such as email address, phone # etc) it is advantageous for users to have the system obtain it from a common profile.

Information Responsibility Principle

Those who have information are obliged to share it with those who need it. This article is based on the article “The Coming of the Knowledge-Based Organization” by Peter Drucker in his 1988 HBR article. Since information is intangible, it is difficult for potential consumers of information to perceive its source and hence the principle. The principle implicitly assumes that the reasons for excluding information from a person do not exist.

Information Ownership

Owners of information must have access to it. This is a corollary of the information responsibility principle. When information changes, the owner has a stake in making the change in the system so it is reasonable to give them the access to do it. Many companies are web-enabling their systems, which illustrate this principle.

References

  • Amaravadi, CS, “The Laws of Information Systems,” Journal of Management Research, Vol. 4, No. 3, pp. 129-137, December 2004.
  • Kurzweil, R., “The Law of Accelerating Returns” [1]
  • Harmon, SY “IEEE International Conference on Systems, Man and Cybernetics, 1998 Volume 1, Issue 11-14 Oct 1998 Page (s): 1009 – 1014 vol.1
  • Brooks, F., “The Mythical Man Month” Addison Wesley 1975.

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