Systems Theory

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Introduction

Systems Theory

Systems theory or general systems theory or systemics is an interdisciplinary field which studies systems as a whole. Systems theory was founded by Ludwig von Bertalanffy, William Ross Ashby and others between the 1940s and the 1970s on principles from physics, biology and engineering and later grew into numerous fields including philosophy, sociology, organizational theory, management, psychotherapy (within family systems therapy) and economics among others. Cybernetics is a closely related field. In recent times complex systems has increasingly been used as a synonym.

Overview

Systems theory focuses on complexity and interdependence. A system is composed of regularly interacting or interdependent groups of activities/parts that form a whole.

Part of systems theory, system dynamics is a method for understanding the dynamic behavior of complex systems. The basis of the method is the recognition that the structure of any system -- the many circular, interlocking, sometimes time-delayed relationships among its components -- is often just as important in determining its behavior as the individual components themselves. Examples are chaos theory and social dynamics.

Systems theory has also been developed within sociology. The most notable scientist in this area is Niklas Luhmann (see Luhmann 1994). The systems framework is also fundamental to organizational theory as organizations are dynamic living entities that are goal-oriented. The systems approach to organizations relies heavily upon achieving negative entropy through openness and feedback.

In recent years, the field of systems thinking has been developed to provide techniques for studying systems in holistic ways to supplement more traditional reductionistic methods. In this more recent tradition, systems theory is considered by some as a humanistic counterpart to the natural sciences.

History

Subjects like complexity, self-organization, connectionism and adaptive systems had already been studied in the 1940s and 1950s, in fields like cybernetics through researchers like Norbert Wiener, William Ross Ashby, John von Neumann and Heinz Von Foerster. They only lacked the right tools, and tackled complex systems with mathematics, pencil and paper. John von Neumann discovered cellular automata and self-reproducing systems without computers, with only pencil and paper. Aleksandr Lyapunov and Jules Henri Poincaré worked on the foundations of chaos theory without any computer at all.

All of the "C"-Theories below - cybernetics, catastrophe theory, chaos theory,... - have the common goal to explain complex systems which consist of a large number of mutually interacting and interwoven parts. Cellular automata (CA), neural networks (NN), artificial intelligence (AI), and artificial life (ALife) are related fields, but they do not try to describe general complex systems. The best context to compare the different "C"-Theories about complex systems is historical, which emphasizes different tools and methodologies, from pure mathematics in the beginning to pure computer science now. Since the beginning of chaos theory when Edward Lorenz accidentally discovered a strange attractor with his computer, computers have become an indispensable source of information. One could not imagine the study of complex systems without computers today.

Timeline

1960 cybernetics (W. Ross Ashby, Norbert Wiener) Mathematical theory of the communication and control of systems through regulatory feedback. Closely related: "control theory" and "general systems theory" founded by Ludwig von Bertalanffy and W. Ross Ashby
1970 catastrophe theory (René Thom, E.C. Zeeman) Branch of mathematics that deals with bifurcations in dynamical systems, classifies phenomena characterized by sudden shifts in behavior arising from small changes in circumstances.
1980 chaos theory (David Ruelle, Edward Lorenz, Mitchell Feigenbaum, Steve Smale, James A. Yorke....) Mathematical theory of nonlinear dynamical systems that describes bifurcations, strange attractors, and chaotic motions.
1990 complex adaptive systems (CAS) (John H. Holland, Murray Gell-Mann, Harold Morowitz, W. Brian Arthur,..) The "new" science of complexity which describes emergence, adaptation and self-organization was established mainly by researchers of the SFI and is based on agents and computer simulations and includes multi-agent systems (MAS) which have become an important tool to study social and complex systems. CAS are still an active field of research.

References

Daniel Durand (1979) La systémique, Presses Universitaires de France
Ludwig von Bertalanffy (1968). General System Theory: Foundations, Development, Applications New York: George Braziller
Gerald M. Weinberg (1975) An Introduction to General Systems Thinking (1975 ed., Wiley-Interscience) (2001 ed. Dorset House).
Niklas Luhmann Soziale Systeme. Grundriss einer allgemeinen Theorie, Frankfurt, Suhrkamp, 1994
Herman Kahn, Techniques of System Analysis

External links

Un-annotated external links

Systems Approach to Instruction

Systems Theory

What is a System

Key words:
entities
connection

A system, S, is viewed as a whole made up of many parts or subsystems which are interconnected. To be considered as a system, S MUST have one or more objectives. In turn, each subsystem may itself be viewed as a system, leading to a hierarchy of systems (or subsystems). The system's parts are working together as a whole to accomplish the system's objective(s) by performing certain tasks. Ex. a computer. A system can also be defined as separate bodies coming together to form a system.

Keywords: system, subsystem, interconnection, objective

Systems Approach

The systems approach developed out of the 1950s and 1960s focus on language laboratories, teaching machines, programmed instruction, multimedia presentations and the use of the computer in instruction. Most systems approaches are similar to computer flow charts with steps that the designer moves through during the development of instruction. Rooted in the military and business world, the systems approach involved setting goals and objectives, analyzing resources, devising a plan of action and continuous evaluation/modification of the program. (Saettler, 1990)

Who This Book is For

How This Book is Organized

Order-Chaos

Systems Theory

Order and Chaos

Systems exhibit behaviors. These behaviors are the result of the application of parameters for the system’s elements - the rules. The subsequent behavior is interpreted based on the knowledge of the current system and the anticipated results of various inputs and processes. When systems behave in the expected manner they are generally regarded as stable and thus, in order. The term “order” is not absolute, however. Generally, terms such as “stable”, “balanced” and “in order” are describing all known (considered) inputs and outputs of a system, and based on those factors alone, the system appears to be exhibiting the desired behaviors.

Order describes the history of a system or system segment. Order illustrates that a system has responded to a rule or rules that have made the system behave in a manner that is expected. Specific forms of order exist in many systems: homeostasis, autonomy and chaos. These forms of order describe the system’s inherent behavior and how fluctuations in a system can occur.

Homeostasis

When systems respond to external forces by eventually returning to their starting points, they are considered homeostatic. An example of this form of order is in living systems. A population of animals supported by an island will, over time, maintain a certain number. An external disease, predator, or other force may diminish the number, but eventually the population will increase (all other forces being equal). If the population spikes, shortages in food will eventually lead to less animals. These natural laws work to create homeostatic order. Systems do not require external forces to create fluctuations, though. Often, systems that maintain the same inputs and processes over time experience diminishing desired outputs due to entropy (see wikipedia:Entropy).

Autonomy

Systems that produce ongoing fluctuations or change but follow an average output yield autonomy. Autonomy is best described as oscillation in a system over a period of time. Although this oscillation is not necessarily harmonic motion, it does tend to be around a general mean. A small change in the input parameters of homeostatic order can create autonomic order.

Chaos

If the system fluctuates further it can become unpredictable. Although this does not indicate system failure, the behavior suggests “deterministic unpredictability” – the concept that the same inputs generate different results. This unpredictability is often considered chaotic. The term “chaos” does not mean that the system is failing or will fail; rather it is a method of describing a system that can not be predicted will full certainty. Most often, systems are considered to be chaotic when the underlying rules are unknown, thus the results can not be known. This, however, does not mean that unpredictable or complex output structured systems are chaotic. When there is no underlying rule that governs the unpredictable system it is considered to be non-deterministic.

An example of a non-deterministic system is a coin flip. Although the coin has only two sides, predicting which side will land face-up is quite difficult. There is no underlying rule that governs the coin flip; rather it is the interaction of several inputs: the force of the flip, the original position, friction of fingernails, wind speed and direction, height of the flip, rotational velocity, current gravity of the object on which the person is standing, etc. Unpredictability results from not being able to consider all of these factors. Although we may assume that after many flips the number of times each side lands face-up will be equal, it is not a definite prediction. This system is not considered autonomic since it does not internally correct itself, nor does the low number of outputs available immediately make it a homeostatic environment.

Thus, chaotic systems can be considered to have a highly complex order, sometimes too complex to understand without the aid of analytical tools or complex mathematics. Most approaches to studying complex and chaotic systems involve understanding graphical plots of fractal nature, and bifurcation diagrams. These models illustrate very complex recurrences of outputs directly related to inputs. Hence, complex order occurs from chaotic systems.

References

Serendip. On Beyond Newton: From Simple Rules to Stability, Fluctuation, and Chaos

http://serendip.brynmawr.edu/complexity/newton

Cybernetics:Principles of Systems and cybernetics: an evolutionary perspective

http://intelegen.com/genius/systems_and_cybernetics.htm#Cybernetics%20The%20Principle%20of%20Autocatalytic%20Growth

Sterman, John. Business Dynamics: Systems Thinking and Modeling for a Complex World. 2000.

Holism

Systems Theory

Holistic View

A holistic view of a system encompasses the complete, entire view of that system. Holism emphasizes that the state of a system must be assessed in its entirety and cannot be assessed through its independent member parts. Dividing a system into its separate parts is considered destructive to that system and single parts within a system should not be prioritized. Thus, holistic views define atomism to ultimately be a threat to the health of a system. Holism focuses on alleviating problems within a system by emphasizing on the system as a whole and understanding that member parts ultimately aggregate to create that whole.

Currently

Today, many industries are integrating holistic practices into their management and operations. U.S. western medical practitioners as well as corporate leaders are becoming aware of the dangers of treating humans or complex corporations as entities made up of isolated, unrelated parts. Understanding the larger system and how individual parts contribute to that system is key. Leaders must grasp the paradigm that the “whole is more than the sum of its parts”. Understanding a complex system entails understanding that the whole can take on a behavior all its own. Understanding this independent behavior is key in holism. Powerful, effective decisions can be made once a holistic view of a system is acquired. Poor, inappropriate decisions can be derived when a limited view is acquired by assessing separate parts without regarding how those parts contribute to the whole. Management practices emphasizing holism seek to eliminate the risk of these poor decisions.

The Goal

The goal of increasing holistic awareness allows leaders to create and implement successful management policies and is highlighted by the International Standards Organization for quality management. The ISO 9001 standard defines improved quality management in which acquiring complete, complex knowledge of an organization is essential in implementing effective management practices. Operations and structures within an organization must be enveloped into the full, complete system context. Pooling these critical definitions together must be done so that a sophisticated and complete, essential perspective of an organization's behavior is acquired. Implementing holistic practices within an organization improves the chances of bringing that system to a stable state. Decisions concerning only one isolated function within the system are never constructed using holistic management practices. Thus, unknown outcomes caused by an underestimation of thorough system behavior are eliminated. Without holistic principles, an entire system’s function can be threatened and ultimately the individual constituting parts can be put into peril.

Places that use Holism

Holistic practices are critical within the IT industry as data networks and intelligent complete systems are designed and built. Viewing independent components of a communications network or intelligent system as stand-alone entities ultimately impacts the overall quality of that system. In networking, care is taken to view a LAN, MANET or WAN as an entire entity. Small iterative changes to parts of a network are not being considered in isolation such that the downstream or upstream impacts aren't anticipated. Seeing a data network as a whole living system increases the chances for success of a stable, reliable system. In building IT intelligent systems, comprised of interacting smaller software and hardware modules, retaining a systems view is necessary so that the complete product can be troubleshot and debugged successfully. Myopic views focusing only on a single subsystem, where the entire system performance is not considered, can create an unpredictable and unreliable final product. If a holistic view is retained by analysts building or maintaining IT systems, the chance for success of that IT system is more realistic. As the analysts' understanding of the entire system increases, he/she is more capable of initiating complex changes such that the global health of the system is protected.

A Final Example

A final illustration of how holism is an immanent and essential theory is the current adaptation of our world to its new global economy. It is imperative that we understand how our global world operates and how we all participate within that entity. Gaining a global economy has been facilitated through readily available international travel and instant, personal communication. As international parties intermix, the world will grow into a very sophisticated and complex system. Retaining a concise and objective perspective of our world and understanding its complexity is critical. Powerful internal influences will begin to impact our global system’s behavior. Every individual is gaining the power to make a fuller impact on his environment and the ability to voice his opinion concerning the state of his world. As more individual voices and wills are exercised, the stability and balance of our global system will be harder to maintain. We must persistently understand our world with a holistic perspective to gain in depth awareness of its behavior as an independent entity. Gaining this holistic view is essential in understanding our future evolution.

Goal Seeking (Intrinsic & Extrinsic)

Systems Theory

Goal Seeking

Control systems or Cybernetics are characterized by the fact that they have goals: states of affairs that they try to achieve and maintain, in spite of obstacles or perturbations

Control systems are combinations of components (electrical, mechanical, thermal, or hydraulic) that act together to maintain actual system performance close to a specified set of performance specifications. Open-loop control systems (e.g. alarm clocks) are those in which the output has no effect on the input. Closed-loop control systems (e.g. automotive cruise-control systems) are those in which the output has an effect on the input in such a way as to maintain the specified output value. A closed-loop system must include some way to measure its output to sense changes so that corrective action can be taken. The speed with which a simple closed-loop control system moves to correct its output is described by its natural frequency and damping ratio. A system with a small damping ratio is characterized by overshooting the desired output before settling down. Systems with larger damping ratios do not overshoot the desired output, but respond more slowly.

Mechanistic World View

In the mechanistic world view, there is no place for goal-directedness or purpose. All mechanical processes are determined by their cause, which lies in the past. A goal, on the other hand, is something that determines a process, yet lies in the future.

The thesis that natural processes are determined by their future purpose is called teleology. It is closely associated with vitalism, the belief that life is animated by a vital force outside the material realm. Our mind is not a goalless mechanism; it is constantly planning ahead, solving problems, trying to achieve goals. How can we understand such goal-directedness without recourse to the doctrine of teleology?

Cybernetics

Probably the most important innovation of cybernetics is its explanation of goal-directedness. An autonomous system, such as a person or an organism, can be characterized by the fact that it pursues its own goals, resisting obstructions from the environment that would make it deviate from its preferred state of affairs. Thus, goal-directedness implies regulation of or control over distractions.

A good example is a room in which the temperature is controlled by a thermostat. The setting of the thermostat determines the desired temperature or goal state. Perturbations may be caused by changes in the outside temperature, opening of windows or doors, drafts, etc. The task of the thermostat is to minimize the effects of such influences, and thus to keep the temperature as much as possible constant with respect to the specified temperature.

Survival

On the most fundamental level, the goal of an autopoietic or autonomous system is survival, that is, maintenance of its essential organization. This goal has been built into all living organisms by natural selection: those that were not focused on survival have simply been eliminated. In addition to this main goal, the system will have various subsidiary goals, such as keeping warm or finding food, that indirectly contribute to its survival. Artificial systems, such as automatic pilots and thermostats, are not autonomous: their primary goals are constructed in them by their designers. They are allopoietic: which means their function is to produce something other ("allo") than themselves.

Goals as States

Goal-directedness can be defined most simply as suppression of deviations from an invariant goal state. In that respect, a goal is similar to a stable equilibrium, to which the system returns after any disruptions. Both goal-directedness and stability are characterized by equifinality: different initial states lead to the same final state, implying the destruction of variety. What distinguishes them is that a stable system automatically returns to its equilibrium state, without performing any work or effort. But a goal-directed system must actively engage to achieve and maintain its goal, which would not be equilibrium otherwise. Control may appear essentially conservative, resisting all departures from a preferred state. But the net effect can be very progressive or dynamic, depending on the complexity of the goal. For example, if the goal is defined as the rate of increase of some quantity, or the distance relative to a moving target, then suppressing deviation from the goal implies constant change. A simple example is a heat-seeking head in a Stinger missile attempting to reach a fast moving enemy jet, cruise missile or helicopter.

A system's "goal" can also be a range of acceptable states, similar to an attractor. The dimensions defining these states are called the essential variables, and they must be kept within a limited range compatible with the survival of the system. For example, a person's body temperature must be kept within a range of approximately 35-40 degrees C. Even more generally, the goal can be seen as a gradient, or "fitness" function, defined on state space, which defines the degree of "preference" or "value" of one state relative to another one. In the latter case, the problem of control becomes one of on-going optimization or maximization of fitness.

Reference

Heylighen F. & Joslyn C. (2001): "Cybernetics and Second Order Cybernetics", in: R.A. Meyers (ed.), Encyclopedia of Physical Science & Technology , Vol. 4 (3rd ed.), (Academic Press, New York), p. 155-170
http://www.answers.com/main/ntquery?method=4&dsid=2040&dekey=controlsy&gwp=8&curtab=2040_1
http://pespmc1.vub.ac.be/GOAL.html

Wiki-Resources

Control Systems

Goal Structure (Teleological Behavior)

Systems Theory

Four Teleological Orders

According to Quentin Smith (1981), the four teleological orders are the relations of one aim to another as (1) a “means to” it, (2) a “part of” it, (3) a “concretion” of it, and (4) as “subsumed” under it. These teleological orders interconnect three different types of aims: (1) ends, (2) goals and (3) purposes. Smith uses the term “ends” to refer to those aims that are directly pursued in voluntary actions. Ends are terms of one or both of the teleological relationships that we are designating as the “means to” relationship and the “part of” relationship. The ends that are terms of these relationships are either physical ends, mental ends, or interpersonal ends. 1. Physical ends are either to alter the physical structure of my surroundings (such as to saw a branch in half), or to alter and move my body for its own sake (as is the case when I engage in exercise). 2. Mental ends are not to change the physical environment or my body, but to “bring to my mind” ideas, images or memories. Examples of mental ends are “to solve a mathematical problem,” “to recall a person’s name,” “to read a poem,” and “to conceptualize in an accurate manner a vague insight.” 3. Interpersonal ends are to influence or affect the consciousness of another person. Goals are formally defined as aims that are constituted by two or more ends. The smallest scale goals are constituted by the fewest ends, and the largest scale goals are constituted by thousands of ends. Goals differ in essence from ends in that they are mediate aims of voluntary actions, whereas ends are immediate aims of these actions. An end is immediately and directly attained by an action: it is the volitional activity and striving that either are the end itself, or that bring about the end as their direct result. A voluntary action, however, cannot immediately attain a goal. A goal must be attained by a series of voluntary actions, such that they become attained only through the attainment of a series of complete actional ends. In this sense the attainment of the goal is mediate: it is mediated by the attainment of a series of actional ends. The explanation of this definition of goals involves a distinction of goals from complete actional ends. Complete actional ends differ from goals in two respects. Complete actional ends are such that: (1) they are continually posited from the moment they are willed by a volition until the moment they have been completely attained by the subsequent voluntary action. This entails that these ends (2) are pursued in a continuous and uninterrupted action and striving, such that I act and strive without intermission from the moment I will the end to be attained until the moment I attain it.

Goals

Goals, on the other hand: (1) are not continually posited from the moment they are willed until the moment they are attained. Rather they are posited as my aims at different times. The pursuit of a goal is interspersed with modes of behavior that do not aim at attaining the goal; these modes of behavior may be actions that aim to attain other goals, or they may be non-actional modes of behavior, such as passions, moods, periods of day-dreaming, sleeping, etc. (2) Corresponding to this discontinuous positing of the goal, the voluntary activities and strivings that aim to realize the goal are engaged in at different times. This definition of goals indicates that they differ from ends in the manner in which they are posited and pursued. They do not necessarily differ in their contents (although they usually do). This occasional similarity between their contents is apparent in the smallest scale goals and the complete actional ends. At one time I may pursue and attain one aim in a single voluntary action, and at another time I may pursue and attain a similar aim in two or three different actions. In the first case, since the aim is attained in a single action, it is nothing more than the complete end of this action. In the second case, it is the mediate aim of several actions: it is the goal to which the several actional ends are ordered. This distinction between ends and goals enables us to note a fundamental difference in kind among the various aims of actions. This difference is usually overlooked. For example, Sartre does not distinguish between the different kinds of aims we empirically desire. For Sartre (L’Etre et le Neaot; Paris: Librairie Gallimard, 1943), “drinking a glass of water” and “conquering Gaul” are undifferentiated instances of the aims we can empirically desire. However we believe there is a fundamental difference in kind between these two aims. The aim of “drinking a glass of water” is achieved in a single and continuous action, whereas the aim of “conquering Gaul” is achieved by a vast number of such actions. The former aim is an end, the immediate aim of one action, whereas the latter aim is a goal, the mediate aim of a number of actions. The relation of goals to purposes is different in kind than the relation of ends to goals. Goals are neither “means to” nor “parts of” purposes, but “concretions” of purposes. Goals “make concrete” the “abstractions” that are purposes. Specifically, goals are the concrete determinations of undetermined instances of universals. It is the nature of purposes to be undetermined instances of universals, and it is the nature of goals to be the particular determinations of these instances. Whereas ends and goals constitute the unique individuality of our actions, purposes are the unconditioned meanings of our actions. They are the ultimate “reason” or “what for” of our actions. If it is asked why or “what for” a person is engaging in an action, the final reason he can give is that he is doing it in order to realize a purpose. While goals are the meaning of ends, in that ends are pursued for the reason that they realize goals, purposes are the meaning of goals, for purposes are the “reason why” we pursue goals.

If we consider this order and distinction between these terms and their relationships, then the order of things may be presented in the way similar to graph above. Once the purpose of the action is formulated in the mind of a person, he/she pursues the goal(s) required to be accomplished, in order to achieve definite (and desired) end(s), with ultimate result of fulfilling this purpose. Ends, goals and purposes, as they are interrelated by the four teleological orders, constitute a teleological structure of voluntary actions. According to this interpretation of goal structure, the goal is mediate aim of a number of actions. Hence, it consists of the series of actions intended to achieve proximal and distal effects. If we use Smith’s example, adapted from Sartre, aim of “conquering Gaul” requires series of actions. In order to achieve this military aim, Julius Caesar might have devised this series of actions: • Divide the territory into areas of responsibility between legions, in order to prevent communication of Gaul tribes. • Ascertain key terrain features that need to be controlled by legions, to establish camps and uninterrupted lines of communication and supplies for the troops, etc. These hypothetical actions of Caesar would represent the series of activities designed to achieve proximal effects. Distal effect of this series of actions would still be, of course, “conquering Gaul.” The structure above has five levels, each level being an interrelationship between some of these aims. The relationship between ends and other ends constitute the level that lies at the basis of this structure: the “part of” and “means to” order between ends is the irreducible foundation of voluntary and aim—directed action. Founded upon this basic level of the interconnection of ends, there is a second level consisting of the “part of” and “means to” relations between ends and goals. And upon this level there is based a third level: the “part of” and “means to” relations that connect goals to other goals. These relations serve as the foundation for a fourth level of this teleological structure: the “concretion” order that links goals to purposes. Upon this level there is built the uppermost level, which is the “subsumption” order that connects purposes to other purposes. The final purpose in this “subsumption” order is the empty purpose of “attaining some purpose.” This is the most general aim of voluntary action, and with it the teleological relationships terminate. Any phenomenon that is to become an aim of human action can only do so through being integrated in an immanent fashion within this interconnected teleological structure.

References

“Four Teleological Orders of Human Action” by Quentin Smith Published in: Philosophical Topics, Vol. 12, No. 3., Winter 1981, pp. 312-335.

Inputs-Outputs

Systems Theory

Inputs and Outputs

Input is something put into a system or expended in its operation to achieve output or a result. The information entered into a computer system, examples include: typed text, mouse clicks, etc. Output is the information produced by a system or process from a specific input. Within the context of systems theory, the inputs are what are put into a system and the outputs are the results obtained after running an entire process or just a small part of a process. Because the outputs can be the results of an individual unit of a larger process, outputs of one part of a process can be the inputs to another part of the process. Output includes the visual, auditory, or tactile perceptions provided by the computer after processing the provided information. Examples include: text, images, sound, or video displayed on a monitor or through speaker as well as text or Braille from printers or embossers. Inputs and Ouputs of the system has been classified viz., i) Dynamic Nature of the Input/Output, ii) temporal nature - a) planned, and b) unplanned inputs, iii) periodicity, (iv) controllability, and (v) duration of application (Hariharan, 2021)

Input-Output Analysis

Input-output analysis is “a technique used in economics for tracing resources and products within an economy. The system of producers and consumers is divided into different branches, which are defined in terms of the resources they require as inputs and what they produce as outputs. The quantities of input and output for a given time period, usually expressed in monetary terms, are entered into an input-output matrix within which one can analyze what happens within and across various sectors of an economy where growth and decline takes place and what effects various subsidies may have” (Krippendorf).

Areas of Consideration

Systems theory is “transdisciplinary study of the abstract organization of phenomena, independent of their substance, type, or spatial or temporal scale of existence” (Universiteit). It can be applied to general systems that exist in nature or, in a business context, organizational or economic systems. In studying systems theory, there are a few common, major aspects to consider.

One must look at the individual objects that compose a system. The objects consist of the parts, elements, or variables that make up the system. The objects that make up a system can be physical objects that actually exist in the world, or they can be abstract objects or ideas that cannot be found physically in the world.

One must also consider the attributes of a system. The attributes consist of the qualities and properties of the aforementioned objects of the system. The attributes may also describe the entire system itself.

A third consideration would be the internal relationships among the objects of a system.

The fourth consideration would be the environment in which the system exists.

All of these aspects of a system play an important role. Using these four characteristics, a system can then be defined as “a set of things that affect one another within an environment and form a larger pattern that is different from any of the parts.” Furthermore, “The fundamental systems-interactive paradigm of organizational analysis features the continual stages of input, throughput (processing), and output, which demonstrate the concept of openness/closedness” (Universiteit).

Systems Defined

A system can be defined by using the definition of desired outputs to understand what inputs are necessary. The following questions can be used for this method:

What essential outputs must the system produce in order to satisfy the system users’ requirements?
What transformations are necessary to produce these outputs?
What inputs are necessary for these transformations to produce the desired outputs?
What types of information does the system need to retain?

Another way of defining a system is to work forward in a stimulus-response method of definition. The following questions can be asked in conjunction with this method in order to define the system:

What are the stimuli, and what are the responses to each stimulus?
For each stimulus response pair, what are the transformations necessary?
What are the essential data that must be maintained?

These methods can be used to define not just the general system in question, but also the subsystems which compose the larger system as a whole (Sauter).

References

Hariharan, T. S., Ganesh, L. S., Venkatraman, V., Sharma, P., & Potdar, V. (2021). Morphological Analysis of general system–environment complexes: Representation and application. Systems Research and Behavioral Science, 1– 23.

Krippendorf (Accessed 2005): "Input-Output Analysis", in: F. Heylighen, C. Joslyn and V. Turchin (editors): Principia Cybernetica Web (Principia Cybernetica, Brussels), URL: http://pespmc1.vub.ac.be/ASC/INPUT-_ANALY.html.

Universiteit Twente de ondernemende universiteit (Accessed 2005): “System Theory”. URL: http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Communication%20Processes/System_Theory.doc/.

Sauter, Vicki L. (Accessed 2005): “Systems Theory”. Last Modified: 2000. URL: http://www.umsl.edu/~sauter/analysis/intro/system.htm.

Transformation Processes

Systems Theory

Transformation Processes

Transformation processes can be described as some change in behavior which is intended to alter the desired outcome. Individuals can go through a transformation process that deals with their intellect as well as their overall persona. Organizations can also go through transformation processes. Usually, organizations will have a change in goal or organizational mission that will trigger for a transformation process to occur, whether it is to have a major down-sizing or to implement new policies and procedures that directly affect the collective behavior of the organization as a whole. The research involving transformation processes to date explore different models to use for the most efficient processes to take place. For instance, Creative Dimensions in Management (CDM), a consulting firm, presented corporate transformation processes based on one-on-one mentoring to a succession of UK banking organizations in the late 80s and 90s. Models drawn from Comprehensive Family Therapy and Progressive Abreactive Regression (PAR) tries to predict a persons attempt to significantly change their performance, they are likely to follow a zig-zag path to growth, alternatively progressing and regressing as in the following diagram:

Concluding the study by CDM suggested that, “The key to managing these regressions lies in increased self-awareness. As the growth goal increases, awareness and self-consciousness must deepen in order to manage the regressive trends that occur. These trends include moving beyond one's illusions about oneself and one's potential; moving beyond the defenses that protect the self from the anxieties of growth; examining and resolving the ambivalence that prevents a total commitment to achieving one's vision; embracing fears and terrors associated with failure and success including shame and abandonment; and, ultimately, discovering one's will - an energy source that can fuel the activation and achievement of any vision.”

Continuous Transformations

Another realm often researched is socio-ecological systems which are often described by society-environment relations which are relevant for sustainable development within a certain problem area. Problem areas may be the supply of human needs, economic sectors, geographic regions etc. Energy, substance flows, technical structures, institutions, and ideas influence the transformation process that is involved in these socio-ecological systems. It is also discovered that this type of system is continuously undergoing transformations due to its underlined nature. The following chart depicts the study of this system and how it relates to transformation processes: File:Graph2.jpg Further research of this realm suggests, “The challenge of sustainable transformation is to understand the complex interactions which underlie the dynamics of structural change, to assess and evaluate the impacts of specific paths of transformation, and to shape transformation processes in order bring about desired outcomes “

Sustainable Transformation

3 specific features of Sustainable Transformation:

- 1. Uncertainty about system behavior, because of non-predictability of complex interactions underlying transformation processes.
- 2. Divergent social goals and differing evaluations of the impact of transformation, with social values being endogenous to the transformation process.
- 3. Distribution of control capacities among a broad range of social actors with specific interests and resources to influence transformation paths.

References

Entropy

Systems Theory

Energy – From Physics to Organizations

The First Law of Thermodynamics states that energy can not be created or destroyed. All energy present in the universe (the largest system we know) simply changes forms throughout the cycles and phases of the system. When we observe a component of the system losing energy, we are observing a displacement of the energy’s location. The energy that was once localized to a specific entity, group, business or person will eventually disperse into the surrounding system.

Example: A hammer is swung with kinetic energy to drive a nail into a piece of wood. When the nail is struck, the hammer’s energy is transferred to the nail. The nail then uses its kinetic energy to move into the wood. The wood uses its potential energy to push back against the nail until the nail no longer continues to move. If the nail had been driven into a perfectly elastic substance it would simply have bounced back. But the wood fibres irreversibly dissipate the lost kinetic energy as heat. Excess energy produces heat and sound during the hammering.

In this small system, energy only changes form and location, but is never created or destroyed. If we were to expand the system, we would see that the kinetic energy in the hammer came from the kinetic energy in the hammerer’s arm. This kinetic energy came from the chemical energy gained from ingesting food. The food’s chemical energy came from the chemical bonds formed in the presence of solar energy. In physics, the changes in the location and form of energy is the mechanism that connects the universe as a very large system. Energy flows through other systems as well. In social organizations, energy is often described in various other terms (people, money, products, information or capital) but still flows in a similar manner.*

Example: A company’s success in bill collecting is declining because the amount of collection calls per week has diminished. The bill collector has been searching for a new job during their working hours. The energy that the collector normally put into calling clients is now being used to conduct personal business. In this example, the energy inputs (40 hours per week from the collector) have remained the same, however, the energy is being dispersed from the company’s system to the collector and other businesses’ systems.

Entropy Defined

Entropy is a tendency for a systems’ outputs to decline when the inputs have remained the same. Most often associated with the Second Law of Thermodynamics, entropy measures the changes in the type and dispersion of energy within an observable system. We measure entropy in a systems thinking by the change in outputs when the inputs have remained the same. Thus, entropy is a direct function of time (temporal).**

Closed Systems and Scope

Entropy occurs in closed systems where only the outputs decline. The system appears closed because the observed scope of the system displays no changes in the normal processes or actions that continue to take place.

Example: An organization manufactures ocean-worthy sailing ships for transportation. The organization has reported fewer revenues every year since the late 1800’s. The inputs (labor, skill, tools and capital) have remained the same throughout the years, however, sales have dropped (no pun intended) due to entropy. Without changes in inputs (creativity, modernism, or market observance) the system is considered closed, and entropy becomes inevitable.

In this example, the success of the transportation industry is not declining, only the success of one organization. Through more convenient transportation, the success of large sailing ships is being transferred from the local environment to the global market. The trans-oceanic travel is still needed, but due to a closed and entropic system, the sailing boat organization will no longer be successful. This organization failed to consider that they are part of a larger system, and must evolve over time. The scope of the system and the complexity greatly effect the length of time in which entropy typically occurs.

Example: A neighborhood child opens a lemonade stand. The lemonade remains the same, and after one month, the sales of lemonade decline as the neighborhood consumers want variety. Entropy within this system sets in quickly. An international soft-drink company that chooses to no longer manufacture more than one type of beverage could similarly fall victim to entropy.

Combating Entropy

Living organisms are often affected by diseases. These diseases represent external and internal threats that degenerate the organism until it no longer can sustain life. We challenge this form of organic entropy by adding inputs (white blood cells, medications, nutrients, etc.). Thus, organisms continue to exist through a spontaneous changes in the inputs and structures of the processes.***

Once social organizations reach a state of static equilibrium, entropy begins to occur. The degradation of the system unit of entire system is then only a function of time. This unintended process occurs until the system is thrown out of its static state with new inputs or process changes, or the system fails. To challenge the onslaught of entropy, system thinkers are required to continuously expand their knowledge of the scope and complexity of their system. Once they can identify a way for the component or process to evolve, the risk of entropy is lessened. After new actions are taken or inputs are changed, the process to avoid system decline due to entropy begins again. Once growth has stopped, the decline of the system is inevitable.

Although the concepts of people, money, products, information, or capital can be created and destroyed, the flow, organization, and displacements of these components are treated like energy for the discussion of organizational entropy.

- Not to confuse physics and systems science: In systems science, entropy is measured by change in outputs over time. In physics, entropy is measured by change in temperature over time.

- - Nobel laureate Ilya Prigogine first described that living systems continuously renew themselves through a process of "spontaneous structuration" which occurs when they are jarred out of a state of equilibrium.

References

More information on entropy can be found at: http://www.entropylaw.com/entropyenergy.html

Swenson, R. (1997a). Autocatakinetics, Evolution, and the Law of Maximum Entropy Production: A Principled Foundation Toward the Study of Human Ecology. Advances in Human Ecology, 6, 1-46.

Swenson, R. and Turvey, M.T. (1991). Thermodynamic Reasons for Perception-Action Cycles. Ecological Psychology, 3(4), 317-348. (Also in Japanese: Translated and reprinted in Perspectives on Affordances, M. Sasaki (ed.). Tokyo: University of Tokyo Press, 1998 ).

Open/Closed System Structure

Systems Theory

Open Systems

A system that interfaces and interacts with its environment, by receiving inputs from and delivering outputs to the outside, is called an open system. They possess permeable boundaries, that permits interaction across its boundary, through which new information or ideas are readily absorbed, permitting the incorporation and diffusion of viable, new ideas. Because of this they can adapt more quickly to changes in the external environment in which they operate. As the environment influence the system, the system also influences the environment. Allowing a system to be open ultimately sustains growth and serves its parent environment, and so both have a stronger probability for survival.

Examples of open systems: Business organization, Hospital system, College or University system.

Conversely, a closed system is more prone to resist incorporating new ideas, that can be deemed unnecessary to its parent environment and risks atrophy. By not adopting or imps, a closed system ceases to properly serve the environment it lives in.

Adaptability and Survival

This adaptability and survival of open systems has been exhibited in society's recent global information age. Current information technology have absorbed new technologies and approaches have sustained long term success. Systems which incorporate efficient data representation, storage, and transfer, and good operating system design and effective use of processor power have retained their public user base. Information technology initiatives which adapted slowly to their rapidly changing environment ultimately lost significance in the industry and have disappeared.Operating technologies such as the Network File System, initiated by Sun Microsystems, and Netscape Internet Browser, designed by Marc Andreessen, are examples of effective open IT systems. These technologies have established the backbone of information technology within society. Both technologies represent open systems with the common initiative for sharing data. NFS is used to distribute access to shared disk file system across several servers within a local network. Netscape is used as a portal to gain access to the volumes of data within the world’s Internet. Both technologies fostered further growth within the environments in which they were introduced. The NFS standard created synergy for the growth of SUN Microsystems and has been adopted by other operating system designers. Netscape ultimately fostered the growth of the amount and types of data made available through the Internet. These technologies foster an open paradigm where data is absorbed by an infinite number of participants.

Increase Chances

The rate of change within the technology sector is extremely rapid, and systems groups have consciously adopted an “open systems” approach to increase their chance of survival. Prioritizing the adaptability of a system for different OS platforms, database designs, and communication protocols has been seen within the IT industry as the wisest approach. Any technologies designed to solely propagate the success of one platform or particular technology have been methodically eliminated. At the risk of domination of the user’s personal computer desktop, Microsoft's introduction of a closed design embedding the company’s internet browser into kernel operating modules was halted by an investigation of monopolistic practices. The initiative of Microsoft to manipulate technology such that the primary benefits were for the propagation of their technical product alone was halted by the natural tensions created by the unhealthy closed system. The system did not serve the environment in which it lived by posing the threat of limiting user’s access to the world’s internet. Outcry from users as well as judicial entities has forced Microsoft to evolve it's operating system into a more accommodating, dynamic and viable system.

Isomorphic Systems

Systems Theory

Isomorphism

Isomorphism is the formal mapping between complex structures where the two structures contain equal parts. This formal mapping is a fundamental premise used in mathematics and is derived from the Greek words Isos, meaning equal, and morphe, meaning shape. Identifying isomorphic structures in science is a powerful analytical tool used to gain deeper knowledge of complex objects. Isomorphic mapping aids biological and mathematical studies where the structural mapping of complex cells and sub-graphs is used to understand equally related objects.

Isomorphic Mapping

Isomorphic mapping is applied in systems theory to gain advanced knowledge of the behavior of phenomena in our world. Finding isomorphism between systems opens up a wealth of knowledge that can be shared between the analyzed systems. Systems theorists further define isomorphism to include equal behavior between two objects. Thus, isomorphic systems behave similarly when the same set of input elements is presented. As in scientific analysis, systems theorists seek out isomorphism in systems so to create a synergetic understanding of the intrinsic behavior of systems. Mastering the knowledge of how one system works and successfully mapping that system’s intrinsic structure to another releases a flow of knowledge between two critical knowledge domains. Discovering isomorphism between a well understood and a lesser known, newly defined system can create a powerful impact in science, medicine or business since future, complex behaviors of the lesser understood system will become revealed.

Methods

General systems theorists strive to find concepts, principles and patterns between differing systems so that they can be readily applied and transferred from one system to another. Systems are mathematically modeled so that the level of isomorphism can be determined. Event graphs and data flow graphs are created to represent the behavior of a system. Identical vertices and edges within the graphs are discovered to identify equal structure between systems. Identifying this isomorphism between modeled systems allows for shared abstract patterns and principles to be discovered and applied to both systems. Thus, isomorphism is a powerful element of systems theory which propagates knowledge and understanding between different groups. The archive of knowledge obtained for each system is increased. This empowers decision makers and leaders to make critical choices concerning the system in which they participate. As future behavior of a system is more well understood, good decision making concerning the potential balance and operation of a system is facilitated.

Uses

Isomorphism has been used extensively in information technology as computers have evolved from simple low level circuitry with a minimal external interface to highly distributed clusters of dedicated application servers. All computer scientific concepts are derived from fundamental mathematical theory. Thus, isomorphic theory is easily applied within the computer science domain. Finding isomorphism between lesser undeveloped and current existing technologies is a powerful goal within the IT industry as scientists determine the proper path in implementing new technologies. Modeling an abstract dedicated computer or large application on paper is much less costly than building the actual instance with hardware components. Finding isomorphism within these modeled, potential computer technologies allows scientists to gain an understanding of the potential performance, drawbacks and behavior of emerging technologies. Isomorphic theory is also critical in discovering “design patterns” within applications. Computer scientists recognized similar abstract data structures and architecture types within software as programs migrated from low level assembler language to the currently used higher level languages. Patterns of equivalent technical solution architectures have been documented in detail. Modularization, functionality, interfacing, optimization, and platform related issues are identified for each common architecture so to further assist developers implementing today’s applications. Examples of common patterns include the “proxy” and “adapter” patterns. The proxy design pattern defines the best way to implement a remote object’s interface, while the adapter pattern defines how to build interface wrappers around frequently instantiated objects. Current research into powerful, new abstract solutions to industry specific applications and the protection of user security and privacy will further benefit from implementing isomorphic principles.

Comparing real vs model

The most powerful use for isomorphic research occurs when comparing a synthetic model of a natural system and the real existence of that system in nature. System theorists build models to potentially solve business, engineering and scientific problems and to gain a valid representation of the natural world. These models facilitate understanding of our natural phenomena. Theorists work to build these powerful isomorphic properties between the synthetic models they create and real world phenomena. Discovering significant isomorphism between the modeled and real world facilitates our understanding of the our own world. Equal structure must exist between the man-made model and the natural system so to ensure an isomorphic link between the two systems. The defined behavior and principles built inside the synthetic model must directly parallel the natural world. Success in this analytical and philosophical drive leads man to gain a deeper understanding of himself and the natural world he lives in.

Evolution & Growth

Systems Theory

Instability: Not Always Bad?

Instability in systems is not always bad. Growth and evolution are both potential properties of unstable systems and can occur only when there is a net change in one or more system stocks.

Growth: Growth, found in many forms, is essentially the change in the quantities of stocks (levels) within a system. Mostly thought of as a positive increase, growth is the change in the quantities of the system given the normal system structures, i.e. without changing the behavior of the system or the interaction of its components. Different forms of growth exist based on the system’s complexity. All that is required for the positive feedback loops to gain dominance is to have a very small net increase in the system.

Exponential Growth: The result of a dominance of positive feedback loops in a system is the exponential growth of its quantities. Exponential growth doubles the system stock quantities each period of time, regardless of the system size or complexity. This growth will continue within a system until it reaches the “carrying-capacity”. This capacity is the natural limits imposed on the system as to what it can sustain. As the limits are met, the negative feedback loops tend to gain dominance over the positive feedback loops. This slows the growth and even can cause decline in the system if the carrying-capacity limits were exceeded.

Limitations

Resources are not infinite, however. Eventually, real world systems must run out of the resources used to increase exponentially and may do so only after doubling the stocks a few times. Exponential growth in a system may be difficult to notice with only one or two iterations. As the time periods increase in the difference in the previous system state (i.e. the ability to notice change) will also double.

Note: To gain a conceptual idea of the true nature of exponential growth, see examples and explanations in pages 268-272 in Business Dynamics: Systems Thinking and Modeling for a Complex World, Sterman, 2000.

S-Shaped Growth: Exponential growth continues until negative feedback loops (possibly in the form of resource restrictions) slow that growth. The slowing of this exponential growth in return to a stable system can produce the common pattern of an “S-Shaped Growth”. This pattern is dependent on the responsiveness of the negative feedback loop. Delays in the negative feedback loop allow the exponential growth to overshoot the equilibrium goal then create an oscillating graph pattern. Additionally, if the carrying capacity of the system is overshot due to this delay in negative feedback, the result will be a decline (collapse) of the system’s stock(s). This decline often remains until the system reaches the carrying capacity and regains equilibrium.

Patterns of Growth

Growth in a system can occur in patterns other than the standard exponential or S-Shaped models. Linear growth, however, is often rare. What is thought to be linear is often a narrow field of view of a system’s growth. If widened, this narrow view often turns out to be exponential growth.

Evolution: When the growth of a system changes the structure or behaviors of a system, instability will occur, if only briefly. Spontaneity in the system or just simply the release of non-advantageous components makes the system evolve. This evolution is the creation of a new generation of the previous system. Evolution of a system is driven by selection processes that effect the growth and stability of the system. These selection processes select against disadvantages in the system, rather than selecting for processes thought to be advantageous.

Example

Examine the growth of a plant over time. Eventually, the plant’s root system will expand to meet the needs of the plant. However, this root system did not grow in a direct manner. Rather, roots grew in various directions with some roots finding less nutrients and water than others. Thus, the plant naturally selects away from underproductive roots and the productive roots in the nutrient-rich areas thrive.

The example illustrates the process of evolution throughout the plant’s root system (a sub-system of its own). Evolution is a by-product of the system’s growth and periodic instability. As the system experiences periods of growth, the evolutionary choices allow the system to remain productive. Without evolution, the system would eventually be unable to compete with other systems for resources.

References

Cybernetics:Principles of Systems and cybernetics: an evolutionary perspective

http://intelegen.com/genius/systems_and_cybernetics.htm#Cybernetics%20The%20Principle%20of%20Autocatalytic%20Growth

Evolutionary Cybernetics.

http://pespmc1.vub.ac.be/EVOLCYB.html

Lucas, Chris. Emergence and Evolution - Constraints on Form.

http://www.calresco.org/emerge.htm

Sterman, John. Business Dynamics: Systems Thinking and Modeling for a Complex World. 2000.

Creativity

Systems Theory

Creativity

The key work on Systems Theory in Creativity was done by Mihaly_Csikszentmihalyi (1988, 1999) in providing a model with which to explain how creative artifacts emerge from the system, which is a confluence model. Csikszentmihalyi's model incorporates three entities, the Individual, The Field and the Domain.

Creativity is a multidisciplinary, multifaceted concept that has held the interest of both theorists and practitioners over many years. It is perhaps more important today than ever before (Runco, 2004) because of the fast and complex changes that characterize the environment in which we live and operate. Some of the more fundamental changes are: a) globalization which, among other things, has introduced diversity in cultures and markets and exposed organizations to increased competitive pressure; b) technology advancements, particularly in communication, that have changed the means and the pace of information flow; c) organizational structures that are leaner and flatter, sometimes with part of the operations physically located on other continents; d) shift from manufacturing dominated to service dominated economies; e) markets that are more informed about products, available choices and civil rights.

All these changes make obsolete the traditional ways of going about business and pose new challenges for decision makers. Individuals, firms and governments alike are must seek novel solutions to the challenges posed by the increasing dynamism and complexity of their environment. Creativity is the first step in the formulation of the novel solutions needed to counter these equally new situations. Indeed since the times of Graham Wallas and his work – Art of Thought - published in 1926, creativity has been recognized as a useful and effective response to evolutionary changes.

Meaning and Scope

Being a multidisciplinary concept, creativity means different things to, and is expressed in different ways by different people. Organizational creativity is different from artistic creativity and both are different from clinical creativity. Emphases will also differ from one discipline to another. A psychiatrist’s interest in creativity will be different from that of a mathematician and both will differ from that of an organizational behaviorist. Consequently, debates abound as to the origins, boundaries, processes and importance of creativity. For organizations and businesses however, the debates are only academic because of the role that creativity plays in innovation and entrepreneurship.

In organizations and businesses, creativity is the process through which new ideas that make innovation possible are developed (Paulus & Nijstad, 2003). Additionally, at least for business organizations, creative ideas must have utility. They must constitute an appropriate response to a gap in the production, marketing or administrative processes of the organization. In this sense, creativity may be defined as the development of original ideas that have functional utility. The generation and development of original ideas is a complex process that has attracted research interest in several disciplines. The model first developed by Wallas in 1926 seems to have gained general acceptance, albeit with modifications over time. Without going into details, the model breaks down the creativity process into five stages namely: idea germination (or problem perception), knowledge accumulation (also called preparation or immersion), incubation, illumination (also called revelation or the Eureka stage) and verification (or evaluation).

There are other interesting perspectives about the meaning and scope of creativity. Only a few of them are mentioned here. First, understanding creativity as an aspect of problem solving seems to suggest that creativity is reactive. While this is true – creativity responds to new demands occasioned by changes that have become part of organizational life – creativity is also proactive (Heinzen, 1994). Understanding creativity as the development of new ideas that have utility pits the concept in a dual role of problem solving and problem finding (Runco, 2004). As developments in the communication industry demonstrate, creativity has a role to play in initiating change and evolution in organizations. Second, whereas originality is necessary, it is not sufficient for creativity. Unless an idea is useful to someone and can be replicated, it is only just that – a bright idea (Drucker, 1994). In addition, originality does not imply that creative ideas are always radical deviations of present day applications. Inventions are momentous when they are introduced on the market but there are very few radical inventions and far between. In many cases creative ideas that result in innovations are a reformulation of existing ideas that makes them more versatile, user-friendlier or just less costly to produce and dispense. Third, creativity is intricately linked to innovation and entrepreneurship (Kao, 1989). Creativity envisions what is possible and is more conceptual than practical. Innovation and entrepreneurship complete the cycle by applying the results of the creative process to economic or social advantage. Fourth, a distinction needs to be made between talent creativity – like that possessed by artists and performers – and self-actualizing creativity (Maslow, 1971) that is deliberately developed for problem solving and competence enhancing. The point here is that creative ability is a process of nature and nurture. Some may have a natural flair for creative thinking while for others it lies latent and must de deliberately developed. Fifth, apart from the categorization along the disciplinary divide, creativity may also be discussed under an alliterative scheme adopted by Runco from Rhodes (1987). The scheme distinguishes between the creative person, process, product and pressures (press) on creative persons or creative processes.

Drivers

Factors that enhance creativity in individuals or organizations may be divided into two categories - personal and environmental. Personal characteristics that enhance creative ability include high evaluation of aesthetic qualities, having broad interests, curiosity and a penchant for discovery, openness to suggestion, attraction to complexity, having independence of judgment thought and action, a love for autonomy, intuition, self-confidence, ability to accommodate ambiguity and to resolve antinomies, intrinsic motivation and a firm belief in self as a creative person.

Environmental factors examine the context in which creative ability is nurtured and in which creative action is required. In an individual’s formative stages, family background and structure (Sulloway, 1996), societal norms and values, social institutions such as schools, religion, role models and peer groups all play a role in the nurturing of creative ability. Contexts that are flexible about rules and regulations, permitting experimentation and independent choices will enhance creativity. Those that are rigid will inhibit it. In the organizational context, situations that avail time to think, resources to spend, encouragement and reward for original solutions, freedom from criticism and that have good role models and norms in which innovation is prized and failure is not fatal, will enhance creativity.

Inhibitors

Among the factors that inhibit creative ability in individuals and organizations are a population that is not curious or inquisitive and unwilling or unable to question assumptions, resistance to change and tendency to conform, fear of failure and criticism, red tape, time pressure (time is important for new ideas to incubate), lack of feedback, inappropriate norms and values, strict adherence to rules, regulations and budgets, an atmosphere characterized by constraint and lack of autonomy, blinked thinking and unrealistic expectations, over analysis of phenomenon (resulting in paralysis), organizational structures that constrain the flow of ideas, lack of recognition and reward for successful ideas, short term orientation and departmental actions that fail to take into account the bigger picture (functional myopia).

There are also a number of factors that may work both ways - that may stimulate or inhibit creativity. Among these are: lack of resources – ideas require extensive resources to be developed but paucity may in itself be an incentive for creativity (Runco, 2004); competition may also act in both directions – it may stimulate creativity to maintain a competitive edge but it may also discourage creative effort by making obsolete innovations before they are able to recoup expenditure on their development.

The Dark Side

While creativity has been associated with problem solving and value enhancing through proactive actions, it has also been linked with potential costs to the individual and to the society at large. For example, creative geniuses have been associated with various disorders including madness and eccentricity (Ludwig 1995), alcoholism (Noble et al 1993) and stress (Carson and Runco, 1999). Runco (1999) suggested that because creativity is strongly linked to originality it is a kind of social deviance. Plucker & Runco (1999) and Runco (1999) observed that there is frequent stigma attached to creativity. Before these, McLaren (1993) made the observation that it is the dark side of creativity has given the world weapons of mass destruction and other evil inventions and techniques. It would of course be naïve to conclude that creative persons are mad, social deviants or terrorists from such correlations.

References

Carson D.K., Runco M.A. 1999. Creativity problem solving and problem finding in young adults: Interconnections with stress, hassles and coping abilities. Journal of Creative Behavior 33:167- 190.
Drucker P. 1994. Innovation and Entrepreneurship: Practice and Principles. Harper Business
Heizen T. 1994. Situational affect: proactive and reactive creativity. In Shaw and Runco eds 1994. Creativity and Affect, NJ: Abex, pp 127- 146.
Kao J. 1989. Creativity, Entrepreneurship and Organization: Texts, Cases and Readings. Sage, Thousand Oaks.
Ludwig A. 1995. The Price of Greatness. New York: Guilford.
Maslow A. H. 1971. The Farther Reaches of Human Nature. New York: Viking Press
McLaren R. 1993. The dark side of creativity. Creativity Research Journal 6 137- 144
Paulus P.P. and Nijstad B.A. eds. 2003. Group Creativity. New York: OUP.
Plucker J and Runco M.A. 1999. Deviance. In Runco and Pritzker eds. 1999. Encyclopedia of Creativity. San Diego, CA: Academic.
Rhodes M. 1987. An analysis of creativity. In Frontiers of Creativity Research: Beyond the Basics, eds SG Isaksen, Buffalo NY: Bearly, pp 216-222.
Runco M.A. 2004. Creativity. In Annual Review of Psychology 55: 657-687.
Runco M.A. 1999 Time for Creativity. In Runco and Pritzker eds. 1999. Encyclopedia of Creativity. San Diego, CA: Academic.
Runco and Pritzker eds. 1999. Encyclopedia of Creativity. San Diego, CA: Academic.
Sulloway F. 1996. Born to Rebel. New York: Patheon.

Coordination

Systems Theory

Coordination

Coordination may be defined as the process of managing dependencies between activities (Malone & Crowston, 1994). The need for coordination arises from the fact that literally all organizations are a complex aggregation of diverse systems, which need to work or be operated in concert to produce desired outcomes. To simplify the picture, one could decompose an organization into three broad components of actors, goals and resources. The actors, comprising of entities such as management, employees, customers, suppliers and other stakeholders perform interdependent activities aimed at achieving certain goals. To perform these activities, the actors require various types of inputs or resources. As explained later in the paper the inputs may themselves be interdependent in the ways that they are acquired, created or used. The goals to which the actors aspire are also diverse in nature. Some of them will be personal while others are corporate. Even where the goals are corporate, they address different sets of stakeholders and may be in conflict.

Calls for coordination are evident is situations where a) temporality is a factor, such that effects of delays or of future consequences of today’s decisions are not immediately apparent b) there is a large number of actors c) there is a large number interactions between actors or tasks in the system or d) where combinations or occurrences in the system involve an aspect of probability (stochastic variability). In summary, the more complex the system (and organizations are complex aggregations) the more coordination is necessary.

Multiple actors and interactions, resources and goals need to be coordinated if common desired outcomes are to be achieved. Viewed from the need to maintain perspective and solve problems that might arise from these multiplicities, coordination links hand in glove with the concept of systems thinking.

In contrast to traditional methods of problem analysis, system thinking focuses on how a component of a system under study interacts with other constituents of the same system (Aronson, 1998). Organizations are systems in the sense that they comprise of elements that interact to produce a predetermined behavior or output. Traditional analysis approaches focus on isolating individual parts. The systems thinking approach instead works by expanding the analytical spectrum to take into account the broader picture of how the constituent parts of the system interact with each other. Change in a constituent part of a system may constrain efficient functioning of other parts of the same system or alter required input or output specifications. Others, especially resources, may need to be used in combination to achieve desired changes. The point here is that looking at small parts of an interacting system involving multiple actors, resources and goals may accentuate a problem that analysis seeks to solve. Coordination, in a systems thinking approach fashion is called for.

Crowston (1998) refers to coordination theory as “a still developing body of theories about how coordination can occur in diverse kinds of systems. According to this theory, actors in organizations are faced with coordination problems. Coordination problems are a consequence of dependencies in the organization that constrain the efficiency of task performance. Dependencies may be inherent in the structure of the organization (for example, departments of a university college interact with each other, constraining the changes that can be made to a single department without interfering with the efficient functioning of the other departments) or dependences may result from processes - task decomposition or allocation to actors and resources (for example, professors teaching complementary courses face constraints on the kind of changes they can make without interfering with the functioning of each other).

The solution to coordination problems, according to coordination theory, lies in the actors performing additional activities called coordination mechanisms. A professor who wishes to change a course module must check if the changes will affect other courses in the department and other departments in the college. The theory maintains that dependences and mechanisms to counter them are general in the sense that they arise in one form or another in nearly every organization. The theory this makes a recommendation that it is essential to identify and study dependences in a system and their related coordination mechanisms before decisions are made or action taken. Actors must also realize that there are several mechanisms to manage a dependency each of which may result in different processes. The ideal one should be based on situational factors and often involves trade offs. To summarize, an organization considering change (or an organization in the process of formation) ought to first identify inherent dependences and coordination problems likely to be faced and then choose from alternatives the coordination mechanism that best achieves the desired goals in the circumstances (Crowston, 1998). A key point here is that coordination mechanisms are variable parts of the organization system and that choice of a specific mechanism has consequences for efficiency and goal achievement.

A simplified typology of the kind of dependences that call for coordination in an organization may be: a) Task-task: o Tasks may have overlapping, conflicting or outputs with the same characteristics; o Common inputs for tasks may be shareable, reusable or non-reusable o The output of one task may be the input of other tasks or a prerequisite for performing subsequent tasks. There may be conflict in specifications that need coordination. b) Task-resource i.e. resources required by a task c) Resource-resource: a situation in which one resource depends on another resource. Each of these dependences requires an appropriate coordination mechanism to manage it.

In conclusion, solution to organizational problems, implementation of change or formation of a new organization involves the management of numerous dependences among tasks, resources and goals. Dependences are best managed by coordination of the dependent parties. The choice of a specific coordination mechanism results in a unique organizational form and/or processes that have consequences for achievement of organizational goals. Coordination is a constituent application of systems thinking in the sense that it requires an organization wide examination in how a change in one component of the organization affects other components of the same system. The aim of coordination is not new; improvement of performance is a universal organizational goal. Approaching the task from a broad perspective differs from the traditional mechanisms of analysis i.e. breaking down the problem into small parts. Finally focusing on dependences and coordination mechanisms is not a one-time effort. For organizations in dynamic environments, it is a recurring theme.

References

Aronson, D. (1998) Overview of Systems Thinking. http://www.thinking.net
Crowston, K. (1997) A coordination Theory Approach to Organizational Process Design, Organization Science 8 (2), 157-175
Greiner, L.E. (1972) Evolution and Revolution as Organizations Grow, Harvard Business Review (July/August)
Malone, T.W. & Crowston, K. (1994) The Interdisciplinary Study of coordination, Computing Surveys, 26 (1), 87-119
Rich, P. (1992) The organizational Taxonomy: Definition and Design. Academy of Management Review, 17 (4), 758-781
Senge, P. (1990) The Fifth Discipline NY: Currency/Doubleday.

Cybernetics

Systems Theory

What is Cybernetics?

There are many different definitions of Cybernetics and many individuals who have influenced the direction of Cybernetics. Cybernetics takes as its domain the discovery or design and application of principles of regulation and communication. Cybernetics treats ways of behaving and not things. Cybernetics does not ask "what is this thing?" but "what does it do?" and "what can it do?" However, questions may also be posed concerning "how it does what it does" which is reflected in higher orders of cybernetics. Because numerous systems in the living, technological and social world may be understood in this way, Cybernetics is a combination of many traditional disciplines. The concepts which Cyberneticians develop thus form a metadisciplinary language through which we may better understand and modify complex systems.

History

Deriving from the Greek word for steersman (kybernetes), Cybernetics was first introduced by the mathematician Wiener, as the science of communication and control in the animal and the machine (to which we now might add: in society and in individual human beings). It grew out of Shannon's information theory, which was designed to optimize the transfer of information through communication channels (e.g. telephone lines), and the feedback concept used in engineering control systems. A more philosophical definition, suggested in 1958 by Louis Couffignal, one of the pioneers of Cybernetics in the 1930s, considers Cybernetics as "the art of assuring efficiency of action". Cybernetics in General Systems Theory is defined as the study of control within a system, typically using combinations of feedback loops. This can be within machines or living structures. First order Cybernetics relates to closed systems, second order includes the observer perspective and third order looks to how these co evolve.

Cybernetics and systems theory study basically the same problem, that of organization independent of the substrate in which it is embodied. Insofar as it is meaningful to make a distinction between the two approaches, we might say that systems theory has focused more on the structure of systems and their models, whereas Cybernetics has focused on how systems function, that is to say how they control their actions, how they communicate with other systems or with their own components. Since structure and function of a system cannot be understood in separation, it is clear that systems theory and Cybernetics should be viewed as two facets of a single approach.

Cybernetics Contributions

The early contributions of Cybernetics were mainly technological, and gave rise to communication technology, feedback control devices, automation of production processes and computers. Another tradition, which emerged from human and social concerns, emphasizes epistemology, how we come to know, and explores theories of self-reference to understand such phenomena as identity, autonomy, and purpose. Some Cyberneticians seek to create a more humane world, while others seek merely to understand how people and their environment have co-evolved. Some Cyberneticians are interested in systems as we observe them, others in systems that do the observing. Some try to develop methods for modelling the relationships among measurable variables. Others seek to understand the dialogue that occurs between models or theories and social systems. Early efforts sought to define and apply principles by which systems may be controlled. More recently, Cyberneticians try to understand how systems describe themselves, control themselves, and organize themselves. Despite its short history, Cybernetics has developed a concern with a wide range of processes involving people as active organizers, as autonomous, and as sharing communicators, responsible individuals. Interest moved soon to numerous sciences, applying Cybernetics to processes of cognition, to such practical pursuits such as psychiatry, family therapy, the development of information and decision systems, government, management, and to efforts to understand complex forms of social organization including communication and computer networks.

Pillars of Cybernetics

Cybernetics theories tend to rest on four basic pillars: circularity, variety, process and observation. Circularity occurs in its earliest theories of circular causation or feedback, later in theories of recursion and of iteration in computing and now involving self-reference in cognitive organization and in autonomous systems of production. This circular form enables Cybernetics to explain systems from within, making no recurse to higher principles or a priori purposes, expressing no preferences for hierarchy. Variety is fundamental to its communication, information and control theories and emphasises multiplicity, alternatives, differences, choices, networks, and intelligence rather than force and singular necessity. Almost all Cybernetic theories involve process and change, from its notion of information, as the difference between two states of uncertainty, to theories of adaptation, evolution and growth processes. A feature of Cybernetics is that it explains such processes in terms of the organization of the system manifesting it, e.g., the circular causality of feedback loops is taken to account for processes of regulation and a system's effort to maintain equilibrium or to reach a goal. Observation including decision making is the process underlying Cybernetic theories of information processing and computing. By extending theories of self-reference to processes of observation including cognition and other manifestations of intelligence, Cybernetics has been applied to itself and is developing an epistemology of systems involving their observers (second-order Cybernetics) qualitatively unlike the earlier interest in the ontology of systems which are observed from the outside (first-order cybernetics).

Focus

While as a meta-theory, the principles and ideas of Cybernetics and Systems Science are intended to be applicable to anything, the "interesting" objects of study that Cybernetics and Systems Science tends to focus on are complex systems such as organisms, ecologies, minds, societies, and machines. Cybernetics and Systems Science regards these systems as complex, multi-dimensional networks of information systems. Cybernetics presumes that there are underlying laws and principles which can be used to unify the understanding of such seemingly disparate types of systems. The characteristics of these systems directly affect the nature of cybernetic theory, resulting in serious challenges to traditional methodology. Some of these characteristics are complexity, mutuality, complementarity, evolvability, constructivity and reflexivity (for additional information consult the appendix). The domain of computing applications has grown so rapidly that labelling anything that uses a computer as "cybernetic" is more obscuring than enlightening. Therefore we would limit the label "cybernetic technology" to those information processing and transmitting tools that somehow increase the general purpose "intelligence" of the user, that is to say the control the user has over information and communication.

The systemic and the analytic approaches are more complementary than opposed, yet neither one is reducible to the other. The analytic approach seeks to reduce a system to its basic elements in order to study in detail and understand the types of interaction that exist between them. By modifying one variable at a time, it tries to conclude general laws that will enable one to predict the properties of a system under very different conditions. To make this forecast possible, the laws of the additivity of elementary properties must be invoked. This is the case in homogeneous systems, those composed of analogous elements and having weak interactions among them. Here the laws of statistics readily apply, enabling one to understand the behaviour of the multitude of disorganized complexity. The laws of the additivity of elementary properties do not apply in very complex systems composed of a large diversity of elements linked together by strong interactions. These systems must be approached by methods such as those which the systemic approach groups together. The purpose of the new methods is to consider a system in its complexity, its totality, and its own dynamics. Through simulation one can "animate" a system and observe in real time the effects of the different kinds of relations among its elements. The study of this behavior leads in time to the determination of rules that can modify the system or design other systems.

Resources and Further Reading

PERSPECTIVES ON THE HISTORY OF THE CYBERNETICS MOVEMENT: THE PATH TO CURRENT RESEARCH THROUGH THE CONTRIBUTIONS OF NORBERT WIENER, WARREN McCULLOCH, AND JOHN VON NEUMANN, Rav, Yehuda (Cybernetics & Systems; Dec2002, Vol. 33 Issue 8, p779, 26p)
THE ORIGINS AND PURPOSES OF SEVERAL TRADITIONS IN SYSTEMS THEORY AND CYBERNETICS, Umpleby, Stuart A. & Dent, Eric B., Cybernetics & Systems; Mar1999, Vol. 30 Issue 2, p79, 25p
CYBERNETIC THEORY DOES NOT EXPLAIN FAMILY AND COUPLE PROCESS: SYSTEMS THEORY AND DIALECTICAL METATHEORY, Moyer, Alfred J., American Journal of Family Therapy; Fall94, Vol. 22 Issue 3, p273, 9p
Major systems theories throughout the world, Bahg, Chang-Gen, Behavioral Science; Apr90, Vol. 35 Issue 2, p79, 29p, 5 diagrams, 2 graphs
http://www.asc-cybernetics.org/foundations/definitions.htm
http://www.worldtrans.org/whole/wholedefs.html
http://www.worldtrans.org/whole.html
http://web.mit.edu/sdg/www/
http://www.well.com/user/dooley/systems.html
http://ssie.binghamton.edu/
http://carbon.cudenver.edu/~mryder/itc_data/complexity.html
http://histm2.free.fr/H.Couffign.htm
http://www.calresco.org/glossary.htm
http://pespmc1.vub.ac.be/CYBSWHAT.html
http://www.hfr.org.uk/cybernetics-pages/origins.htm
http://pespmc1.vub.ac.be/ASC/CYBERNETICS.html
http://pespmc1.vub.ac.be/ANALSYST.html

Systems Learning

Systems Theory

Systems Learning

Systems Learning is a complex concept that involves understanding and gathering information about systems that entail small organizations all the way up to global entities. Systems learning can be placed beside the term organizational learning and has been more evident in recent knowledge management research. This subject encompasses a variety of disciplines such as mathematics, physics, engineering, as well as, sociology and economics. Most research has experimented with organizational learning only on one specific discipline and has not taken it to the next level by using it with other known models. This curriculum diversification has hindered an accurate definition of organizational learning. Several meanings exist which some stand out in the research than others. One definition expressed by popular researchers is “it is undertaken by members of an organization to achieve organizational purposes, takes place in teams or small groups, is distributed widely throughout the organization, and embeds its outcomes in the organization’s system, structures and culture (Snyder and Cummings, 1998). Since there is no hard evidence of what really goes on in an organization, it is difficult to state whether organizational learning is a reality for the organization. The traditional view of learning in an organization involves informal processes. Today, organizational learning is more structured, as employees are expected to direct and inform in a way that is highly correlated to work performance. Argyris and Shcon are the two pioneers of organizational learning who brought forth the sociocultural approach and described it as “the growth of a culture of open communication, in which members of an organization collaborate in ‘organizational enquiries’ to discover better ways of achieving the organizations purpose”(Borham and Morgan, 2004).

Systems Dynamics

Systems dynamics suggest that learning is a process that can be acquired through making choices and learning from them. By learning from your mistakes an individual can grow and become more able to adapt to certain circumstances. Since systems dynamics experiment with models that can only imitate reality, it is hard to create an experimental environment to enable a successful learning atmosphere. Some say that there is an underlined political dynamics influence that cannot be ignored by organizational learning. The works of Crossan, Lane and White (1999) developed a model of organizational learning, which involves a four-step process. The 4I process starts with individual learning and then graduates to group learning which ends up with organizational learning. Crossan’s four processes included in this model are intuiting, interpreting, integrating, and institutionalizing. Intuiting by Crossan is defined as, “the preconscious recognition of the pattern and/or possibilities inherent in a personal stream of experience” (1999:525). Intuiting starts with the individual where they learn from their experiences and then use these experiences for future mental models. Interpreting is explained by Crossan as, “the explaining of, through words and/or actions, of an insight or idea to one’s self and to others” (Crossan 1999:525). Interpreting allows one to map out ideas to use in external domains. Group level starts at the interpreting process and is explained as, “the process of developing shared understanding among individuals and of taking coordinated action through mutual adjustment” (Crossan 1999:525). The victory of collective behavior and action is the group feel of interpretation. Institutionalizing is described by Crossan as, “systems, structures, procedures and strategies” (Crossan 1999:525) that is the core piece of organizational learning. A learning loop begins to form with the involvement of these four processes and take hold of the organizational learning entity. The transformation into the institutional element of organizational learning has a definite political component as individuals deal with power in an organization and how ideas are reflected through this underlying element.

References

A sociocultural analysis of organisational learning. By: Boreham, Nick; Morgan, Colin. Oxford Review of Education, Sep2004, Vol. 30 Issue 3, p307, 19p; DOI: 10.1080/0305498042000260467; (AN 14573925)
Argyris, C. & Schon, D. (1978) Organizational Learning: A Theory of Action Perspective, Addision-Wesley, Reading, MA.
Crossan, M., Lane, H. & White, R. An Organizational Learning Framework: From Intuition to Institution, The Academy of Management Review, Jul 1999, Vol. 24, No. 3 pp. 522-537

Decision Structure

Systems Theory

Decisions

What is a decision? Merriam- Webster’s dictionary defines a word as a noun, with meaning “a position arrived at after consideration.”

Mallach, defines a decision as a reasoned choice among alternatives. Examples of this are:

Where to advertise a new product,
What stock to buy,
What movie to see,
Where to go for dinner.

Some other authors define the term in similar manner, but with more obvious mathematical approach to definition (Zvi Covaliu, 2001, A Fair Isaak White Paper). For Covaliu, a decision, refers to a point in time when the decision maker has to choose one alternative, out of a domain of available alternatives, that could be discrete or continuous, simple or combined. An alternative can be a combination of values to be chosen at the decision point for a number of decision variables.

Different Decisions

What separates one decision from another is the difference in the information set available to the decision maker before each decision is made. The information set corresponding to a decision is the set of all observations available to the decision maker prior to making that decision.

Hammond, Kenney and Raiffa equate the decision with, what they call, “smart choice.” According to them, every “smart choice” can be broken down to eight elements:

Problem
Objectives
Alternatives
Consequences
Trade-Offs
Uncertainty
Risk Tolerance
Linked Decisions

First five of these elements are applicable to virtually any decision. Last three- uncertainty, risk tolerance and linked decisions- help clarify decisions in volatile or evolving environments. Some decisions won’t involve these elements, but many most important decisions will.

Mallach, on the other hand defines decision elements in different terms. For him, the decision comprises of three elements:

Decision Statement- What are we trying to decide?
Alternative- What are the options?
Decision Criteria- How are we going to judge the merits of each alternative?

Further, the decision can be classified in three types, based on the type of structure or nature of the task:

Structured Problems
- Routine and repetitive with standard solution
- Well defined decision making procedure
- Given a well-defined set of input, a well defined set of output is defined
Semi-structured Problems
- Has some structured aspect
- Some of the inputs or outputs or procedures are not well defined
Unstructured Problems
- All phases of decision making process are unstructured

Simon, on the other hand, classifies decisions into two categories:

Programmed decisions, and
Non-programmed decisions.

The programmed decision is repetitive and routine. The non-programmed decisions are novel, unstructured, and call for intelligent adaptive and problem oriented action when solving the problem. Simon states that the traditional techniques for these non-programmed decisions are judgment, creativity, and intuition.

C.I. Barnard categorizes decisions into logical and non-logical. Logical decision making involves conscious thinking and reasoning, and the process is expressible in words or other symbols. Non-logical decision making is not capable of being expressed in words, or as reasoning, and is made known only by the action itself.

The decisions are made in order to solve a particular problem. According to Duncker (1945) solving a problem means continuous narrowing of the range of options through modifications of representations. This repetitive process may continue until there may be only one alternative left to consider. Hence, it is not so strange to see “decision-making” and “problem solving” used interchangeably in common language and discussion. As stated in the paragraph above, decision has to be reached; it has to be made through a deliberate process of decision-making. As such, once the term is used, certain action is expected on behalf of the agent that makes a decision (decision-maker). According to Simon, the decision-making process consists of three main stages:

Intelligence: Fact finding, problem and opportunity sensing, analysis, and exploration.
Design: Formulation of solutions, generation of alternatives, modeling and simulation.
Choice: Goal maximization, alternative selection, decision making, and implementation.

Other authors also consider the implementation to be integral element of the decision-making process, building upon Simon’s observations.

For Hammond et al, in order to make the right decision, one must:

work on the right decision problem
specify the objectives
create imaginative alternatives
understand the consequences
grapple with trade-offs
clarify the uncertainties
think hard about risk tolerance
consider linked decisions

Decision making can be divided into 3 types: strategic, management control and operations control. Within each of these levels, decision making can be classified as either structured or unstructured, closely paralleling the Simon’s classification of the decision (i.e. structured decision making uses programmed decisions).

Decisions are difficult. There are four key areas that determine the relative difficulty of a decision:

Structure – in general, the more structure, the less information required
Cognitive limitations – the human mind is limited to handling 5 to 9 distinct pieces of information
Uncertainty – the amount is based on how complete and accuracy of the information
Alternatives and multiple objectives – the selection of one alternative may impede the progress towards a different goal

There are two traditional models of decision making:

The rational man model is associated with economics. The rational decision-maker systematically searches for the decision that will maximize results. Goal is to optimize or maximize.

Know the goals, know the goal that optimizes.
Know all the alternative courses of action.
Know the relation of courses of action to goals, this is you can predict consequences of actions, i.e. can predict outcomes of actions.

Alternative model called the administrative model associated with Herbert Simon. Simon says the rational man model is prescriptive or normative, the way it is supposed to be, rather than the way it is.

Simon famously discussed limits on decision making. According to him, there are two limits:

Bounded rationality: decision-maker receives imperfect information about goals and courses of action and relation of means to ends. Decision-making is then based on these restrictions.
Bounded discretion: constraints on optimizing, prior commitments, moral and ethical standards, laws, and social standards.

Influenced by these limitations, decision makers are often forced to make decisions that are just good enough. This is what Simon referred to as “satisficing.”

Futurology

If you could see your future, would you try to make it better? If you were a Soviet in 1980 and you knew that spiraling debt would destroy your country, would you do something to stop it? If you were a German in 1933 and knew that the Decree of the Reich President for the Protection of People and State would lead to a world war, tens of millions of deaths, and the leveling of your nation, would you oppose it?

Its safe to assume that we would all say yes to these questions. Our only excuse in letting these patterns reoccur is a claim that we can't see the future with any degree of certainty, but is this claim true? Couldn't the Soviets have deduced their overwhelmingly probable financial failure based on a combination of proven economic and socio-political models? Couldn't the Germans have deduced from history that support of absolute power always leads to failure? Why would the Germans have supported a document that circumvented their basic human rights when they had hundreds of examples throughout history that such documents are always eventually used for tyranny and oppression? How could Soviets have possibly thought that the government could endlessly support citizens who took more than they gave to the collective without going bankrupt? On top of that, how did they expect their government to survive financially while maintaining a horrendously expensive and unwinnable war in the Middle East? How could they have not seen the future when it was so evident? What good is history if we cannot learn from its mistakes and plot a better course for the future? These are some of the questions that futurology attempts to illuminate.

Futurology also uses aspects of multiple disciplines to anticipate forces of nature and predict how we will react to those forces. In our fight for survival against natural threats, our ability to plan for a future that has no historic precedent gives us an advantage over other life-forms on this planet, including those that were extinguished eons ago. For instance, taking into consideration various aspects of weather forecasting, mathematical probability, economics, and hydraulic engineering, a futurologist could predict that the New Orleans foundation code requiring only seven days of submersion allowance would not be economically sound. This would be based on the likelihood of a category-five hurricane, the engineered strength of the dikes in the face of such a hurricane, the amount of seawater likely to breach the dikes, how long it would take get rid of the water, and how much it would cost to replace the foundations of those structures that were underground for more than seven days. A hundred years ago, foundations in New Orleans were built so that they could withstand being submerged for weeks without losing structural integrity. The building costs associated with that level of stability came to be considered overkill, not because the likelihood of level-five hurricanes diminished, but because of one additional futurology consideration: a hundred years ago, the federal government would never have paid for the rebuilding of New Orleans. Even though Hurricane Katrina was entirely a force of nature, a futurologist must also take into consideration socio-political trends and the needs of his client. In externalizing costs associated with long-term risks, futurologists have become invaluable resources for shaping corporate-sponsored government legislation.

Methods of quantifying the effectiveness of corporate futurologists have become a complex science in itself, one that seeks to objectively define the difference between fantasy, science fiction, and science. This textbook explores those differences and enables society to use the power of strategic forecasting for more than corporate and political gain.

Introduction to Futurology
In a nutshell
The differences between science, science fiction, and fantasy
Star Trek vs. the singularity
Definition of time
Can we really know what time is?
Obstacles to conventional scientific means
Being our own temporal police
Free Will
Being your own man.
Methods and proofs
Using principles of quantum probability to objectify the future
Unconventional Means of Predicting the Future
the Pseudoscience Approach to Futurology

Part II

Building principles and axioms based on past futurology successes
Can we use the "self-evident" "truths" about human nature that founded successful nations as forecasting agents, or were these assumptions just dumb luck?"
Predicting which political ideologies, species traits, and commercial paradigms will be most fit to survive
Using the Darwinism method of futurology
Proofing dystopia and utopia
Can modern methods of futurology prove or disprove the conjectures of Saint Thomas More, Hitler, etc.?
US Presidential Election 2008: Who Will Win?
Can we tell at this point in time who will be the next Mr. President?
Cosmological forecasting
At its largest scale, can futurology help humanity plan our potential in the universe, venture to other suns, and avoid the fate of the dinosaurs?

Additional reading

Norbert Wiener, "Kybernetik. Regelung und Nachrichtenübertragung im Lebewesen und in der Maschine", Düsseldorf (Econ-Verlag) 1992.
Claude E. Shannon u. Warren Weaver, "The Mathematical Theory of Communication", Urbana/Ill. (University of Illinois Press) 1949.
James W. Carey, "A cultural approach to communication", Communication 1 (2), 1975.
Daniel Bell, "Die nachindustrielle Gesellschaft", Frankfurt a.M. (Campus) 1989.
Walt W. Rostow, "The Stages of Economic Growth. A Non-Communist Manifesto", Cambridge 1960.
Nicholas Negroponte, "Total digital. Die Welt zwischen 0 und 1 oder Die Zukunft der Kommunikation", München (Bertelsmann) 1995.
Zbigniew Brzezinski, "Between Two Ages. America's Role in the Technotronic Era", New York (Viking Press) 1969.
Joseph S. Nye u. William A. Owens, "America's Information Edge", Foreign Affairs 75 (2), 1996.

Wikibooks resources

Systems Theory
Future
Choosing The Right File Format talks a lot about "future proofing", which requires some consideration of likely future events.

Please add {{alphabetical}} only to book title pages.

Decision Behavior

Systems Theory

Behavioral Decisions

Behavioral decision research is concerned with how people make judgments and choices, and with how the processes of decision might be improved. The field of behavioral decision research is intensely interdisciplinary, employing concepts and tools from psychology, economics, statistics, and other disciplines.

Recently, experimental research on judgment and choice was said to be “psychology’s leading intellectual export to the social sciences as well as to a host of applied fields” (Tetlock, 2002, Psychological Review).

Studies in the psychology of individual choice have identified numerous cognitive, informational, temporal, and other limitations which bound human rationality, often producing systematic errors and biases in judgment and choice. One of the greatest factors in forming any decision is a perception of the decision maker. Perception is a process by which individuals organize and interpret their sensory impressions in order to give meaning to their environment. People’s behavior is based on their perception of what reality is, not on reality itself i.e. the world as it is perceived is the world that is behaviorally important.

Types

There are three types of factors that influence perception:

Factors in the perceiver:

Attitudes
Motives
Experiences
Interests
Expectations

Factors in the situation:

Time
Work setting
Social Setting

Factors in the target:

Novelty
Motion
Sounds
Size
Background
Proximity
Similarity

When individuals observe behavior, they attempt to determine whether it is internally or externally caused. This is the essence of attribution theory. When the individual behavior is observed there are three types of interpretation of this behavior, according to the attribution theory: (1) Distinctiveness: different behaviors in different situations; (2) Consensus: response is the same as others to same situation: (3) Consistency: response is the same over time.

Errors

As is the case with most theories, errors and biases are possible in the attribution theory. The fundamental attribution error is the tendency to underestimate the influence of external factors and overestimate the influence of internal factors when making judgments about the behavior of others. Self-serving bias is the tendency for individuals to attribute their own successes to internal factors while putting the blame for failures on external factors.

Perception and Decision

There is an obvious link between perception and decision. Decision is passed through a prism of individual decision-maker’s perception. This decision-making was (and still is) the subject of research of multiple disciplines. Few prominent decision-making models emerged. One that has been described and used the most is the Rational Decision- Making Model, which describes how individuals should behave in order to maximize some outcome. Economists favor theories based on axioms of rational choice. Decision-making behavior is assumed to be rational and consistent. Agents maximize utility or profits, and the information required to do so is either freely available or optimally purchased. In the most extreme form, exemplified today by rational expectations models, agents have perfect models of the economy and never systematically err. However, in order for this model to work, it should be in conformance with numerous assumptions:

Problem is clear,
Options are known,
Preferences are clear and constant,
No time or cost constraints are present, and
Maximum payoff.

Steps to a Decision

Steps in the rational decision-making model are:

Define the problem.
Identify the decision criteria.
Allocate weights to the criteria.
Develop the alternatives.
Evaluate the alternatives.
Select the best alternative.

However, we can judge from our experiences that people are not always rational, nor that they behave in the rational manner. To support this observation psychologists have documented numerous experimental results of departures from optimal behavior in a wide variety of decision-making tasks.

Limitations

Rationality is bounded by limitations of information, time, and cognitive capability. This is what Herbert Simon referred to as bounded rationality. Individuals make decisions by constructing simplified models that extract the essential features from problems without capturing all their complexity. This bounded rationality influences how the decisions are made in the organizations.

In a 1981 review Hogarth laments the "insufficient attention" paid "to the effects of feedback between organism and environment." By feedback is meant not merely outcome feedback but changes in the environment, in the conditions of choice, which are caused, directly and indirectly, by a subject's past actions. For example, a firm's decision to increase production feeds back through the market to influence the price of goods, profits, and demand; greater output may tighten the markets for labor and materials; competitors may react - all influencing future production decisions. Such multiple feedbacks are the norm rather than the exception in real problems of choice. As a result it has been difficult for behavioral decision theory to make much headway in analyzing the dynamics of aggregate organizations such as a firm or industry.

Types of People

Decision Theory does not in general provide a good description of people's decision behavior. Under some conditions Decision Theory may be approximately accurate: especially when experienced people are making consequential decisions (financial investors, lawyers negotiating a law suit settlement, etc.). But, even in these situations there are many violations of "rationality." Compared to Decision Theory's predictions, people are:

Boundedly Rational

they violate "obviously rational" axioms
they cannot "cognitively compute" all the probability X payoff values

Boundedly Selfish

they care about fairness
they are altruistic
have Bounded Self-Control

Resources

Systems Theory

Related Wikibooks

Systems Theory/Printable version

Introduction

Overview

History

Timeline

References

See also

External links

Un-annotated external links

Systems Approach to Instruction

What is a System

Systems Approach

Who This Book is For

How This Book is Organized

Order-Chaos

Order and Chaos

References

Holism

Holistic View

Currently

The Goal

Places that use Holism

A Final Example

Goal Seeking (Intrinsic & Extrinsic)

Goal Seeking

Mechanistic World View

Cybernetics

Survival

Goals as States

Reference

Wiki-Resources

Goal Structure (Teleological Behavior)

Four Teleological Orders

Goals

References

Inputs-Outputs

Inputs and Outputs

Input-Output Analysis

Areas of Consideration

Systems Defined

References

Transformation Processes

Transformation Processes

Continuous Transformations

Sustainable Transformation

References

Entropy

Energy – From Physics to Organizations

Entropy Defined

Closed Systems and Scope

Combating Entropy

References

Open/Closed System Structure

Open Systems

Adaptability and Survival

Increase Chances

Isomorphic Systems

Isomorphism

Isomorphic Mapping

Methods

Uses

Comparing real vs model

Evolution & Growth

Instability: Not Always Bad?

Limitations

Patterns of Growth

Example

References

Creativity

Creativity

Meaning and Scope

Drivers

Inhibitors

The Dark Side

References

Coordination

Coordination

References

Cybernetics

What is Cybernetics?