How would you define complexity?What issues are causing it?

 Based on Andy lane, 2002 , " systems thinking , principles and practice"

Complexity is a situation that is too rich and varied for us to understand in simple common mechanistic or linear ways. Complex situation is one with interconnected and intricate parts.

Complexity is something we experience and thus what is complex will differ depending on who is experiencing – this is sometimes described as perceived complexity.

Complexity is a term that is contested by different interest. As yet we do not have the right language to speak about the range of concepts to which complexity is attributed.

Complexity means something that exists and can be discovered, measured, modeled, manipulated, maintained or predicted. Complexity  is used to describe a new way of thinking and help for learning.

Organizational complexity is the new normal, driven by growth, globalization and disruptive technologies; properly addressing it requires time and focus. In order to do it decisively and sustainably, holistic thinking approach takes business fundamentals such as corporate strategy and operating model into account to ensure that we understand its root causes. We then address each one of these causes simultaneously with focused, practical recommendations and pragmatic incentives for both management and the frontline.

There’s no questioning the fact that companies today are faced with growing complexity. Environmental, political, and competitive changes conspire to create a challenging and complex operating environment.

In response to these ever evolving pressures, companies often try to mirror external complexity in their internal environments. For example, they may respond to more sophisticated customer demands by creating tailored products and services. They may address the need for cost cutting and innovation by building matrix organizational structures. They may attempt to add new processes to address evolving market needs. In isolation, each of these responses makes sense, but in combination, they can significantly affect organizational performance. In the current business environment, there are several common causes of complexity: a proliferation of products and/or services, inconsistent and overlapping processes, misaligned incentives, byzantine organizational structures, and poorly articulated strategies, often in some form of combination.

How should organizations deal with complexity?

The answer depends on the type of complexity being considered. Some complexity provides competitive differentiation; this is good complexity and should be optimized. Most complexity, however, does not add value and needs to be reduced or eliminated.

More things interlinked with each other is basically be seen as complex to segregate and look for a solution for the problem.

Issues causing may be

- misunderstanding

- miscommunication

- don't want to understand

- creating wantedly

- wrong perception of things.

few causes i can think of. Thanks.

Definitions of complexity often depend on the concept of a "system"—a set of parts or elements that have relationships among them differentiated from relationships with other elements outside the relational regime. Many definitions tend to postulate or assume that complexity expresses a condition of numerous elements in a system and numerous forms of relationships among the elements. However, what one sees as complex and what one sees as simple is relative and changes with time

Complexity is generally used to characterize something with many parts where those parts interact with each other in multiple ways

Complexity in my opinion is the inability to perform tasks due to an external factor. 

Causes are a lot,

- but i think bureaucracy is number 1

- depending on others to perform their job instead of finishing the whole task

- the cost of hiring experts 

Complexity arises to to some of all of below:

1. Volatile global political situation
2. Diversity
3. Consumerism
4. Competition
5. Technological advances

Thanks for the invite I agree with the rest of the answers

Thanks for invitation

I amagreeing with my colleague’s answer Ms. Ghada Eweda  

In several scientific fields, "complexity" has a precise meaning:

• In computational complexity theory, the amounts of resources required for the execution of algorithms is studied. The most popular types of computational complexity are the time complexity of a problem equal to the number of steps that it takes to solve an instance of the problem as a function of the size of the input (usually measured in bits), using the most efficient algorithm, and the space complexity of a problem equal to the volume of the memory used by the algorithm (e.g., cells of the tape) that it takes to solve an instance of the problem as a function of the size of the input (usually measured in bits), using the most efficient algorithm. This allows to classify computational problems by complexity class (such as P, NP ... ). An axiomatic approach to computational complexity was developed by Manuel Blum. It allows one to deduce many properties of concrete computational complexity measures, such as time complexity or space complexity, from properties of axiomatically defined measures.
• In algorithmic information theory, the Kolmogorov complexity (also called descriptive complexity, algorithmic complexity or algorithmic entropy) of a string is the length of the shortest binary program that outputs that string. Minimum message length is a practical application of this approach. Different kinds of Kolmogorov complexity are studied: the uniform complexity, prefix complexity, monotone complexity, time-bounded Kolmogorov complexity, and space-bounded Kolmogorov complexity. An axiomatic approach to Kolmogorov complexity based on Blum axioms (Blum 1967) was introduced by Mark Burgin in the paper presented for publication by Andrey Kolmogorov (Burgin 1982). The axiomatic approach encompasses other approaches to Kolmogorov complexity. It is possible to treat different kinds of Kolmogorov complexity as particular cases of axiomatically defined generalized Kolmogorov complexity. Instead, of proving similar theorems, such as the basic invariance theorem, for each particular measure, it is possible to easily deduce all such results from one corresponding theorem proved in the axiomatic setting. This is a general advantage of the axiomatic approach in mathematics. The axiomatic approach to Kolmogorov complexity was further developed in the book (Burgin 2005) and applied to software metrics (Burgin and Debnath, 2003; Debnath and Burgin, 2003).
• In information processing, complexity is a measure of the total number of properties transmitted by an object and detected by an observer. Such a collection of properties is often referred to as a state.
• In physical systems, complexity is a measure of the probability of the state vector of the system. This should not be confused with entropy; it is a distinct mathematical measure, one in which two distinct states are never conflated and considered equal, as is done for the notion of entropy in statistical mechanics.
• In mathematics, Krohn–Rhodes complexity is an important topic in the study of finite semigroups and automata.
• In Network theory complexity is the product of richness in the connections between components of a system.
• In software engineering, programming complexity is a measure of the interactions of the various elements of the software. This differs from the computational complexity described above in that it is a measure of the design of the software.
• In abstract sense - Abstract Complexity, is based on visual structures perception [9] It is complexity of binary string defined as a square of features number divided by number of elements (0’s and 1’s). Features comprise here all distinctive arrangements of 0’s and 1’s. Though the features number have to be always approximated the definition is precise and meet intuitive criterion.

Other fields introduce less precisely defined notions of complexity:

• A complex adaptive system has some or all of the following attributes:[3]
  • The number of parts (and types of parts) in the system and the number of relations between the parts is non-trivial – however, there is no general rule to separate "trivial" from "non-trivial";
  • The system has memory or includes feedback;
  • The system can adapt itself according to its history or feedback;
  • The relations between the system and its environment are non-trivial or non-linear;
  • The system can be influenced by, or can adapt itself to, its environment; and
  • The system is highly sensitive to initial conditions.

Complexity has always been a part of our environment, and therefore many scientific fields have dealt with complex systems and phenomena. From one perspective, that which is somehow complex-—displaying variation without being random – is most worthy of interest given the rewards found in the depths of exploration.

The use of the term complex is often confused with the term complicated. In today's systems, this is the difference between myriad connecting "stovepipes" and effective "integrated" solutions.[10] This means that complex is the opposite of independent, while complicated is the opposite of simple.

While this has led some fields to come up with specific definitions of complexity, there is a more recent movement to regroup observations from different fields to study complexity in itself, whether it appears in anthills, human brains, or stock markets. One such interdisciplinary group of fields is relational order theories.

Complex behaviour

The behavior of a complex system is often said to be due to emergence and self-organization. Chaos theory has investigated the sensitivity of systems to variations in initial conditions as one cause of complex behaviour.

Complex mechanisms

Recent developments around artificial life, evolutionary computation and genetic algorithms have led to an increasing emphasis on complexity and complex adaptive systems.

Complex simulations

In social science, the study on the emergence of macro-properties from the micro-properties, also known as macro-micro view in sociology. The topic is commonly recognized as social complexity that is often related to the use of computer simulation in social science, i.e.: computational sociology.

Complex systems

Systems theory has long been concerned with the study of complex systems (in recent times, complexity theory and complex systems have also been used as names of the field). These systems are present in the research of a variety disciplines, including biology, economics, and technology. Recently, complexity has become a natural domain of interest of real world socio-cognitive systems and emerging systemics research. Complex systems tend to be high-dimensional, non-linear, and difficult to model. In specific circumstances, they may exhibit low-dimensional behaviour.

Complexity in data

In information theory, algorithmic information theory is concerned with the complexity of strings of data.

Complex strings are harder to compress. While intuition tells us that this may depend on the codec used to compress a string (a codec could be theoretically created in any arbitrary language, including one in which the very small command "X" could cause the computer to output a very complicated string like "18995316"), any two Turing-complete languages can be implemented in each other, meaning that the length of two encodings in different languages will vary by at most the length of the "translation" language—which will end up being negligible for sufficiently large data strings.

In addition to the answer I would like to briefly list the factors that may cause complexity:

1. Broken communication skills

2. Weak negotiation

3. Unclear situation/problem

4. Interrelated tasks

5. Indirect communication

Thank You

thanks for invite agree with ms ghada answer

   I completely agree with experts answer ..