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[[Complexity (glossary)]] is one of the most important and difficult to define system concepts. Is a system's complexity in the eye of the beholder or is there inherent complexity in how systems are organised? Is there a single definitive definition of complexity and if so how can it be assessed and measured?  
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'''''Lead Author:''''' ''Rick Adcock'', '''''Contributing Authors:''''' ''Hillary Sillitto, Sarah Sheard''
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This article is part of the [[Systems Science]] knowledge area (KA). It gives the background of and an indication of current thinking on {{Term|Complexity (glossary)|complexity}} and how it influences {{Term|Systems Engineering (glossary)|systems engineering}} (SE) practice. 
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Complexity is one of the most important and difficult to define {{Term|System (glossary)|system}} {{Term|Concept (glossary)|concepts}}. Is a system's complexity in the eye of the beholder, or is there inherent complexity in how systems are organized? Is there a single definitive definition of complexity and, if so, how can it be assessed and {{Term|Measure (glossary)|measured}}? This topic will discuss how these ideas relate to the general definitions of a system given in [[What is a System?]], and in particular to the different {{Term|Engineered System (glossary)|engineered system}} {{Term|Context (glossary)|contexts}}. This article is closely related to the [[Emergence|emergence]] topic that follows it.
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==Defining System Complexity==
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Complexity has been considered by a number of authors from various perspectives; some of the discussions of complexity relevant to systems are described in the final section of this article.  Sheard and Mostashari (Sheard and Mostashari 2011) synthesize many of these ideas to categorize complexity as follows:
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#'''Structural Complexity''' looks at the system elements and relationships. In particular, structural complexity looks at how many different ways system elements can be combined. Thus, it is related to the potential for the system to adapt to external needs.
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#'''Dynamic Complexity''' considers the complexity which can be observed when systems are used to perform particular tasks in an environment. There is a time element to dynamic complexity. The ways in which systems interact in the short term is directly related to system behavior; the longer-term effects of using systems in an environment is related to system evolution.
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#'''Socio-Political Complexity''' considers the effect of individuals or groups of people on complexity. People-related complexity has two aspects. One is related to the perception of a situation as complex or not complex, due to multiple stakeholder {{Term|Viewpoint (glossary)|viewpoints}} within a system context and social or cultural biases which add to the wider influences on a system context. The other involves either the “irrational” behavior of an individual or the swarm behavior of many people behaving individually in ways that make sense; however, the {{Term|Emergence (glossary)|emergent}} behavior is unpredicted and perhaps counterproductive. This latter type is based on the interactions of the people according to their various interrelationships and is often graphed using systems dynamics formalisms.
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Thus, complexity is a measure of how difficult it is to understand how a system will behave or to predict the consequences of changing it. It occurs when there is no simple relationship between what an individual element does and what the system as a whole will do, and when the system includes some element of adaptation or problem solving to achieve its goals in different situations. It can be affected by objective attributes of a system such as by the number, types of and diversity of system elements and relationships, or by the subjective perceptions of system observers due to their experience, knowledge, training, or other sociopolitical considerations.
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This view of complex systems provides insight into the kind of system for which systems thinking and a {{Term|Systems Approach (glossary)|systems approach}} is essential.
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==Complexity and Engineered Systems==
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The different perspectives on complexity are not independent when considered across a {{Term|System Context (glossary)|systems context}}.  The structural complexity of a {{Term|System-of-Interest (glossary)|system-of-interest}} (SoI) may be related to dynamic complexity when the SoI also functions as part of a wider system in different problem scenarios. People are involved in most system contexts, as part of the problem situation, as system elements and part of the operating environment. The human activity systems which we create to identify, design, build and support an {{Term|Engineered System (glossary)|engineered system}} and the wider social and business systems in which they sit are also likely to be complex and affect the complexity of the systems they produce and use.
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Sheard and Mostashari (2011) show the ways different views of complexity map onto {{Term|Product System (glossary)|product system}}, {{Term|Service System (glossary)|service system}} and {{Term|Enterprise System (glossary)|enterprise system}} contexts, as well as to associated development and sustainment systems and project {{Term|Organization (glossary)|organizations}}.  Ordered systems occur as system {{Term|Component (glossary)|components}} and are the subject of traditional {{Term|Engineering (glossary)|engineering}}. It is important to understand the behaviors of such systems when using them in a complex system.  One might also need to consider both truly random or chaotic natural or social systems as part of the context of an engineered system.  The main focus for systems approaches is '''organized complexity '''(see below).  This kind of complexity cannot be dealt with by traditional analysis techniques, nor can it be totally removed by the way we design or use solutions. A systems approach must be able to recognize and deal with such complexity across the life of the systems with which it interacts.
  
This article will give the background to the ways in which complexity has been describe, how this relates to all types of system [[Natural System (glossary)]], [[Social System (glossary)]] or [[Engineered System (glossary)]] and an indication of current thinking on what it is and how it influences Systems Engineering Practice.  
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Sillitto (2014) considers the link between the types of system complexity and system {{Term|Architecture (glossary)|architecture}}. The ability to understand, manage and respond to both objective and subjective complexity in the problem situation, the systems we develop or the systems we use to develop and sustain them is a key component of the [[Systems Approach Applied to Engineered Systems]] and hence to the practice of systems engineering.
  
 
==Origins and Characteristics of Complexity==
 
==Origins and Characteristics of Complexity==
  
In this section some of the prevailing ideas on complexity as described. Different authors have used different language to express these ideas and, while a number of common threads can be seen, some of the ideas take different viewpoints or are even contradictory.
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This section describes some of the prevailing ideas on complexity. Various authors have used different language to express these ideas. While a number of common threads can be seen, some of the ideas take different {{Term|Viewpoint (glossary)|viewpoints}} and may be contradictory in nature.
 
 
Weaver (Weaver 1948) gives one of the earliest definitions as the degree of difficulty in predicting the properties of a system, if the properties of the system's parts are given.  This is related to the number of elements and connections between them.  However, complexity should not be confused with complicated.  Complexity is a system property related to the kinds of elements and relationships not simply to their number.
 
  
Ordered systems have fixed relations between elements and are not adaptable. (Page 2009) cites a watch as an example of something which can be considered an ordered system. Such a system is complicated, with many elements working togetherIts components are based on similar technologies with a clear mapping between form and function. If the operating environment changes outside prescribed limits or one key component is removed, the watch will cease to perform its function.  
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One of the most widely used definitions of complexity is the degree of difficulty in predicting the properties of a system if the properties of the system's parts are given (generally attributed to Weaver).  This, in turn, is related to the number of {{Term|Element (glossary)|elements}} and connections between them.  Weaver (Weaver 1948) relates complexity to types of elements and how they interact. He describes simplicity as {{Term|Problem (glossary)|problems}} with a finite number of variables and interaction, and identifies two kinds of complexity:
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#'''Disorganized Complexity''' is found in a system with many loosely coupled, disorganized and equal elements, which possesses certain average properties such as temperature or pressure.  Such a system can be described by “19th Century” statistical analysis techniques.
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#'''Organized Complexity''' can be found in a system with many strongly coupled, organized and different elements which possess certain {{Term|Emergence (glossary)|emergent}} properties and phenomena such as those exhibited by economic, political or social systemsSuch a system cannot be described well by traditional analysis techniques.
  
In common usage [[Chaos (glossary)]] is a state of disorder or unpredictability characterised by elements which are not interconnected and behave randomly with no adaptation or control. Chaos Theory (Kellert 1993) is applied to certain dynamic system (e.g. the weather) which, although they have structure and relationships, exhibit unpredictable [[Behavior (glossary)]]. These systems may include aspects of randomness but are be described by deterministic models from which their behavior can be described given a set of initial conditions. However, their structure is such that (un-measurably) small perturbations in inputs or environmental conditions may result in unpredictable changes in behaviour.  Such systems are referred to as Deterministically Chaotic, or simply Chaotic Systems. Simulations of chaotic systems can be created and, with increases in computing power, reasonable predictions of behavior are possible at least some of the time.
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Weaver's ideas about this new kind of {{Term|Complex (glossary)|complex}} problem are some of the foundational ideas of {{Term|Systems Thinking (glossary)|systems thinking}}. (See also [[Systems Thinking]].)
  
On a spectrum of order to complete disorder, complexity is somewhere in the middle, with more flexibility and change that complete order, and more stability than complete disorder (Sheard and Mostashari 2009).  
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Later authors, such as Flood and Carson (1993) and Lawson (2010), expand organized complexity to systems which have been organized into a {{Term|Structure (glossary)|structure}} intended to be understood and thus amenable to {{Term|Engineering (glossary)|engineering}} and {{Term|Life Cycle Management (glossary)|life cycle management}} (Braha et al. 2006).  They also suggest that disorganized complexity could result from a heterogeneous {{Term|Complex (glossary)|complex}} system evolving without explicit architectural {{Term|Control (glossary)|control}} during its life (complexity creep).  This is a different use of the terms “organized” and “disorganized” to that used by Weaver. Care should be taken in mixing these ideas
  
Complex systems may evolve “to the edge of chaos” resulting in systems which can appear deterministic but which exhibit counter intuitive behavior compared to that or more ordered systems. The statistics of chance events in a complex system are often characterised by a Power Law distribution, the “signature of complexity” [Sheard 2005]. The Power Law distribution is found in a very wide variety of natural and man-made phenomena and it means that the probability of a low probability large impact event is much higher than Gaussian statistics would suggest.  Such a systems may react in a non-linear way to exhibit abrupt “phase changes”, which may be reversible or irreversibleThis has a major impact on engineered systems in terms of the occurrence, impact and public acceptance of risk and failure.
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Complexity should not be confused with "complicated"Many authors make a distinction between ordered and disordered collections of elements.
  
'''Objective''' complexity is an attribute of complex systems and is a measure of where a system sits on this spectrum, it is defined as the extent to which future states of the system cannot be predicted with certainty and precision however good our knowledge of current state and history. '''Subjective''' complexity is a softer measure of how easy it is for an observer to understand a system or predict what it will do nextAs such it exists in peoples’ minds and can be mitigated by consistent clear communication and good stakeholder engagement (Sillitto, 2009).  
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Ordered systems have fixed relationships between elements and are not adaptable. Page (2009) cites a watch as an example of something which can be considered an ordered system. Such a system is complicated, with many elements working togetherIts {{Term|Component (glossary)|components}} are based on similar technologies, with clear mapping between form and function. If the operating {{Term|Environment (glossary)|environment}} changes beyond prescribed limits, or one key component is removed, the watch will cease to perform its {{Term|Function (glossary)|function}}.  
  
A number of authors, (Weaver 1948), (Flood and Carson 1993) and (Lawson 2010), describe similar complexity categorization related to these two types of complexity:
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In common usage, {{Term|Chaos (glossary)|chaos}} is a state of disorder or unpredictability characterized by elements which are not interconnected and behave randomly with no adaptation or control. Chaos Theory (Kellert 1993) is applied to certain dynamic systems (e.g., the weather) which, although they have structure and relationships, exhibit unpredictable {{Term|Behavior (glossary)|behavior}}. These systems may include aspects of randomness but can be described using deterministic models from which their behavior can be described given a set of initial conditions. However, their structure is such that (un-measurably) small perturbations in inputs or environmental conditions may result in unpredictable changes in behaviorSuch systems are referred to as deterministically chaotic or, simply, chaotic systems. {{Term|Simulation (glossary)|Simulations}} of chaotic systems can be created and, with increases in computing power, reasonable predictions of behavior are possible at least some of the time.
*'''Organized Simplicity''' occurs when there are a small number of essential factors and large number of less significant or insignificant factors. Initially, a situation may seem to be complex, but on investigation the less significant and insignificant factors are taken out of the picture and hidden simplicity is found. This is also the basis for the process of [[Abstraction (glossary)]]; creating systems of greater general applicability, but with lower level of detail.  
 
*'''Organized Complexity''' is prevalent in physical and abstract systems where the structure of the system is organized in order to be understood and thus amenable to scientists in describing complex behaviorsThis also forms the basis for structuring the engineering and [[Life Cycle Management (glossary)]] of complex systems (Braha et al. 2006).
 
*'''Disorganized Complexity''' occurs when there are many variables that exhibit a high level of unpredictable behavior. It can also result from a heterogeneous complex system evolving without explicit architectural control during its life (complexity creep).
 
*'''People-related Complexity''', where perception fosters a feeling of complexity or human behavior adds to complexity. People may be viewed as “observing systems” or as system elements which contribute to the other types of complexity (Axelrod and Cohen 1999). The rational or irrational behavior of individuals in particular situations is of course a vital factor in respect to complexity (Kline 1995).  
 
  
The literature has evolved to a fairly consistent definition of the characteristics of system elements and relationships for objective systems complexity; the following summary is given by (Page 2009):
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On a spectrum of order to complete disorder, complexity is somewhere in the middle, with more flexibility and change than complete order and more stability than complete disorder (Sheard and Mostashari 2009).  
#'''Independence''': Autonomous system elements which are able to make their own decisions; influenced by information from other elements and the adaptability algorithms it carries with it (Sheard and Mostashari 2009)..
 
#'''Interconnectedness''': System elements connect via a physical connection, shared data or simply a visual awareness of where the other elements are and what they are doing as in the case of the flock of geese or the squadron of aircraft.
 
#'''Diversity''': System elements which are different in some way either technologically or functionally. Elements may be carrying different adaptability algorithms, for example.
 
#'''Adaptability''': Self-organizing system element which can do what it wants to do to support itself or the entire system in response to their environment (Sheard and Mostashari 2009). Adaptability is often achieved by human elements but can be achieved with software. (Pollock and Hodgson 2004) describe how this can be done in a variety of complex system types including power grids and enterprise systems.  .  
 
  
The inclusion of people in a system is often a factor in their complexity, due to the variability of human behavior as part of a system and the perceptions of people out-side the system. Some of this complexity can be reduced by education, training or familiarity with a system; some must be managed as part of a problem or solution. (Checkland 1999) argues that a group of stakeholders will have its own world views which lead them to form different, but equally valid, understandings of a system context. These differences cannot be explained away or analyzed out, but must be understood and considered in the formulation of problems or the creation of potential solutions.
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Complex systems may evolve “to the edge of chaos,” resulting in systems which can appear deterministic but which exhibit counter intuitive behavior compared to that of more ordered systems. The statistics of chance events in a complex system are often characterized by a power-law distribution, the “signature of complexity” (Sheard 2005). The power-law distribution is found in a very wide variety of natural and man-made phenomena, and it means that the probability of a low probability—large impact event is much higher than a Gaussian distribution would suggest. Such a system may react in a non-linear way to exhibit abrupt phase changes. These phase changes can be either reversible or irreversible. This has a major impact on engineered systems in terms of the occurrence, impact and public acceptance of {{Term|Risk (glossary)|risk}} and failure.
  
(Warfield 2006) developed a powerful methodology for addressing complex issues, particularly in the socio-economic field, based on a relevant group of people developing an understanding of the issue in the form of a set of interacting problems - what he called the “problematique”. The complexity is then characterized by several measures, such as the number of significant problems, their interactions, and the degree of consensus about the nature of the problems. Thus, what becomes clear is that how, why, where and by whom a system is used may all contribute to its perceived complexity.  
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'''Objective''' complexity is an attribute of complex systems and is a measure of where a system sits on this spectrum. It is defined as the extent to which future {{Term|State (glossary)|states}} of the system cannot be predicted with certainty and precision, regardless of our knowledge of current state and history. Subjective complexity is a measure of how easy it is for an observer to understand a system or predict what it will do next. As such, it is a function of the perspective and comprehension of each individual. It is important to be prepared to mitigate subjective complexity with consistent, clear communication and strong {{Term|Stakeholder (glossary)|stakeholder}} engagement (Sillitto 2009).
  
(Sheard and Mostashari 2011) sort the attributes of complexity into causes and effects. Attributes that cause complexity include: many pieces, nonlinear, emergent, chaotic, adaptive, tightly coupled, self-organized, decentralized, open, political (vs. scientific), and multi-scale. The effects of those attributes which make a system seem complex, often as perceived by people, include: uncertain, difficult to understand, unclear cause and effect, unpredictable, uncontrollable, unstable, unrepairable and unmaintainable, costly, and takes too long to build.
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The literature has evolved to a fairly consistent definition of the characteristics of system elements and relationships for objective systems complexity. The following summary is given by Page (2009):
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#'''Independence''': Autonomous system elements which are able to make their own decisions, influenced by information from other elements and the adaptability algorithms the autonomous elements carry with themselves (Sheard and Mostashari 2009).
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#'''Interconnectedness''': System elements connect via a physical connection, shared data or simply a visual awareness of where the other elements are and what they are doing, as in the case of the flock of geese or the squadron of aircraft.  
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#'''Diversity''': System elements which are either technologically or functionally different in some way. For example, elements may be carrying different {{Term|Adaptability (glossary)|adaptability}} algorithms.  
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#'''Adaptability''': Self-organizing system elements which can do what they want to do to support themselves or the entire system in response to their environment (Sheard and Mostashari 2009). Adaptability is often achieved by human elements but can be achieved with software. Pollock and Hodgson (2004) describe how this can be done in a variety of complex system types, including power grids and {{Term|Enterprise (glossary)|enterprise}} systems.  
  
==Defining System Complexity==
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Due to the variability of human behavior as part of a system and the perceptions of people outside the system, the inclusion of people in a system is often a factor in their complexity. People may be viewed as observing systems or as system elements which contribute to the other types of complexity (Axelrod and Cohen 1999). The rational or irrational behavior of individuals in particular situations is a vital factor in respect to complexity (Kline 1995). Some of this complexity can be reduced through education, training and familiarity with a system. Some is irreducible and must be managed as part of a problem or solution. Checkland (1999) argues that a group of stakeholders will have its own world views which lead them to form different, but equally valid, understandings of a system context. These differences cannot be explained away or analyzed out, and must be understood and considered in the formulation of problems and the creation of potential solutions.
(Sheard and Mostashari 2011) synthesis much of the ideas described above to categorize complexity as follows:
 
#'''Structural Complexity''' looks at the system elements and relationships. In particular, structural complexity looks at how many different ways system elements can be combined, and thus the potential for the system to adapt to external needs.
 
#'''Dynamic Complexity''' considers the complexity which can be observed when systems are used to perform particular tasks in an environment. There is a time element to dynamic complexity. The ways in which systems interact in the short term is directly related to system behavior (glossary); the longer term effects of using systems in an environment is related to system evolution.  
 
#Finally, '''Socio-political Complexity''' considers the effect of individuals or groups of people on complexity. People related complexity has two aspects, one related to the perception of a situation as complex or not, due to multiple stakeholder viewpoints within a system context and social or cultural biases which add to the wider influences on a system context. The other involves either irrational behavior of an individual or swarm-effects of many people behaving individually in ways that make sense but the emergent behavior is unpredicted and perhaps counterproductive.  This latter type is based on the interactions of the people according to their various interrelationships and is often graphed using systems dynamics formalisms.
 
  
Thus, complexity is a measure of how difficult it is to understand how a system will behave or to predict the consequences of changing it. It occurs when there is no simple relationship between what an individual element does and what the system as a whole will do, and when the system includes some element of adaptation or problem solving to achieve its goals in different situations. It can be effected by objective attributes of a system such as by the number, types of and diversity of system elements and relationships; or by the subjective perceptions of system observers, both due to their experience, knowledge or training or due to other socio political considerations.  
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Warfield (2006) developed a powerful methodology for addressing complex issues, particularly in the socio-economic field, based on a relevant group of people developing an understanding of the issue in the form of a set of interacting problems - what he called the “problematique”. The complexity is then characterized via several {{Term|Measure (glossary)|measures}}, such as the number of significant problems, their interactions and the degree of consensus about the nature of the problems. What becomes clear is that how, why, where and by whom a system is used may all contribute to its perceived complexity.  
  
This view of complex systems is very much the kind of system for which a [[Systems Approach (glossary)]] is essential.
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Sheard and Mostashari (2011) sort the attributes of complexity into causes and effects. Attributes that cause complexity include being non-linear; emergent; chaotic; adaptive; tightly coupled; self-organized; decentralized; open; political (as opposed to scientific); and multi-scale; as well as having many pieces. The effects of those attributes which make a system be perceived as complex include being uncertain; difficult to understand; unpredictable; uncontrollable; unstable; unrepairable; unmaintainable and costly; having unclear cause and effect; and taking too long to build.
  
==Complexity and Engineered Systems==
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==Complexity and its relationship with difficultly==
  
The views of complexity are not independent when considered across a [[System Context (glossary)]]. Problem situations and potential solutions may contain both subjective and objective complexity, and structural complexity of a system-of-interest be related to dynamic complexity when the system-of-interest is used as part of the wider system in different problem scenarios. People are involved in most system contexts, as system elements and as part of the operating environment. People are also involved with systems throughout the lifetimes of those systems.  
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The Collins Dictionary (2024) defines difficulty as “a task, problem, etc., that is hard to deal with”, while the Oxford English dictionary (2024) defines difficult as “Needing much effort or skill to accomplish, deal with, or understand”.
  
(Sheard and Mostashari 2011) also show how the different views of complexity map onto [[Product System (glossary)]], [[Service System (glossary)]] and [[Enterprise System (glossary)]] contexts; and to associated Development and Sustainment systems and Project organizations.  Ordered systems occur as system components, and are the subject of traditional engineering. It is important to understand the behaviors of such systems when using them in a complex system. One might also need to consider both truly random or chaotic natural or social systems as part of the context of an engineered system. The main focus for systems approaches is '''Organised Complexity''', the ways we choose to structure system elements to help manage and mitigate both objective and subjective complexity.
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There are many types of systems that fall into the difficult category: Complex, complicated and constrained systems are examples of difficult systems from the perspective of systems engineering.  Complicated or intricate systems can be hard to understand, and hence difficult. Complex systems are hard to predict because they are not understood, or otherwise leading to a breakdown in causality, and hence are difficult. Achieving an outcome when heavily constrained by cost, resource or time can also be very difficult.  
  
(Sillitto 2009) considers the link between the types of system complexity and system [[Architecture (glossary)]]. The ability to understand, manage and respond to both objective and subjective complexity be they in the problem situation, the systems we develop or the systems we use to develop and sustain them is a key component of the [[Systems Approach as Applied to Engineered Systems]] and hence to the practice of Systems Engineering.
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A richer understanding of Complex systems and proposed definitions for complex, complicated and simple systems are available in the “A Complexity Primer for Systems Engineers, Revision 1, 2021.  
  
 
==References==
 
==References==
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Checkland, P. 1999.  ''Systems Thinking, Systems Practice''. New York, NY, USA: John Wiley & Sons.  
 
Checkland, P. 1999.  ''Systems Thinking, Systems Practice''. New York, NY, USA: John Wiley & Sons.  
  
Flood, R. L., and E.R. Carson. 1993. ''Dealing with Complexity: An Introduction to The Theory and Application of Systems Science", 2nd ed.''. New York, NY, USA: Plenum Press.
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Flood, R. L., and E.R. Carson. 1993. ''Dealing with Complexity: An Introduction to The Theory and Application of Systems Science'', 2nd ed. New York, NY, USA: Plenum Press.
  
Lawson, H. W. 2010. ''A Journey Through the Systems Landscape''.  Kings College, UK: College Publications.  
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Kellert, S. 1993. ''In the Wake of Chaos: Unpredictable Order in Dynamical Systems'', Chicago, IL, USA: University of Chicago Press.
  
Kellert, S. 1993. ''In the Wake of Chaos: Unpredictable Order in Dynamical Systems'', Chicago, IL, USA: University of Chicago Press. p. 32.
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Kline, S. 1995. ''Foundations of Multidisciplinary Thinking''. Stanford, CA, USA: Stanford University Press.
  
Kline, S. 1995. ''Foundations of Multidisciplinary Thinking''. Stanford, CA, USA: Stanford University Press.
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Lawson, H. W. 2010. ''A Journey Through the Systems Landscape''. Kings College, UK: College Publications.
  
 
Page, Scott E. 2009. ''Understanding Complexity''. Chantilly, VA, USA: The Teaching Company.
 
Page, Scott E. 2009. ''Understanding Complexity''. Chantilly, VA, USA: The Teaching Company.
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Senge, P.M. 1990. ''The Fifth Discipline: The Art & Practice of The Learning Organization''. New York, NY, USA: Doubleday/Currency.
 
Senge, P.M. 1990. ''The Fifth Discipline: The Art & Practice of The Learning Organization''. New York, NY, USA: Doubleday/Currency.
  
Sheard, S.A. and A. Mostashari. 2008. "Principles of Complex Systems for Systems Engineering." ''Systems Engineering'', 12(4): 295-311.
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Sheard, S.A. 2005. "Practical applications of complexity theory for systems engineers". ''Proceedings of the Fifteenth Annual International Council on Systems Engineering,'' vol. 15, no. 1.
  
Sheard, SA. and A. Mostashari. 2011. "Complexity Types:  From Science to Systems Engineering." Proceedings of the 21st Annual of the International Council on Systems Engineering (INCOSE) International Symposium, 20-23 June 2011, Denver, Colorado, USA.
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Sheard, S.A. and A. Mostashari. 2009. "Principles of complex systems for systems engineering." ''Systems Engineering'', vol. 12, no. 4, pp. 295-311.
  
Sillitto H.G. 2009. "On Systems Architects and Systems Architecting: Some Thoughts on Explaining The Art and Science of System Architecting."  Proceedings of the 19th Annual International Council on Systems Engineering (INCOSE) International Symposium, 20-23 July 2009, Singapore.
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Sheard, SA. and A. Mostashari. 2011. "Complexity types: From science to systems engineering."  Proceedings of the 21st Annual of the International Council on Systems Engineering (INCOSE) International Symposium, Denver, Colorado, USA, 20-23 June 2011.
  
Weaver, W. 1948. "Science and Complexity." ''American Science.'' 36: 536-544.
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Sillitto, H. 2014. "Architecting Systems - Concepts, Principles and Practice", London, UK: College Publications.
  
 
Warfield, J.N. 2006. ''An Introduction to Systems Science''.  London, UK: World Scientific Publishing.
 
Warfield, J.N. 2006. ''An Introduction to Systems Science''.  London, UK: World Scientific Publishing.
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Weaver, W. 1948. "Science and complexity." ''American Science,'' vol. 36, pp. 536-544.
  
 
===Primary References===
 
===Primary References===
 +
Flood, R. L., & E.R. Carson. 1993. ''[[Dealing with Complexity]]: An Introduction to The Theory and Application of Systems Science'', 2nd ed. New York, NY, USA: Plenum Press
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 +
INCOSE, 2021. A Complexity Primer for Systems Engineers.
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Page, Scott E. 2009. ''[[Understanding Complexity]]''. Chantilly, VA, USA: The Teaching Company.
 
Page, Scott E. 2009. ''[[Understanding Complexity]]''. Chantilly, VA, USA: The Teaching Company.
  
Flood, R. L., & E.R. Carson. 1993. ''[[Dealing with Complexity]]: An Introduction to The Theory and Application of Systems Science'', 2nd ed. New York, NY, USA: Plenum Press.
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Sheard, S.A. and A. Mostashari. 2009. "[[Principles of Complex Systems for Systems Engineering|Principles of complex systems for systems engineering]]". ''Systems Engineering'', vol. 12, no. 4, pp. 295-311.
 
 
Sheard, S.A. and A. Mostashari. 2008. "[[Principles of Complex Systems for Systems Engineering]]". ''Systems Engineering'', 12(4): 295-311.
 
  
 
===Additional References===
 
===Additional References===
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Ashby, W.R. 1956. ''An Introduction to Cybernetics''. London, UK: Chapman and Hall.
 
Ashby, W.R. 1956. ''An Introduction to Cybernetics''. London, UK: Chapman and Hall.
  
Aslaksen, E.W. 2004. "System Thermodynamics: A Model Illustrating Complexity Emerging from Simplicity". ''Systems Engineering'', 7(3). Hoboken, NJ, USA: Wiley.
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Aslaksen, E.W. 2004. "System thermodynamics: A model illustrating complexity emerging from simplicity". ''Systems Engineering'', vol. 7, no. 3. Hoboken, NJ, USA: Wiley.
  
 
Aslaksen, E.W. 2009. ''Engineering Complex Systems: Foundations of Design in the Functional Domain''.  Boca Raton, FL, USA: CRC Press.
 
Aslaksen, E.W. 2009. ''Engineering Complex Systems: Foundations of Design in the Functional Domain''.  Boca Raton, FL, USA: CRC Press.
  
Aslaksen, E.W. 2011. "Elements of a Systems Engineering Ontology". ''Proceedings of SETE 2011'', Canberra, Australia.
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Aslaksen, E.W. 2011. "Elements of a systems engineering ontology". Proceedings of SETE 2011, Canberra, Australia.
  
 
Eisner, H. 2005. ''Managing Complex Systems: Thinking Outside the Box''. Hoboken, NJ, USA: John Wiley & Sons.
 
Eisner, H. 2005. ''Managing Complex Systems: Thinking Outside the Box''. Hoboken, NJ, USA: John Wiley & Sons.
  
Jackson, S., D. Hitchins, and H. Eisner. 2010. What is the Systems Approach? INCOSE ''Insight'' 13(1) (April 2010): 41-43.
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Jackson, S., D. Hitchins, and H. Eisner. 2010. “What is the Systems Approach?INCOSE ''Insight,'' vol. 13, no. 1, April, pp.  41-43, 2010.
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MITRE. 2011. "Systems engineering strategies for uncertainty and complexity."  ''Systems Engineering Guide.''  Accessed 9 March 2011.  Available at: http://www.mitre.org/work/systems_engineering/guide/enterprise_engineering/comprehensive_viewpoint/sys_engineering_strategies_uncertainty_complexity.html.
 +
 
 +
Ryan, A. 2007. "Emergence is coupled to scope, not Level, complexity".  A condensed version appeared in INCOSE ''Insight'', vol. 11, no. 1, January, pp.  23-24, 2008.
  
MITRE. 2011. "Systems Engineering Strategies for Uncertainty and Complexity."  ''Systems Engineering Guide.''  Accessed 9 March 2011 at [[http://www.mitre.org/work/systems_engineering/guide/enterprise_engineering/comprehensive_viewpoint/sys_engineering_strategies_uncertainty_complexity.html]].
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Sillitto H.G. 2009. "On systems architects and systems architecting: Some thoughts on explaining the art and science of system architecting."  Proceedings of the 19th Annual International Council on Systems Engineering (INCOSE) International Symposium, Singapore, 20-23 July 2009.
  
Ryan, A. 2007. "Emergence Is Coupled to Scope, Not Level, Complexity".  A condensed version appeared in INCOSE ''Insight'', 11(1) (January 2008): 23-24.
 
 
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Latest revision as of 22:03, 2 May 2024


Lead Author: Rick Adcock, Contributing Authors: Hillary Sillitto, Sarah Sheard


This article is part of the Systems Science knowledge area (KA). It gives the background of and an indication of current thinking on complexitycomplexity and how it influences systems engineeringsystems engineering (SE) practice.

Complexity is one of the most important and difficult to define systemsystem conceptsconcepts. Is a system's complexity in the eye of the beholder, or is there inherent complexity in how systems are organized? Is there a single definitive definition of complexity and, if so, how can it be assessed and measuredmeasured? This topic will discuss how these ideas relate to the general definitions of a system given in What is a System?, and in particular to the different engineered systemengineered system contextscontexts. This article is closely related to the emergence topic that follows it.

Defining System Complexity

Complexity has been considered by a number of authors from various perspectives; some of the discussions of complexity relevant to systems are described in the final section of this article. Sheard and Mostashari (Sheard and Mostashari 2011) synthesize many of these ideas to categorize complexity as follows:

  1. Structural Complexity looks at the system elements and relationships. In particular, structural complexity looks at how many different ways system elements can be combined. Thus, it is related to the potential for the system to adapt to external needs.
  2. Dynamic Complexity considers the complexity which can be observed when systems are used to perform particular tasks in an environment. There is a time element to dynamic complexity. The ways in which systems interact in the short term is directly related to system behavior; the longer-term effects of using systems in an environment is related to system evolution.
  3. Socio-Political Complexity considers the effect of individuals or groups of people on complexity. People-related complexity has two aspects. One is related to the perception of a situation as complex or not complex, due to multiple stakeholder viewpointsviewpoints within a system context and social or cultural biases which add to the wider influences on a system context. The other involves either the “irrational” behavior of an individual or the swarm behavior of many people behaving individually in ways that make sense; however, the emergentemergent behavior is unpredicted and perhaps counterproductive. This latter type is based on the interactions of the people according to their various interrelationships and is often graphed using systems dynamics formalisms.

Thus, complexity is a measure of how difficult it is to understand how a system will behave or to predict the consequences of changing it. It occurs when there is no simple relationship between what an individual element does and what the system as a whole will do, and when the system includes some element of adaptation or problem solving to achieve its goals in different situations. It can be affected by objective attributes of a system such as by the number, types of and diversity of system elements and relationships, or by the subjective perceptions of system observers due to their experience, knowledge, training, or other sociopolitical considerations.

This view of complex systems provides insight into the kind of system for which systems thinking and a systems approachsystems approach is essential.

Complexity and Engineered Systems

The different perspectives on complexity are not independent when considered across a systems contextsystems context. The structural complexity of a system-of-interestsystem-of-interest (SoI) may be related to dynamic complexity when the SoI also functions as part of a wider system in different problem scenarios. People are involved in most system contexts, as part of the problem situation, as system elements and part of the operating environment. The human activity systems which we create to identify, design, build and support an engineered systemengineered system and the wider social and business systems in which they sit are also likely to be complex and affect the complexity of the systems they produce and use.

Sheard and Mostashari (2011) show the ways different views of complexity map onto product systemproduct system, service systemservice system and enterprise systementerprise system contexts, as well as to associated development and sustainment systems and project organizationsorganizations. Ordered systems occur as system componentscomponents and are the subject of traditional engineeringengineering. It is important to understand the behaviors of such systems when using them in a complex system. One might also need to consider both truly random or chaotic natural or social systems as part of the context of an engineered system. The main focus for systems approaches is organized complexity (see below). This kind of complexity cannot be dealt with by traditional analysis techniques, nor can it be totally removed by the way we design or use solutions. A systems approach must be able to recognize and deal with such complexity across the life of the systems with which it interacts.

Sillitto (2014) considers the link between the types of system complexity and system architecturearchitecture. The ability to understand, manage and respond to both objective and subjective complexity in the problem situation, the systems we develop or the systems we use to develop and sustain them is a key component of the Systems Approach Applied to Engineered Systems and hence to the practice of systems engineering.

Origins and Characteristics of Complexity

This section describes some of the prevailing ideas on complexity. Various authors have used different language to express these ideas. While a number of common threads can be seen, some of the ideas take different viewpointsviewpoints and may be contradictory in nature.

One of the most widely used definitions of complexity is the degree of difficulty in predicting the properties of a system if the properties of the system's parts are given (generally attributed to Weaver). This, in turn, is related to the number of elementselements and connections between them. Weaver (Weaver 1948) relates complexity to types of elements and how they interact. He describes simplicity as problemsproblems with a finite number of variables and interaction, and identifies two kinds of complexity:

  1. Disorganized Complexity is found in a system with many loosely coupled, disorganized and equal elements, which possesses certain average properties such as temperature or pressure. Such a system can be described by “19th Century” statistical analysis techniques.
  2. Organized Complexity can be found in a system with many strongly coupled, organized and different elements which possess certain emergentemergent properties and phenomena such as those exhibited by economic, political or social systems. Such a system cannot be described well by traditional analysis techniques.

Weaver's ideas about this new kind of complexcomplex problem are some of the foundational ideas of systems thinkingsystems thinking. (See also Systems Thinking.)

Later authors, such as Flood and Carson (1993) and Lawson (2010), expand organized complexity to systems which have been organized into a structurestructure intended to be understood and thus amenable to engineeringengineering and life cycle managementlife cycle management (Braha et al. 2006). They also suggest that disorganized complexity could result from a heterogeneous complexcomplex system evolving without explicit architectural controlcontrol during its life (complexity creep). This is a different use of the terms “organized” and “disorganized” to that used by Weaver. Care should be taken in mixing these ideas

Complexity should not be confused with "complicated". Many authors make a distinction between ordered and disordered collections of elements.

Ordered systems have fixed relationships between elements and are not adaptable. Page (2009) cites a watch as an example of something which can be considered an ordered system. Such a system is complicated, with many elements working together. Its componentscomponents are based on similar technologies, with clear mapping between form and function. If the operating environmentenvironment changes beyond prescribed limits, or one key component is removed, the watch will cease to perform its functionfunction.

In common usage, chaoschaos is a state of disorder or unpredictability characterized by elements which are not interconnected and behave randomly with no adaptation or control. Chaos Theory (Kellert 1993) is applied to certain dynamic systems (e.g., the weather) which, although they have structure and relationships, exhibit unpredictable behaviorbehavior. These systems may include aspects of randomness but can be described using deterministic models from which their behavior can be described given a set of initial conditions. However, their structure is such that (un-measurably) small perturbations in inputs or environmental conditions may result in unpredictable changes in behavior. Such systems are referred to as deterministically chaotic or, simply, chaotic systems. SimulationsSimulations of chaotic systems can be created and, with increases in computing power, reasonable predictions of behavior are possible at least some of the time.

On a spectrum of order to complete disorder, complexity is somewhere in the middle, with more flexibility and change than complete order and more stability than complete disorder (Sheard and Mostashari 2009).

Complex systems may evolve “to the edge of chaos,” resulting in systems which can appear deterministic but which exhibit counter intuitive behavior compared to that of more ordered systems. The statistics of chance events in a complex system are often characterized by a power-law distribution, the “signature of complexity” (Sheard 2005). The power-law distribution is found in a very wide variety of natural and man-made phenomena, and it means that the probability of a low probability—large impact event is much higher than a Gaussian distribution would suggest. Such a system may react in a non-linear way to exhibit abrupt phase changes. These phase changes can be either reversible or irreversible. This has a major impact on engineered systems in terms of the occurrence, impact and public acceptance of riskrisk and failure.

Objective complexity is an attribute of complex systems and is a measure of where a system sits on this spectrum. It is defined as the extent to which future statesstates of the system cannot be predicted with certainty and precision, regardless of our knowledge of current state and history. Subjective complexity is a measure of how easy it is for an observer to understand a system or predict what it will do next. As such, it is a function of the perspective and comprehension of each individual. It is important to be prepared to mitigate subjective complexity with consistent, clear communication and strong stakeholderstakeholder engagement (Sillitto 2009).

The literature has evolved to a fairly consistent definition of the characteristics of system elements and relationships for objective systems complexity. The following summary is given by Page (2009):

  1. Independence: Autonomous system elements which are able to make their own decisions, influenced by information from other elements and the adaptability algorithms the autonomous elements carry with themselves (Sheard and Mostashari 2009).
  2. Interconnectedness: System elements connect via a physical connection, shared data or simply a visual awareness of where the other elements are and what they are doing, as in the case of the flock of geese or the squadron of aircraft.
  3. Diversity: System elements which are either technologically or functionally different in some way. For example, elements may be carrying different adaptabilityadaptability algorithms.
  4. Adaptability: Self-organizing system elements which can do what they want to do to support themselves or the entire system in response to their environment (Sheard and Mostashari 2009). Adaptability is often achieved by human elements but can be achieved with software. Pollock and Hodgson (2004) describe how this can be done in a variety of complex system types, including power grids and enterpriseenterprise systems.

Due to the variability of human behavior as part of a system and the perceptions of people outside the system, the inclusion of people in a system is often a factor in their complexity. People may be viewed as observing systems or as system elements which contribute to the other types of complexity (Axelrod and Cohen 1999). The rational or irrational behavior of individuals in particular situations is a vital factor in respect to complexity (Kline 1995). Some of this complexity can be reduced through education, training and familiarity with a system. Some is irreducible and must be managed as part of a problem or solution. Checkland (1999) argues that a group of stakeholders will have its own world views which lead them to form different, but equally valid, understandings of a system context. These differences cannot be explained away or analyzed out, and must be understood and considered in the formulation of problems and the creation of potential solutions.

Warfield (2006) developed a powerful methodology for addressing complex issues, particularly in the socio-economic field, based on a relevant group of people developing an understanding of the issue in the form of a set of interacting problems - what he called the “problematique”. The complexity is then characterized via several measuresmeasures, such as the number of significant problems, their interactions and the degree of consensus about the nature of the problems. What becomes clear is that how, why, where and by whom a system is used may all contribute to its perceived complexity.

Sheard and Mostashari (2011) sort the attributes of complexity into causes and effects. Attributes that cause complexity include being non-linear; emergent; chaotic; adaptive; tightly coupled; self-organized; decentralized; open; political (as opposed to scientific); and multi-scale; as well as having many pieces. The effects of those attributes which make a system be perceived as complex include being uncertain; difficult to understand; unpredictable; uncontrollable; unstable; unrepairable; unmaintainable and costly; having unclear cause and effect; and taking too long to build.

Complexity and its relationship with difficultly

The Collins Dictionary (2024) defines difficulty as “a task, problem, etc., that is hard to deal with”, while the Oxford English dictionary (2024) defines difficult as “Needing much effort or skill to accomplish, deal with, or understand”.

There are many types of systems that fall into the difficult category: Complex, complicated and constrained systems are examples of difficult systems from the perspective of systems engineering.  Complicated or intricate systems can be hard to understand, and hence difficult. Complex systems are hard to predict because they are not understood, or otherwise leading to a breakdown in causality, and hence are difficult. Achieving an outcome when heavily constrained by cost, resource or time can also be very difficult.

A richer understanding of Complex systems and proposed definitions for complex, complicated and simple systems are available in the “A Complexity Primer for Systems Engineers, Revision 1, 2021.

References

Works Cited

Axelrod, R. and M. Cohen. 1999. Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York, NY, USA: Simon and Schuster.

Braha, D., A. Minai, and Y. Bar-Yam (eds.). 2006. Complex Engineered Systems: Science Meets Technology. New York, NY, USA: Springer.

Checkland, P. 1999. Systems Thinking, Systems Practice. New York, NY, USA: John Wiley & Sons.

Flood, R. L., and E.R. Carson. 1993. Dealing with Complexity: An Introduction to The Theory and Application of Systems Science, 2nd ed. New York, NY, USA: Plenum Press.

Kellert, S. 1993. In the Wake of Chaos: Unpredictable Order in Dynamical Systems, Chicago, IL, USA: University of Chicago Press.

Kline, S. 1995. Foundations of Multidisciplinary Thinking. Stanford, CA, USA: Stanford University Press.

Lawson, H. W. 2010. A Journey Through the Systems Landscape. Kings College, UK: College Publications.

Page, Scott E. 2009. Understanding Complexity. Chantilly, VA, USA: The Teaching Company.

Pollock, J.T. and R. Hodgson. 2004. Adaptive Information. Hoboken, NJ, USA: John Wiley & Sons.

Senge, P.M. 1990. The Fifth Discipline: The Art & Practice of The Learning Organization. New York, NY, USA: Doubleday/Currency.

Sheard, S.A. 2005. "Practical applications of complexity theory for systems engineers". Proceedings of the Fifteenth Annual International Council on Systems Engineering, vol. 15, no. 1.

Sheard, S.A. and A. Mostashari. 2009. "Principles of complex systems for systems engineering." Systems Engineering, vol. 12, no. 4, pp. 295-311.

Sheard, SA. and A. Mostashari. 2011. "Complexity types: From science to systems engineering." Proceedings of the 21st Annual of the International Council on Systems Engineering (INCOSE) International Symposium, Denver, Colorado, USA, 20-23 June 2011.

Sillitto, H. 2014. "Architecting Systems - Concepts, Principles and Practice", London, UK: College Publications.

Warfield, J.N. 2006. An Introduction to Systems Science. London, UK: World Scientific Publishing.

Weaver, W. 1948. "Science and complexity." American Science, vol. 36, pp. 536-544.

Primary References

Flood, R. L., & E.R. Carson. 1993. Dealing with Complexity: An Introduction to The Theory and Application of Systems Science, 2nd ed. New York, NY, USA: Plenum Press

INCOSE, 2021. A Complexity Primer for Systems Engineers.

Page, Scott E. 2009. Understanding Complexity. Chantilly, VA, USA: The Teaching Company.

Sheard, S.A. and A. Mostashari. 2009. "Principles of complex systems for systems engineering". Systems Engineering, vol. 12, no. 4, pp. 295-311.

Additional References

Ashby, W.R. 1956. An Introduction to Cybernetics. London, UK: Chapman and Hall.

Aslaksen, E.W. 2004. "System thermodynamics: A model illustrating complexity emerging from simplicity". Systems Engineering, vol. 7, no. 3. Hoboken, NJ, USA: Wiley.

Aslaksen, E.W. 2009. Engineering Complex Systems: Foundations of Design in the Functional Domain. Boca Raton, FL, USA: CRC Press.

Aslaksen, E.W. 2011. "Elements of a systems engineering ontology". Proceedings of SETE 2011, Canberra, Australia.

Eisner, H. 2005. Managing Complex Systems: Thinking Outside the Box. Hoboken, NJ, USA: John Wiley & Sons.

Jackson, S., D. Hitchins, and H. Eisner. 2010. “What is the Systems Approach?” INCOSE Insight, vol. 13, no. 1, April, pp. 41-43, 2010.

MITRE. 2011. "Systems engineering strategies for uncertainty and complexity." Systems Engineering Guide. Accessed 9 March 2011. Available at: http://www.mitre.org/work/systems_engineering/guide/enterprise_engineering/comprehensive_viewpoint/sys_engineering_strategies_uncertainty_complexity.html.

Ryan, A. 2007. "Emergence is coupled to scope, not Level, complexity". A condensed version appeared in INCOSE Insight, vol. 11, no. 1, January, pp. 23-24, 2008.

Sillitto H.G. 2009. "On systems architects and systems architecting: Some thoughts on explaining the art and science of system architecting." Proceedings of the 19th Annual International Council on Systems Engineering (INCOSE) International Symposium, Singapore, 20-23 July 2009.


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