Scientific Societies and Institutes Having a Systems Perspective 

A number of scientific societies and institutes provide extensive resources for understanding systems and systems behavior.  This section provides links to some of the major entities involved in systems-oriented research and application.

Membership Societies:

  1. International Society for the Systems Sciences (ISSS). .The ISSS is a worldwide organization for the systems sciences. It was started in 1956 as the Society for General Systems Research.  It became the first interdisciplinary and international organization in the field of systems theory and systems science. It was renamed the International Society for the Systems Sciences in 1988.  Among the presidents of the ISSS have been many of the most prominent authorities in the systems sciences, including several Nobel laureates.
  2.  The Institute of Electrical and Electronics Engineers Systems, Man, and Cybernetics Society (IEEE SMCS). . The IEEE SMCS has a complex evolutionary lineage, becoming its current form in 1972.  SMCS addresses the theory, practice, and interdisciplinary aspects of systems science and engineering, human-machine systems, and cybernetics.  It works in three principal technical areas:
    1. Systems Science and Engineering—Addresses the systematic formulation, interpretation, analysis, and modelling of issues and decision-making in large and complex systems.
    2. Human-Machine Systems—Addresses cognitive ergonomics, human information processing, and organizational interactions.
    3. Cybernetics—Addresses communication and control across machines or among machine, human and organizations. Subareas include computational intelligence and machine learning, neural networks, computer vision, evolutionary algorithms, and fuzzy systems. 
  3. International Federation for Systems Research (IFSR). . The IFSR is an international federation of societies in the field of systems science. It was founded in 1980, and is constituted of some forty-five member organizations from various countries.
  4. System Dynamics .The Systems Dynamics Society is headquartered in Albany, New York and has chapters in 14 other countries.  It focuses on computer-aided approaches applied to dynamic system interactions occurring in complex social, managerial, economic, or ecological systems, characterized by interdependence, mutual interaction, information feedback, and circular causality.
  5. Complex Systems Society . The Complex Systems Society has the purpose to promote the development of all aspects of complex systems science in the countries of Europe, as well as the whole international scientific community.  It was founded in 2004.
  6. American Society for Cybernetics. .Founded in 1964, the American Society for Cybernetics.  It focuses on the advancement of cybernetics as an interdisciplinary science and the application of cybernetics methods and techniques to manage complex systems.
  7. Cybernetics Society. . Founded in 1968, the Cybernetics Society is the United Kingdom-based learned society for the understanding of cybernetics.
  8. Cognitive Science Society. .Founded in 1979, the Cognitive Science Society is a professional society for the interdisciplinary field of cognitive science, bringing together researchers from many fields that are focused on understanding the nature of the human mind.
  9. Association for the Advancement of Artificial Intelligence (AAAI). .Founded in 1979, the Association for the Advancement of Artificial Intelligence is an international scientific society devoted to promoting research in, and the responsible use of, artificial intelligence.  It focuses on the mechanisms underlying thought and intelligent behavior and their embodiment in machines. Many of the leading figures in the development of artificial intelligence have been prominently associated with the AAAI.
  10. International Council on Systems Engineering (INCOSE). .  INCOSE is the international professional society in the field of systems engineering.  It was founded in 1990 and has approximately 70 local chapters globally. It certifies systems engineers based on its Systems Engineering Handbook. 
  11. Ecological Society of America (ESA). .The Ecological Society of America was founded in 1915.  It is the professional society for ecological scientists and the study of ecological systems.  Although headquartered in the United States, it has members in over 90 countries. In the 1940s, the Society decided that it should focus on research and not pursue an activist or political focus on ecological preservation.  As a consequence, the first president of the Ecological Society founded what became The Nature Conservancy.


Research Institutes:

  1. New England Complex Systems Institute (NECSI). . NECSI was founded in 1996 by faculty of various New England academic institutions, among them MIT, Harvard, and Brandeis to foster collaboration among researchers studying complex systems.  It is located in Cambridge, Massachusetts.
  2. Santa Fe Institute (SFI).  .The SFI  is an independent, nonprofit theoretical research institute devoted to dedicated to multidisciplinary/interdisciplinary study of the fundamental principles of complex adaptive systems.  These include physical, computational, biological, and social systems, among others. It is located in Santa Fe, New Mexico.  
  3. International Institute for Applied Systems Analysis (IIASA). . The IIASA is a not-for-profit research institute located in Laxenburg, near Vienna, in Austria.  Founded in 1972, it conducts interdisciplinary scientific studies on environmental, economic, technological, and social issues with a focus on global change conditions using applied systems analysis. Its mission is to provide insights and guidance to policymakers worldwide.  It is funded by scientific organizations in 22 member countries, including the U.S.

Books on Systems Topics

Following is a short list of recent major books on system topics.

  1. Peter Senge, The Fifth Discipline: The Art and Practice of the Learning Organization, Doubleday, 1990, revised 2006,  466 pages, list price $28.00, ISBN-10: 0385517254.  The most widely cited book on systems topics.
  2. John D. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw Hill, 2000, 982 pages, (textbook--used copies only), ISBN-10: 0072311355. A widely used textbook.
  3. John Gall, The Systems Bible: The Beginner's Guide to Systems Large and Small. General Systemantics Press, 2002, 316 pages, list price $27.95, ISBN-10:0961825170.  A humorous discussion of how systems go awry.
  4. Barry Richmond, An Introduction to Systems Thinking with STELLA, ISEEE Systems,, 2004, 165 pages, list price $39.00, ISBN-10: 0970492111. Includes an introduction to the STELLA systems modeling software.
  5. John Boardman and Brian Sauser, Systems Thinking: Coping With 21st Century Problems, CRC Press, 2008, 242 pages, list price $120.00,  ISBN-10: 1420054910. A college textbook on systems thinking.
  6. Donella Meadows, Thinking in Systems: A Primer,. Chelsea Green, 2008, 242 pages, list price $19.95, ISBN-10: 1603580557.  An excellent, widely-acclaimed, and very accessible introduction to system dynamics concepts. 
  7. Jamshid Garajedaghi, Systems Thinking: Managing Chaos and Complexity::A Platform for Designing Business Architecture, Morgan Kaufmann, 2011, 376 pages, list price $39.95, ISBN-10: 0123859158.  Systems concepts applied to business topics.
  8. David Peter Stroh, Systems Thinking for Social Change: A Practical Guide to Solving Complex Problems, Avoiding Unintended Consequences, and Achieving Lasting Results,  Chelsea Green,  2015, 266 pages, list price $24.95, ISBN-10: 160358580X.  Used in a number of university courses on systems concepts.
  9. Asish Ghosh, Dynamic Systems for Everyone: Understanding How Our World Works, Springer, 2015, 239 pages, list price $119.00,  ISBN-10: 3319107348. An expensive text on dynamic systems.
  10. Chiang H. Ren, How Systems Form and How Systems Break: A Beginner's Guide for Studying the World, Springer, 2016, 187 pages, list price $139.99, ISBN-10-978-3-44029-2.  Another expensive text..

Videos on Systems Topics

A number of online videos provide useful information on systems topics.  Following is a short list of examples with links.

TED talks:

  1. Tom Wujec, Got a wicked problem?  First, tell me how you make toast…February 5, 2015,
  2. Eli Stefanski, Making systems thinking sexy, October 21, 2011,
  3. Igor Nikolic, Complex adaptive systems, September 13, 2010,
  4. Rebecca Mills, Systems thinking for a better world, October 9, 2014,
  5. Joe Simkins, The complexity of emergent systems, January 26, 2014,
  6. Eric Berlow, Simplifying complexity, November 12, 2010,
  7. Nicholas Perony, Puppies! Now that I’ve got your attention, complexity theory, complexity_theory/discussion

Other YouTube videos:

  1. The value of systems thinking, October 26, 2017,
  2. How thinking in systems can change the world, October 5, 2012,
  3. Systems thinking—rethink everything, February 22, 2018,
  4. Peter Senge, Systems thinking, October 23, 2016,
  5. Peter Senge, Introduction to systems thinking, August 5, 2014,
  6. Peter Senge, Systems thinking in a digital world, February 7, 2016,
  7. 5 principles of systems thinking, January 4, 2017,
  8. Systems thinking, March 2, 2015,
  9. Intro to systems thinking, June 11, 2017,
  10. James Swanson, Systems thinking white boarding animation project, December 17, 2011,
  11. Dave Snowden, Complex adaptive systems, June 12, 2018,
  12. Gerald Midgeley, An introduction to systems thinking, October 2, 2014

Educational Programs Focused on Systems Science

A number of university programs provide education on systems sciences.  This section identifies some of the more important of these programs.  

Not included are programs on systems engineering. These are widely available.  A number of them are specialized in particular application areas, such as industrial systems engineering, control systems engineering, computer systems engineering, aerospace systems engineering, manufacturing systems engineering, and agricultural and biological systems engineering.  Also not included in this listing are programs in operations research.

University Departments and Degree Programs in the Area of Systems Science:

  1. Massachusetts Institute of Technology, Sloan School of Management 
  2. Portland State University, MS and Ph.D. in systems sciences 
  3. University of Hull (UK), Centre for Systems Studies 
  4. Saybrook University, Ph.D. in Managing Organizational Systems 
  5. Washington University in St. Louis, McKelvey School of Engineering, Systems Science and Engineering.
  6. University of Michigan Ann Arbor, Center for the Study of Complex Systems 
  7. Binghamton University, State University of New York, Systems Science and Industrial Engineering Department 
  8. The Open University, Systems Thinking in Practice, MS in systems sciences 

Selected University Courses in the Area of Systems Science (including online):

  1. University of Toronto, CSC2720H Systems Thinking for Global Problems 
  2. McGill University MSUS 402 Systems Thinking and Sustainability 
  3. Georgia Tech Online, DEF 4523P, Applied Systems Thinking 
  4. Oregon State University, IE 575 Systems Thinking Theory and Practice 
  5. Dartmouth College, ENGS 18 System Dynamics in Policy Design and Analysis 
  6. The California State University Chico, BADM 495, Applied Strategic Decision Making 
  7. University of California San Diego Extension, MAE-40017 Introduction to Systems Thinking  
  8. Purdue University, SYS53000 Practical Systems Thinking 
  9. University of Michigan Online, (no number) Model Thinking 
  10. James Madison University, ISAT290 Introduction to Systems Thinking for Complex Problems 
  11. Future Learn, Systems Thinking and Complexity 

University Certificate Programs:

  1. Cornell University, Systems Thinking Certificate 

Some Selected Systems Thinking Websites and Blogs

  1. The Donella Meadows Project:
  2. Systems Thinker:
  3. Free Management Library: Systems Thinking, System Tools, and Chaos Theory:
  4. Cognitive Edge:
  5. SE Scholar:
  6. Facile Things:
  7. Bridgeway Partners:
  8. JotForm Systems Thinking:
  9. Waters Center for Systems Thinking:

Key figures in the Development of Systems Thinking

Following is a selected list of individuals that have been important in various aspects of the development of systems thinking. The links are Wikipedia articles on each individual.

Some selected systems innovators presently alive:

Some Online Articles on Systems-Related Topics

Following are some relevant online articles on systems-related topics. Each is a Wikipedia article. The internal links expand on the primary wikipedia topic in each case and are worth following for additional insight.

1. System. See

2. Systems theory. See

3. Systems science. See

4. Systems modeling. See

5. System dynamics. See

6. Systems architecture. See

7. Systems analysis. See

8. Systems engineering. See

9. Chaos theory. See

10. Complex system. See

11. Complex adaptive system. See

12. Cybernetics. See

13. Control system. See

14. Systems biology. See

15. Systems ecology. See

16. Systems psychology. See

17. Holism. See

18. Emergence and emergent behavior. See

19. Synergy. See

20. Feedback loops. See

21. Black box. See

22. Homeostasis. See

23. Tragedy of the Commons. See

24. Sustainability. See

25. Self-organization. See

26. Cognitive biases. See

Some Tools for Analyzing, Understanding, and Synthesizing Systems

A number of disciplines connected with different aspects of systems thinking have developed useful tools to aid in the analysis, understanding, and synthesis of systems, both natural systems and human-designed technical systems such as complex information systems.  Following is a brief discussion of such tools along with Wikipedia article links describing each in more detail.

Not included in this discussion are system-oriented project management tools such as PERT (Program Evaluation and Review Technique), CPM (Critical Path Method), event chain diagrams, and the Gantt chart for scheduling.

Causal loop diagram.  A causal loop diagram depicts how different variables in a system are interrelated.  In particular, the diagram describes the feedback loops present in the system, either reinforcing or balancing feedback loops.  Causal loop diagrams describe a system qualitatively.  Annotation of the nodes and arcs in the diagram can increase its descriptive power.  See

System dynamics stock and flow diagram.  To facilitate analysis of dynamic systems involving varying quantities, a causal loop diagram can be transformed to a stock and flow diagram.  A stock is any reservoir that accumulates or depletes over time. A flow is a transfer into or out of a reservoir, with a rate of change in the stock as a result of the flow. Stock and flow models are usually built and simulated using computer software.  They are particularly useful for performing what-if analyses to evaluate alternative policies, etc.  See

System dynamics simulation software.  System dynamics computer simulations model the time-dependent dynamic behavior of a system involving stocks and flows.  For a comparison of some current software tools serving this purpose, see

Agent-based model (ABM).  An agent-based model is a computational model used to simulate the dynamic behavior of sets of autonomous agents and evaluate their effects on the system as a whole. The agents can be individuals or collections, such as organizations.  See

Agent-based model software.  For a comparison of some existing agent-based simulation software systems, see

Bayesian network.  A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional dependencies using a directed acyclic graph. In particular, a Bayesian network is useful for considering an event that occurred and estimating the probability that any one of the several possible known causes was the key contributing factor. See

Influence diagram.  Alternate terms for the influence diagram include relevance diagram, decision diagram, and decision network.  A generalization of a Bayesian network, it provides a graphical and mathematical representation of a decision situation.  It is useful for modeling both probabilistic inference problems and decision making problems.  See

Ishikawa diagram (cause-and-effect diagram).  An Ishikawa diagram is used to depict the causes of a specific event or overall effect (for example, the causes of a quality defect).  It is helpful to identify root causes.  The format allows one to visualize all causes simultaneously.  See

 Issue-based information system (IBIS).  IBIS is an argumentation-based approach helpful in clarifying so-called wicked problems, which are complex, poorly-defined problems involving multiple stakeholders and multiple interests.  Creating diagrams using IBIS notation is often called issue mapping.  IBIS techniques are used to guide the identification, structuring, and resolution of issues. See

Block diagram.  A block diagram represents the principal parts or functions of a system by blocks connected by lines showing the relationships of the blocks.  They are extensively used in engineering for hardware and software design. See

System context diagram (SCD).  This diagram depicts the boundary between the system, or part of a system, and its environment, showing the entities that interact with it.  The diagram shows the system as a whole and its various inputs and outputs from/to external factors.   See

Flowchart.  A flowchart is a diagrammatic representation of an algorithm or workflow, a step-by-step process for accomplishing a task.  See

Data-flow diagram (DFD).  A data-flow diagram represents the flow of data in a process or a system, including information about the inputs and outputs of each entity and the process itself. The diagram does not describe control flow, and there are no decision rules and no loops.  See

Control flow diagram (CFD).  A control-flow diagram depicts the control flow of a process or a system. See

Bond graph.  A bond graph provides a graphical depiction of a physical dynamic system using a state-space representation.  It is similar to a block diagram or signal-flow graph, except that the arcs in bond graphs represent the bi-directional exchange of physical energy rather than the unidirectional exchange of information.  Bond graphs can include multiple energy domains (e.g. mechanical, electrical, hydraulic, etc.) and are domain neutral. See

N-squared chart.  An N-squared chart is in the form of a matrix, representing interfaces between system elements. The chart is used to systematically identify, define, design, and analyze functional, informational, and physical interfaces.  See

Fault tree analysis (FTA).  Fault tree analysis provides a top-down, deductive failure analysis.   An undesired state of a system is analyzed using Boolean logic to determine a set of progressively lower-level causative events. See

Failure mode, effects, and criticality analysis (FMECA).  FMECA provides a bottom-up, inductive analysis of failures and their consequent effects.  It includes a criticality analysis, which considers the probability of particular failure modes against the severity of their consequences.  See,_effects,_and_criticality_analysis

Venn diagram.  A Venn diagram illustrates the possible logical relations between a collection of different sets. See

 Mind map.  A mind map visually organizes information, showing relationships among pieces of the whole in a hierarchical structure, typically drawn around a single concept.  Major ideas are linked to the central concept, and other ideas branch out in turn from the major ideas.  See

Concept map.  Concept maps extend the idea of mind maps by allowing multiple concepts as hubs in the diagram with relationships indicated between the concepts.  See

Argument diagram.  An argument diagram (or argument map) provides a visual representation of the structure of an argument.  Key components of the argument include the conclusion (or contention) and the premises (or reasons).  Additional components can include co-premises, objections, counterarguments, rebuttals, and lemmas.  See

Formal concept analysis (FCA).  FCA is used to derive a concept hierarchy or formal ontology from a collection of objects and their associated properties.  See

Semantic network.  A semantic network is organized as a knowledge base describing semantic relations between concepts. It is a common form of knowledge representation.  The diagram has vertices, which represent concepts, and edges, which represent semantic relations between concepts.  See

Unified modeling language (UML).  UML is a general-purpose modeling language intended to provide a standard means of visualizing the design of a system, particularly, but not limited to, an information system.  It constitutes a set of highly-structured diagrams describing the underlying model of the system. See

Following are some of the key diagrams utilized in the UML:

Use case diagram.  A use case diagram describes a user's interaction with the system.  It shows the relationship between the user and the user’s different use cases. It can identify the different types of users and the different use cases.  See

Class diagram.  A class diagram illustrates the structure of a software system.   It shows the system's classes, their attributes, operations (or methods), and the relationships among objects.  See See also classes for a discussion of system classes.

Composite structure diagram.  A composite structure diagram shows the internal structure of a class and the collaborations that this structure makes possible.  Elements of the diagram include internal parts, ports allowing the parts to interact with each other or through which instances of the class interact with the external world and with the parts, and connectors between parts or ports. The set of interconnected elements collaborate at runtime to achieve some purpose.  See

Deployment diagram.  A deployment diagram depicts the physical deployment of the elements of the system on hardware components and the software components that run on each of the hardware components, along with how the components are connected. See

Activity diagram.  An activity diagram represents workflows of stepwise activities and actions, including depiction of choices, iterations, and concurrency. The diagram shows the data flows involved with the activities in addition to the overall flow of control.  See

Interaction overview diagram.  An interaction overview diagram is similar to an activity diagram but pictures each individual activity as a frame which can contain a nested interaction diagram. See

Sequence diagram.  A sequence diagram depicts object interactions in a scenario, including the messages exchanged, arranged in time sequence. See

State diagram.  State diagrams provide an abstract description of the behavior of a system, represented as a series of events occurring in one or more possible states. The state diagram tracks the different states of the system’s objects.  See

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