Part 1-(Social) Complexity Basics: Challenges to Understanding the Problem Domain
A somewhat different interpretation of (social) complexity — a multi-part introductory series
I’ve recently started writing about the volatile, uncertain, complex and ambiguous (VUCA) environments that organisations face today on my company’s Medium page.
However, VUCA or other acronyms such as BANI (brittle, anxious, non-linear and incomprehensible, see Cascio 2020, Mitzkus 2022) are somehow misleading because they tend to emphasize only certain aspects of (social) complexity in general, which itself is often poorly understood.
On the other hand, the paradigm of (social) complexity isn’t easy to grasp. It’s rather a tricky beast because it seems to be as complex as the phenomena to be studied (i.e., self-organizing dynamic systems and networks) themselves.
This means (see, for example, James Ladyman / Karoline Wiesner (2020), What are complex systems?, the website of the Santa Fe Institute complexityexplorer.org and these Wikipedia articles about complex systems and social complexity):
- Many definitions: There is no universally accepted definition of complexity or a complex system. And, for various reasons, there will never be such a consensus among complexity researchers. This is true for related key notions such as system, emergence, self-organization / autopoiesis, etc., as well.
- Multitude of features: There is an open-ended list of features that may characterize a complex system.
- Complexity is relative: Complexity can be interpreted as a phenomenon that is in the eye of the beholder. In short, it´s observer-dependent.
- Transdisciplinary: There are many scientific disciplines (computer science, physics, biology, psychology, economics, sociology, etc.) involved so that a universal consensus among complexity researchers about the tools, methods, theories, and concepts to be used is unlikely. This means further:
- First, it´s impossible to give a thorough account of the paradigm of complexity.
- Second, a certain degree of ignorance and non-communication among complexity researchers is to be expected because of various epistemological, theoretical, methodological, and conceptual obstacles that might sometimes be too hard to overcome. - Multiple subdisciplines: Even within a single scientific discipline such as sociology there are various subdisciplines (computational sociology, relational sociology, social network analysis, sociocybernetics, etc.) that are studying complex phenomena.
- Multiple approaches: Within each subdiscipline such as sociocybernetics there are various approaches (systems and form theory, second order cybernetics, etc.) with different theoretical options.
- Multiple epistemologies: The guiding distinction in this context is constructivism vs realism. But, there isn´t a single constructivist or realist position. Instead, there are many variations of them.
In subsequent Medium posts related to this introductory post, I’d like to focus on some of the aspects mentioned above, based on the systems-theoretical approach of the Bielefeld School (Niklas Luhmann, Dirk Baecker, Peter Fuchs, etc.) in sociology.
This theory is interesting because it’s not only quite different from the better known theories of complex adaptive systems (CAS), but also contains one of the most fruitful organizational theories available today.
This implies further that this introduction to (social) complexity isn’t an academic end in itself! Instead, it is meant to lay a framework to better understand the interplay of:
- Organizations / Startups (with respect to Lean Startup, etc.)
- Business Process Management (BPM), especially with respect to the automation of processes through
- Process Mining
- Robotic Process Automation (RPA)
- Machine Learning (ML) / Artificial intelligence (AI) and
- Hyperautomation
as well as New Work approaches such as
- Business Agility
- Holacracy / Sociocracy
etc.
Here is an overview of the following parts of this introductory series:
- Part 2 — (Social) Complexity Basics: A General Definition Problem
- Part 3 — (Social) Complexity Basics: An Intro to Complex Systems
- Part 4 — (Social) Complexity Basics: Features of Complexity and the Scalability Problem
- Part 5 — (Social) Complexity Basics: System Formation and Maintenance Related to the Distinction System/ Environment
- Part 6 — (Social) Complexity Basics: System Formation and Maintenance Related to the Distinction System/ Element
- Part 7 — (Social) Complexity Basics: The “God complex” and Other Biases when Dealing with Complex Systems
- Part 8 — (Social) Complexity Basics: Emergence
- Part 8 — (Social) Complexity Basics: Recursion, Self-Reference and Feedback Loops
- Part 9 — (Social) Complexity Basics: Memory and Adaptation (Learning).
- Part 10 — (Social) Complexity Basics: Coordination Mechanisms
- Part 11— (Social) Complexity Basics: Types of Systems according to Niklas Luhmann
- Part 12 — (Social) Complexity Basics: Niklas Luhmann’s Operative Constructivism
- Part 13 — (Social) Complexity Basics: Models of Communication
- Part 14 —(Social) Complexity Basics: Containers all the Way down
- Part 15 — (Social) Complexity Basics: Medium — World Dualism and Non-Dualism
- Part 16— (Social) Complexity Basics: What Kind of Theory is Luhmann’s Sociological Systems Theory?
- Part 17 — (Social) Complexity Basics: The Interplay of Mind and Communication
- Part 18 — (Social) Complexity Basics: Types of Social Systems (Interactions, Organizations and Function Systems)
- Part 19 — (Social) Complexity Basics: Societal Complexity and Differentiation Modes
- Part 20 — (Social) Complexity Basics: Modern Function Systems
- Part 21 — (Social) Complexity Basics: Thresholds of Improbability in Communication Processes
- Part 22 — (Social) Complexity Basics: Dissemination Media, Information Catastrophes and Societal Evolution
- Part 23 — (Social) Complexity Basics: Media, Technology and Social Evolution (an Heuristic Model)
- Part 25— And what about Artificial Intelligence (Nassehi, Baecker, Esposito)?
- Part 26 — Summary
- Addendum 1: Network Theory
- Addendum 2: Contexture Analysis
