Question
Where do the different ideas completely contradict each other and what are the principles that underpin these differences?
Based on the provided sources, the authors contradict one another primarily on the definition of complexity (is it random or organized?), its location (is it in the world or in the mind?), and the methodology required to handle it (should we optimize and solve, or probe and adapt?).
These contradictions stem from fundamentally different underlying principles regarding ontology (what exists), epistemology (how we know it), and teleology (the purpose of intervention).
1. The Definition Wars: Randomness vs. Organization
The most stark contradiction exists between the information theorists and the systems thinkers regarding the relationship between complexity and order.
• The Contradiction:
Complexity is Randomness: David L. Abel explicitly defines “maximum complexity” as “randomness” and the lack of order or pattern[1][2]. In this view, a sequence is complex if it cannot be compressed; therefore, complexity is “antithetical to order” and blind to function[2][3]. This aligns with Herb Simon’s note that in some frameworks, randomness is equated with complexity[4].
Complexity is Organization: Conversely, Paul Cilliers, Max Boisot, and the Relational Biologists argue the exact opposite. Cilliers states that complexity is not merely a state of having many parts (which is “complicated”) but arises from distinct, non-linear relationships[5]. Boisot distinguishes “crude complexity” (random noise) from “effective complexity” (regularities)[6]. Warren Weaver (cited in Shannon/Kahlen) distinguishes “disorganized complexity” from “organized complexity,” which involves organic correlation[7].
Underlying Principle: This difference rests on Information Theory vs. Systems Theory. Abel relies on algorithmic information theory (Kolmogorov/Shannon), where information is a measure of unpredictability. The systems thinkers (Checkland, Cilliers) rely on structuralism and cybernetics, where complexity is a measure of functional interconnectedness.
2. The Location of Complexity: Objective vs. Subjective
Is complexity a physical property of the system, or is it a failure of the observer’s mind?
• The Contradiction:
It is Subjective (Observer-Dependent): James Wilk calls complexity a “perceptual bug” and a “fault in our maps” rather than a feature of the territory[8][9]. John Warfield defines it as a “sensation of frustration” in the mind[10]. The TOG source agrees, calling it an “observer phenomenon” dependent on framing[11][12]. Stafford Beer and Patrick Hoverstadt also emphasize that complexity is “relative to the observer” who decides what constitutes a “state”[13].
It is Objective (Intrinsic): The Relational Biologists (Rosen, Pattee, Noble) contend that complexity is an “intrinsic system property” characterized by “impredicative loops” and closed cycles of causation[14]. It is not just about how we see it; it is about the physical reality of systems that cannot be simulated by Turing machines[14]. Cilliers also views it as a real condition of non-linear interaction and history, not just a mental construct[15].
Underlying Principle: This is a conflict between Constructivism (reality is constructed by our perceptions) and Critical Realism (reality exists independently of our perceptions but is difficult to model).
3. The Methodology of Intervention: Optimization vs. Adaptation
This contradiction dictates whether one should try to “fix” the system or simply navigate it.
• The Contradiction:
Solve and Optimize: The Triz and Theory of Constraints (Dettmer) sources advocate for finding precise technical solutions. Triz seeks an “Ideal Final Result” by resolving contradictions to zero cost[16]. Dettmer advises finding the “root cause” and the “constraint” to maximize system performance[17][18].
Dissolve and Learn: Russ Ackoff and the “Soft Systems” thinkers (Checkland, Rittel) argue that you cannot “solve” complex messes. Ackoff suggests “dissolving” them (redesigning the system)[19]. Checkland urges abandoning “optimization” in favor of a “learning cycle”[20]. Rittel warns that there is no “stopping rule” or correct answer, only “good enough”[21]. ◦
Probe and Sense: Dave Snowden explicitly rejects “best practice” and “root cause analysis” for complex systems. He argues that because cause and effect are only visible in retrospect, one must “probe-sense-respond” rather than analyze and optimize[22][23].
Underlying Principle: This stems from Mechanistic Determinism (the world is like a machine/chain we can engineer) vs. Evolutionary Adaptation (the world is an ecology we must survive).
4. Structure and Analysis: Decomposition vs. Irreducibility
How should we study a complex system? By taking it apart or by looking at the whole?
• The Contradiction:
Decompose It: Herb Simon argues for “hierarchic decomposition,” stating that complex systems are “nearly decomposable.” He advises studying subsystems independently to simplify analysis[24]. Triz and Dettmer also use trees and functional analysis to break problems down[18].
Do Not Decompose: The Relational Biologists argue that reductionism “fails for complex systems” because it breaks the internal causal loops (entailment) that define them[25]. Cilliers agrees that if you take a complex system apart, you “destroy the very interactions that define it”[26]. Ackoff states that improving parts taken separately often makes the whole worse[19].
Underlying Principle: This is the classic debate of Reductionism (understanding the whole via the parts) vs. Holism (the whole is greater than the sum of the parts).
5. The Role of Constraint: Negative vs. Positive
Authors disagree on whether constraints are obstacles to be removed or essential tools for management.
• The Contradiction:
Remove Constraints: In Theory of Constraints (Dettmer), the constraint is the “weakest link” or bottleneck that limits performance and must be exploited and elevated[17]. In Triz, constraints (contradictions) are barriers to the Ideal Final Result[27].
Utilize Constraints: Alicia Juarrero, Dave Snowden, and Philip Ladyman argue that you should manage constraints to channel behavior. Snowden speaks of “enabling constraints” that allow patterns to emerge[28]. Ladyman and Ashby suggest discovering constraints to reduce variety and make regulation possible[29].
**Underlying Principle:**Linear Causality (constraints block flow) vs. Cybernetic Control (constraints provide structure and direction).
Summary Table of Principles
| Conflict Area | Idea A (Rational/Engineering) | Idea B (Soft/Cybernetic/Evolutionary) |
|---|---|---|
| Ontology | Complexity is random/disordered (Abel, Shannon). | Complexity is organized/relational (Cilliers, Luhmann). |
| Epistemology | Complexity is an intrinsic property (Relational Biologists). | Complexity is an observer’s frustration (Wilk, Warfield). |
| Methodology | Solve, optimize, find root causes (Triz, Dettmer). | Probe, learn, dissolve, negotiate (Snowden, Checkland). |
| Analysis | Decompose into parts/hierarchies (Simon). | Holism; decomposition destroys the system (Ackoff, Cilliers). |
| Nature of Rules | Universal laws/best practices exist (Triz). | Context is everything; rules fail (Snowden, Juarrero). |
References
[1] David L. Abel.md [2] David L. Abel.md [3] David L. Abel.md [4] Herb Simon.md [5] Paul Cilliers.md [6] Max Boisot.md [7] Claude Shannon.md [8] James Wilk.md [9] James Wilk.md [10] John Warfield.md [11] Mastering the Muddle - Systemic Perspectives on Complexity Management.md [12] TOG - Mastering the Muddle.md [13] Patrick Hoverstadt.md [14] Relational Biologists - Robert Rosen Howard Pattee Dennis Noble.md [15] Paul Cilliers.md [16] Triz.md [17] Theory of Constraints - William Dettmer.md [18] Theory of Constraints - William Dettmer.md [19] Russ Ackoff.md [20] Peter Checkland.md [21] Horst Rittel.md [22] Dave Snowden.md [23] Dave Snowden.md [24] Herb Simon.md [25] Relational Biologists - Robert Rosen Howard Pattee Dennis Noble.md [26] Paul Cilliers.md [27] Triz.md [28] Dave Snowden.md [29] Philip Ladyman and Ross Ashby.md