Based on the works of W. Ross Ashby and his commentators provided in the sources, these five concepts—constraint, causality, non-ergodicity, speciation, and hierarchy—are not separate phenomena but are logically interlocked stages in the behavior of complex, state-determined systems.
The “untangling” begins with Constraint as the foundational building block and proceeds to explain how complex biological and social structures emerge from it.
1. Constraint as the Foundation of Organization
In Ashby’s cybernetics, constraint is the most primitive concept. It is the opposite of chaos.
• Definition: Constraint exists whenever the variety (number of possible states) of a system is less than the maximum possible variety it could exhibit if all its parts were independent[1].
• Relation to Causality: Ashby redefines causality not as a metaphysical force, but as a rigorous form of constraint. If A causes B, then the variety of states B can take is constrained by the state of A[2],[3].
• Organization: Therefore, “organization” is defined objectively as the presence of constraints. A system is organized precisely because its parts are not independent; they constrain each other’s behavior[4],[1].
2. From Causality to Non-Ergodicity (The Origin of Memory)
Once a system is constrained by laws (causality), it behaves in specific ways over time. Here, the distinction between ergodic and non-ergodic systems becomes the dividing line between simple physics and biology/intelligence.
• Ergodic Systems: In classical communication theory (Shannon), systems are often assumed to be ergodic, meaning they eventually visit all possible states and their probabilities remain stationary. They have no permanent history[5].
• Non-Ergodicity: Ashby argues that adaptive systems (living organisms) are necessarily non-ergodic. To adapt means to find a stable solution and stick to it, thereby permanently rejecting the “bad” behaviors. If a system were ergodic, it would eventually repeat its mistakes, destroying its memory[5].
• The Link: Non-ergodicity allows a system to have a history and memory. Memory is defined as a constraint acting over time—a “transduction” where the state at time t constrains the state at time t+k[6],[7].
3. Speciation as Adaptation to Local Constraints
Speciation (or reproduction) is explained not as a magical biological drive, but as a necessary dynamic outcome of a non-ergodic system interacting with a specific type of environment.
• Localized Disturbance: Ashby argues that reproduction is a specialized adaptation to environments where disturbances are localized (e.g., a rock falls here, not everywhere at once)[8],[9].
• Dispersion: In such an environment, a single massive organism is vulnerable. Survival is maximized by dispersing many small, identical copies (species/individuals) so that a local disaster destroys only a few, leaving the rest to regenerate the population[8],[10].
• Selection by Equilibrium: Speciation is the result of a dynamic system running to equilibrium. Forms that are “self-reproducing” are simply those that are uniquely resistant to the operations of the environment. Over time, only these resistant forms remain[11],[12].
4. Hierarchy as the Result of “Weak” Constraints
Hierarchy emerges when we analyze the degree of constraint between parts of a large system.
• Reducibility: Most large systems (like brains or societies) are not fully connected (where every part constrains every other part). Instead, they are “nearly decomposable”[13].
• Cylindrance: Ashby introduced the concept of cylindrance to measure this. If a relation between 100 variables can be reconstructed by looking at them in groups of 3, the system has a low cylindrance (it is simple)[14],[15].
• Hierarchy of Information: This informational structure allows the system to be treated as a hierarchy of subsystems. Strong constraints exist within subsystems (high interaction), while weak constraints exist between subsystems[16]. This hierarchical structure is efficient for information processing and evolution because it minimizes the “coordination rate” required to maintain stability[17].
Summary of the Relationship
1. Causality is simply the presence of Constraint (rules) in a system.
2. These constraints force the system to settle into stable patterns (Equilibria).
3. Because the system sticks to these stable patterns and does not revisit unstable ones, it is Non-Ergodic (it has memory and learns).
4. Because the external constraints (disturbances) are usually local, the system adapts by breaking into repetitive, dispersed units, leading to Speciation.
5. Because the internal constraints are not absolute (not everything connects to everything), the system naturally decomposes into a Hierarchy of subsystems, which allows for efficient regulation.
In short, Order (organization) is the accumulation of constraints, and Life (speciation/hierarchy) is the specific structural form that those constraints take when a system adapts to a localized, non-ergodic environment.
References
[1] Ashby Mechanisms.pdf [2] Ashby - Requisite Variety and its implications for the Control of Complex Systems.pdf [3] Ashby Mechanisms.pdf [4] Ashby Mechanisms.pdf [5] Ashby - Systems and Information.pdf [6] Ashby - Systems and Information.pdf [7] Ashby Mechanisms.pdf [8] Ashby Mechanisms.pdf [9] Ashby Mechanisms.pdf [10] Ashby Mechanisms.pdf [11] Ashby Mechanisms.pdf [12] Ashby Mechanisms.pdf [13] conant1991.pdf [14] Ashby Mechanisms.pdf [15] Ashby-Mechanisms_of_intelligence_ocr.pdf [16] conant1991.pdf [17] conant1991.pdf