Hoverstadt 33 Laws and Principles

The 33 Laws and Principles

What are the 33 Laws

Based on the provided sources, there is no specific list of exactly “31 laws.” However, Patrick Hoverstadt’s book The Grammar of Systems introduces a foundational collection of 33 Systems Laws and Principles. (Additionally, there is an appendix listing Humberto Maturana’s 30 Systems Laws).

It is highly likely you are referring to the primary 33 Systems Laws and Principles that form the bedrock of the author’s systems thinking framework. Here is the complete list and their brief definitions:

Boundaries, Identity, and Structure

1. Law of Calling: Difference creates boundaries and boundaries create difference.
2. The Law of Crossing: Crossing a boundary is a change of state.
3. Network Power Law: Structural complexity goes up exponentially with the number of elements.
4. Law of Sufficient Complexity: The system does what it does because it is what it is.
5. Fractal Principle: Systems replicate their own form.
6. Root Structuration Theorem: Structuring a system to have the same number of sub-systems at each level reduces its complexity.
7. Scaling Stasis Principle: The more complex a system is, the more constraints it has.

Viability, Stability, and Change

8. Viability Principle: A system’s viability depends on how well it can balance autonomy with cohesion and stability with change over time.
9. Homeostasis Principle: A system will be stable if all its key variables remain within their physiological limits.
10. System Stability Principle: Systems are patterns that are recognisable over several observations.
11. System Survival Theorem: Systems fail if their environment changes more than the system.
12. Conservation of Adaptation Principle: Change is the only constant in the relationship between a system and its environment.
13. Structural Viability Theorem: A system has optimal viability when its change rate/environmental change rate is similar to that of its sub-systems.
14. Steady State Principle: Stability of the system depends on the level of stability of its sub-systems and vice versa.
15. Order Osmosis Principle: Sub-systems migrate from less ordered to more ordered systems.
16. Relaxation Time Principle: A system that is repeatedly shocked at shorter intervals than its recovery time may never stabilize.

Management, Complexity, and Control

17. Law of Requisite Variety (Ashby’s Law): How well any system manages depends on how well it matches the variety it faces.
18. Conant-Ashby Theorem: The ability to deal with any situation depends on how good your model of it is.
19. Power Structuration Theorem: A system has optimal agency when its needs for agency are balanced with those of its sub-systems.
20. Redundancy of Potential Command Principle: Your ability to be effective in a complex situation depends on bringing together the right mix of information.
21. Adams’ 3rd Law: A system’s overall risk depends on balancing the risk across levels of the system.
22. Darkness Principle: There is always something about a system you can’t know.

Feedback, Dynamics, and Emergence

23. 1st Circular Causality Principle: Positive feedback drives state change.
24. 2nd Circular Causality Principle: Negative feedback drives stability.
25. Feedback Dominance Theorem: Loops with strong feedback will take you where they take you, irrespective of the size of the input.
26. Law of Reciprocity of Connections: If ‘A’ connects to ‘B’, then ‘B’ also connects to ‘A’.
27. Self-organised Criticality: Systems whose dynamics drive the system to collapse.
28. Complexity Instability Principle: Systems with too many changing parts tend to become unstable.
29. System Resonance Principle: Resonance occurs because of similarities in systems.
30. Self-Organising Principle: Parts generate wholes.
31. & 32. The Two Black Box Principles (1st & 2nd): The outputs of a black box are predictable. (Note: These are counted as two principles to reach the total of 33).
32. Principle of Emergence: The whole is more than the sum of its parts.

Can you provide a clustering of the 33 laws

Patrick Hoverstadt groups the 33 systems laws and principles into three main clusters (or domains) that sit on a gradient between order and chaos. He uses a “Triquetra” (a three-cornered knot) to illustrate these groupings, emphasizing that the boundaries between these categories are fluid, and many laws slide into one another or play a dual role.

The three primary clusters are:

1. Knowing / Uncertainty (Understanding) This cluster deals with the limits of human perception, how we make distinctions, and the role of models in helping us navigate what we cannot fully comprehend.

• Laws in this domain: The Darkness Principle, 1st and 2nd Black Box Principles, Redundancy of Potential Command Principle, System Resonance Principle, Law of Calling, Law of Crossing, Conant-Ashby Theorem, Law of Sufficient Complexity, and the System Stability Principle.

2. Dynamic Complexity This cluster focuses on the temporal behaviors of systems, exploring how rates of change, feedback loops, and adaptation drive a system toward either stability or collapse.

• Laws in this domain: 1st and 2nd Circular Causality Principles, Homeostasis Principle, Relaxation Time Principle, Conservation of Adaptation Principle, System Survival Theorem, Self-organised Criticality, Feedback Dominance Theorem, and the Steady State Principle.

3. Structural Complexity This cluster is concerned with the architecture of systems, looking at how the number of elements, network interdependencies, boundaries, and structural levels generate complexity and constraints.

• Laws in this domain: Network Power Law, Complexity Instability Principle, Root Structuration Theorem, Power Structuration Theorem, Law of Reciprocity of Connections, Scaling Stasis Principle, Adams’ 3rd Law, Self-Organising Principle, Order Osmosis Principle, and the Structural Viability Theorem.

The Intersections Because the clusters overlap in Hoverstadt’s Triquetra model, several foundational concepts sit at the boundaries between them:

• The Principle of Emergence sits squarely in the center, overlapping all three domains.
• The Viability Principle sits at the intersection of Knowing and Structural Complexity.
• Ashby’s Law of Requisite Variety sits at the intersection of Knowing and Dynamic Complexity.
• The Fractal Principle sits at the intersection of Structural and Dynamic Complexity.

Hoverstadt notes that when taken together, these three clusters reveal two dominant storylines that run through the entire grammar of systems: the continuous interplay between order and chaos, and the limits of human knowledge regarding the interplay between reality and our perception.

The Storylines of Order and Chaos

How do the two storylines of order and chaos with reality and perception evolve and map to each other

The two dominant storylines running through systems thinking—the interplay between order and chaos, and the interplay between reality and perception—are deeply intertwined. In the author’s framework, these storylines map to the three domains of systems laws: Structural Complexity, Dynamic Complexity, and Knowing/Uncertainty,.

Here is how these two storylines evolve and continuously map to one another.

1. The Storyline of Order and Chaos (The Mechanics of Reality)

This storyline focuses on the physical and structural realities of systems as they constantly navigate the continuum between static order and total chaos.

• Structural Complexity: Structure is the primary mechanism systems use to absorb complexity and maintain order. By drawing boundaries and grouping elements into sub-systems, organizations prevent a chaotic explosion of interdependencies,. However, according to the Network Power Law, if structure fails and too many parts connect directly, the system breaches a threshold and the Complexity Instability Principle takes over, driving the system into chaos.
• Dynamic Complexity: Order and chaos are also temporal states driven by feedback loops. Negative feedback (Homeostasis) regulates the system, driving stability and order. Conversely, positive feedback (the 1st Circular Causality Principle) drives exponential change, which, if unchecked, leads to Self-Organised Criticality—a dynamic where the system’s own momentum guarantees its eventual catastrophic collapse into chaos,.

2. The Storyline of Reality and Perception (The Limits of Knowing)

This storyline focuses on the observer’s epistemology. It dictates that humans cannot interact with the pure, objective reality of a complex system; we can only interact with our mental models or perceptions of it.

• The Act of Distinction: Reality is carved into perceptible chunks through the Law of Calling. Drawing a boundary to separate a system from its environment is a subjective cognitive choice based on differences the observer decides are important,.
• The Observer Effect: According to the Law of Crossing, where the observer stands relative to a boundary fundamentally alters their perception. The logic, emotions, and worldview of an insider are entirely different from an outsider,.
• Inescapable Uncertainty: The Darkness Principle establishes that complex reality is infinitely fractal and dynamic, meaning no system can ever be known completely. Furthermore, as George Box noted, “all models are wrong, but some are useful.” Because we rely on simplified models, there is a permanent gap between our perception (the map) and reality (the territory),.

How the Two Storylines Evolve and Map to Each Other

These two narratives do not run in parallel; they form a self-referencing, continuous loop. How an observer perceives a system directly alters the system’s dynamic trajectory between order and chaos, and the system’s chaotic or stable nature constantly challenges the observer’s perception.

Emergence as the Unifying Intersection The concept of Emergence sits exactly where these two storylines collide. Emergent properties (like life, speed, or organizational culture) are real, physical phenomena generated by the dynamics and structures of the system (Order/Chaos),. However, emergence is uniquely elusive because it cannot be seen by looking at the system’s parts. It requires the observer to use “Holism”—a specific perceptual technique of looking up to the wider meta-system,. Therefore, emergence is a reality that only exists when the structural order of the system meets the correct perceptual model of the observer.

Difference Drives Both Cognition and Chaos The concept of “difference” acts as a bridge between the two storylines.

• In perception: The most basic cognitive act is recognizing “the difference that makes a difference.” The observer uses perceived differences to draw boundaries and construct their model of reality,.
• In reality (Order/Chaos): Differences across a boundary (differentials in speed or structure) inject dynamic energy into a relationship. If the difference between two structurally coupled systems is too high, it generates violent dynamics that push the relationship toward chaos,. Therefore, the observer’s cognitive capacity to perceive difference dictates their ability to manage the real-world dynamics of chaos.

The Constructivist Loop (Models Create Reality) The map and the territory continuously co-evolve. When a manager holds a specific model of reality (e.g., Theory X vs. Theory Y management), that perception dictates how they interact with the system. The system then reacts to that intervention, altering its internal order and dynamics. Believing that thought is separate from reality is defined as the “Cartesian Fallacy”; in human systems, the act of observing and modeling (perception) actually produces a state change in the system itself (reality),.

Doubly True Uncertainty Ultimately, the two storylines fuse in the experience of uncertainty, which the author describes as “doubly true”.

1. The system itself is objectively uncertain because it exists in a state of dynamic flux, constantly threatening to slip from order into chaotic Self-Organized Criticality.
2. The observer’s perception is subjectively uncertain because they are relying on simplified, inherently flawed models that may be ignoring critical variables,.

To survive this dual evolution, the systems thinker must constantly “close the loop”—testing their perceptual models against the chaotic reality of the real world, and updating their map the moment reality diverges from expectation,.

Stories and Narratives - Reality and Facts

How do stories and narratives relate to concrete reality and facts

Stories, narratives, and myths relate to concrete reality not as objective reflections of the present, but as mental models, vehicles for tacit knowledge, and dynamic forces that actively shape an organization’s future.

Based on the sources, the relationship between stories and concrete reality operates in several distinct ways:

1. Stories as Carriers of Survival Knowledge Organizations use stories to capture and transmit concrete, life-or-death realities across time. A prime example is the regimental system of the British Army, where stories and traditions are used to pass down critical tacit knowledge about how to behave in war. Because wars are infrequent, an army cannot rely on direct, first-hand experience to teach soldiers how to survive; instead, it relies on organizational memory preserved in regimental stories. These narratives encode the reality of past successes against impossible odds, building the esprit de corps necessary for future survival. In this way, businesses also have “life stories” where past events form cultural “memes” that shape organizational identity and directly predict future behavior.

2. Narratives as Substitutes for Non-Existent Facts In systems thinking, there is a strict separation between managing the present (“running the business”) and managing the future (“changing the business”). The present is a world of concrete facts, figures, and data. However, strategy deals with the future, and “there are no facts about the future… because it has not happened yet”. Because the future consists entirely of possibilities, probabilities, and opinions, managers cannot rely on concrete facts to formulate strategy. Instead, they must construct narratives—such as scenario plans—to model different possible futures and explore how their organization might fit into them.

3. Myths Actively Constructing Reality (Self-Fulfilling Prophecies) Narratives do not simply describe the world; they actively construct it. The sources illustrate this with the story of a mongrel dog that believes a “myth” that the world is full of other dogs waiting to attack it. Acting on this narrative, the dog behaves aggressively, which provokes other dogs to attack, thereby making the false myth a concrete reality. Managers do the exact same thing. When managers hold a specific belief or myth about how the world works (e.g., believing that business is strictly a zero-sum game), they interact with the world in a way that forces the environment to respond in kind, making the myth factually true.

4. Myth-Making as a Flight from Reality While stories can encode useful knowledge, they can also act as pathological defence mechanisms when concrete reality becomes too stressful. During strategic crises, management teams frequently exhibit stereotypical “Fantasist” behaviors, which include turning inward and engaging in “myth-making” about the problem situation. By constructing an elaborate fantasy rather than investigating the concrete facts, managers decouple themselves from reality (for example, concocting a complex narrative about why clients are withholding payment, rather than discovering the factual reality that the software simply didn’t work).

5. The Danger of Borrowed Anecdotes When faced with confusing concrete realities where traditional strategic planning fails, managers often abandon underlying theory and instead seek guidance from “anecdotes” and “attractive stories” found in business books. This is a dangerous relationship to reality, because managers are trying to apply a narrative of success that belongs to an entirely different context and organization, rather than dealing with the specific systemic facts of their own situation.

6. Ancient Myths as Expressions of Systemic Facts Finally, ancient myths are often used in systems thinking to express fundamental, concrete laws of physics and organization. For example, the creation myth of Rome (where Romulus kills his twin Remus for jumping over the newly built wall) or the Norse myth of the Midgard Serpent, are not historical facts, but they perfectly encode the concrete systemic reality of boundaries. The narrative of the myth communicates the factual reality that defining a boundary is what creates an identity, and failing to respect that boundary leads to systemic destruction.

“There are no facts about the future, there cannot be because it has not happened yet, nor is there any data. Instead, there are possibilities, probabilities, opinions, views and intuitions.”

Facts and the Future

"There are no facts about the future, there cannot be because it has not happened yet, nor is there any data. Instead, there are possibilities, probabilities, opinions, views and intuitions." how is this statement understood in v2-combined

The statement perfectly captures a foundational consensus across the sources regarding the nature of complex systems, human affairs, and the limits of prediction. Traditional, mechanistic science operated on the illusion that if one possessed enough data about the present, the future could be reliably predicted and controlled,. However, the complexity paradigm asserts that because complex systems are open, non-linear, and populated by agents with free will, the future is fundamentally unknowable,.

Here is how the sources in the collection understand and operationalize the reality that the future contains no facts, only possibilities, probabilities, and subjective views:

1. The Ontological Reality of an Unknowable Future The sources firmly agree that the future cannot yield facts because it is pure potential. Dee Hock explicitly states that the future “does not exist in actuality… but has only the potential to exist”. Nassim Nicholas Taleb emphasizes that the future is completely opaque and governed by unpredictable, high-impact “Black Swans,” making attempts to forecast it using standard historical data a dangerous deception,. Paul Cilliers notes that complex systems are “incompressible” and historically contingent; because we must inevitably exclude some variables from our models, and because those excluded variables can multiply non-linearly into massive effects, we are fundamentally incapable of calculating the system’s exact future state,.

2. Aleatory vs. Epistemic Uncertainty (Probabilities and Lack of Data) The absence of “facts” or “data” about the future maps directly to how the sources define aleatory uncertainty,. David Spiegelhalter and Roger James define aleatory uncertainty as the physical, unavoidable randomness regarding the future—what we cannot know because it relies on stochastic mechanisms and simply hasn’t happened yet,,. This is contrasted with epistemic uncertainty, which is our subjective ignorance regarding facts that currently exist but are missing from our mental models,,.

Furthermore, David Spiegelhalter notes that outside of subatomic physics, objective “probability does not exist” in the real world. Instead, probability is merely a constructed, subjective mathematical tool that humans use to express their lack of knowledge about these future possibilities.

3. The Reliance on Opinions, Views, and Intuitions Because there are no objective facts about the future, the sources emphasize that human beings must rely on subjective constructions to navigate it.

• Appreciative Settings and Worldviews: Geoffrey Vickers and Peter Checkland highlight that we view the world through “appreciative settings” and “Weltanschauungen” (worldviews),. Because there is no single objective future to uncover, differing opinions are not errors; they are valid, subjective lenses required to make sense of a complex reality.
• Problems vs. Puzzles: Reg Revans argues that while “puzzles” have factual answers that experts can discover, the future presents us with “problems”—situations with no single right answer, where different reasonable people will advocate completely different actions based on their subjective intuitions and values.
• The Birth of Ethics: Rather than viewing the lack of future facts as a cause for despair, Paul Cilliers points out that this uncertainty is the very birthplace of ethics. If we could calculate the future perfectly with objective data, decision-making would be automated; because we cannot, we are forced to make subjective choices and take moral responsibility for actions taken in the dark.

4. From “Predict and Control” to “Prepare and Adapt” If there are no facts about the future, organizations must radically alter their management strategies:

• Prepare, don’t plan: Dee Hock advises that because the future is indeterminate, organizations must stop wasting the present trying to plan a fixed future and instead focus on “preparing, not planning for surprise” by maximizing structural fluidity.
• Make it happen: Russ Ackoff argues that the traditional “predict and prepare” paradigm is obsolete. Because the future cannot be forecasted, “the best way to predict the future is to make it happen” through continuous, participative design of the desired possibilities.
• Probe and Tinker: Dave Snowden advises abandoning the “idealist fallacy” of designing future states. Instead, leaders should manage the “evolutionary potential of the present” using small, “safe-to-fail probes” to see what possibilities actually emerge,,.
• Antifragility: Taleb champions abandoning prediction entirely. Instead of trying to guess the probabilities of future chaos, we must build “antifragile” systems that are positioned to actively benefit and grow from the unknown, volatile possibilities that time will inevitably bring,,.

33 Systems Laws and V2-Combined

How do the 33 systems laws and principles in Hoverstadt align to the authors in V2-Combined

The 33 systems laws and principles in Hoverstadt’s Grammar of Systems are deeply rooted in the work of many authors featured in the V2-Combined collection. Hoverstadt explicitly acknowledges that these laws were developed and codified by the pioneers of systems thinking.

The following alignment shows how these laws map to the authors and core concepts found in the V2-Combined text:

Stafford Beer

Hoverstadt heavily relies on Beer’s work, particularly for the structural and operational laws of organizations.

• Viability Principle: Directly attributed to Beer, stating that a system’s viability depends on balancing autonomy with cohesion and stability with change.

• Fractal Principle: Derived from Beer’s Viable System Model (VSM), asserting that systems replicate their own functional form at every level.

• System Stability Principle: Defined as a pattern of relationships stable enough to be recognized over time, a concept central to Beer’s diagnostic approach.

• POSIWID (Purpose Of a System Is What It Does): While categorized under the Law of Sufficient Complexity, Hoverstadt explicitly references Beer’s dictum that the purpose is what is actually achieved, not intended.

Ross Ashby

Ashby’s work forms the “skeleton of linked concepts” for much of the Grammar’s laws on control and variety.

• Law of Requisite Variety: The core law stating that only variety can destroy variety; a regulator must match the variety of the system it manages.

• 1st and 2nd Circular Causality Principles: Ashby’s principles for positive feedback (driving state change) and negative feedback (driving stability).

• The Two Black Box Principles: Based on Ashby’s work, these state that outputs are predictable even if internal workings are unknown.

• Conant-Ashby Theorem: Stating that every good regulator of a system must be a model of that system.

Humberto Maturana

Maturana’s biological systems theories align with the laws concerning how systems interact with their environment.

• Conservation of Adaptation Principle: Links directly to Maturana’s concept of “structural coupling,” where the only constant is the change in the relationship between a system and its environment.

• Self-Organising Principle: While attributed to Smuts, Hoverstadt links its mechanism (the closure of relational loops) to Maturana and Varela’s Autopoiesis.

Warren McCulloch

McCulloch’s work on neural networks provides the foundation for information and structural laws.

• Redundancy of Potential Command Principle: Asserts that effectiveness in complex situations depends on an adequate concatenation of information.

• Law of Sufficient Complexity: States that a complex system constitutes its own simplest behavioral description.

Christopher Alexander

Hoverstadt uses Alexander’s design principles to explain how systems structure themselves to manage complexity.

• Power Structuration Theorem: Links to Alexander’s definition of “organic order” as a perfect balance between the needs of the parts and the needs of the whole.

• Root Structuration Theorem: References Alexander’s Notes on the Synthesis of Form as the basis for structuring systems into sub-systems to reduce exponential complexity.

Other Key Author Alignments

• Jan Smuts / G.H. Lewes: Together they provide the Principle of Emergence, asserting that the whole is more than the sum of its parts.

• Spencer-Brown: Provides the Law of Calling (making distinctions to create boundaries) and the Law of Crossing (crossing a boundary constitutes a change of state).

• Claude Shannon: Hoverstadt notes that the Law of Requisite Variety is similar to, but goes significantly beyond, Shannon’s 10th theorem regarding telecommunications variety.

• Russ Ackoff: His definition of “Synthesis” is the basis for Holism, moving up a level to understand a system by its role in the containing whole.

• Donella Meadows: Her “12 leverage points” are used as a miscellany tool for high-leverage intervention in systems.

• Peter Checkland: His Soft Systems Methodology (SSM) is used to apply the Law of Calling and Law of Crossing when defining system boundaries from multiple perspectives.

Case Study - Why Some National Challenges Do Not Respond to Reform

Can you apply the Hoverstadt's 33 laws to the paper why some national

The core premise of the paper “Why Some National Challenges Do Not Respond to Reform” is that persistent issues like structural unemployment are not failures of intent or data, but failures of structural design. This aligns directly with Patrick Hoverstadt’s 33 Systems Laws and Principles, which provide the theoretical “grammar” to explain why these patterns persist and how they can be realigned.

The following alignment applies Hoverstadt’s laws to the reasoning chain presented in the article:

1. The Structural Basis of Persistence

• Law of Sufficient Complexity: Hoverstadt states that “The system does what it does because it is what it is”. The article echoes this, arguing that structural unemployment is an inescapable output of an economy that is “structurally prepared to build” for only 20% absorption while demographic inflow automatically expands.

• Feedback Dominance Theorem: This law asserts that loops with strong feedback will “take you where they take you,” regardless of the size of the input. The article traces a “closing chain” of reinforcing loops—from household formation to STEM density to production depth—that keeps the national trajectory consistent despite energetic but episodic reforms.

• Principle of Emergence: Hoverstadt defines emergence as a property of a system not found in its parts. The article treats unemployment as an emergent property of interlinked silos (demography, education, industry, and social formation) rather than an isolated “employment problem”.

2. Managing Variety and Absorption

• Law of Requisite Variety: A regulator must match the variety of the system it manages. The article highlights an “Absorption Gap”: the economy’s “variety” (absorption capacity) is overwhelmed by the “variety” of entrants generated by births eighteen years earlier.

• Scaling Stasis Principle: “The more complex a system is, the more constraints it has”. The article identifies specific structural constraints, such as a 1% STEM graduate distribution compared to 30–35% in industrial nations, which limits the system’s ability to scale production.

• Steady State Principle: This law states that for a system to be in equilibrium, its sub-systems must also be in equilibrium. The article demonstrates that national production cannot reach a “steady state” if the sub-system of the household fails to align output with economic capacity.

3. Time Horizons and Stability

• Relaxation Time Principle: A system shocked at intervals shorter than its recovery time may never stabilize. The article notes that reversing a 40-year structural slope requires a 10-to-15-year commitment, but is often interrupted by three-year ministerial terms and electoral cycles.

• System Stability Principle: Systems are patterns recognizable over time. The article’s method involves plotting Behaviour Over Time curves to move beyond “event-based” reactions and recognize the persistent structural pattern.

• Self-Organised Criticality: This describes systems whose dynamics drive them toward collapse. The 2043 Forecast in the article—where unemployment is projected to double—is a diagnostic of a system reaching a critical threshold due to current demographic-production imbalances.

4. Governance and Shared Seeing

• Conant-Ashby Theorem: “Every good regulator of a system must be a model of that system”. The article calls for “shared seeing” and “placing the pattern in the room” so that stakeholders (Ministers of Agriculture, Trade, Finance, and Education) have a shared, accurate mental model of the structure they are trying to regulate.

• Redundancy of Potential Command Principle: Effectiveness in complex situations depends on “bringing together the right mix of information”. The article emphasizes that “no single office moves it alone,” requiring aligned responsibility across portfolios to pull structural levers simultaneously.

• Darkness Principle: “There is always something about a system you can’t know”. The paper addresses this by calling for leadership that surfaces “blind spots” without defensiveness or humiliation.

5. Structural Integrity and Fractals

• Fractal Principle: Systems replicate their own form at different levels. The article observes that household environments model the long-horizon planning and discipline required for technical fields, effectively “seeding” the structural capacity of the national economy at the micro-level.

• Root Structuration Theorem: This provides a guide for dividing complex systems into sub-systems to reduce instability. The article proposes “Cluster Formation” (concentrated ecosystems where technical training is embedded in production sites) as the structural spine to manage the complexity of sector scaling.

• Law of Reciprocity of Connections: If A connects to B, then B connects to A. The article illustrates this through “Social Feedback,” where limited economic absorption reshapes household modelling, which in turn influences the future capability of labour entrants.