Framework Composition: How to Combine Models Without Confusion

Last Updated June 9, 2026

Framework composition is the disciplined practice of combining models without turning them into a confusing pile of diagrams, templates, checklists, and terminology. A framework can clarify a problem. Several frameworks can clarify a larger problem. But when models are combined without purpose, sequence, hierarchy, or governance, they can create contradiction instead of insight.

Framework Composition: How to Combine Models Without Confusion examines how writers, strategists, educators, researchers, public institutions, content teams, and analysts can combine frameworks responsibly. It focuses on purpose, scope, model role, conceptual fit, sequencing, translation, conflict detection, audience pathway, evidence alignment, governance, and review. The article treats framework composition as a form of knowledge architecture: a way to connect models without flattening their differences or overloading the reader.

This article explains how framework composition supports complex communication across strategy, education, policy, sustainability, science communication, technology communication, public reasoning, systems explanation, and content architecture. It examines when frameworks should be layered, sequenced, nested, translated, or kept separate. It also includes computational workflows for auditing model compatibility, conceptual overlap, sequencing clarity, audience burden, evidence alignment, contradiction risk, and governance priority.

Why Framework Composition Matters

Framework composition matters because complex problems often require more than one model. A single framework may explain audience need, but not policy context. Another may explain strategy, but not evidence strength. Another may explain systems behavior, but not public participation. Another may clarify measurement, but not values or tradeoffs. Combining frameworks can help, but only if the composition is deliberate.

Without composition discipline, frameworks can collide. A persuasion model may be combined with a public reasoning model in a way that confuses advocacy with deliberation. A strategic model may be combined with a systems model in a way that hides feedback loops. A decision matrix may be combined with a values framework in a way that makes contested judgment appear objective. A scenario model may be combined with a forecast in a way that makes plausible futures look like predictions.

Framework composition helps communicators keep models in the right role. It clarifies which framework diagnoses the problem, which organizes evidence, which structures options, which explains systems behavior, which supports public reasoning, which guides action, and which governs review.

The goal is not to use more frameworks. The goal is to use fewer frameworks better, and to combine them only when the combination adds clarity.

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What Framework Composition Is

Framework composition is the structured combination of two or more models so that each model contributes a distinct explanatory, analytical, strategic, educational, or governance function. It is not a collage of diagrams. It is a design process for connecting models while preserving their boundaries, assumptions, strengths, and limits.

A composed framework may combine a systems map with a decision matrix, a stakeholder map with a theory of change, a scenario framework with an early warning system, a message house with evidence architecture, or a public reasoning framework with a participation model. The composition works when users can understand why each model is included and how the models relate.

Framework composition is successful when the combined structure is easier to use than the separate pieces would be on their own.

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Framework Stacking vs Framework Composition

Framework stacking happens when multiple models are placed next to one another without integration. The result may look comprehensive, but it often creates repetition, conflicting terms, unclear sequence, and unnecessary cognitive load. Framework composition is different. It defines how the models relate, where they overlap, where they differ, and how users move between them.

Stacking says, “Here are five useful models.” Composition says, “Use this model first to define the problem, this model second to map the system, this model third to evaluate options, and this model fourth to govern review.”

More frameworks do not automatically create deeper analysis. They can also create more ways to misunderstand the problem.

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Start With Model Purpose

Every framework composition should begin with purpose. The first question is not, “Which models do we like?” The first question is, “What kind of thinking does this problem require?” Different frameworks support different forms of reasoning: diagnosis, explanation, comparison, decision-making, communication, participation, measurement, forecasting, scenario thinking, learning, governance, or ethical review.

When purpose is unclear, models are chosen because they are familiar, popular, visually appealing, or easy to explain. That creates a mismatch between model and task. A SWOT analysis may help structure an initial strategic scan, but it will not explain feedback loops. A message house may clarify communication hierarchy, but it will not evaluate uncertainty. A decision matrix may compare options, but it will not reveal hidden values unless values are explicitly modeled.

Purpose protects framework composition from becoming model collecting.

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Scope, Boundaries, and Level of Analysis

Frameworks often fail in composition because they operate at different levels of analysis. One model may describe individual motivation. Another may describe organizational capability. Another may describe system structure. Another may describe public legitimacy. Combining these models can be powerful, but only if their levels are explicit.

A model designed for audience segmentation should not be treated as a systems model. A systems map should not be treated as a message strategy. A policy framework should not be treated as a brand positioning tool. A measurement framework should not be treated as a theory of change unless it includes causal assumptions.

Composition becomes clearer when each model is labeled by level, purpose, and boundary before it is connected to another model.

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The Roles Frameworks Can Play

Frameworks should not all do the same job. In a strong composition, one framework may define the problem, another may explain the system, another may evaluate options, another may organize communication, and another may manage governance. When those roles are explicit, the composition becomes easier to follow.

Framework role clarity also prevents overextension. A framework becomes dangerous when it is asked to do work it was not designed to do. A persona can help explore audience needs, but it should not be treated as evidence that all members of a group think the same way. A scoring matrix can help compare options, but it should not be treated as neutral if the criteria are value-laden.

Framework composition becomes much easier when each model has one primary job.

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Common Framework Composition Patterns

Frameworks can be combined in several ways. The right pattern depends on the problem, audience, content format, and decision need. Some frameworks should be sequenced. Others should be layered. Others should be nested. Others should be connected through translation tables or governance checkpoints.

The most common mistake is using a hybrid without documenting what was combined, why it was combined, and what was lost in the combination.

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Translation Interfaces Between Models

A translation interface explains how one framework hands information to another. This is often the missing piece in framework composition. Teams may complete a stakeholder map, a system map, a scenario set, and a decision matrix, but fail to explain how insights from one artifact shape the next.

For example, a systems map may identify feedback loops. Those loops can become risks in a scenario framework. Scenario risks can become criteria in a decision matrix. Decision criteria can become public reasoning questions. Public reasoning questions can become content pathways. This chain needs an explicit translation layer.

Translation interfaces make framework composition reusable because they show how thinking moves from one model to another.

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Conflict, Contradiction, and Conceptual Drift

Frameworks can conflict. One model may assume rational choice while another emphasizes bounded rationality. One may prioritize persuasion while another prioritizes deliberation. One may focus on individual behavior while another focuses on structural constraint. One may reduce complexity to scores while another warns against false precision.

Conflict is not always a problem. It can reveal important tensions. The problem arises when conflicts are hidden. A strong composition names contradictions and decides whether to resolve them, sequence them, separate them, or use them as points of analysis.

Conceptual drift occurs when a framework changes meaning as it moves across contexts. Composition governance should track that drift before the model becomes misleading.

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Audience Pathways and Cognitive Load

Composite frameworks can become cognitively heavy. Even when each model is useful, the combined structure may overwhelm the audience. Readers may not know where to start, which diagram matters most, which terms are repeated, or whether the composition is a method, a report, a strategy, a teaching tool, or a decision workflow.

Audience pathways help solve this problem. A pathway defines how users move through the composition. It may begin with a plain-language overview, then a problem map, then a systems explanation, then a decision layer, then a governance layer. Different audiences may need different entrances into the same composite framework.

A framework that is technically elegant but impossible to follow has failed as a communication framework.

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Evidence Alignment Across Frameworks

Composite frameworks must keep evidence aligned across models. A scenario framework may rely on weak signals. A decision framework may rely on quantitative performance estimates. A public reasoning framework may rely on stakeholder input and values. A systems explanation may rely on causal interpretation. These evidence types are not interchangeable.

Evidence alignment means showing what kind of evidence each model uses, how confident the analysis is, and where evidence is missing. It also means preventing the authority of one model from being transferred to another without justification. A strong dataset in one layer does not automatically validate assumptions in another layer.

Evidence alignment is especially important when composite frameworks are used for public communication, policy explanation, or strategic decisions.

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Governance for Composite Frameworks

Composite frameworks require governance because each component can change. Evidence can become stale, assumptions can drift, terminology can shift, decision criteria can be revised, stakeholder conditions can change, and linked articles can move. Without governance, a composite framework may look stable while its internal parts become inconsistent.

Governance should define ownership, review dates, dependencies, update triggers, evidence status, audience pathway status, and conflict resolution rules. It should also identify which parts of the composition are reusable templates and which parts are context-specific interpretations.

Composite frameworks should be treated as living knowledge systems, not one-time diagrams.

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Practical Uses of Framework Composition

Framework composition can support article maps, educational pathways, policy explainers, strategic planning, sustainability communication, public reasoning, decision support, technical communication, content audits, research communication, and governance systems.

Framework composition is especially useful when a single model cannot responsibly carry the full explanatory burden.

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The Limits of Framework Composition

Framework composition has limits. Some models should not be combined. Some models operate from incompatible assumptions. Some combinations create false precision. Some hybrids erase important differences between evidence, values, systems, strategy, persuasion, and public reasoning. Some compositions become too complicated for their intended audience.

A composed framework is not automatically better than a simple one. Simplicity is sometimes the responsible choice. The test is whether the composition improves understanding, judgment, action, or governance. If it does not, it should be simplified or separated.

The strongest framework composition is often restrained. It combines only what needs to be combined.

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Relationship to Public Reasoning, Systems Explanation, Decision Science, and Foresight

Framework composition connects naturally to public reasoning, systems explanation, decision science, strategic foresight, evidence architecture, content governance, and educational scaffolding. Each of these areas often requires more than one model, but each also risks confusion when models are combined without design discipline.

Framework composition is the design layer that helps these models work together without collapsing into one another.

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How Framework Composition Supports Content Frameworks

Framework composition supports content frameworks by helping a knowledge system connect multiple models across articles, templates, repositories, datasets, visuals, governance queues, and audience pathways. It allows a site to build depth without making each article carry every possible framework at once.

For a knowledge platform, composition can guide when to link to another article, when to summarize a model, when to reuse a template, when to create a companion repository, when to add a governance queue, and when to split an overloaded article into a separate page.

In a Catalyst Canvas-ready system, framework composition can become structured data: model role, input, output, boundary, assumption, evidence source, dependency, conflict flag, review owner, and update status.

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Ethics, Power, and Composite Models

Composite frameworks carry ethical risk because they can make decisions appear more comprehensive, neutral, or authoritative than they are. When many models are combined, the resulting structure can look rigorous even if important assumptions, affected publics, values, or evidence gaps are hidden.

Ethical framework composition requires transparency about why models were selected, what each model contributes, what each model leaves out, whose perspectives shaped the composition, and how conflicts are handled. It should also avoid using model complexity to overwhelm audiences or shield conclusions from challenge.

• Selection transparency: Explain why each framework is included.
• Boundary honesty: State what each model can and cannot explain.
• Value visibility: Identify values embedded in criteria, pathways, and recommendations.
• Stakeholder visibility: Show who is represented, affected, excluded, or empowered.
• Evidence discipline: Connect model claims to sources, methods, confidence, and limits.
• Conflict disclosure: Name contradictions between models rather than hiding them.
• Audience respect: Avoid complexity that prevents scrutiny.
• Review discipline: Update composite frameworks when evidence, context, or dependencies change.

Composite frameworks should clarify judgment, not disguise it.

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Mathematics, Computation, and Modeling

Framework composition can be supported by computational audits that score model-role clarity, purpose fit, boundary clarity, sequencing clarity, conceptual compatibility, translation quality, evidence alignment, audience burden, conflict risk, and governance readiness. These scores do not determine whether a composition is correct. They identify where review is needed.

A framework composition quality score can average core composition layers:

A confusion risk score can combine low role clarity, high audience burden, low translation quality, and high conflict risk:

A review priority score can combine low composition quality and high confusion risk:

Computational audits should support editorial judgment, not replace it. Their value is in surfacing composition risks before confusing models reach readers.

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Python Workflow: Framework Composition Audit

The Python workflow below evaluates composite frameworks by purpose fit, model role clarity, boundary clarity, sequencing clarity, translation quality, evidence alignment, governance readiness, audience burden, conflict risk, and review status. The companion repository version extends this into a Catalyst Canvas-ready module with schemas, package-style Python, tests, JSON exports, Canvas cards, shared contracts, and governance queues.

# framework_composition_audit.py
# Dependency-light workflow for framework composition governance.

from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
import csv
from statistics import mean

ARTICLE_ROOT = Path(__file__).resolve().parents[1]
TABLES = ARTICLE_ROOT / "outputs" / "tables"


@dataclass
class FrameworkCompositionItem:
    item: str
    composition_type: str
    description: str
    purpose_fit: float
    role_clarity: float
    boundary_clarity: float
    sequence_clarity: float
    translation_quality: float
    evidence_alignment: float
    governance_readiness: float
    audience_burden: float
    conflict_risk: float
    owner: str
    status: str

    def quality_score(self) -> float:
        return mean([
            self.purpose_fit,
            self.role_clarity,
            self.boundary_clarity,
            self.sequence_clarity,
            self.translation_quality,
            self.evidence_alignment,
            self.governance_readiness,
        ])

    def confusion_risk(self) -> float:
        return min(
            1.0,
            (1 - self.role_clarity) * 0.25
            + self.audience_burden * 0.25
            + (1 - self.translation_quality) * 0.25
            + self.conflict_risk * 0.25,
        )

    def review_priority_score(self) -> float:
        return min(
            1.0,
            (1 - self.quality_score()) * 0.50
            + self.confusion_risk() * 0.50,
        )

    def review_priority(self) -> str:
        if self.status == "revise" or self.review_priority_score() >= 0.45:
            return "high"
        if self.status == "review" or self.confusion_risk() >= 0.40:
            return "medium"
        return "standard"


def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    if not rows:
        raise ValueError(f"No rows to write: {path}")
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys()))
        writer.writeheader()
        writer.writerows(rows)


def main() -> None:
    items = [
        FrameworkCompositionItem("Strategy diagnosis sequence", "sequence", "Uses PESTLE for external context SWOT for synthesis and OKRs for execution.", 0.84, 0.82, 0.78, 0.86, 0.76, 0.72, 0.70, 0.34, 0.18, "strategy", "active"),
        FrameworkCompositionItem("Public reasoning composite", "governed hybrid", "Combines claims evidence values tradeoffs stakeholder visibility participation and accountability.", 0.82, 0.78, 0.76, 0.74, 0.80, 0.78, 0.76, 0.42, 0.22, "governance", "active"),
        FrameworkCompositionItem("Scenario decision bridge", "translation", "Translates scenario drivers into decision criteria signposts and robust options.", 0.80, 0.74, 0.72, 0.76, 0.84, 0.70, 0.68, 0.38, 0.24, "foresight", "review"),
        FrameworkCompositionItem("Overloaded content model", "stack", "Combines too many frameworks without clear sequence audience pathway or governance owner.", 0.48, 0.38, 0.44, 0.36, 0.32, 0.50, 0.40, 0.82, 0.76, "editorial", "revise"),
        FrameworkCompositionItem("Systems to message pathway", "translation", "Uses systems map outputs to define public explanation priorities and message boundaries.", 0.78, 0.76, 0.80, 0.72, 0.74, 0.70, 0.68, 0.36, 0.20, "communication", "active"),
    ]

    rows = []

    for item in items:
        rows.append({
            "item": item.item,
            "composition_type": item.composition_type,
            "description": item.description,
            "purpose_fit": item.purpose_fit,
            "role_clarity": item.role_clarity,
            "boundary_clarity": item.boundary_clarity,
            "sequence_clarity": item.sequence_clarity,
            "translation_quality": item.translation_quality,
            "evidence_alignment": item.evidence_alignment,
            "governance_readiness": item.governance_readiness,
            "audience_burden": item.audience_burden,
            "conflict_risk": item.conflict_risk,
            "quality_score": round(item.quality_score(), 3),
            "confusion_risk": round(item.confusion_risk(), 3),
            "review_priority_score": round(item.review_priority_score(), 3),
            "owner": item.owner,
            "status": item.status,
            "review_priority": item.review_priority(),
        })

    rows = sorted(rows, key=lambda row: row["review_priority_score"], reverse=True)
    write_csv(TABLES / "framework_composition_audit.csv", rows)

    governance_queue = [
        row for row in rows
        if row["review_priority"] != "standard"
    ]

    write_csv(TABLES / "framework_composition_governance_queue.csv", governance_queue)

    print("Framework composition audit complete.")


if __name__ == "__main__":
    main()

This workflow helps identify overloaded framework stacks, unclear model roles, weak translation interfaces, high audience burden, conceptual conflicts, and composite frameworks that need review before publication or reuse.

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R Workflow: Framework Composition Diagnostics

The R workflow below creates a framework composition dataset, calculates quality score, confusion risk, review priority score, and review status, then exports summary tables and base R plots. It is intentionally portable and uses only base R.

# framework_composition_report.R
# Base R workflow for framework composition diagnostics.

args <- commandArgs(trailingOnly = FALSE)
file_arg <- grep("^--file=", args, value = TRUE)

if (length(file_arg) > 0) {
  script_path <- normalizePath(sub("^--file=", "", file_arg[1]), mustWork = TRUE)
  article_root <- normalizePath(file.path(dirname(script_path), ".."), mustWork = TRUE)
} else {
  article_root <- getwd()
}

setwd(article_root)

tables_dir <- file.path(article_root, "outputs", "tables")
figures_dir <- file.path(article_root, "outputs", "figures")

if (!dir.exists(tables_dir)) {
  dir.create(tables_dir, recursive = TRUE)
}

if (!dir.exists(figures_dir)) {
  dir.create(figures_dir, recursive = TRUE)
}

items <- data.frame(
  item = c(
    "Strategy diagnosis sequence",
    "Public reasoning composite",
    "Scenario decision bridge",
    "Overloaded content model",
    "Systems to message pathway"
  ),
  composition_type = c(
    "sequence",
    "governed hybrid",
    "translation",
    "stack",
    "translation"
  ),
  purpose_fit = c(0.84, 0.82, 0.80, 0.48, 0.78),
  role_clarity = c(0.82, 0.78, 0.74, 0.38, 0.76),
  boundary_clarity = c(0.78, 0.76, 0.72, 0.44, 0.80),
  sequence_clarity = c(0.86, 0.74, 0.76, 0.36, 0.72),
  translation_quality = c(0.76, 0.80, 0.84, 0.32, 0.74),
  evidence_alignment = c(0.72, 0.78, 0.70, 0.50, 0.70),
  governance_readiness = c(0.70, 0.76, 0.68, 0.40, 0.68),
  audience_burden = c(0.34, 0.42, 0.38, 0.82, 0.36),
  conflict_risk = c(0.18, 0.22, 0.24, 0.76, 0.20),
  owner = c("strategy", "governance", "foresight", "editorial", "communication"),
  status = c("active", "active", "review", "revise", "active"),
  stringsAsFactors = FALSE
)

items$quality_score <- rowMeans(items[, c(
  "purpose_fit",
  "role_clarity",
  "boundary_clarity",
  "sequence_clarity",
  "translation_quality",
  "evidence_alignment",
  "governance_readiness"
)])

items$confusion_risk <- pmin(
  1,
  (1 - items$role_clarity) * 0.25 +
    items$audience_burden * 0.25 +
    (1 - items$translation_quality) * 0.25 +
    items$conflict_risk * 0.25
)

items$review_priority_score <- pmin(
  1,
  (1 - items$quality_score) * 0.50 +
    items$confusion_risk * 0.50
)

items$review_priority <- ifelse(
  items$status == "revise" | items$review_priority_score >= 0.45,
  "high",
  ifelse(
    items$status == "review" | items$confusion_risk >= 0.40,
    "medium",
    "standard"
  )
)

items <- items[order(items$review_priority_score, decreasing = TRUE), ]

write.csv(
  items,
  file.path(tables_dir, "framework_composition_summary.csv"),
  row.names = FALSE
)

governance_queue <- items[items$review_priority != "standard", ]

write.csv(
  governance_queue,
  file.path(tables_dir, "framework_composition_governance_queue.csv"),
  row.names = FALSE
)

png(file.path(figures_dir, "framework_composition_confusion_risk.png"), width = 1200, height = 700)
barplot(
  items$confusion_risk,
  names.arg = items$item,
  las = 2,
  ylab = "Confusion risk",
  main = "Framework Composition Confusion Risk"
)
grid()
dev.off()

png(file.path(figures_dir, "framework_composition_quality.png"), width = 1000, height = 700)
barplot(
  items$quality_score,
  names.arg = items$item,
  las = 2,
  ylab = "Composition quality score",
  main = "Framework Composition Quality"
)
grid()
dev.off()

print(items[, c("item", "composition_type", "quality_score", "confusion_risk", "review_priority_score", "review_priority")])

This workflow turns framework composition into an auditable content-governance artifact. It helps identify where combined models need clearer roles, stronger boundaries, better translation interfaces, less audience burden, and stronger governance.

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GitHub Repository

The companion repository for this article supports framework composition as a Catalyst Canvas-ready content-framework module. It includes model-role audits, purpose-fit scoring, boundary clarity, sequence clarity, translation quality, evidence alignment, governance readiness, audience burden, conflict risk, JSON schemas, package-style Python, tests, Canvas card outputs, markdown governance queues, synthetic datasets, SQL views, documentation, and multi-language scaffolds for composite framework governance.

articles/framework-composition-how-to-combine-models-without-confusion/
├── canvas/
│   ├── canvas_manifest.json
│   ├── input_schema.json
│   ├── output_schema.json
│   ├── canvas_cards.json
│   └── governance_queue.json
├── html/
├── css/
├── php/
├── java/
├── python/
│   ├── framework_composition_canvas/
│   │   ├── __init__.py
│   │   ├── __main__.py
│   │   ├── cli.py
│   │   ├── models.py
│   │   ├── scoring.py
│   │   ├── validation.py
│   │   ├── governance.py
│   │   └── exporters.py
│   ├── tests/
│   │   └── test_framework_composition_canvas.py
│   └── run_framework_composition_canvas_audit.py
├── r/
│   ├── framework_composition_report.R
│   └── run_all_framework_composition_workflows.R
├── sql/
│   ├── canvas_schema.sql
│   └── canvas_queries.sql
├── docs/
├── data/
├── outputs/
│   ├── figures/
│   ├── json/
│   ├── markdown/
│   └── tables/
├── notebooks/
├── shared/
└── README.md

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Why Framework Composition Needs Discipline

Framework composition needs discipline because models are powerful. They shape what people notice, how they reason, what evidence they accept, what values become visible, what options seem possible, and what decisions feel justified. Combining models multiplies that power.

Used poorly, framework composition creates jargon, redundancy, false precision, hidden assumptions, and conceptual confusion. Used well, it creates a pathway through complexity. It shows which model does what, how models connect, where they conflict, what evidence supports them, what audience burden they create, and how they should be maintained.

Framework composition is not about building the biggest model. It is about designing the smallest useful combination of models that helps people understand, decide, communicate, and govern responsibly over time.

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Related Articles

• Public Reasoning and Framework Design
• Systems Explanation Frameworks
• Frameworks for Strategic Foresight and Scenario Thinking
• Content Audits and Framework Governance
• Editorial Metadata and Content Systems
• Scaling Knowledge Through Frameworks

Further Reading

• OECD (2017) Systems Approaches to Public Sector Challenges: Working with Change. Paris: OECD Publishing. Available at: https://www.oecd.org/en/publications/systems-approaches-to-public-sector-challenges_9789264279865-en.html
• OECD Observatory of Public Sector Innovation (n.d.) Systems Approaches. Paris: OECD OPSI. Available at: https://oecd-opsi.org/work-areas/systems-approaches/
• National Academies of Sciences, Engineering, and Medicine (2017) Communicating Science Effectively: A Research Agenda. Washington, DC: The National Academies Press. Available at: https://www.nationalacademies.org/publications/23674/communicating-science-effectively-a-research-agenda
• National Academies of Sciences, Engineering, and Medicine (2016) Science Literacy: Concepts, Contexts, and Consequences. Washington, DC: The National Academies Press. Available at: https://www.nationalacademies.org/publications/23595/science-literacy-concepts-contexts-and-consequences
• Meadows, D.H. (1999) Leverage Points: Places to Intervene in a System. The Sustainability Institute. Available at: https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/
• Argyris, C. and Schön, D.A. (1978) Organizational Learning: A Theory of Action Perspective. Reading, MA: Addison-Wesley. Available at: https://archive.org/details/organizationalle00chri
• Meadows, D.H. (2008) Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing.
• Sterman, J.D. (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin/McGraw-Hill.
• Checkland, P. (1981) Systems Thinking, Systems Practice. Chichester: Wiley.
• Checkland, P. and Scholes, J. (1990) Soft Systems Methodology in Action. Chichester: Wiley.
• Simon, H.A. (1996) The Sciences of the Artificial. 3rd edn. Cambridge, MA: MIT Press.
• Rittel, H.W.J. and Webber, M.M. (1973) ‘Dilemmas in a general theory of planning’, Policy Sciences, 4(2), pp. 155–169.
• Ostrom, E. (2005) Understanding Institutional Diversity. Princeton, NJ: Princeton University Press.
• Stone, D. (2012) Policy Paradox: The Art of Political Decision Making. 3rd edn. New York: W.W. Norton.

References

• OECD (2017) Systems Approaches to Public Sector Challenges: Working with Change. Paris: OECD Publishing. Available at: https://www.oecd.org/en/publications/systems-approaches-to-public-sector-challenges_9789264279865-en.html
• OECD Observatory of Public Sector Innovation (n.d.) Systems Approaches. Paris: OECD OPSI. Available at: https://oecd-opsi.org/work-areas/systems-approaches/
• National Academies of Sciences, Engineering, and Medicine (2017) Communicating Science Effectively: A Research Agenda. Washington, DC: The National Academies Press. Available at: https://www.nationalacademies.org/publications/23674/communicating-science-effectively-a-research-agenda
• National Academies of Sciences, Engineering, and Medicine (2016) Science Literacy: Concepts, Contexts, and Consequences. Washington, DC: The National Academies Press. Available at: https://www.nationalacademies.org/publications/23595/science-literacy-concepts-contexts-and-consequences
• Meadows, D.H. (1999) Leverage Points: Places to Intervene in a System. The Sustainability Institute. Available at: https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/
• Argyris, C. and Schön, D.A. (1978) Organizational Learning: A Theory of Action Perspective. Reading, MA: Addison-Wesley. Available at: https://archive.org/details/organizationalle00chri
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