Problem Framing and Problem Definition

Last Updated June 4, 2026

Problem framing and problem definition are foundational processes in strategic thinking that determine how challenges are constructed, interpreted, and ultimately addressed. Before solutions can be generated, evaluated, optimized, or implemented, decision-makers must establish what the problem actually is. In complex environments, this task is never trivial. Problems are not simply objective entities waiting to be discovered. They are constructed through cognitive, institutional, political, analytical, and systems-level processes that shape how reality is perceived and acted upon.

Strategic reasoning therefore begins not with answers, but with the structure of the question itself. The way a problem is framed determines the space of possible solutions. It defines what counts as relevant information, which causal relationships are considered, which stakeholders are visible, which constraints are treated as fixed, and which interventions appear viable. In this sense, problem framing is not a preliminary step before strategy. It is one of the primary determinants of strategic direction.

At its deepest level, framing is a way of organizing reality for action. A frame is not merely a description. It is a selective model of what matters, who matters, how causality works, where boundaries lie, what tradeoffs are acceptable, and what success would mean. That is why two actors can confront the same situation and generate entirely different strategic responses. They are not merely disagreeing about solutions. They are often working from different constructions of the problem itself.

Rittel and Webber’s classic treatment of wicked problems, Tversky and Kahneman’s work on framing, Deborah Stone’s account of policy narratives, Herbert Simon’s work on bounded rationality, and systems thinking traditions all reinforce the same lesson: strategy depends profoundly on how a problem is represented before action begins. If the representation is weak, even sophisticated analysis may produce strategically shallow answers. If the representation is stronger, the entire possibility space changes.

This article examines problem framing and problem definition as core capabilities in strategic ideation. It explains why problems are constructed rather than simply discovered, how frames shape solution spaces, why institutional power influences problem definition, how complex systems complicate framing, which advanced techniques improve framing quality, how organizations can build framing capability, and how weak framing produces strategic failure. It also includes practical audit tools, mathematical representations, reproducible modeling examples, and a companion GitHub repository structure for professional strategic analysis.

Problems Are Constructed, Not Discovered

In classical decision theory, problems are often treated as clearly defined optimization challenges. A decision-maker is assumed to know the goal, the available alternatives, the relevant constraints, and the criteria for evaluating success. This assumption rarely holds in real-world strategic contexts. Many strategic problems are ambiguous, contested, dynamic, and embedded in systems whose boundaries are uncertain.

Horst Rittel and Melvin Webber described such challenges as wicked problems: problems that are difficult to define, interconnected with other problems, shaped by stakeholder disagreement, and resistant to final resolution. In wicked problem contexts, the problem does not arrive with a stable boundary, a settled objective function, or a universally accepted causal model. What counts as the problem depends on how the situation is interpreted.

That means the first strategic task is not solution generation. It is construction of the problem. This construction involves selection. Some variables are included and others are excluded. Some actors are treated as central and others as peripheral. Some causal stories are legitimized and others are ignored. Some time horizons are emphasized and others disappear. Some values are treated as nonnegotiable and others become tradeoffs.

There is no neutral problem definition. Every formulation reflects a particular way of seeing the system. Strategy therefore begins with epistemology: how knowledge about the problem is constructed, stabilized, challenged, and treated as actionable. This connects directly to mental models, which shape representation, and to cognitive bias, which constrains what is perceived in the first place.

Problem framing is not the act of naming a problem. It is the act of constructing a strategic reality from which action becomes possible.

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Why Problem Framing Matters in Strategic Ideation

Problem framing matters because ideas are downstream from representation. The same situation can produce radically different ideas depending on how it is framed. A decline in participation can be framed as a communications problem, a trust problem, an access problem, a relevance problem, a governance problem, a workflow problem, a capacity problem, or a symptom of a wider systems failure. Each frame generates a different idea space.

If the problem is framed as a communications issue, the team may generate campaigns, messaging improvements, content changes, or awareness initiatives. If the problem is framed as a trust issue, the team may generate transparency mechanisms, governance changes, consent models, repair strategies, or accountability structures. If the problem is framed as a workflow issue, the team may generate service redesign, interface changes, integration improvements, or journey-mapping interventions. If the problem is framed as an incentive issue, the team may redesign roles, metrics, rewards, or authority structures.

This is why framing is a strategic act. It determines not only which solutions are preferred, but which solutions become thinkable. Poor framing does not merely lead to weak answers. It prevents better answers from appearing. Strong framing expands the range of meaningful possibilities by clarifying the underlying structure of the situation.

Framing also shapes evidence. Teams gather evidence that appears relevant under the chosen frame. A communications frame encourages message testing. A trust frame encourages stakeholder interviews and legitimacy analysis. A systems frame encourages feedback-loop mapping. A resource frame encourages capacity analysis. The chosen frame therefore guides not only solution generation, but also what the organization learns.

The quality of strategic ideation depends on the quality of the problem representation that precedes it.

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Problem Framing as a Search Over Representations

Problem framing can be understood as a search process—not over solutions, but over representations of the problem itself. Each frame defines a different problem space, with its own structure, constraints, causal assumptions, stakeholder map, and possible interventions.

For example, declining organizational performance may be framed as a leadership failure, a market-positioning problem, a cultural breakdown, a talent problem, a resource constraint, a coordination failure, a strategy-to-execution gap, or a systems-level incentive problem. Each framing produces a different search space. Different variables become salient, different causal mechanisms are emphasized, and different solutions emerge as viable.

This reveals a deeper insight: strategic ideation is constrained not only by the absence of solutions, but by the representation of the problem. Teams often believe they are stuck because they lack ideas. In reality, they may be stuck because they are searching inside a weak frame. The idea space looks exhausted because the problem representation is too narrow.

This aligns with Lateral Thinking in Strategy, which shifts frames, and Analogical Thinking and Idea Transfer, which imports alternative representations. Tversky and Kahneman’s work is especially relevant because it shows that reasoning itself changes when the same underlying choice is posed in a different way.

Strategic reframing expands the search by changing the representation that governs what counts as a possible answer.

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From Framing to Definition: Imposing Structure

If framing selects perspective, problem definition imposes structure. It translates an interpretive lens into an actionable representation that can guide analysis, evidence gathering, ideation, prioritization, and decision-making.

Problem definition usually involves specifying the core issue, system boundaries, relevant actors and stakeholders, constraints and resources, causal assumptions, criteria for success, risks, and the time horizon over which effects matter. In formal domains such as operations research, systems engineering, policy analysis, design research, or strategic planning, this step may be expressed through models, decision trees, causal diagrams, system maps, optimization criteria, or theory-of-change structures.

However, even formal representations remain contingent on prior framing choices. A decision tree can be technically precise while excluding the most important stakeholders. A systems map can reveal feedback while hiding power. A performance model can be mathematically valid while optimizing the wrong outcome. A theory of change can be elegant while resting on weak causal assumptions. Definition is therefore not a neutral translation. It is a structured commitment to a particular view of reality.

Problem definition is where interpretation becomes operational. Once the problem is defined, resources, attention, metrics, authority, and accountability begin to align around it. That is why definition must be treated with care. The wrong definition can institutionalize the wrong strategy.

A problem definition is not a description of reality. It is a strategic model for action.

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Framing Determines the Possibility Space

Problem framing defines the boundaries of the possibility space. It determines which variables are included or excluded, which causal relationships are considered, which constraints are treated as fixed, which outcomes are prioritized, which tradeoffs are accepted, and which stakeholders are recognized as strategically relevant.

This has a direct impact on ideation. Poor framing produces shallow solution spaces. Strong framing expands the range of viable interventions. A narrow frame may make the problem look technically manageable while excluding the dynamics that generate it. A broader frame may initially feel messier, but it often reveals more meaningful leverage points.

For example, a university seeking to improve student retention might frame the problem as student motivation, administrative complexity, financial stress, advising access, belonging, mental health support, curriculum structure, or institutional trust. Each frame creates a different portfolio of possible interventions. The same data can support different strategic pathways depending on the frame that organizes it.

This connects to Heuristics in Strategic Ideation, which guide search, and Divergent vs Convergent Thinking, which expands and narrows that space. Strong framing improves both divergence and convergence. It expands the field of possible ideas while also improving the criteria used to evaluate them.

The way a problem is framed determines not only what will be solved, but what can even be imagined as solvable.

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Framing, Power, and Institutional Context

Problem framing is not purely analytical. It is also institutional and political. Different stakeholders construct problems in ways that reflect their interests, incentives, responsibilities, values, risks, and positions within a system. A regulator may frame a challenge in terms of compliance and systemic risk. Executives may emphasize growth, efficiency, and profitability. Employees may emphasize fairness, workload, and feasibility. Communities may emphasize legitimacy, exclusion, harm, or voice.

These competing frames influence which solutions gain legitimacy. Deborah Stone’s Policy Paradox is especially important here because it shows how public problems are constructed through narratives of causality, responsibility, symbols, values, and decision rules rather than through neutral technical description alone.

This introduces a critical strategic capability: reframing. The ability to shift how a problem is understood can redefine the entire strategic landscape. Sometimes the most consequential strategic move is not solving the accepted problem better, but showing that the accepted problem was misframed to begin with.

Power enters framing in several ways. Powerful actors may define the problem in ways that preserve their authority. Institutions may frame problems in ways that match existing budgets, departments, metrics, or legal responsibilities. Consultants may frame problems in ways that match available methods. Technical teams may frame human problems as technical gaps. Public agencies may frame structural problems as compliance issues. Organizations may frame trust problems as communication problems because communication is easier to change than governance.

This does not mean every frame is manipulative. It means frames are never innocent. They are social claims about what matters and whose definition of reality will govern action.

Frames are never just cognitive. They are also social claims about what matters and whose reality will govern action.

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Problem Framing in Complex Systems

In complex systems, framing becomes even more consequential. Complex systems are characterized by feedback loops, nonlinearity, delays, adaptation, emergence, interdependence, and shifting boundaries. Linear framing often misrepresents these dynamics by treating system-level issues as if they were isolated variable problems.

For example, treating a coordination failure as a staffing problem may lead to more capacity without changing information bottlenecks or incentives. Treating institutional mistrust as a communication problem may improve messaging while leaving the underlying drivers of distrust intact. Treating poor implementation as a motivation problem may ignore role ambiguity, conflicting metrics, authority gaps, or governance failure.

This is why problem framing must be integrated with Systems Thinking in Ideation and Complex Systems and Strategic Uncertainty. Accurate framing requires understanding how structure produces behavior over time. The strongest frame is often not the simplest one. It is the one that best explains the recurring pattern while remaining useful for action.

Complexity also requires humility. A frame is a model, not the system itself. It may clarify some relationships while hiding others. It may represent the perspective of those who created it more than those who experience the problem. It may become obsolete as the system adapts. Strong framing therefore includes revision triggers. It asks when the problem definition should be reopened.

In complex systems, weak framing often mistakes symptoms for causes and local visibility for systemic importance.

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Strong problem framing is not simply clearer language. It is a better architecture for strategic inquiry.

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Advanced framing does not merely sharpen a single lens. It compares lenses before deciding which one should govern action.

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Problem Framing as Organizational Capability

Strong problem framing is not just an individual skill. It is an organizational capability. Some institutions are structured to inherit old frames and defend them. Others create space for reframing through cross-functional interpretation, field observation, challenge processes, stakeholder inquiry, systems mapping, and iterative learning.

Organizations with better framing capability tend to delay premature closure on diagnosis. They treat frontline information as strategically relevant. They make assumptions discussable rather than invisible. They revisit definitions when interventions fail. They distinguish between administrative convenience and analytical accuracy. They preserve decision memory so that abandoned frames, rejected assumptions, and revised definitions are not lost.

These are not minor process preferences. They shape whether the institution can adapt to changing conditions or remains trapped in inherited representations of reality. A stagnant organization may still produce many ideas, but those ideas remain trapped inside old definitions. An adaptive organization may produce fewer ideas at first, but the ideas emerge from better frames and are more likely to address the real structure of the situation.

Problem-framing capability also requires leadership restraint. If senior leaders define the problem too early, teams may perform analysis inside a predetermined frame. Strong leaders do not avoid judgment. They sequence judgment. They allow diagnostic work to widen the frame before narrowing it into decisions.

Often, what distinguishes adaptive organizations from stagnant ones is not that they solve faster, but that they reframe better.

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The cost of poor framing is that intelligence gets spent optimizing the wrong reality.

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From Problem Framing to Strategic Ideation

Problem framing is the gateway to ideation. It determines what questions are asked, what ideas are generated, what evidence is gathered, what solutions are pursued, and what strategies become legitimate. This leads to a fundamental principle: innovation emerges from better problem definitions, not just better solutions.

Reframing a problem from “How do we increase sales?” to “How do we create value within a changing ecosystem?” transforms the entire ideation process. Different actors matter. Different causal mechanisms become visible. Different interventions become thinkable. Different evidence becomes relevant. Different risks and tradeoffs appear.

This connects directly to What Is Strategic Ideation?, where the quality of ideas depends on the structure of inquiry. A richer frame does not guarantee a better solution, but it greatly increases the chance that the search will move in a strategically meaningful direction.

Strong problem framing improves strategic ideation in four ways. It expands the possibility space by revealing alternative interpretations. It improves idea quality by clarifying mechanisms and constraints. It improves evaluation by defining meaningful criteria. And it improves learning by identifying when the frame itself should be revised.

The quality of ideation is downstream of the quality of framing.

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A problem-framing audit protects strategy from solving the wrong problem with impressive discipline.

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Mathematical Lens: Frames, Boundaries, and Search Spaces

A problem frame can be represented conceptually as a mapping:

The resulting solution space can be represented as:

Reframing can then be represented as:

Problem-definition quality can be represented as a weighted relationship among boundary breadth, causal depth, stakeholder inclusion, assumption clarity, and actionability:

The mathematical lens clarifies a core strategic principle: changing the frame changes the visible intervention space.

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Advanced R Workflow: Comparing Problem-Framing Profiles

The R workflow below compares stylized framing contexts across boundary breadth, stakeholder inclusion, systems awareness, causal depth, assumption clarity, reframing capacity, and institutional lock-in risk.

# Install packages if needed.
# install.packages(c("tidyverse"))

library(tidyverse)

# ------------------------------------------------------------
# R Workflow: Comparing Problem-Framing Profiles
# Purpose:
#   Build stylized profiles across framing contexts using
#   boundary breadth, stakeholder inclusion, systems awareness,
#   causal depth, assumption clarity, reframing capacity,
#   and institutional lock-in risk.
# ------------------------------------------------------------

contexts <- tibble(
  context = c(
    "Narrow Administrative Frame",
    "Balanced Strategic Frame",
    "Systems-Oriented Frame",
    "Politically Contested Frame",
    "Stakeholder-Grounded Frame"
  ),
  boundary_breadth = c(0.28, 0.71, 0.86, 0.63, 0.82),
  stakeholder_inclusion = c(0.34, 0.74, 0.79, 0.88, 0.92),
  systems_awareness = c(0.26, 0.72, 0.91, 0.61, 0.78),
  causal_depth = c(0.31, 0.76, 0.84, 0.58, 0.74),
  assumption_clarity = c(0.24, 0.70, 0.78, 0.62, 0.76),
  reframing_capacity = c(0.22, 0.73, 0.82, 0.69, 0.80),
  institutional_lock_in_risk = c(0.82, 0.40, 0.32, 0.56, 0.34)
)

contexts <- contexts %>%
  mutate(
    framing_profile =
      0.18 * boundary_breadth +
      0.16 * stakeholder_inclusion +
      0.18 * systems_awareness +
      0.18 * causal_depth +
      0.14 * assumption_clarity +
      0.16 * reframing_capacity -
      0.10 * institutional_lock_in_risk,
    diagnosis = case_when(
      framing_profile >= 0.70 ~ "strong_problem_framing_capacity",
      institutional_lock_in_risk >= 0.70 ~ "institutional_lock_in_risk",
      boundary_breadth < 0.45 ~ "boundary_myopia_risk",
      causal_depth < 0.45 ~ "symptom_framing_risk",
      TRUE ~ "develop_with_frame_comparison"
    )
  )

print(contexts)

contexts_long <- contexts %>%
  pivot_longer(
    cols = c(
      boundary_breadth,
      stakeholder_inclusion,
      systems_awareness,
      causal_depth,
      assumption_clarity,
      reframing_capacity,
      institutional_lock_in_risk
    ),
    names_to = "dimension",
    values_to = "value"
  )

ggplot(contexts_long, aes(x = dimension, y = value, fill = context)) +
  geom_col(position = "dodge") +
  labs(
    title = "Stylized Problem-Framing Dimensions",
    x = "Dimension",
    y = "Value",
    fill = "Context"
  ) +
  theme_minimal(base_size = 12) +
  coord_flip()

ggplot(contexts, aes(x = reorder(context, framing_profile), y = framing_profile)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Stylized Problem-Framing Profile",
    x = "Context",
    y = "Profile Score"
  ) +
  theme_minimal(base_size = 12)

write_csv(contexts, "problem_framing_profiles.csv")

This workflow should not be treated as an objective measurement system. Its purpose is to make framing criteria visible so that teams can compare problem representations before committing to one definition.

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Advanced Python Workflow: Simulating Framing Shifts and Idea-Space Expansion

The Python workflow below simulates stylized framing contexts over repeated cycles, showing how stronger boundary breadth, systems awareness, stakeholder inclusion, and reframing capacity expand the quality of available strategic options.

# Install packages if needed:
# pip install pandas numpy matplotlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# ------------------------------------------------------------
# Python Workflow: Simulating Problem Framing
# Purpose:
#   Compare framing contexts whose option quality depends on
#   boundary breadth, systems awareness, stakeholder inclusion,
#   causal depth, and reframing capacity.
# ------------------------------------------------------------

time_steps = np.arange(1, 31)

def simulate_context(
    boundary,
    systems,
    stakeholders,
    causal_depth,
    reframing,
    lock_in_risk,
    initial_state=0.30
):
    state = np.zeros(len(time_steps))
    state[0] = initial_state

    for t in range(1, len(time_steps)):
        gain = (
            0.14 * boundary +
            0.16 * systems +
            0.14 * stakeholders +
            0.16 * causal_depth +
            0.18 * reframing
        )

        lock_in_drag = 0.10 * lock_in_risk

        # Reframing compounds modestly when the organization learns.
        learning_bonus = 0.01 * systems * reframing * np.log1p(t)

        state[t] = state[t - 1] + gain / 5 - lock_in_drag / 5 + learning_bonus
        state[t] = np.clip(state[t], 0, 1.8)

    return state

narrow_frame = simulate_context(
    boundary=0.28,
    systems=0.26,
    stakeholders=0.34,
    causal_depth=0.31,
    reframing=0.22,
    lock_in_risk=0.82
)

balanced_frame = simulate_context(
    boundary=0.71,
    systems=0.72,
    stakeholders=0.74,
    causal_depth=0.76,
    reframing=0.73,
    lock_in_risk=0.40
)

systems_frame = simulate_context(
    boundary=0.86,
    systems=0.91,
    stakeholders=0.79,
    causal_depth=0.84,
    reframing=0.82,
    lock_in_risk=0.32
)

stakeholder_grounded_frame = simulate_context(
    boundary=0.82,
    systems=0.78,
    stakeholders=0.92,
    causal_depth=0.74,
    reframing=0.80,
    lock_in_risk=0.34
)

df = pd.DataFrame({
    "time": time_steps,
    "Narrow Administrative Frame": narrow_frame,
    "Balanced Strategic Frame": balanced_frame,
    "Systems-Oriented Frame": systems_frame,
    "Stakeholder-Grounded Frame": stakeholder_grounded_frame
})

print(df.head())

plt.figure(figsize=(10, 6))
for col in df.columns[1:]:
    plt.plot(df["time"], df[col], label=col)

plt.xlabel("Framing Cycle")
plt.ylabel("Option-Space Quality")
plt.title("Framing Shifts and Idea-Space Expansion")
plt.legend()
plt.tight_layout()
plt.show()

df.to_csv("problem_framing_simulation.csv", index=False)

This workflow can be extended with real framing audits, stakeholder interviews, systems maps, implementation evidence, and decision-memory records. Its purpose is to show that better framing does not merely clarify language. It changes the quality of available strategic options over time.

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GitHub Repository

The companion repository for this article will provide advanced strategist-facing workflows for problem-framing diagnostics, frame comparison, boundary analysis, stakeholder frame mapping, causal-depth review, assumption audits, reframing triggers, problem-definition scoring, idea-space expansion modeling, and decision-memory records.

The repository structure is designed to support professional strategic analysis rather than generic coding demonstrations. The python/ folder can model framing-profile scores, boundary breadth, stakeholder inclusion, systems awareness, causal depth, assumption clarity, reframing capacity, institutional lock-in risk, and option-space expansion over time. The r/ folder can compare problem-framing profiles and visualize frame strengths and weaknesses. The julia/ folder can support scenario-based sensitivity analysis for competing frames. The sql/ folder can define schemas for problems, frames, boundaries, stakeholders, assumptions, evidence, causal models, reframing events, decision records, and idea-space outputs.

Additional folders can support command-line diagnostics, lower-level scoring utilities, and reproducible documentation. The rust/ folder can provide a command-line framing diagnostics scaffold. The go/ folder can provide a frame-comparison utility. The cpp, fortran, and c folders can provide efficient scoring examples and low-level utilities. The docs, data, outputs, and notebooks folders can support article notes, modeling principles, synthetic datasets, generated outputs, and notebook placeholders.

This code should be understood as a transparent learning and modeling scaffold. It is intended for synthetic-data research, methods demonstration, institutional learning, strategic analysis, and reproducible workflow development. It is not a substitute for stakeholder engagement, ethical review, domain expertise, accountable governance, or participatory judgment.

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Conclusion

Problem framing and problem definition are central to strategic thinking. They determine how reality is interpreted, how systems are represented, how stakeholders are recognized, how evidence is gathered, how ideas are generated, and how solutions are evaluated.

In complex environments, the ability to construct and reconstruct problems becomes a primary strategic capability. It enables decision-makers to uncover deeper structures, challenge assumptions, expand the range of possible interventions, and avoid wasting effort on the wrong problem. Ultimately, the effectiveness of strategy depends not only on the answers provided, but on the questions that guide inquiry and the frames that make those questions possible.

The most dangerous strategic errors often begin before execution. They begin when a weak frame becomes accepted as reality. Once that happens, intelligent people may work hard, analyze carefully, and implement competently while still solving the wrong problem. The solution may be efficient, measurable, and well managed, yet strategically misdirected.

Strong problem framing does not guarantee success. But it improves the odds that strategy is aimed at the real structure of the situation rather than at the most visible symptom, the most convenient administrative category, or the most politically acceptable story.

To improve strategic ideation, improve the frame. Better ideas begin with better problem definitions.

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Related Articles

• Strategic Ideation
• Systems Thinking in Ideation
• Complex Systems and Strategic Uncertainty
• Mental Models in Strategic Thinking
• Cognitive Bias in Idea Generation
• Lateral Thinking in Strategy
• Analogical Thinking and Idea Transfer
• Heuristics in Strategic Ideation
• First Principles Thinking in Strategy
• Boundary Setting in Strategic Ideation
• Assumption Mapping for Strategic Ideas
• Theory of Change and Strategic Logic

Further Reading

• Kahneman, D. (2011) Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
• Kahneman, D. and Tversky, A. (1981) ‘The framing of decisions and the psychology of choice’, Science, 211(4481), pp. 453–458. Available at: https://www.science.org/doi/10.1126/science.7455683
• Rittel, H.W.J. and Webber, M.M. (1973) ‘Dilemmas in a general theory of planning’, Policy Sciences, 4(2), pp. 155–169.
• Schön, D.A. (1983) The Reflective Practitioner: How Professionals Think in Action. New York: Basic Books.
• Simon, H.A. (1996) The Sciences of the Artificial. 3rd edn. Cambridge, MA: MIT Press. Available at: https://mitpress.mit.edu/9780262690232/the-sciences-of-the-artificial/
• Stone, D. (2012) Policy Paradox: The Art of Political Decision Making. 3rd edn. New York: W. W. Norton.
• Stanford Encyclopedia of Philosophy (no date) Decision Theory. Available at: https://plato.stanford.edu/entries/decision-theory/
• MIT Sloan (no date) System Dynamics. Available at: https://mitsloan.mit.edu/faculty/academic-groups/system-dynamics/about-us

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References

• Kahneman, D. and Tversky, A. (1981) ‘The framing of decisions and the psychology of choice’, Science, 211(4481), pp. 453–458. Available at: https://www.science.org/doi/10.1126/science.7455683
• MIT Sloan (no date) System Dynamics. Available at: https://mitsloan.mit.edu/faculty/academic-groups/system-dynamics/about-us
• Rittel, H.W.J. and Webber, M.M. (1973) ‘Dilemmas in a general theory of planning’, Policy Sciences, 4(2), pp. 155–169.
• Schön, D.A. (1983) The Reflective Practitioner: How Professionals Think in Action. New York: Basic Books.
• Simon, H.A. (1996) The Sciences of the Artificial. 3rd edn. Cambridge, MA: MIT Press. Available at: https://mitpress.mit.edu/9780262690232/the-sciences-of-the-artificial/
• Stone, D. (2012) Policy Paradox: The Art of Political Decision Making. 3rd edn. New York: W. W. Norton.

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