Mental Models in Strategic Thinking

Last Updated June 4, 2026

Mental models in strategic thinking are the internal representations through which decision-makers interpret environments, infer causality, anticipate consequences, and select courses of action. Strategy is never formed from data alone. It is formed through conceptual structures that determine what counts as relevant evidence, how uncertainty is framed, which relationships are treated as causal, and what futures appear plausible or actionable.

In this sense, strategy is not merely a matter of planning. It is a matter of cognition. Every strategic judgment is mediated by a model of how the world works, even when that model remains implicit. Decision-makers do not confront raw reality directly. They confront an interpreted version of it: a simplified, structured, and value-laden representation shaped by experience, professional training, institutional routines, incentives, power, ideology, memory, and available evidence.

Mental models are indispensable because strategic environments are too complex to be apprehended in unfiltered form. Yet they are also hazardous. Any model that simplifies reality necessarily excludes part of it. Strategic failure often emerges not from the absence of intelligence, but from reliance on models that are too narrow, too rigid, too linear, too culturally insulated, too institutionally protected, or too resistant to feedback. The deepest challenge of strategy is therefore not simply to think harder, but to think through better representations of the world.

This article examines mental models as the cognitive infrastructure of strategy. It explores why they matter, how they represent causality, how they shape organizational interpretation, why model monoculture is dangerous, how foresight depends on model revision, and why strategic failure often begins when a once-useful representation becomes too authoritative to question.

Why Mental Models Matter in Strategy

Strategic thinking depends on abstraction. No decision-maker can directly grasp the full complexity of a competitive field, institutional environment, technological system, ecological system, public-policy landscape, or social context. Strategy therefore depends on intermediate representations: conceptual maps that reduce complexity into intelligible structure.

These maps may be explicit, as in scenario frameworks, risk models, system diagrams, causal maps, competitive theories, stakeholder maps, or portfolio models. More often, they are implicit, embedded in habits of interpretation, professional training, institutional routines, governance structures, budget categories, historical analogies, metrics, and inherited assumptions. What matters is not whether a model exists, but whether it is adequate to the environment in which it is being used.

The decisive importance of mental models lies in the fact that they shape perception before they shape choice. They determine what is noticed and what is ignored, what is treated as signal versus noise, where opportunity is located, how threats are interpreted, which harms are made visible, and what forms of intervention are considered legitimate. For this reason, mental models are not peripheral to strategy. They are part of its cognitive infrastructure.

This is why organizations can collect more data and still fail to learn. Data enters strategy through interpretive systems. If the governing model is too narrow, evidence that contradicts it may be dismissed, reclassified, delayed, or absorbed without changing the underlying frame. If the model is too rigid, new information may be treated as anomaly rather than signal. If the model is politically protected, evidence may be filtered before it can challenge strategic assumptions.

Strong strategic thinking therefore requires model awareness. Strategists must ask not only what they know, but how they are organizing what they know. They must examine the categories, causal assumptions, analogies, metrics, and institutional routines through which reality becomes strategically intelligible.

The quality of strategic thinking depends directly on the quality, diversity, and revisability of the models through which reality is interpreted.

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Mental Models as Representations of Causality

At the core of strategic reasoning lies an attempt to understand causality: what produces growth, what drives decline, what generates resilience, what produces legitimacy, what triggers crisis, what creates trust, what undermines coordination, and what causes interventions to succeed or fail. Mental models provide provisional answers to these questions. They are not merely narratives. They are working hypotheses about how the system operates.

Some models are linear, assuming relatively direct relationships between cause and effect. These can be useful in bounded, stable, procedural environments where variables are well understood and feedback is limited. But many strategic domains do not possess those conditions. In complex systems, causality is often delayed, recursive, nonlinear, and distributed across relationships. Actions produce second- and third-order effects. Improvements in one domain may destabilize another. Short-term optimization may generate long-term fragility. Attempts to solve visible symptoms may intensify underlying causes.

This is why Systems Thinking in Ideation, Complex Systems and Strategic Uncertainty, and Second-Order Effects and Unintended Consequences are essential to advanced strategy. As complexity increases, reliance on simplistic causal models becomes progressively more dangerous.

A mental model is therefore not evaluated only by whether it is elegant, familiar, or easy to communicate. It must be evaluated by whether it captures the kind of causality the environment actually exhibits. A linear model applied to a nonlinear system can produce confident failure. A mechanistic model applied to an adaptive social system can misread resistance as irrationality. A short-term financial model applied to a legitimacy problem can make the organization more efficient at destroying trust.

Strategic cognition depends on matching the model to the causal structure of the environment. The purpose is not to abandon simple models entirely. The purpose is to know when they are useful, when they are insufficient, and when they become dangerous.

A model is strategically useful to the extent that it captures the kind of causality the environment actually exhibits.

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The Cognitive Foundations of Strategic Modeling

Cognitive theory suggests that human reasoning operates through internal representations rather than through purely formal logic. Mental-model theory holds that individuals construct structured internal depictions of situations, relationships, constraints, and possibilities. These internal models allow them to simulate outcomes, compare alternatives, and imagine consequences before acting.

From a strategic standpoint, this is crucial. Decision-making rarely occurs under conditions of complete information. It unfolds under uncertainty, ambiguity, time pressure, contested evidence, institutional constraint, and incomplete feedback. In such environments, formal optimization is often impossible. Instead, actors depend on models that simplify enough of the world to make judgment possible.

What distinguishes strong strategists from weak ones is not the absence of mental models, but their quality. Strong strategists develop models that are structurally rich enough to capture key relationships, flexible enough to incorporate new information, plural enough to allow multiple interpretations, and revisable enough to absorb disconfirming evidence.

Weak strategists rely on models that are narrow, static, and resistant to revision. The problem is rarely that they have no model. The problem is that they do not know enough about the one they are using. They confuse familiarity with validity. They confuse confidence with evidence. They confuse professional fluency with environmental fit.

Strategic cognition improves when decision-makers can examine not only the content of a model, but the qualities that make that model usable, limited, revisable, and accountable.

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Bounded Rationality and Model Awareness

Classical strategy often assumed decision-makers capable of relatively coherent optimization under stable conditions. Modern research complicates that view. Human reasoning is shaped by bounded rationality, heuristics, framing effects, overconfidence, loss aversion, availability bias, confirmation bias, and other systematic deviations from idealized rational choice.

The implication is not that strategy becomes impossible, but that it becomes reflective. Strategists must ask not only, What should we do? but also, What assumptions about the world are making this option appear sensible? That second question is often more important. Poor strategic decisions frequently originate upstream, in flawed conceptual frames.

If an organization assumes stability where volatility dominates, or linearity where feedback governs outcomes, downstream choices may appear rational while producing failure. If a leadership team assumes that stakeholders respond only to incentives, it may misread legitimacy, identity, trust, grievance, or institutional memory. If a strategy team assumes that the future is a continuation of recent trends, it may optimize for a world that is already disappearing.

This connects directly to Decision-Making Under Uncertainty, Heuristics in Strategic Ideation, and Cognitive Bias in Idea Generation, where judgment is shaped as much by perception as by analysis.

Model awareness is therefore a discipline of bounded rationality. It does not make decision-makers perfectly rational. It helps them locate where their rationality is bounded: by information, time, cognitive capacity, institutional norms, professional identity, political pressure, and inherited categories. Once those bounds become visible, they can be managed more intelligently.

Model awareness is what turns bounded rationality from a hidden liability into a manageable condition of thought.

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The strongest strategic thinking does not rely on one model alone. It integrates multiple lenses appropriate to different layers of reality. Model strength often comes less from depth within one frame than from the ability to move intelligently across several.

Strategic maturity depends on knowing which model is useful, which model is missing, and which model has become too dominant.

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The Problem of Model Monoculture

One of the most dangerous conditions in strategy is model monoculture: the dominance of a single interpretive framework. Organizations often privilege one lens at the expense of others. A financial model may ignore legitimacy and trust. A technical model may ignore adoption, politics, and narrative resistance. An incentive model may ignore norms, identity, and institutional culture. An efficiency model may ignore resilience and long-term adaptability.

From the inside, model monoculture can feel like rigor. The organization appears disciplined because everyone uses the same vocabulary, metrics, dashboards, and assumptions. In reality, it may be narrowing its field of perception. It becomes efficient at noticing what the dominant model can see and increasingly incompetent at noticing what that model excludes.

Model monoculture is especially dangerous when it becomes embedded in governance systems. Once a model controls funding criteria, performance indicators, reporting formats, hiring preferences, and executive language, it becomes more than a way of thinking. It becomes an institution. Alternative interpretations then appear unserious, inefficient, political, emotional, impractical, or outside scope.

This matters for marginalized voices and contested systems. Communities most affected by strategic decisions often experience realities that dominant institutional models fail to register. A transportation model may count throughput while ignoring displacement. A public-health model may count service delivery while ignoring trust. A technology model may count adoption while ignoring surveillance, labor displacement, or unequal access. Model pluralism is therefore not only analytically useful. It is an accountability practice.

Exceptional strategic thinking requires model pluralism: the disciplined use of multiple frameworks and the ability to move between them without collapsing into incoherence. Pluralism does not mean all models are equally valid. It means that complex realities should not be interpreted through a single authorized lens.

When only one model is permitted to explain the world, strategy becomes efficient at noticing only what that model can see.

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Mental Models, Foresight, and the Future

Strategic thinking is inherently anticipatory. It connects present action to future conditions that do not yet exist. Mental models determine how that future is imagined.

They shape whether the future is seen as a continuation of present trends, a branching field of scenarios, a contested space of strategic interaction, a fragile system vulnerable to tipping points and cascading effects, or a field of political imagination in which different groups struggle over what futures are desirable and legitimate.

This is why Scenario Planning and Futures Thinking and Strategic Foresight and Long-Term Thinking are not merely forecasting tools. At a deeper level, they are methods for revising mental models. In volatile environments, the inability to revise one’s models is often more dangerous than incorrect prediction.

Foresight work is valuable because it reveals assumptions about continuity. It asks what would happen if key variables shift, if weak signals intensify, if institutional legitimacy declines, if technological adoption follows a different path, if ecological thresholds are crossed, or if actors respond strategically rather than passively. It forces organizations to see that the future is not simply a projected line. It is a contested and uncertain field.

Institutions do not become obsolete only because the world changes. They become obsolete because they continue to interpret the world through models built for earlier conditions. A model that once created strategic advantage can become a source of vulnerability when the environment changes faster than the model can adapt.

Foresight is valuable not only because it imagines futures, but because it reveals which models of the future an organization has been taking for granted.

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Mental Models in Organizations and Institutions

Mental models do not exist only within individuals. They become embedded in organizations through structures, metrics, governance frameworks, processes, routines, technologies, professional norms, and institutional memory.

Dashboards, KPIs, planning systems, budgeting logics, risk registers, procurement rules, strategic plans, reporting structures, and institutional playbooks all encode assumptions about what matters, how change occurs, what success looks like, which risks are visible, whose knowledge counts, and what forms of failure are acknowledged.

These institutionalized models can outlast the conditions that once made them useful. Strategic renewal therefore often requires not new data, but new models. This is why organizational learning is best understood as model adaptation rather than information accumulation alone. Many institutions collect evidence without learning because the interpretive frame through which evidence is processed remains unchanged.

Institutional mental models also shape power. The model used by an organization determines which departments gain influence, which forms of knowledge become legitimate, which warnings are heard, which communities are treated as stakeholders, and which costs are considered external. A model is never only cognitive. It is also organizational and political.

Organizations often fail to learn not because information is absent, but because the model that receives the information is no longer capable of changing shape.

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Strategic Failure as Model Failure

Many strategic failures can be understood as failures of representation rather than failures of effort. Organizations miss inflection points because they assume continuity where rupture is emerging. They misread competition because they define rivals too narrowly. They intensify crises because they misidentify symptoms as causes. Leadership teams defend obsolete business models because sunk investments reinforce explanatory habits. Public institutions repeat ineffective interventions because the governing model cannot see the lived reality of affected communities.

The common pattern is not lack of intelligence. It is an inability to represent reality adequately. Strategy fails when the governing model cannot register the environment, or when institutions become too attached to revise it.

In this respect, humility is not merely a moral virtue in strategic life. It is a methodological necessity. A strategist who cannot question the model cannot reliably interpret evidence. An organization that cannot revise its model cannot adapt before the environment forces adaptation through crisis.

Strategic failure often begins when a model becomes too authoritative to be questioned and too outdated to remain reliable.

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A mental-model audit turns strategic reflection from a vague virtue into a practical discipline.

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Toward a Reflective Practice of Strategic Thinking

A mature strategic practice requires metacognition: the ability to examine the models through which one is thinking. This is not separate from strategy. It is part of strategy, because the quality of action depends on the quality of representation that precedes action.

Key questions include:

• What assumptions about causality are structuring this strategy?
• What variables are being treated as fixed that may actually be dynamic?
• Where is linear reasoning being applied to a nonlinear system?
• Which stakeholder perspectives are missing from the model?
• What evidence would force revision?
• Are we mistaking confidence for validity?
• Which model has become politically protected?
• Which form of evidence is being treated as illegitimate because it does not fit the dominant frame?
• What would this problem look like through a systems, institutional, ethical, or futures lens?

These questions do not eliminate uncertainty. They improve the quality of judgment under uncertainty. They move strategy from reactive analysis toward reflective practice. Reflective strategy is not slower because it is indecisive. It is stronger because it makes the architecture of judgment visible enough to be revised.

This reflective practice also changes the meaning of expertise. Expertise is not only the possession of a powerful model. It is the ability to know when that model is useful, when it is incomplete, and when another model must be brought into the conversation. In complex environments, the most dangerous expert is often the one who knows one model too well and every alternative too poorly.

To think strategically at an advanced level is not only to choose among options, but to interrogate the cognitive machinery that made those options visible in the first place.

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Mathematical Lens: Representation, Uncertainty, and Model Revision

A mental model can be represented conceptually as a mapping from an environment to an interpreted internal representation. The point is not that the model reproduces the environment exactly. The point is that it transforms environmental complexity into something usable for judgment.

Strategic choice then operates over the interpreted state rather than raw reality:

Model revision can be represented as:

Model error can be represented conceptually as the distance between the interpreted model and the environment it attempts to represent:

The formal structure clarifies why strategy fails when models cannot revise. If \(M_t\) remains fixed while \(E_t\) changes, the gap between reality and interpretation grows. Strategy may remain internally coherent while becoming externally obsolete.

The mathematical lens shows that strategic judgment depends not only on choosing actions, but on continuously improving the model through which actions are chosen.

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Advanced R Workflow: Comparing Strategic Mental-Model Profiles

The R workflow below compares stylized strategic contexts across systems richness, probabilistic depth, model flexibility, institutional embedding, and revision capacity. It is designed as a transparent demonstration of how model quality can be profiled, compared, and questioned.

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

library(tidyverse)

# ------------------------------------------------------------
# R Workflow: Comparing Strategic Mental-Model Profiles
# Purpose:
#   Build stylized profiles across strategic contexts using
#   systems richness, probabilistic depth, flexibility,
#   institutional embedding, and revision capacity.
# ------------------------------------------------------------

contexts <- tibble(
  context = c(
    "Rigid Linear Strategy Context",
    "Balanced Reflective Strategy Context",
    "Systems-Rich Adaptive Context",
    "Institutionally Locked Context"
  ),
  systems_richness = c(0.24, 0.71, 0.89, 0.33),
  probabilistic_depth = c(0.21, 0.73, 0.84, 0.29),
  model_flexibility = c(0.19, 0.77, 0.88, 0.18),
  institutional_embedding = c(0.58, 0.64, 0.71, 0.91),
  revision_capacity = c(0.22, 0.76, 0.87, 0.16)
)

contexts <- contexts %>%
  mutate(
    mental_model_profile =
      0.24 * systems_richness +
      0.20 * probabilistic_depth +
      0.22 * model_flexibility -
      0.10 * institutional_embedding +
      0.24 * revision_capacity,
    diagnosis = case_when(
      model_flexibility < 0.35 & revision_capacity < 0.35 ~ "rigid_model_failure_risk",
      systems_richness < 0.40 & probabilistic_depth < 0.40 ~ "linear_overconfidence_risk",
      institutional_embedding > 0.80 & revision_capacity < 0.35 ~ "institutional_lock_in_risk",
      mental_model_profile >= 0.70 ~ "adaptive_model_strength",
      TRUE ~ "requires_model_review"
    )
  )

print(contexts)

contexts_long <- contexts %>%
  pivot_longer(
    cols = c(
      systems_richness,
      probabilistic_depth,
      model_flexibility,
      institutional_embedding,
      revision_capacity
    ),
    names_to = "dimension",
    values_to = "value"
  )

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

ggplot(contexts, aes(x = reorder(context, mental_model_profile), y = mental_model_profile)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Stylized Strategic Mental-Model Profile",
    x = "Context",
    y = "Profile Score"
  ) +
  theme_minimal(base_size = 12)

write_csv(contexts, "mental_models_profiles.csv")

This workflow can be expanded by adding evidence quality, stakeholder diversity, model plurality, causal richness, feedback routing, and decision-memory measures. Its purpose is not to produce a universal score. Its purpose is to make model assumptions visible enough for structured review.

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Advanced Python Workflow: Simulating Model Revision Under Uncertainty

The Python workflow below simulates stylized strategic contexts over repeated cycles, showing how model flexibility and revision capacity improve strategic quality under uncertainty. The simulation is simplified, but it demonstrates an important idea: models that cannot revise eventually become strategic liabilities.

# 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 Strategic Mental Models
# Purpose:
#   Compare strategic contexts whose performance depends on
#   systems richness, flexibility, and revision capacity.
# ------------------------------------------------------------

time_steps = np.arange(1, 31)

def simulate_context(richness, flexibility, revision, rigidity, initial_state=0.30):
    state = np.zeros(len(time_steps))
    state[0] = initial_state

    for t in range(1, len(time_steps)):
        gain = (
            0.16 * richness +
            0.18 * flexibility +
            0.18 * revision
        )

        drag = 0.14 * rigidity

        # Environments punish rigid models more over time.
        time_pressure = 1 + (t / len(time_steps)) * 0.20

        state[t] = state[t - 1] + gain / 5 - (drag * time_pressure) / 6
        state[t] = np.clip(state[t], 0, 1.8)

    return state

rigid_linear = simulate_context(
    richness=0.24,
    flexibility=0.19,
    revision=0.22,
    rigidity=0.78
)

balanced_reflective = simulate_context(
    richness=0.71,
    flexibility=0.77,
    revision=0.76,
    rigidity=0.42
)

systems_adaptive = simulate_context(
    richness=0.89,
    flexibility=0.88,
    revision=0.87,
    rigidity=0.29
)

institutionally_locked = simulate_context(
    richness=0.33,
    flexibility=0.18,
    revision=0.16,
    rigidity=0.91
)

df = pd.DataFrame({
    "time": time_steps,
    "Rigid Linear Strategy Context": rigid_linear,
    "Balanced Reflective Strategy Context": balanced_reflective,
    "Systems-Rich Adaptive Context": systems_adaptive,
    "Institutionally Locked Context": institutionally_locked
})

print(df.head())

plt.figure(figsize=(10, 6))

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

plt.xlabel("Strategic Cycle")
plt.ylabel("Strategic Judgment Quality")
plt.title("Model Revision Under Uncertainty")
plt.legend()
plt.tight_layout()
plt.show()

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

This simulation can be developed into a more serious workflow by adding model error, disconfirming evidence, stakeholder feedback, scenario shocks, institutional lock-in, and differential revision speed. In practice, the most important question is not whether a model performs well under familiar conditions. It is whether the model can revise before its errors become costly.

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GitHub Repository

The companion repository for this article will provide advanced strategist-facing workflows for mapping mental models, diagnosing model rigidity, comparing causal frames, assessing model diversity, identifying institutional lock-in, and simulating model revision under uncertainty.

The repository structure is designed to support professional strategic analysis rather than generic coding demonstrations. The python/ folder can model strategic mental-model profiles, model-revision dynamics, evidence interpretation, and institutional lock-in. The r/ folder can compare mental-model dimensions, visualize model plurality, and flag revision risks. The julia/ folder can support scenario-based model comparison and adaptive revision examples. The sql/ folder can define schemas for mental models, assumptions, evidence, model audits, feedback events, revision triggers, and decision-memory records.

Additional folders can support command-line diagnostics, lower-level scoring utilities, and reproducible documentation. The rust/ folder can provide a command-line model-diagnostics scaffold. The go/ folder can provide a model-audit 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

Mental models are the invisible architecture of strategy. Every serious act of strategic judgment depends on some representation of how the world works, how change occurs, what actors matter, what risks are visible, what futures are plausible, and what forms of action appear legitimate.

To think strategically is therefore not only to choose among options. It is to examine and refine the models that make those options visible in the first place. The most advanced strategists are distinguished not by perfect foresight, but by model awareness. They recognize that complexity punishes simplistic frames, that uncertainty cannot be eliminated, that institutions can become trapped inside their own categories, and that survival often depends on revising one’s understanding before reality forces revision from the outside.

Strong strategy is not simply planning ahead. It is disciplined cognition under uncertainty. It requires the ability to build models, compare models, challenge models, revise models, and know when a model that once helped explain the world has become a barrier to seeing it clearly.

Strategic maturity begins when decision-makers stop asking only what they should do and begin asking what model of reality is making that action appear sensible.

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Related articles

• What Is Strategic Ideation?
• Strategy vs Tactics vs Ideation
• First Principles Thinking in Strategy
• Systems Thinking in Ideation
• Complex Systems and Strategic Uncertainty
• Second-Order Effects and Unintended Consequences
• Decision-Making Under Uncertainty
• Heuristics in Strategic Ideation
• Cognitive Bias in Idea Generation
• Scenario Planning and Futures Thinking
• Strategic Foresight and Long-Term Thinking

Further reading

• American Psychological Association (2018) Mental model. APA Dictionary of Psychology. Available at: https://dictionary.apa.org/mental-model
• American Psychological Association (2018) Cognitive model. APA Dictionary of Psychology. Available at: https://dictionary.apa.org/cognitive-model
• Pitt, D. (2024) ‘Mental representation’, in The Stanford Encyclopedia of Philosophy. Available at: https://plato.stanford.edu/entries/mental-representation/
• Johnson-Laird, P.N. (1983) Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press.
• Kahneman, D. (2011) Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
• Kahneman, D. and Tversky, A. (1979) ‘Prospect theory: An analysis of decision under risk’, Econometrica, 47(2), pp. 263–291.
• MIT Sloan School of Management (n.d.) About us: System Dynamics. Available at: https://mitsloan.mit.edu/faculty/academic-groups/system-dynamics/about-us
• MIT Sloan School of Management (n.d.) MIT Sloan Beer Game Online. Available at: https://mitsloan.mit.edu/teaching-resources-library/mit-sloan-beer-game-online
• Sterman, J.D. (2006) ‘Learning from evidence in a complex world’, American Journal of Public Health, 96(3), pp. 505–514. Available at: https://mitsloan.mit.edu/shared/ods/documents?PublicationDocumentID=4328
• OECD (n.d.) Strategic foresight. Available at: https://www.oecd.org/en/about/programmes/strategic-foresight.html
• OECD (2025) Foresight toolkit for resilient public policy. Available at: https://www.oecd.org/en/publications/foresight-toolkit-for-resilient-public-policy_bcdd9304-en.html

References

• American Psychological Association (2018) Mental model. APA Dictionary of Psychology. Available at: https://dictionary.apa.org/mental-model
• American Psychological Association (2018) Cognitive model. APA Dictionary of Psychology. Available at: https://dictionary.apa.org/cognitive-model
• Johnson-Laird, P.N. (1983) Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press.
• Kahneman, D. (2011) Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
• Kahneman, D. and Tversky, A. (1979) ‘Prospect theory: An analysis of decision under risk’, Econometrica, 47(2), pp. 263–291.
• MIT Sloan School of Management (n.d.) About us: System Dynamics. Available at: https://mitsloan.mit.edu/faculty/academic-groups/system-dynamics/about-us
• Nobel Prize Outreach AB (2002) Daniel Kahneman – Facts. Available at: https://www.nobelprize.org/prizes/economic-sciences/2002/kahneman/facts/
• OECD (n.d.) Strategic foresight. Available at: https://www.oecd.org/en/about/programmes/strategic-foresight.html
• OECD (2025) Foresight toolkit for resilient public policy. Available at: https://www.oecd.org/en/publications/foresight-toolkit-for-resilient-public-policy_bcdd9304-en.html
• Pitt, D. (2024) ‘Mental representation’, in The Stanford Encyclopedia of Philosophy. Available at: https://plato.stanford.edu/entries/mental-representation/
• Sterman, J.D. (2006) ‘Learning from evidence in a complex world’, American Journal of Public Health, 96(3), pp. 505–514. Available at: https://mitsloan.mit.edu/shared/ods/documents?PublicationDocumentID=4328