Design Thinking in Public Policy

Last Updated May 28, 2026

Design thinking in public policy applies human-centered, iterative, evidence-seeking, and systems-aware design methods to the making of laws, services, programs, regulatory systems, administrative processes, and institutional interventions. In its strongest sense, this is not a decorative attempt to make government feel more creative, nor a simple effort to import private-sector workshop techniques into public administration. It is a serious attempt to improve how public institutions understand lived experience, frame policy problems, test interventions, learn from implementation, and translate abstract public goals into systems that people can actually navigate.

Public policy does not fail only because goals are poorly chosen. It also fails because institutions misunderstand the situations they are governing, mistake administrative convenience for public intelligibility, overlook unequal burdens, and implement reforms whose real-world effects were never sufficiently explored. A benefit may exist in law while remaining inaccessible in practice. A right may be formally recognized while remaining difficult to claim. A service may be available in theory while requiring forms, documents, literacy, time, digital access, transportation, language fluency, and institutional confidence that many people do not possess.

That is why design thinking matters in public policy. Governments operate in environments defined by legal authority, political contestation, fiscal constraint, bureaucratic complexity, uneven trust, and the reality that policy decisions are experienced differently across class, geography, race, disability, language, age, immigration status, housing status, and institutional position. Under such conditions, design thinking offers a disciplined way of asking how policies are actually encountered, where friction and exclusion arise, what kinds of interpretive or administrative burdens are imposed on citizens and residents, and how prototypes, pilots, participatory inquiry, and implementation learning can improve public judgment before failure is scaled across entire populations.

At its best, design thinking in public policy links empathy and stakeholder research, problem framing, insight generation, prototyping, testing and validation, iteration and experimentation, implementation and scaling, design evaluation, learning, and outcome measurement, and design thinking and systems thinking into a more reflective model of public decision-making. It does not replace democratic politics, legal reasoning, policy analysis, ethics, public finance, administrative law, or institutional accountability. It complements them by making policy development more empirical, interpretive, participatory, and responsive to the realities of public life.

Policy design becomes most consequential when public institutions recognize that implementation is not a final administrative stage after policymaking. It is where policy becomes real. Laws, rules, eligibility formulas, benefit systems, licensing procedures, public-service portals, enforcement routines, appeals processes, and public communications are not neutral delivery mechanisms. They shape who receives help, who gives up, who trusts the state, who is monitored, who is punished, who is believed, and who must carry the hidden labor of making public systems work.

What Design Thinking Means in Public Policy

Design thinking in public policy refers to the use of human-centered inquiry, iterative development, participatory methods, systems mapping, prototyping, service analysis, implementation learning, and evidence-informed revision in the formation of public systems. In contrast to policy approaches that begin and end with abstract targets, legal drafting, economic models, administrative categories, or political messaging, design thinking asks how institutions are encountered by the people subject to them. It studies not only what policy intends to do, but how policy is interpreted, experienced, trusted, navigated, resisted, and lived.

This does not mean that policy becomes a matter of consumer preference or service polish. Public policy remains bound to law, public reason, legitimacy, distribution, rights, obligation, coercive authority, democratic accountability, and institutional responsibility. But design thinking adds something essential to that environment: a method for discovering where policy design fails at the level of lived reality. It asks whether official problem definitions are adequate, whether public services are intelligible, whether participation processes are credible, whether administrative burdens are justified, and whether the state has mistaken its own categories for the social world it is trying to govern.

In this sense, design thinking in public policy is not a substitute for policy expertise. It is a discipline of public translation. It helps move policy from abstract intention into workable civic form. It asks whether a legal or programmatic idea can survive contact with real people, real institutions, real inequities, real administrative constraints, and real political consequences.

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Why Design Thinking Matters for Public Policy

Public policy matters because governments make decisions that shape housing, healthcare, transport, education, welfare, disability support, infrastructure, energy, migration, environmental protection, taxation, public safety, labor protections, licensing, public benefits, and administrative access. These decisions are usually made under pressure, with incomplete information, within institutional systems that are not neutral and often not easily changed. Design thinking matters because it introduces a disciplined way of learning from that complexity rather than pretending it can be fully mastered in advance.

Its value lies in helping policymakers become more responsive to how policy systems function in practice. That includes attention to public experience, service friction, interpretive burden, trust, inclusion, compliance, procedural dignity, and the hidden labor people perform to navigate bureaucratic systems. Where conventional policy analysis may emphasize aggregate modeling, statutory design, fiscal projections, or formal incentives, design thinking helps policymakers see what is otherwise flattened by abstraction: the phone call that never connects, the form that requires impossible documentation, the eligibility rule that appears neutral but excludes those with unstable housing, the digital portal that assumes access, the deadline that punishes disability or language barriers, and the appeal process that exists only for those with time and confidence to use it.

The deeper contribution of design thinking is that it treats public systems as designed systems. Forms, rules, portals, services, letters, queues, eligibility criteria, field offices, call centers, inspection routines, enforcement thresholds, public dashboards, participation formats, and appeals processes are not secondary details. They are the material through which public authority becomes lived reality. Improving them can change whether policy is experienced as support, confusion, exclusion, surveillance, dignity, or punishment.

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Historical Development of Design Thinking in Government

The movement toward design thinking in public policy did not emerge from nowhere. It developed out of broader twentieth-century work on design methods, systems thinking, organizational learning, public administration, service design, participatory planning, behavioral science, implementation research, and the practical reasoning required in complex sociotechnical environments. Herbert Simon’s work on the sciences of the artificial provided an early intellectual foundation for seeing design as a domain concerned with how things ought to be arranged. Richard Buchanan’s work on wicked problems later emphasized that many public and institutional challenges cannot be solved through simple technical deduction because the problem itself is unstable, socially embedded, and interpretively contested.

As design thinking expanded from products into services, organizations, institutions, and policy settings, governments began experimenting with design labs, behavioral units, public-service redesign teams, innovation offices, digital-service teams, civic-tech units, and participatory policy methods. These efforts drew on several overlapping traditions: service design, public-sector innovation, human-centered design, behavioral insights, participatory design, systems thinking, ethnographic research, and digital transformation. Over time, the field became associated with efforts to make policymaking more people-centered, experimental, and open to iteration.

This expansion created genuine methodological innovation, but it also introduced the risk of dilution, especially when design thinking was reduced to facilitation language without corresponding institutional change. The strongest public-policy design work therefore treats design not as a creative style, but as a disciplined civic practice: one that must remain accountable to law, democracy, evidence, equity, and the lived consequences of public authority.

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The Complexity of Public Policy Systems

Public policy problems are rarely simple technical challenges. Healthcare access, climate adaptation, housing affordability, migration governance, education reform, disability support, public safety, income support, infrastructure investment, and labor-market transitions all arise within systems shaped by law, bureaucracy, political incentives, budgets, infrastructures, norms, historical inequality, and unequal social power. A policy designed to improve one part of a system may generate unanticipated burdens elsewhere. A reform that appears elegant in legislation may become opaque, punitive, or fragile in implementation.

Design thinking helps policymakers work through this complexity by resisting the illusion that policies can be perfected in advance through top-down abstraction alone. It introduces a mode of inquiry grounded in observation, stakeholder engagement, service analysis, pilot testing, and revision. This is where public policy design comes into close contact with design thinking and systems thinking, since the success of an intervention depends not only on whether it is desirable in principle, but on how it interacts with the wider institutional environment.

The complexity of public policy systems makes design thinking both useful and limited. Useful, because it brings policy closer to lived reality before scale. Limited, because many public problems cannot be solved by better process alone. The role of design thinking is to improve public learning, not to pretend that complexity can be removed.

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Citizen Experience and the Design of Public Services

Many government programs are designed primarily through legal, administrative, or budgetary frames rather than through the experiences of the people who use them. As a result, services may become fragmented across agencies, procedurally confusing, inaccessible to people with limited time or literacy, or silently dependent on background knowledge that many citizens and residents do not possess. A benefit may exist formally while remaining practically unattainable. A right may exist legally while remaining obscure in use.

Design thinking begins by asking how citizens, residents, patients, claimants, students, workers, tenants, migrants, caregivers, business owners, frontline staff, and communities encounter public systems in everyday life. That requires more than consultation. It requires serious attention to lived experience. This is why policy design depends so heavily on human-centered problem solving and empathy and stakeholder research. Institutions need to understand not only how a program is written, but how it is actually navigated by those who bear its consequences.

Public-service experience is not superficial. It determines whether rights and benefits become meaningful. When the design of access is confusing, humiliating, opaque, or burdensome, public systems can deny people in practice while claiming to serve them in principle. Design thinking helps expose that gap.

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Observation, Interpretation, and Research in Policy Design

Research in design-oriented policymaking involves more than traditional surveys or public comment periods. It often includes interviews, ethnographic observation, contextual inquiry, journey mapping, film ethnography, diary methods, service walkthroughs, co-design sessions, case reviews, call-center analysis, front-desk observation, document audits, form testing, field shadowing, administrative-data review, and interpretive synthesis. These methods help make visible the hidden frictions and asymmetries that formal policy models can miss.

That is crucial because policy failure is often interpretive before it is statistical. A public service may technically function while remaining humiliating, stigmatizing, or impossible to understand. A compliance process may be rational from an institutional perspective while remaining incoherent to the people it governs. A participation process may be open in formal terms while inaccessible to those who work multiple jobs, lack childcare, lack transportation, speak a minority language, mistrust institutions, or fear retaliation. Design thinking contributes a method for reconnecting policy analysis to social experience, which in turn can improve institutional judgment before large-scale implementation occurs.

The strongest policy-design research treats evidence as layered. Qualitative evidence shows how policy is lived. Quantitative evidence shows patterns at scale. Legal analysis shows what is authorized or prohibited. Implementation research shows what institutions can deliver. Systems mapping shows how causes, burdens, incentives, and delays interact. Design thinking becomes credible when it brings these evidence forms together rather than treating anecdote or workshop output as sufficient.

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Problem Framing and the Public Definition of Need

Problem framing is especially important in public policy because the way a problem is defined determines what kinds of solutions become thinkable. A government may frame low benefit uptake as a communication problem when it is actually a documentation burden problem. It may frame late rent payment as individual irresponsibility when it is shaped by wage volatility, childcare instability, or benefit delays. It may frame climate adaptation as infrastructure hardening while ignoring housing insecurity, heat exposure, disability access, insurance gaps, and public-health vulnerability.

Design thinking strengthens policy work by making problem definitions contestable. It asks who defined the problem, who was excluded from definition, what evidence was used, what assumptions were embedded, what historical conditions matter, and what alternative frames might reveal. This is not merely semantic. Different problem frames create different policy instruments, accountability structures, and moral interpretations.

Public problem framing is never neutral. It can blame individuals for structural conditions, hide institutional failure, or make some forms of suffering more visible than others. A serious design-thinking approach treats framing as an ethical and analytical act. Before asking what solution to build, it asks whether the institution has understood the problem honestly.

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Prototyping Policy Solutions

One of the most distinctive contributions of design thinking to public policy is the use of prototypes and pilots. Rather than implementing a policy at full scale immediately, policymakers can test a service, communication strategy, eligibility pathway, workflow, interface, appeal process, inspection routine, benefit-renewal method, public-participation format, or behavioral intervention in a limited setting. These trials make policy visible as something that can be examined and revised before it becomes fixed through statewide or national rollout.

In that respect, policy prototyping serves a similar function to prototyping elsewhere in design practice. It converts abstract proposals into observable realities. This allows governments to see how interventions are interpreted, whether people can actually use them, where operational bottlenecks emerge, which groups remain excluded, and which consequences remain hidden at the planning stage. The point is not simply to be experimental for its own sake. It is to reduce the likelihood that institutional overconfidence will be mistaken for policy adequacy.

Policy prototyping must also be ethically bounded. Governments cannot experiment with people carelessly. Public pilots require attention to consent, transparency, legal authority, equitable selection, risk, reversibility, privacy, due process, and whether a prototype could deny, delay, or distort access to rights and essential services. Public prototyping is legitimate only when it strengthens public learning without treating affected people as disposable test subjects.

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Behavioral Insights and Policy Design

Policy outcomes depend not only on laws or incentives, but on how people interpret information, perceive institutions, manage complexity, and respond to burdens. Behavioral economics and social psychology have shown repeatedly that people do not behave according to simplified models of rational optimization. Framing, defaults, norms, time pressure, trust, identity, stigma, confusion, scarcity, fear, trauma, institutional memory, cognitive overload, and administrative complexity can all shape how policies are encountered in practice.

Design thinking complements these insights by examining how policy is actually presented and experienced. The design of forms, interfaces, letters, deadlines, service channels, waiting rooms, offices, call scripts, public notices, websites, identity-verification processes, and administrative language all influence participation, uptake, compliance, and trust. This creates natural links to work elsewhere on the site in areas such as heuristics and biases, social norms, implicit bias, and behavioral economics. Public systems are not encountered by abstract rational actors, but by situated human beings moving through real institutional conditions.

Behavioral insights are most useful when they reduce unjust burden and make public systems more legible. They are most dangerous when used to manipulate, discipline, or nudge people into compliance while leaving structural problems untouched. Design thinking can help keep behavioral policy grounded in lived experience, consent, and public accountability.

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Policy Innovation Labs and Experimental Government

Governments and public institutions in many countries have established policy innovation labs, public-sector design teams, behavioral units, digital-service teams, civic-innovation offices, and experimental units intended to bring design-led methods into statecraft. These initiatives often combine designers, policymakers, technologists, ethnographers, researchers, data analysts, community stakeholders, and frontline staff in order to improve how governments understand problems and test responses.

The significance of these labs lies not simply in generating new ideas. Their deeper significance lies in changing how institutions learn. Public-sector design labs can create protected spaces for experimentation, cross-disciplinary collaboration, and direct engagement with citizens and residents. They can also help public agencies move from abstract policy intent to testable service, implementation, and governance models. Organizations such as the OECD Observatory of Public Sector Innovation, the UK Policy Lab, and public-sector design initiatives documented by the Design Council have helped formalize a field in which experimentation, participation, and systems thinking are treated as serious parts of policy development rather than optional embellishments.

Policy innovation labs are strongest when they are not isolated from ordinary government. Their purpose should not be to create a small island of creativity within an unchanged bureaucracy. Their purpose should be to improve how public institutions define problems, test options, engage affected people, learn from evidence, and revise policy before harm becomes durable.

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Institutional Constraints and Political Realities

Despite its promise, design thinking does not remove the institutional constraints that shape public policy. Governments operate within constitutions, statutes, administrative law, fiscal limits, political bargaining, electoral pressures, procurement systems, data restrictions, collective-bargaining agreements, agency cultures, media scrutiny, and bureaucratic routines that can either support or inhibit experimentation. Policy decisions often involve competing values, unequal power, and distributive conflict rather than merely technical problem-solving.

For that reason, design thinking in public policy should be understood as a complement to traditional policy analysis rather than a substitute for it. It can improve how institutions define problems, gather evidence, prototype interventions, and learn from implementation. It cannot abolish politics, nor can it resolve disputes about legitimacy, justice, taxation, coercion, public priority, or democratic authority by procedural creativity alone.

Design thinking becomes more credible when it acknowledges these constraints rather than pretending they can be bypassed through creativity. A serious public-design practice understands that policy innovation must be lawful, governable, fundable, legitimate, equitable, and politically intelligible. The design task is not only to imagine better interventions, but to understand the institutional conditions under which they can responsibly exist.

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Design Thinking and Democratic Governance

Design thinking can strengthen democratic governance when it enables more meaningful forms of listening, participation, and institutional responsiveness. When policymakers engage communities through well-structured people-centered methods, they gain access to forms of evidence that are often excluded from elite policy debate. They can better understand how policies affect different groups, how trust is built or damaged, how official assumptions diverge from public experience, and where public legitimacy depends on more than procedural compliance.

But participatory design is not democratic by default. Much depends on who is invited, who is legible to the institution, whose input is translated into action, and whether participation is merely consultative or actually consequential. Public participation can be used to broaden legitimacy, but it can also become symbolic if institutions selectively hear only the voices that fit existing frameworks. Serious design thinking therefore requires a more reflective account of representation, power, and public accountability.

Public design practice should therefore be careful with the language of co-creation. Co-creation is meaningful only when participants have real influence, adequate information, safe conditions, fair compensation when appropriate, and visible pathways from participation to institutional change. Without that, design thinking can become another way of making unequal systems appear more inclusive than they are.

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Administrative Burden, Access, and Public Legibility

Administrative burden is one of the most important concepts for connecting design thinking to public policy. Burden appears when people must learn complex rules, gather documents, complete forms, wait on hold, travel to offices, prove eligibility repeatedly, navigate appeals, interpret letters, manage deadlines, or absorb psychological stress in order to access something the public system claims to provide. These burdens are not evenly distributed. They often fall hardest on people already facing poverty, disability, unstable housing, language barriers, immigration precarity, digital exclusion, caregiving responsibilities, or low trust in institutions.

Design thinking helps make administrative burden visible. It treats the path to access as part of the policy itself rather than a secondary implementation detail. A policy that is difficult to use is not fully designed. A benefit that people cannot claim is not fully available. A right that requires extraordinary procedural competence is not equally enjoyed. Public legibility—the ability of people to understand what the state is doing, why it is doing it, what choices they have, and how to challenge decisions—is a core design problem for democratic administration.

Reducing administrative burden is not always simple. Some verification, documentation, and procedural safeguards may be legally or ethically necessary. The design question is not whether every burden should disappear. It is whether burdens are necessary, proportionate, intelligible, equitable, and placed on the institution whenever possible rather than transferred to the people with the least capacity to carry them.

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Systems Thinking, Implementation, and Policy Feedback

Public policy design becomes much more powerful when it is combined with implementation analysis and systems thinking. Policies do not act on blank space. They enter ecosystems of agencies, jurisdictions, legacy systems, frontline workers, digital platforms, contractors, communities, courts, regulators, advocacy groups, media narratives, and public expectations. A well-intentioned intervention can fail if it generates burdens downstream, collides with institutional capacity, shifts costs onto actors who were never considered in the original design, or creates feedback loops that undermine its own goals.

This is why implementation must be treated as part of the design problem itself. Testing and piloting are useful only if the lessons learned are integrated back into policy revision. In that sense, policy design is inseparable from policy feedback. Governments need to see how a policy performs in practice, not only how it was justified at the moment of adoption.

Design thinking helps public institutions test how policy is experienced. Systems thinking helps them understand why that experience is produced and why it may persist. Implementation analysis helps determine whether change can survive the ordinary conditions of government. The strongest policy-design practice integrates all three.

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Ethics, Power, and Inclusion in Public Design Practice

Ethics, power, and inclusion are central to design thinking in policy because public institutions do not affect all groups equally. A service redesign that appears efficient may deepen exclusion if it assumes digital access, literacy, linguistic fluency, transport availability, stable housing, disability accommodation, or freedom from stigma. A participatory process may appear inclusive while privileging those with time, confidence, social capital, and prior institutional familiarity. A policy pilot may appear successful while excluding non-users, dropouts, undocumented residents, unhoused people, informal workers, caregivers, disabled people, or communities with deep reasons to distrust government.

Serious public design practice therefore asks not only whether a policy works, but for whom, under what conditions, with what authority, and at whose expense. It asks whether burdens are being redistributed invisibly, whether marginal voices are genuinely shaping the design, whether participation is consequential, and whether the state is using design language to soften or obscure harder questions of justice. Human-centered practice without attention to power is too easily absorbed into administrative self-congratulation.

Power is not external to public policy design. It is embedded in eligibility criteria, documentation requirements, enforcement discretion, data categories, service channels, public notices, consultation processes, procurement systems, and evaluation metrics. A serious design-thinking approach does not hide power behind empathy. It studies how public systems distribute it.

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AI-Assisted Public Policy Design and Its Limits

AI-assisted tools can support public policy design by helping teams synthesize consultation responses, cluster service feedback, identify recurring administrative barriers, analyze complaint themes, summarize qualitative research, compare pilot portfolios, model uncertainty, translate documents into plain language drafts, and detect patterns in implementation data. Used carefully, these tools can help public institutions inspect complexity more systematically and document design assumptions more transparently.

However, AI-assisted policy design also carries significant risks. Public institutions can use AI to make weak evidence appear precise, automate flawed categories, scale surveillance, obscure accountability, or flatten minority experiences into generic summaries. AI systems may encode historical bias, exclude people who are missing from data, and encourage institutions to optimize measurable outputs while ignoring dignity, legitimacy, trust, and procedural justice. In public policy, automation is never merely technical. It is an exercise of public power.

AI is most useful in public design when it strengthens traceability, comparison, accessibility, and disciplined inquiry. It is most dangerous when it becomes a shortcut around democratic participation, legal accountability, ethical judgment, and direct engagement with affected people. Public institutions should use AI to support public reasoning, not to evade it.

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Methods and Measurement in Public Policy Design

Public policy design needs stronger methods and more disciplined forms of measurement if it is to remain more than an attractive vocabulary. Not everything that matters can be reduced to a single outcome metric, but policymakers still need robust ways to assess whether a design-led intervention is improving access, comprehension, uptake, completion, compliance, procedural dignity, satisfaction, trust, equity, administrative effectiveness, and long-term public value.

That assessment should usually combine qualitative and quantitative evidence. A pilot may show improved completion rates while worsening stigma. A redesign may reduce transaction time while increasing exclusion for people with limited digital access. A new digital service may increase throughput while reducing appeal access. A participatory process may increase attendance while leaving decision power unchanged. Design thinking becomes most credible in policy settings when it combines institutional data with contextual inquiry, implementation feedback, public accountability, and a transparent account of what counts as improvement.

Public measurement should also include a learning agenda. What assumptions are being tested? What evidence would justify scale? What evidence would require revision? What harms would trigger pause? Who has authority to change the policy based on findings? Without these questions, measurement becomes reporting rather than learning.

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The Limits of Design Thinking in Policy

Design thinking provides valuable tools for improving the quality of policy development, but it cannot solve every public problem. Some policy disputes are not the result of poor design alone. They are rooted in disagreements about taxation, distribution, policing, welfare, migration, ecological responsibility, labor rights, sovereignty, public goods, property, and democratic authority. These are normative and political conflicts, not simply design failures.

The danger arises when design thinking is treated as a universal solvent for public disagreement. In such cases, political contestation can be reframed misleadingly as a matter of empathy gaps, process inefficiency, poor communication, or insufficient creativity. That weakens the field. Design thinking is strongest when it is modest about what it can do: clarify problems, reveal lived realities, improve service design, make implementation more empirical, reduce avoidable burden, support participation, and help institutions learn before scaling error.

Design thinking is not enough by itself. Public policy also requires law, democratic contestation, public finance, institutional capacity, civil rights, administrative accountability, labor protections, regulatory competence, and moral clarity. Design thinking becomes valuable when it strengthens those public responsibilities rather than replacing them with aesthetic or procedural optimism.

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Cross-Pillar Connections

Design thinking in public policy connects naturally to several knowledge areas. It depends on systems thinking because policy effects arise from relationships among agencies, rules, incentives, information flows, feedback loops, and public behavior. It connects to behavioral economics because defaults, friction, scarcity, trust, loss aversion, and status quo bias shape how people encounter public choices. It connects to institutions and governance because public policy is never only an intervention; it is an exercise of authority.

It also connects to social psychology, organizational psychology, data systems, artificial intelligence, sustainability, and risk and resilience. Public policy is not a narrow administrative activity. It is a social, institutional, technological, legal, and moral practice. Design thinking becomes especially useful when it helps connect those dimensions rather than reducing public problems to service-interface issues.

The broader lesson is that public design cannot be isolated from the disciplines that explain public life. It must be human-centered, but also legal, institutional, behavioral, technological, ethical, and systems-aware. That is what gives design thinking in public policy its seriousness.

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Mathematical Lens: Modeling Public Policy Design Under Constraint

Design thinking in public policy is not reducible to equations, but formal models can clarify the trade-offs institutions are already making. One useful abstraction is to treat a proposed policy intervention \(i\) as a candidate design evaluated across several public dimensions:

Here \(A_i\) represents administrative usability or accessibility, \(F_i\) feasibility, \(L_i\) legitimacy or public acceptability, \(E_i\) equity adequacy, and \(R_i\) implementation risk. The weights \(w_a, w_f, w_l, w_e,\) and \(w_r\) represent public priorities or institutional emphasis. This does not turn policy into pure optimization. It simply makes explicit the fact that governments are always balancing competing considerations, whether they articulate those balances clearly or not.

Policy learning across pilots can also be modeled iteratively. Let policy quality at round \(t\) depend on changes in uptake \(U_t\), citizen friction \(C_t\), and implementation error \(I_t\):

Administrative burden can be represented as a distributed quantity across citizens, frontline staff, community intermediaries, and future maintainers:

A portfolio approach is also useful. If each pilot has probability \(p_i\) of scaling successfully, expected portfolio value may be expressed as:

This matters because some pilots are valuable not only when they succeed, but also when they reveal critical design flaws early enough to prevent statewide or national policy failure. In that sense, design thinking in public policy can be linked productively to decision science, implementation research, evaluation, and systems modeling without losing its interpretive and human-centered core.

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R Workflow: Policy Pilot Prioritization Under Competing Public Goals

The R workflow below evaluates a portfolio of public-policy pilot concepts across accessibility, feasibility, legitimacy, equity, administrative-burden reduction, implementation durability, and implementation risk. It then performs scenario-based sensitivity analysis to show how rankings change when policymakers emphasize different public goals. This is useful when teams need to make trade-offs more transparent and less dependent on intuition alone.

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

library(tidyverse)
library(scales)

# -------------------------------------------------------------------
# Example public-policy pilot portfolio.
# Each concept is scored on multiple public-value dimensions.
# Higher risk means greater implementation penalty.
# -------------------------------------------------------------------

policy_pilots <- tibble(
  pilot = c(
    "Benefits Application Simplification",
    "Mobile Community Health Enrollment",
    "Digital Licensing Renewal Redesign",
    "School Meals Access Outreach",
    "Tenant Rights Navigation Service",
    "Disability Benefit Renewal Redesign",
    "Climate Resilience Grant Access Pilot",
    "Small Business Compliance Assistance"
  ),
  policy_domain = c(
    "social_protection",
    "public_health",
    "licensing",
    "education",
    "housing",
    "disability_support",
    "climate_adaptation",
    "economic_development"
  ),
  accessibility = c(8.9, 8.4, 7.5, 8.7, 8.6, 8.8, 8.1, 7.9),
  feasibility = c(8.1, 7.2, 8.5, 7.8, 7.0, 6.9, 6.8, 8.2),
  legitimacy = c(8.0, 8.6, 7.4, 8.5, 8.7, 8.4, 8.2, 7.8),
  equity = c(8.8, 9.1, 6.9, 8.9, 9.0, 9.2, 8.7, 7.5),
  burden_reduction = c(9.0, 8.0, 7.6, 8.4, 8.7, 9.1, 8.3, 8.1),
  durability = c(7.8, 7.4, 8.0, 7.6, 7.3, 7.2, 7.1, 7.9),
  risk = c(3.2, 4.4, 3.8, 3.5, 4.8, 5.0, 4.9, 3.7),
  evidence_quality = c(0.78, 0.72, 0.76, 0.75, 0.70, 0.69, 0.71, 0.77),
  stakeholder_coverage = c(0.74, 0.77, 0.66, 0.72, 0.82, 0.80, 0.76, 0.68)
)

# -------------------------------------------------------------------
# Weighted public design value function.
# This makes policy trade-offs explicit and reviewable.
# -------------------------------------------------------------------

score_policy_pilots <- function(data, wa, wf, wl, we, wb, wd, wr) {
  data %>%
    mutate(
      evidence_strength =
        0.55 * evidence_quality +
        0.45 * stakeholder_coverage,
      policy_value =
        wa * accessibility +
        wf * feasibility +
        wl * legitimacy +
        we * equity +
        wb * burden_reduction +
        wd * durability -
        wr * risk,
      evidence_adjusted_value =
        policy_value * (0.75 + 0.25 * evidence_strength),
      learning_priority =
        0.30 * risk +
        0.20 * (1 - evidence_quality) * 10 +
        0.20 * (1 - stakeholder_coverage) * 10 +
        0.15 * (10 - equity) +
        0.15 * (10 - burden_reduction)
    ) %>%
    arrange(desc(policy_value))
}

# -------------------------------------------------------------------
# Strategic weighting scenarios.
# These reflect different public-policy orientations.
# -------------------------------------------------------------------

scenarios <- tribble(
  ~scenario,               ~wa,  ~wf,  ~wl,  ~we,  ~wb,  ~wd,  ~wr,
  "Balanced",              0.20, 0.16, 0.16, 0.20, 0.14, 0.08, 0.06,
  "Feasibility-first",     0.16, 0.34, 0.12, 0.14, 0.10, 0.08, 0.06,
  "Equity-first",          0.14, 0.12, 0.12, 0.38, 0.14, 0.06, 0.04,
  "Legitimacy-first",      0.16, 0.12, 0.34, 0.16, 0.10, 0.08, 0.04,
  "Burden-reduction",      0.18, 0.12, 0.12, 0.18, 0.34, 0.04, 0.02,
  "Durability-first",      0.14, 0.16, 0.14, 0.16, 0.10, 0.26, 0.04,
  "Risk-sensitive",        0.17, 0.16, 0.16, 0.18, 0.13, 0.06, 0.14
)

# -------------------------------------------------------------------
# Evaluate all pilots under each scenario.
# -------------------------------------------------------------------

scenario_results <- scenarios %>%
  rowwise() %>%
  do(
    score_policy_pilots(
      policy_pilots,
      wa = .$wa,
      wf = .$wf,
      wl = .$wl,
      we = .$we,
      wb = .$wb,
      wd = .$wd,
      wr = .$wr
    ) %>%
      mutate(scenario = .$scenario)
  ) %>%
  ungroup()

# Rank pilots within each scenario.
ranked_results <- scenario_results %>%
  group_by(scenario) %>%
  arrange(desc(policy_value), .by_group = TRUE) %>%
  mutate(rank = row_number()) %>%
  ungroup()

print(ranked_results)

# -------------------------------------------------------------------
# Rank stability across public priorities.
# -------------------------------------------------------------------

rank_stability <- ranked_results %>%
  group_by(pilot, policy_domain) %>%
  summarize(
    mean_rank = mean(rank),
    best_rank = min(rank),
    worst_rank = max(rank),
    rank_range = worst_rank - best_rank,
    mean_policy_value = mean(policy_value),
    mean_evidence_adjusted_value = mean(evidence_adjusted_value),
    mean_learning_priority = mean(learning_priority),
    .groups = "drop"
  ) %>%
  arrange(mean_rank, rank_range)

print(rank_stability)

# -------------------------------------------------------------------
# Visualize how policy rankings shift across scenarios.
# -------------------------------------------------------------------

ggplot(ranked_results, aes(x = pilot, y = policy_value, group = scenario)) +
  geom_point(size = 3) +
  geom_line(aes(color = scenario), linewidth = 1) +
  coord_flip() +
  labs(
    title = "Public Policy Pilot Value Across Strategic Weighting Scenarios",
    x = "Policy Pilot",
    y = "Weighted Public Design Value"
  ) +
  theme_minimal(base_size = 12)

# -------------------------------------------------------------------
# Count how often each pilot ranks first.
# This gives a simple robustness summary.
# -------------------------------------------------------------------

top_rank_summary <- ranked_results %>%
  filter(rank == 1) %>%
  count(pilot, name = "times_ranked_first") %>%
  arrange(desc(times_ranked_first))

print(top_rank_summary)

# -------------------------------------------------------------------
# Export scored results for reporting or dashboard use.
# -------------------------------------------------------------------

write_csv(ranked_results, "public_policy_design_pilot_prioritization.csv")
write_csv(rank_stability, "public_policy_design_rank_stability.csv")
write_csv(top_rank_summary, "public_policy_design_top_rank_summary.csv")

This workflow supports more mature policy discussion because it reveals how institutional priorities affect public decisions. A pilot that looks strongest under a feasibility-first frame may not be strongest under an equity-first, burden-reduction, legitimacy-first, or risk-sensitive frame. Making those differences visible is part of responsible public design.

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Python Workflow: Uncertainty Analysis for Public Policy Prototypes

The Python workflow below extends the same logic with Monte Carlo simulation. Instead of assuming that pilot scores are known with certainty, it models uncertainty in accessibility, feasibility, legitimacy, equity, administrative-burden reduction, durability, and implementation risk. This helps public teams estimate which pilot is most robust when the available evidence remains incomplete.

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

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

# ---------------------------------------------------------------------
# Example public-policy pilot concepts.
# ---------------------------------------------------------------------

policy_pilots = pd.DataFrame({
    "pilot": [
        "Benefits Application Simplification",
        "Mobile Community Health Enrollment",
        "Digital Licensing Renewal Redesign",
        "School Meals Access Outreach",
        "Tenant Rights Navigation Service",
        "Disability Benefit Renewal Redesign",
        "Climate Resilience Grant Access Pilot",
        "Small Business Compliance Assistance"
    ],
    "policy_domain": [
        "social_protection",
        "public_health",
        "licensing",
        "education",
        "housing",
        "disability_support",
        "climate_adaptation",
        "economic_development"
    ],
    "accessibility": [8.9, 8.4, 7.5, 8.7, 8.6, 8.8, 8.1, 7.9],
    "feasibility": [8.1, 7.2, 8.5, 7.8, 7.0, 6.9, 6.8, 8.2],
    "legitimacy": [8.0, 8.6, 7.4, 8.5, 8.7, 8.4, 8.2, 7.8],
    "equity": [8.8, 9.1, 6.9, 8.9, 9.0, 9.2, 8.7, 7.5],
    "burden_reduction": [9.0, 8.0, 7.6, 8.4, 8.7, 9.1, 8.3, 8.1],
    "durability": [7.8, 7.4, 8.0, 7.6, 7.3, 7.2, 7.1, 7.9],
    "risk": [3.2, 4.4, 3.8, 3.5, 4.8, 5.0, 4.9, 3.7],
    "evidence_quality": [0.78, 0.72, 0.76, 0.75, 0.70, 0.69, 0.71, 0.77],
    "stakeholder_coverage": [0.74, 0.77, 0.66, 0.72, 0.82, 0.80, 0.76, 0.68]
})

# ---------------------------------------------------------------------
# Baseline public-policy design weights.
# ---------------------------------------------------------------------

weights = {
    "accessibility": 0.20,
    "feasibility": 0.16,
    "legitimacy": 0.16,
    "equity": 0.20,
    "burden_reduction": 0.14,
    "durability": 0.08,
    "risk": 0.06
}

# ---------------------------------------------------------------------
# Weighted policy value function.
# Higher risk reduces final value.
# ---------------------------------------------------------------------

def compute_policy_value(df, weights_dict):
    result = df.copy()

    result["evidence_strength"] = (
        0.55 * result["evidence_quality"] +
        0.45 * result["stakeholder_coverage"]
    )

    result["policy_value"] = (
        weights_dict["accessibility"] * result["accessibility"] +
        weights_dict["feasibility"] * result["feasibility"] +
        weights_dict["legitimacy"] * result["legitimacy"] +
        weights_dict["equity"] * result["equity"] +
        weights_dict["burden_reduction"] * result["burden_reduction"] +
        weights_dict["durability"] * result["durability"] -
        weights_dict["risk"] * result["risk"]
    )

    result["evidence_adjusted_value"] = (
        result["policy_value"] *
        (0.75 + 0.25 * result["evidence_strength"])
    )

    result["learning_priority"] = (
        0.30 * result["risk"] +
        0.20 * (1 - result["evidence_quality"]) * 10 +
        0.20 * (1 - result["stakeholder_coverage"]) * 10 +
        0.15 * (10 - result["equity"]) +
        0.15 * (10 - result["burden_reduction"])
    )

    return result.sort_values("policy_value", ascending=False)

baseline_results = compute_policy_value(policy_pilots, weights)

print("Baseline policy pilot ranking:")
print(
    baseline_results[
        [
            "pilot",
            "policy_domain",
            "policy_value",
            "evidence_adjusted_value",
            "learning_priority"
        ]
    ]
)

# ---------------------------------------------------------------------
# Monte Carlo simulation.
# Allow each score to vary around current estimates.
# This approximates uncertainty in early policy development.
# ---------------------------------------------------------------------

np.random.seed(42)
n_simulations = 10000
simulation_winners = []
simulation_records = []

score_columns = [
    "accessibility",
    "feasibility",
    "legitimacy",
    "equity",
    "burden_reduction",
    "durability",
    "risk"
]

for simulation_id in range(n_simulations):
    simulated = policy_pilots.copy()

    for col in score_columns:
        simulated[col] = np.random.normal(
            loc=policy_pilots[col],
            scale=0.6
        ).clip(1, 10)

    simulated_results = compute_policy_value(simulated, weights)
    winner = simulated_results.iloc[0]["pilot"]
    simulation_winners.append(winner)

    simulated_results = simulated_results.reset_index(drop=True)

    for rank, row in simulated_results.iterrows():
        simulation_records.append({
            "simulation_id": simulation_id,
            "pilot": row["pilot"],
            "policy_domain": row["policy_domain"],
            "policy_value": row["policy_value"],
            "evidence_adjusted_value": row["evidence_adjusted_value"],
            "learning_priority": row["learning_priority"],
            "rank": rank + 1
        })

# ---------------------------------------------------------------------
# Estimate the probability each pilot ranks first.
# ---------------------------------------------------------------------

winner_summary = (
    pd.Series(simulation_winners)
    .value_counts(normalize=True)
    .rename("probability_ranked_first")
    .reset_index()
)

winner_summary.columns = ["pilot", "probability_ranked_first"]
winner_summary["probability_ranked_first"] *= 100

print("\nProbability each pilot ranks first:")
print(winner_summary)

# ---------------------------------------------------------------------
# Rank stability under uncertainty.
# ---------------------------------------------------------------------

simulation_df = pd.DataFrame(simulation_records)

rank_stability = (
    simulation_df
    .groupby(["pilot", "policy_domain"])
    .agg(
        mean_policy_value=("policy_value", "mean"),
        sd_policy_value=("policy_value", "std"),
        mean_evidence_adjusted_value=("evidence_adjusted_value", "mean"),
        mean_learning_priority=("learning_priority", "mean"),
        median_rank=("rank", "median"),
        mean_rank=("rank", "mean"),
        best_rank=("rank", "min"),
        worst_rank=("rank", "max")
    )
    .reset_index()
    .sort_values(["median_rank", "mean_rank"])
)

print("\nRank stability:")
print(rank_stability)

# ---------------------------------------------------------------------
# Random-weight sensitivity.
# This tests how rankings change when public priorities shift.
# ---------------------------------------------------------------------

criteria = [
    "accessibility",
    "feasibility",
    "legitimacy",
    "equity",
    "burden_reduction",
    "durability",
    "risk"
]

n_weight_samples = 10000
random_weight_winners = []

for _ in range(n_weight_samples):
    sampled = np.random.dirichlet(np.ones(len(criteria)))
    sampled_weights = dict(zip(criteria, sampled))

    sampled_results = compute_policy_value(policy_pilots, sampled_weights)
    random_weight_winners.append(sampled_results.iloc[0]["pilot"])

weight_sensitivity = (
    pd.Series(random_weight_winners)
    .value_counts(normalize=True)
    .rename("probability_winning_under_random_weights")
    .reset_index()
)

weight_sensitivity.columns = [
    "pilot",
    "probability_winning_under_random_weights"
]

weight_sensitivity["probability_winning_under_random_weights"] *= 100

print("\nWeight sensitivity:")
print(weight_sensitivity)

# ---------------------------------------------------------------------
# Plot the robustness of each pilot under uncertainty.
# ---------------------------------------------------------------------

plt.figure(figsize=(10, 6))
plt.bar(winner_summary["pilot"], winner_summary["probability_ranked_first"])
plt.xticks(rotation=20, ha="right")
plt.ylabel("Probability of Ranking First (%)")
plt.title("Robustness of Public Policy Prototypes Under Uncertainty")
plt.tight_layout()
plt.show()

# ---------------------------------------------------------------------
# Export summaries for reporting or policy review.
# ---------------------------------------------------------------------

baseline_results.to_csv("baseline_public_policy_design_scores.csv", index=False)
winner_summary.to_csv("public_policy_prototype_uncertainty_results.csv", index=False)
rank_stability.to_csv("public_policy_prototype_rank_stability.csv", index=False)
weight_sensitivity.to_csv("public_policy_prototype_weight_sensitivity.csv", index=False)
simulation_df.to_csv("public_policy_prototype_simulation_records.csv", index=False)

This workflow is especially useful in public settings because it discourages false confidence. Rather than treating one policy prototype as decisively superior on the basis of preliminary scoring, it shows how rankings behave when uncertainty is introduced. That is often a more honest representation of real policy design conditions.

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GitHub Repository

The companion repository provides a reproducible technical workspace for exploring the modeling, simulation, documentation, and implementation ideas associated with this article. The article folder is organized for multi-language public-policy design research and includes folders for Python, R, Julia, C++, Fortran, C, Rust, Go, SQL, notebooks, documentation, raw data, processed data, and outputs.

The repository structure is designed to support reproducible public-policy design research rather than isolated code examples. The language-specific folders allow the same policy-prioritization logic to be explored across statistical, scientific, systems, and database workflows. The documentation and data folders help preserve assumptions, provenance, evidence limits, public-value weights, stakeholder coverage, administrative-burden analysis, risk registers, implementation notes, and learning artifacts so that policy-design judgments remain traceable.

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Conclusion

Design thinking in public policy matters because governments are asked to act in situations where the stakes are high, the systems are complex, the knowledge is incomplete, and the consequences of institutional error are borne unequally. Under those conditions, the value of design thinking lies not in creative spectacle but in disciplined inquiry. It helps public institutions understand how policies are actually lived, how services are actually used, where burdens and exclusions are produced, and how prototypes, pilots, and participatory methods can improve policy judgment before full-scale failure occurs.

Its strongest contribution is to make policymaking more empirical, interpretive, and responsive without pretending that politics disappears. It complements law, administration, policy analysis, and democratic governance by reconnecting them to lived reality. It also deepens public reasoning by showing that implementation, intelligibility, accessibility, legitimacy, procedural dignity, and equity are not afterthoughts. They are part of policy design from the beginning.

The field is weakened when it is reduced to a bland vocabulary of innovation or treated as a substitute for democratic conflict. It is strongest when treated as a serious method for learning within institutions: one that draws on observation, systems awareness, experimentation, public accountability, ethics, and implementation evidence. In that sense, design thinking in public policy is not merely about improving services. It is about improving how public institutions learn from the people they govern.

A mature public-policy design practice does not ask only whether an intervention is administratively efficient. It asks whether the policy is understandable, lawful, equitable, accessible, legitimate, dignified, operationally feasible, and capable of learning from its own consequences. That is a demanding standard. It is also the standard public policy requires.

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Related articles

• What Is Design Thinking?
• Human-Centered Problem Solving
• Empathy and Stakeholder Research in Design Thinking
• Design Research Methods: Contextual Inquiry and Synthesis
• Problem Framing in Design Thinking
• Insight Generation in Design Thinking
• Prototyping in Design Thinking
• Testing and Validation in Design Thinking
• Iteration and Experimentation in Design Thinking
• Implementation and Scaling in Design Thinking
• Design Evaluation, Learning, and Outcome Measurement
• Design Thinking and Systems Thinking
• Design Thinking for Sustainability

Further reading

• Bason, C. (2014) Design for Policy. Farnham: Gower. Available at: https://www.routledge.com/Design-for-Policy/Bason/p/book/9781472423870.
• Bason, C. (ed.) (2010) Leading Public Sector Innovation: Co-creating for a Better Society. Bristol: Policy Press. Available at: https://policy.bristoluniversitypress.co.uk/leading-public-sector-innovation.
• Bason, C. (2017) Leading Public Design: Discovering Human-Centred Governance. Bristol: Policy Press. Available at: https://policy.bristoluniversitypress.co.uk/leading-public-design.
• Buchanan, R. (1992) ‘Wicked problems in design thinking’, Design Issues, 8(2), pp. 5–21. Available at: https://www.jstor.org/stable/1511637.
• Design Council (2013) Design for Public Good. Available at: https://www.designcouncil.org.uk/fileadmin/uploads/dc/Documents/Design%20for%20Public%20Good.pdf.
• Design Council (no date) Design in the Public Sector. Available at: https://www.designcouncil.org.uk/our-resources/archive/case-studies/design-in-the-public-sector/.
• Herd, P. and Moynihan, D.P. (2018) Administrative Burden: Policymaking by Other Means. New York: Russell Sage Foundation. Available at: https://www.russellsage.org/publications/administrative-burden.
• Kimbell, L. and Bailey, J. (2021) Public Sector Understanding and Use of Design and Design Skills. Design Council. Available at: https://www.designcouncil.org.uk/fileadmin/uploads/dc/Documents/DE21/Design_Economy_2021_Papers_Paper_4.pdf.
• OECD (2017) Fostering Innovation in the Public Sector. Paris: OECD Publishing. Available at: https://www.oecd.org/content/dam/oecd/en/publications/reports/2017/04/fostering-innovation-in-the-public-sector_g1g75c6c/9789264270879-en.pdf.
• OECD (2019) Declaration on Public Sector Innovation. Available at: https://legalinstruments.oecd.org/en/instruments/oecd-legal-0450.
• OECD (2020) The Public Sector Innovation Lifecycle. Available at: https://www.oecd.org/content/dam/oecd/en/publications/reports/2020/05/the-public-sector-innovation-lifecycle_2359c685/0d1bf7e7-en.pdf.
• OECD (no date) Government Innovation. Available at: https://www.oecd.org/en/topics/government-innovation.html.
• Policy Lab (2020) ‘Introducing a “Government as a System” toolkit’, Policy Lab. Available at: https://openpolicy.blog.gov.uk/2020/03/06/introducing-a-government-as-a-system-toolkit/.
• Policy Lab (2024) ‘People-centred and participatory policymaking’, Policy Lab. Available at: https://openpolicy.blog.gov.uk/2024/11/19/people-centred-and-participatory-policymaking/.
• Simon, H.A. (1996) The Sciences of the Artificial. 3rd edn. Cambridge, MA: MIT Press. Available at: https://mitpress.mit.edu/9780262537537/the-sciences-of-the-artificial/.

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References

• Bason, C. (ed.) (2010) Leading Public Sector Innovation: Co-creating for a Better Society. Bristol: Policy Press. Available at: https://policy.bristoluniversitypress.co.uk/leading-public-sector-innovation.
• Bason, C. (2014) Design for Policy. Farnham: Gower. Available at: https://www.routledge.com/Design-for-Policy/Bason/p/book/9781472423870.
• Bason, C. (2017) Leading Public Design: Discovering Human-Centred Governance. Bristol: Policy Press. Available at: https://policy.bristoluniversitypress.co.uk/leading-public-design.
• Buchanan, R. (1992) ‘Wicked problems in design thinking’, Design Issues, 8(2), pp. 5–21. Available at: https://www.jstor.org/stable/1511637.
• Design Council (2011) Design for Innovation. Available at: https://www.designcouncil.org.uk/fileadmin/uploads/dc/Documents/DesignForInnovation_Dec2011.pdf.
• Design Council (2013) Design for Public Good. Available at: https://www.designcouncil.org.uk/fileadmin/uploads/dc/Documents/Design%20for%20Public%20Good.pdf.
• Herd, P. and Moynihan, D.P. (2018) Administrative Burden: Policymaking by Other Means. New York: Russell Sage Foundation. Available at: https://www.russellsage.org/publications/administrative-burden.
• Kimbell, L. and Bailey, J. (2021) Public Sector Understanding and Use of Design and Design Skills. Design Council. Available at: https://www.designcouncil.org.uk/fileadmin/uploads/dc/Documents/DE21/Design_Economy_2021_Papers_Paper_4.pdf.
• OECD (2017) Fostering Innovation in the Public Sector. Paris: OECD Publishing. Available at: https://www.oecd.org/content/dam/oecd/en/publications/reports/2017/04/fostering-innovation-in-the-public-sector_g1g75c6c/9789264270879-en.pdf.
• OECD (2019) Declaration on Public Sector Innovation. Available at: https://legalinstruments.oecd.org/en/instruments/oecd-legal-0450.
• OECD (2020) The Public Sector Innovation Lifecycle. Available at: https://www.oecd.org/content/dam/oecd/en/publications/reports/2020/05/the-public-sector-innovation-lifecycle_2359c685/0d1bf7e7-en.pdf.
• OECD (no date) Government Innovation. Available at: https://www.oecd.org/en/topics/government-innovation.html.
• Policy Lab (2020) ‘Introducing a “Government as a System” toolkit’, Policy Lab. Available at: https://openpolicy.blog.gov.uk/2020/03/06/introducing-a-government-as-a-system-toolkit/.
• Policy Lab (2024) ‘Reflecting on 10 years of design, innovation and people-centred approaches in policy’, Policy Lab. Available at: https://openpolicy.blog.gov.uk/2024/03/21/reflecting-on-10-years-of-design-innovation-and-people-centred-approaches-in-policy/.
• Policy Lab (2024) ‘People-centred and participatory policymaking’, Policy Lab. Available at: https://openpolicy.blog.gov.uk/2024/11/19/people-centred-and-participatory-policymaking/.
• Policy Lab (no date) About Policy Lab. Available at: https://openpolicy.blog.gov.uk/about/.
• Simon, H.A. (1996) The Sciences of the Artificial. 3rd edn. Cambridge, MA: MIT Press. Available at: https://mitpress.mit.edu/9780262537537/the-sciences-of-the-artificial/.

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