Democracy needs shared standards of evidence and institutions that can learn.
Democracy does not require agreement on every
factual claim, but it does require shared procedures for distinguishing
evidence, interpretation, uncertainty, and deliberate deception. A common
epistemic foundation therefore consists not of one official account of reality,
but of institutions and practices through which claims can be tested,
challenged, corrected, and provisionally relied upon[1].
Misinformation—false or misleading information
regardless of intent—must be distinguished from deliberate disinformation,
reasonable factual disagreement, value conflict, satire, and scientific
uncertainty. Treating these different phenomena as one category produces
ineffective policy and excessive control. Regulation should focus on
identifiable conduct and system design, such as impersonation, concealed
sponsorship, coordinated manipulation, fraudulent provenance, and platform
incentives, while leaving contested interpretation and lawful dissent open to
public inquiry.
The epistemic order is therefore not a by-product
of successful democracy but one of its constitutive conditions. Maintaining it
requires active institutional support for education, science, independent
research, journalism, public statistics, and reliable information systems.
These institutions produce, verify, distribute, and correct knowledge
independently of immediate political or commercial interests.
Education should develop more than vocational
competence. Scientific literacy, media literacy, source evaluation, and
argumentative reasoning are necessary democratic capacities. Citizens must be
able to distinguish evidence from interpretation and manipulation. Because
educational and socioeconomic inequalities produce unequal capacities to assess
information, epistemic equality requires both access to reliable knowledge and
the practical ability to understand and use it[2].
Scientific institutions, statistical agencies,
and independent research organizations need legal autonomy, adequate funding,
transparent methods, open access to findings, and protection against political
or economic pressure. Their authority should not rest on claims of
infallibility, but on procedures that expose knowledge to criticism,
verification, and revision[3].
Independence also requires disclosure and
management of conflicts of interest, protection of research integrity,
transparent funding, accessible data and methods where lawful, and procedures
for correcting the public record.
Digital platforms and media infrastructures
increasingly determine which information becomes visible and influential. Attention-based
platform design can create incentives for emotionally engaging or sensational
material, but effects on polarization, knowledge, and participation vary by
platform, user group, issue, and political context. Regulation should therefore
focus on demonstrable systemic risks, transparency, researcher access, and
accountable design processes rather than assume one uniform causal effect. Platform
regulation should require transparency, accountability, pluralism, and
independent scrutiny of algorithmic selection[4].
Its aim should be to protect the conditions under which information can be
evaluated, rather than to impose an official doctrine or political censorship[5].
Freedom of expression remains indispensable for
pluralism, criticism, and social learning. Yet meaningful expression
presupposes an environment in which claims can be tested and corrected. The
objective is not to eliminate disagreement or alternative knowledge, but to
preserve sufficient common ground for disagreement to remain intelligible and
politically productive.
Epistemic correction must be institutionally
organized through peer review, journalistic verification, fact-checking, public
rectification, transparent government communication, and explicit
acknowledgment of uncertainty. Policymakers must explain their evidence,
assumptions, and limitations. Corrections should be visible and capable of
influencing subsequent decisions rather than remaining isolated technical
exercises.
Efforts to protect shared knowledge also create
serious risks. Government involvement may politicize research agendas and
information provision. Concentrating epistemic authority in a small group of
institutions can marginalize minority perspectives and local knowledge.
Excessive platform regulation may suppress legitimate criticism or be perceived
as censorship. Since scientific knowledge is inherently provisional, attempts
to enforce epistemic stability can obstruct learning.
The epistemic infrastructure must therefore be
pluralistic and correctable. No institution should possess unquestionable
authority, and systems designed to combat misinformation must themselves remain
transparent, independently reviewable, and open to contestation. Democratic
knowledge requires a continuing balance between common standards and diversity,
institutional guidance and independence, and freedom and responsibility.
This need for correction extends to institutional
organization more broadly. Democratic institutions often remain formally stable
while becoming materially rigid. Bureaucratic standardization, procedural
delay, and weak responsiveness prevent them from adapting to social,
technological, and ecological change. Tensions accumulate beneath an appearance
of continuity until correction becomes more difficult, disruptive, and
expensive. Stability without learning can therefore produce long-term
fragility.
Institutional adaptability should not mean
unlimited flexibility. It requires structured and legally bounded mechanisms
for learning and revision. Laws and policies should include periodic
evaluations, monitoring, review clauses, and procedures through which evidence
can produce actual amendment. Decision-making should be understood as an
iterative cycle rather than a linear process ending in a final decision.
A complete learning cycle should identify the
responsible institution, specify indicators and review dates, publish evidence
and uncertainty, receive complaints and counterevidence, decide whether
revision is required, and explain how the decision changes implementation.
Evaluation without a duty to respond does not create institutional learning[6].
Context-sensitive implementation can supplement
standardized rules, especially in healthcare, education, and social security.
Professionals may need discretion to respond to individual circumstances, but
that discretion must operate within transparent standards and remain subject to
review. Differentiation should refine equal treatment rather than license
arbitrariness.
Polycentric and multilevel governance can also
increase adaptability. Different institutions and levels of government can
experiment with alternative solutions, exchange lessons, and prevent failure at
one level from becoming total system failure. Higher levels can provide
coordination and minimum standards, while lower levels contribute contextual
knowledge and responsive implementation.
Participation strengthens this learning capacity.
Citizens’ assemblies, participatory evaluation, consultation, and participatory
budgeting can bring practical experience into policymaking and expose
institutional blind spots. Digital tools can accelerate feedback and widen
participation, but only if they remain democratically controlled and do not
create new dependencies, surveillance systems, or concentrations of
technological power.
Meta-governance offers a complementary role for
government. Instead of attempting to control every social process directly,
public authorities can establish legal boundaries, coordinate networks, connect
levels, and safeguard correction. This recognizes the limits of institutional
engineering while retaining public responsibility for the conditions under
which decentralized actors operate[7].
Adaptation nevertheless generates its own risks.
Frequent revision can weaken predictability and legal certainty. Polycentric
systems may produce unclear responsibilities and coordination failures.
Discretion and participatory opportunities may be used more effectively by
privileged groups, deepening inequality. Constant responsiveness to current
pressure can also displace long-term objectives and produce fragmented policy.
Adaptivity must therefore remain connected to
equality, transparency, accountability, and a stable normative direction.
Flexibility requires procedural safeguards; institutional complexity requires
clear responsibility; participation requires equal access; and short-term
adjustment must remain oriented toward long-term human and ecological
development.
Stability and adaptivity are not opposites.
Durable stability is the capacity to absorb change without losing fundamental
rights, legal certainty, equality, or democratic legitimacy. Stability without
adaptation becomes rigidity, while adaptation without stability becomes
volatility. A resilient democratic order combines reliable epistemic
institutions with controlled institutional change: it preserves a shared and
correctable reality while continuously learning how to govern within it.
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[1] Longino, Science as Social Knowledge; Sheila Jasanoff, ed., States
of Knowledge (London: Routledge, 2004).
[2] UNESCO, Global Framework of Reference on Digital Literacy Skills
for Indicator 4.4.2 (Montreal: UNESCO Institute for Statistics, 2018); OECD,
21st-Century Readers (Paris: OECD Publishing, 2021), https://doi.org/10.1787/a83d84cb-en.
[3] National Academies of Sciences, Engineering, and Medicine,
Fostering Integrity in Research (Washington, DC: National Academies Press,
2017), https://doi.org/10.17226/21896;
UNESCO, Recommendation on Open Science, adopted 23 November 2021.
[4] European Union, Regulation (EU) 2022/2065 on a Single Market for
Digital Services, OJ L 277, 27 October 2022, https://eur-lex.europa.eu/eli/reg/2022/2065/oj/eng
[5] Soroush Vosoughi, Deb Roy, and Sinan Aral, “The Spread of True and
False News Online,” Science 359, no. 6380 (2018): 1146–1151, https://doi.org/10.1126/science.aap9559;
Reuters Institute, Digital News Report 2026, https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2026.
[6] Chris Argyris and Donald A. Schön, Organizational Learning II
(Reading, MA: Addison-Wesley, 1996); Charles F. Sabel, “Learning by
Monitoring,” in The Handbook of Economic Sociology, ed. Neil J. Smelser and
Richard Swedberg (Princeton, NJ: Princeton University Press, 1994), 137–165.
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