Experimental governance
Experimental governance lets authorities test new rules, technologies or services under controlled conditions before deciding how to regulate them at scale. Regulatory sandboxes, innovation hubs, pilot regimes and testbeds trade defined flexibility or guidance for close supervision and shared learning. This page sets out how such experiments are designed, where they go wrong and how their evidence is evaluated. It makes no regulatory claims: only competent authorities run sandboxes and decide what follows from them.
- 01In briefThe short answer
- 02The questionExperimental governance: how the risk develops
- 03Key termsTerms used on this page
- 04IndicatorsWhat is measured, and how well
- 05CanvasFrom sources to decisions
- 06SourcesWhere the evidence comes from
- 07DecisionsWho decides, and what the evidence serves
GRF · Established · service level R0
The short answer.
Regulatory sandboxes are supervised arrangements in which authorities let firms test new products or services under defined conditions before deciding how to regulate them. With innovation hubs, pilot regimes and testbeds they form experimental governance, which trades limited flexibility or guidance for close supervision and published learning.
People and hazards in Europe.
Where people live, night-time lights and the floods and fires public institutions answer to, framed on Europe as an example geography for this subject; each layer is a dated NASA product.
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People and hazards
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Natural Earth
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Where people live, night-time lights and the floods and fires public institutions answer to, framed on Europe.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Data: NASA GIBS · ASTER GDEM · Natural EarthSources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). CC BY 4.0
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Active fires and thermal anomalies, last day: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
Regulatory sandbox board, in outline.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Regulatory sandbox board
Experimental governance · Regulatory sandbox · Cohort 3
Cohort
| Parametric micro-cover | Insurance | Agreed | 7 of 12 | Testing |
| Flood sensor data market | Data | Partial | 4 of 12 | Conditions |
| AI permit triage | Public service | Agreed | 6 of 12 | Testing |
| Drone deliveries, medical | Logistics | Agreed | 11 of 12 | Exit test |
Exit criteria
- Consumer harm
- none recorded
- Evidence
- independent evaluation
- Rule change
- regulator decides
- Wind-down
- plan on file
Sandbox cycle
- Applyeligibility
- Admitsafeguards
- Testbounded
- Evaluateindependent
- Exitor rule change
- Publishlessons
Experimental governance: how the risk develops.
Sandboxes and evaluation without regulatory claims. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Learning under supervision
A sandbox admits a limited number of participants under agreed safeguards, so the regulator observes real behaviour before writing rules. The value lies in what is learned and published, not in the admission.
Selection shapes results
Participants who apply are often better resourced and more compliant than the market as a whole. Results from a sandbox cohort may not hold when rules apply to everyone.
Advantage and capture
Admission can signal approval to investors and customers, and close contact can tilt a regulator toward participants. Transparent criteria, equal access and exit rules keep the experiment fair to competitors.
Pilots that never end
Temporary regimes can persist without evaluation, leaving firms and consumers in long uncertainty. An experiment needs an end date, a decision point and a published evaluation to become governance.
Terms used on this page.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Innovation hub | A regulator's point of contact that gives firms guidance on rules without a testing arrangement. |
| Experimental governance | Governing through provisional rules that are tested, evaluated and revised. |
| Exit criteria | The conditions under which a firm leaves a sandbox and the regulator decides on permanent treatment. |
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Active regulatory sandboxes by sector | count of programmes | annual | Regulator registers and announcements | OFFICIAL |
| Sandbox applications and admissions | count per cohort | per cohort | Regulator cohort reports | OFFICIAL |
| Sandboxes with published entry, exit and evaluation criteria | percent of programmes | annual | Regulator programme documents | OFFICIAL |
| Time from sandbox exit to regulatory decision | months | per experiment | Regulator dockets | OFFICIAL |
| Published sandbox evaluations | count of evaluations | annual | Regulator and audit reports | OFFICIAL |
| Consumer complaints and incidents during testing | count per experiment | per experiment | Regulator supervision records | REPORTED |
| Cross-border and multi-regulator testing arrangements | count of arrangements | annual | Regulator network and memorandum records | OFFICIAL |
From sources to decisions.
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| UK Financial Conduct Authority regulatory sandbox records | Cohort lists, eligibility criteria and lessons-learned reports | Open, attribution (FCA terms) |
| World Bank and CGAP regulatory sandbox research | Inventory and design features of fintech sandboxes worldwide | Free, attribution (CC BY IGO) |
| Global Financial Innovation Network | Cross-border testing arrangements among financial regulators | Public records, terms per member |
| EUR-Lex: EU AI Act provisions on sandboxes | Legal basis and conditions for AI regulatory sandboxes and testing in real-world conditions | Free reuse, attribution (Commission reuse decision) |
| OECD Indicators of Regulatory Policy and Governance (iREG) | Practice of impact assessment, consultation and ex post evaluation by country | Open, attribution (OECD terms) |
| National regulators' registers and annual reports | Sandbox programmes in energy, health, transport, data and telecommunications | Public records, terms per regulator |
Who decides, and what the evidence serves.
Innovation Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Sector regulators (financial, energy, health, telecommunications, data protection) | Whether to open a sandbox, on what eligibility and safeguards, and how to end it | Sandbox design guides; comparative registers of other regulators' criteria and outcomes |
| National competent authorities under the EU AI Act | How to run AI regulatory sandboxes and supervise testing in real-world conditions | Legal provisions, implementing acts and comparative experience from other sectors |
| Legislatures and regulatory oversight bodies | Whether a regulator holds the mandate to experiment and how experiments are reviewed | Comparative legal bases for sandboxes; ex post evaluation practice |
| Competition authorities | Whether admission to a sandbox gives participants an unfair advantage | Admission criteria, cohort composition and access records |
Outputs this topic produces
- Sandbox design guides covering eligibility, safeguards, exit and evaluation
- Comparative registers of experimental regimes with their criteria and published outcomes
- Evaluation frameworks that separate learning for rule-making from participant success
- Dialogue formats that bring regulators across sectors and borders together on shared experiments
What experts ask.
The questions concern what is tested, under whose authority, and what is learned.
- What exactly is being tested: a technology, a business model, a rule or an institutional process?
- Which authority has the power to permit the test, and under what conditions?
- How are participants and the public protected during the test?
- How will results be evaluated, and will they be published?
- What happens at the end: permanent rules, extension or withdrawal?
Mechanisms and drivers.
A regulatory sandbox is a framework set up by a regulator that allows firms to test innovations under supervision, sometimes with tailored requirements, for a limited time. Only a regulator with the relevant legal powers can create one, and it applies only within that regulator's remit. Pilots and test beds are broader tools that public bodies and others use to try new approaches at small scale.
Experiments produce learning only if they are designed to: with clear questions, comparison groups where possible, and honest publication of results, including failures. Common weaknesses include selection of participants who would have succeeded anyway, short duration, and pressure to declare success.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Evaluation design, including comparison groups | Whether an intervention made a difference | Randomization is often impractical |
| Process evaluation | How the test was implemented | Does not establish effect |
| Comparative analysis of sandboxes | How frameworks differ across jurisdictions | Outcomes are rarely published in comparable form |
| Systems engineering test protocols | Structured testing of complex systems | Controlled tests may not reflect real use |
How it is studied, and what the methods cannot say.
- Policy, institutional and decision scienceMandate and gap mapping · Programme evaluation design · Comparative regulatory analysisApplied here
- Systems engineeringRequirements traceability for AI governance · Readiness gates for transfer programmesApplied here
- Artificial intelligence and machine learningCoding policy and legal texts · Mapping patents and papers to technologiesApplied here
Questions and answers.
Does a sandbox exempt firms from the law?
What is the difference between a sandbox and an innovation hub?
How should a sandbox be evaluated?
Does Innovation Nexus run sandboxes or test products?
Does Innovation Nexus run a regulatory sandbox?
Is testing in Nexus Universe a regulatory approval?
Why publish failed experiments?
Around Innovation Nexus.
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