Learning from Policy Experiments
in Adaptation Governance
Belinda McFadgen, PhD researcher, IVM, VU University, Amsterdam
Overview
Research gap and questions
Proposed analytical framework
Application of framework to case study in the Netherlands
Conclusions
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Research context and questions
Knowledge for Climate-
“climate proof the Netherlands”
Adaptation governance-
learning
policy experiments
“learning our way out”
“develop governance arrangements that connect new ideas”
Questions:
What design features are most successful at enhancing learning effects?
what is learning and how can it be measured?
what is a policy experiment and how can we evaluate its design features?
how does design relate to learning?
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Policy Experiments
 Aim: to find a definition applicable to adaptation governance
• Bearing in mind the relevance to social-ecological systems!
 Found in the literature:
• Social experiments- e.g. RCTs
• Real-life experiments- e.g. scientific studies in real world context
• Sustainability experiments- e.g. innovation outside traditional governance context
 Need for boundaries
• Experimental gap
• Controlled environment
• Application to policy making
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Tentative definition
 1) A policy experiment attempts to test a policy innovation in a field setting, whether an innovation
in technology, concept, or governance process. Testing ranges from explicit findings of cause and
effect to establishing a baseline for monitoring effects in a contextualized setting;
 2) An experiment provides a “protected space” by temporarily changing the institutional context;
and
 3) It produces evidence for policy making so it has a connection with government policy and seeks
to influence it.
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Analytical model
Policy Experiment:
Analysed using an institutional arrangements approach- Ostrom’s
rule typology.
Learning:
‘relatively enduring alterations of thought or behavioural intentions
that result from experience and that are concerned with the
attainment (or revision) of public policy’ (Sabatier 1987).
Policy impact:
Within experiment:
Cognitive
Normative
Credible
Salient
Legitimate knowledge
Relational learning
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Analytical model
 Systematic framework for describing experiments: theory on what learning
effects are expected from different designs.
boundary rule
access
position rule
initiator, facilitator
information rule
knowledge distribution
information rule
level of interaction
information rule
source of knowledge
information rule
type of knowledge
choice rule
power distribution at decision nodes
aggregation rule
how decisions are made
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Ideal Types
Technocratic
experiment:
• Experts as participants
• Closed invitation
• Produce generic, scientific
information
• Expert knowledge shared
• Regular interaction
• Even information distribution
• The initiator maintains authority
over each decision node
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Broad participation
Open access
Position includes facilitator
Technical and reflexive knowledge
Expert and lay knowledge shared
Regular interaction with all participants
Even information distribution
All participants have full decision
making powers
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•
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Cognitive L- ***
Normative L- ***
Relational L- ***
Credible K- **
Salient K- ***
Legitimate K-***
Cognitive L- ***
Normative LRelational L- *
Credible K- ***
Salient K- *
Legitimate K- *
Advocacy experiment:
Boundary experiment:
• Limited participation for those who
contribute resources;
• Facilitator or project manager
• Limited technical knowledge
• Expert and lay knowledge shared if
support dominant interests
• Irregular interaction
• Selected information is distributed
regularly to garner support
• Dominant interests take decisions at
each decision node.
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Cognitive L- *
Normative LRelational LCredible K- *
Salient K- ***
Legitimate K-
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Case study- Oosterschelde ecosystem engineer policy
experiment
•
Location- Oosterschelde National Park, Zeeland, Southwest Netherlands. Unique for its outstanding natural
features, shellfish industry, and recreation opportunities
(e.g. diving).
•
Problem- Dike causing erosion of sand causing loss of
inter-tidal flats in estuary.
•
Effects- Nature: habitat loss for migratory birds and other
mammals; Safety: dike partially vulnerable to wave impact.
•
Solution- Larger project- sand nourishment; specific
project- use of oyster reefs as “eco engineers”- 200m long
structures that work to prevent erosion and maintain
biodiversity as “living reefs.
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Results
Boundary
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Main stakeholder types represented;
Open access- as a requirement;
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Information
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Expert and lay knowledge utilised;
Regular discussion of alternative policy options
Regular interaction and dissemination of scientific information
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Position
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Initator non-state actor, collaborative effort
Facilitator role by state officials
Power
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3 decision nodes each with “Arnstein’s Ladder”
70% engaged early on
Different actors at different nodes
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Results
 Cognitive learning-***
 Normative learning- *
 Relational learning- *
 Credibility- ***
 Salience- **
 Legitimacy- **
 Result- Technocratic type with boundary characteristics
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Discussion and conclusion
 Surprises:
• Despite reflexiveness (discussion on alternative goals) there was little normative learning
• Established actor networks meant no apparent change in relational learning
• Salience and legitimacy of the knowledge very high- perhaps because of the high regard for
scientific knowledge
 Science-policy issues:
• Length of experiment
• Generalisability of results- introduction of oysters to other areas; meeting carrying capacity
 Conclusion:
• Framework is broad and allows for a systematic analysis with focus on participation, information,
and power and how these features relate to learning;
• Recognises role of science as cornerstone of experimenting with policy.
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