Designing policy experimentationHowtoiden*fy,design,runandlearnfrominnova*vepolicyinterven*ons

Christian BasonChief Executive, Danish Design Centre

Policy Press 2010 Gower Ashgate 2014 Policy Press 2017

PART IA model for policy experimentation 9.30 Welcome Embracing double-edged complexity Towards a new conception of the role of governmentA holistic approach to policy experiments From challenges and opportunities to impactHorizon scanning Sensing the next policy challengeCo-designing hypotheses to test How to build an experiment portfolioCo-producing by experimentation Prototyping, programming and scaling using design approachesLearning from experiments Measuring outcomes10.20 Q&A

10.30 End session

Public policy: A design problem “How can you make sensible policy or strategy in a nondeterministic, evolutionary, highly complex world, that is, a world where the most desirable outcomes are unknown but there may be many possible acceptable outcomes, where change is characterized by both path dependence and unpredictability, and where there are many diverse components, interactions, and feedback among components and multiple dimensions to each problem? This is the design problem with respect to public policy.”

Carlsson (2004:36)

When government policy fails…

• United States: Obamacare digital platform

• Denmark: Runaway applications for solar energy scheme

• Germany: Voluntary Technical Year

• Singapore: Relationship programmes

Embracing double-edged complexity

”[Incomplexsettings]insteadofattemptingtoimposeacourseofaction,leadersmustpatientlyallowthepathforwardtorevealitself.Theyneedtoprobefirst,thensense,andthenrespond.” DavidSnowden

”The state has not just fixed markets, but actively created them”. Marianna Mazzucato

”[We] suggest institutional changes that shift innovation policy towards a more experimental conception of the role of the state in facilitating entrepreneurship, and thereby innovation”. Hasan Bakshi

“This country needs, and unless I mistake its temper, the country demands bold persistent experimentation. It is common sense to take a method and try it. If it fails, admit it frankly and try another. But above all, try something.”

Franklin Delano Roosevelt

“An appalling piece of political stupidity.”

Louis Howe, adviser to Franklin Delano Roosevelt

A policy experimentation model

Horizon scanningSensing the next policy challenge

A policy experimentation model

Horizon scanning

What?

• Sensing coming trends and developments with potential policy or organisational consequence

• Establishing insight, foresight and scenarios to visualize plausible futures

Why?

• Creating awareness of context factors of importance to the organisation

• Preparedness, resilience in view of possible disruptions

• Basis for policy planning and action

Key questions?

• Which political, economic, environmental, societal and technological factors should we care about?

• How could these driving forces influence us in the future?

• What should we do now to shape our future in a desirable direction?

Horizon scanning

Cases

• The Singaporean Government: 2050 foresight strategy

• Policy Horizons Canada: IMPACT - a serious foresigt board game for public servants

• OECD: Schooling for tomorrow

• Danish Design Center: Scenarios Healthcare Denmark 2050

• UAE: Museum of government futures

• Dubai Future Foundation

Co-designDesigning policy with people, not for them

A policy experimentation model

Co-design What?

• Exploring problems from end user perspective

• Co-creating new ideas with users and stakeholders

• Prototyping and testing early ideas “in the lab”

Why?

• To build an early validation of fit and function of a policy idea

• Create basis for redesign and ultimately for decision-making

Key questions?

• Who are the end users?

• How might this policy intervention work for them?

• Which other aspects do we need to take into account?

Source: MindLab

Source: MindLab

Co-design

Cases:

The lab movement

Co-design

What we do

EXPLORING THE

PROBLEM SPACE

GENERATING ALTERNATIVE

SCENARIOS

ENACTING NEW

PRACTICES

Ethnographic research

Prioritization

Concept Development

Ideation

Visualisation

Prototyping

User testing

RealisationPattern

recognition

Co-producing by experimentationEstablishing hypotheses of change for realizing interventions

A policy experimentation model

Co-producing by experimentation

What?

• Organising and implementing policy through collaborative networks

• Leveraging all relevant resources to produce policy outcomes

• Establishing the hypotheses of change to experiment with policy by co-production

• Ensuring rigorous collection of qualitative and quantitative

Why?

• To be explicit about which actions and factors we expect will create intended change

• Raise awareness about critical success factors

• To know what to measure to track changes, including unintended consequences

Key questions?

• Based on our co-design process, which hypothesis is it now we are testing?

• What inputs, activities and outputs do we expect to realize?

• What would outcomes look like, if we are successful?

Co-producing by experimentation

What we do

View all policy interventions as essentially experimental

Realise co-production at three scales

• Prototype: High on experimentingKey questions: How does the intervention work? Who does it work for (who benefits)?

• Program: High on learning Key questions: How can we learn from this now that the design is being realized?

• Scale: High on sharingKey questions: How can we share our insights and tools? Which actors can embed activities to go to scale? How can we reach more people/businesses?

• Finland PMO: Government experimentation programme and funding platform for citizen-led experiments

• UK CO: Government Digital Services

• UAE: Dubai Future Accelerators Programme

Co-producing by experimentation

Cases

EFFEKTER

AKTIVITETER

PRODUKTER LEVERANCER

OUTPUT

AKTIVITET AKTIVITET AKTIVITET AKTIVITET

AKTIVITET

CRITICAL SUCCESS FACTORS

INPUT ACTIVITIES OUTPUTS OUTCOMES LONG-TERM

OUTCOMES SHORT-TERM

CRITICAL SUCCESS FACTORS CRITICAL SUCCESS FACTORS CRITICAL SUCCESS FACTORS

Hypotheses of change frameworkProblem: … Hypotheses: …

Measuring outcomesDocumenting, learning and improving performance

A policy experimentation model

“If you don’t measure outcomes, you cannot tell the difference between success and failure. That means you might be rewarding failure.”

Ray Rist, former senior advisor, World Bank

Outcome measurement

What?

• Establishing a systematic set of methodologies to document inputs, activities, outputs, and short- and long term outcomes of interventions

• Establishing key perfomance indicators: Best indications of what success could look like

• Collecting data systematically

Why?

• Using data to document for accountability and transparency

• Drive continuous learning, and increase organisational performance

• Produce stronger outcomes

Key questions?

• Do our hypotheses hold?

• Are we achieving the positive change and outcomes we intended?

• What are unintended consequences - what should we adjust?

The evidence ladder

No knowledge about outcomes

Proven outcomes

Documented outcomes

Apparent outcomes

No documentation

“Sunshine stories”

Systematic documentation eg surveys, rigorous case studies, A/B testing

Research-based documentation eg randomized controlled trials, meta studies

Different approaches to measuring outcomes Case: iTeams report from Nesta and Bloomberg

Measuring outcomes

What do we do?

Outcome measurement system

Measures outcomes systematically around three overall strategic objectives:

• Contribution to business growth (economic value)

• Contribution to branding of Danish design (economic value)

• Contribution to societal impacts (societal outcomes in education and sustainability)

This is done by assessing progress against a logic model of hypotheses of change effect chains.

Quantitatively: Surveys among businesses, media impact data, etc.

Qualitatively: Observation studies, interviews, design research

Enables cost benefit analysis: What is the return of investing in the Danish Design Centre?

User feedback surveys

• Net promoter scores, evaluation of projects et promotor score

• Measures loyalty from participants in seminars and events

Measuring design impact for business

• Comprehensive case research methodology

• Survey data

• National statistical data

Measuring outcomes

How does it differ in experimental policy?

Traditional policy(operations)

Experimental policy (innovation)

Purpose Documentation, accountability, performance

Learning, adaptation, redesign (or termination)

Focus Optimizing the use of existing resources

Discovering additional resources to be leveraged

Data Mainly quantitative Quantitative and qualitative

Tools Statistics, surveys, other A/B test, RCT’s, cases, design research, future probes, etc.

Time horizon Long-term systematic measurements; on-going

Tailored on concrete prototype or programme design

Challenge Setting right KPIs - and meeting them!

Capturing causal elements of hypotheses of change

EXPLORING THE

PROBLEM SPACE

GENERATING ALTERNATIVE SCENARIOS ENACTING

NEW PRACTICES

#1 Challenging assumptions

#2 Leveraging empathy

#3 Stewarding divergence

#4 Navigating

the unknown

#6 Insisting on

value- creation

#5 Making the

future concrete

Design engagement:An experimental mindset.

Towards experimental government?• Which approaches do you use today from design to

measurement?

• How could systematic experimentation become part of the “new normal” of governing?

• What would be the benefits?

• Which challenges to you foresee?

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