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Designing Policy Experimentation

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How to identify, design, run and learn from innovative policy interventions.
By Christian Bason, Chief Executive, Danish Design Centre.

Publicado en: Liderazgo y gestión
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Designing Policy Experimentation

  1. 1. Designing policy experimentation How to iden*fy, design, run and learn from innova*ve policy interven*ons Christian Bason
 Chief Executive, Danish Design Centre
  2. 2. Policy Press 2010 Gower Ashgate 2014 Policy Press 2017
  3. 3. PART I A model for policy experimentation 9.30 Welcome Embracing double-edged complexity Towards a new conception of the role of government A holistic approach to policy experiments From challenges and opportunities to impact Horizon scanning Sensing the next policy challenge Co-designing hypotheses to test How to build an experiment portfolio Co-producing by experimentation Prototyping, programming and scaling using design approaches Learning from experiments Measuring outcomes 10.20 Q&A 10.30 End session
  4. 4. 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)
  5. 5. When government policy fails… • United States: Obamacare digital platform • Denmark: Runaway applications for solar energy scheme • Germany: Voluntary Technical Year • Singapore: Relationship programmes
  6. 6. Embracing double-edged complexity
  7. 7. ”[In complex settings] instead of attempting to impose a course of action, leaders must patiently allow the path forward to reveal itself. They need to probe first, then sense, and then respond.” David Snowden
  8. 8. ”The state has not just fixed markets, but actively created them”. Marianna Mazzucato
  9. 9. ”[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
  10. 10. “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
  11. 11. “An appalling piece of political stupidity.” Louis Howe, adviser to Franklin Delano Roosevelt
  12. 12. A policy experimentation model
  13. 13. Horizon scanning Sensing the next policy challenge
  14. 14. A policy experimentation model
  15. 15. 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?
  16. 16. 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
  17. 17. Co-design Designing policy with people, not for them
  18. 18. A policy experimentation model
  19. 19. 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?
  20. 20. Source: MindLab
  21. 21. Source: MindLab
  22. 22. Co-design Cases: The lab movement
  23. 23. 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 Realisation Pattern recognition
  24. 24. Co-producing by experimentation Establishing hypotheses of change for realizing interventions
  25. 25. A policy experimentation model
  26. 26. 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?
  27. 27. Co-producing by experimentation What we do View all policy interventions as essentially experimental Realise co-production at three scales • Prototype: High on experimenting
 Key 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 sharing
 Key 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?
  28. 28. • 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
  29. 29. EFFEKTERAKTIVITETER PRODUKTER LEVERANCER OUTPUT AKTIVITET AKTIVITETAKTIVITET AKTIVITET AKTIVITET CRITICAL SUCCESS FACTORS INPUT ACTIVITIES OUTPUTS OUTCOMES LONG-TERM OUTCOMES SHORT-TERM CRITICAL SUCCESS FACTORSCRITICAL SUCCESS FACTORSCRITICAL SUCCESS FACTORS Hypotheses of change framework Problem: … Hypotheses: …
  30. 30. Measuring outcomes Documenting, learning and improving performance
  31. 31. A policy experimentation model
  32. 32. “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
  33. 33. 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?
  34. 34. 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
  35. 35. Different approaches to measuring outcomes Case: iTeams report from Nesta and Bloomberg
  36. 36. 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
  37. 37. 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
  38. 38. 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.
  39. 39. 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?
  40. 40. ddc.dk

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