UNDP 2018: Evidence based vs experimentation based policy

Recently I have a remote talk to a UNDP event about Evidence based versus experimentation based policy. Below are the notes.
  • We invented all of this, and we can reinvent it. We can co-create a better future for everyone, if we choose. But if we settle for making things just a bit better, a bit more sustainable, a bit anything, then we will fundamentally fail the world because change and complexity is growing exponentially, and we need an exponential response to keep up.
  • There is a dramatic shift in paradigm from control to enablement, from being a king in a castle to a node in a network, which assumes a more collaborative approach to governance.
  • Evidence based approaches are great to identify issues, but we need experimentation based approaches, equitably co-designed with communities, so create sustainable and effective solutions. Evidence based solutions often are normative rather than transformative.
  • We need both evidence and experimentation based policy making, combined with system thinking and public engagement to make a real difference.
  • Digital transformation is often mistaken for meaning the digitisation of or service design led improvement of services, but digital transformation means creating institutions that are fit for purpose for the 21st century, from policy, regulation, services, public engagement, a full rethink and redesign of our social, economic and political systems.
  • History in implementation, and we realised that it was the disconnect between policy and implementation, the idea of policy as separate to implementation is undermining the possibility of meeting the policy intent through implementation.
  • Measurement ends up being limited to the context of function rather than outcomes.
  • Urgently need to reform how we do policy, regulation and legislation, to embrace an outcomes based approach, to bring design thinking and system design into the process from the start, from policy development in the first instance.
  • Working in the open is essential to getting both the demand and supply of evidence based policy, and working openly also means engaging in the shared design of policy and services with the communities we serve, to draw on the experience, expertise and values of the communities.
  • Public Values Management
  • Evidence based AND experimentation based policy.
  • Examples:
    • Service Innovation Lab – NZ
      • Service design and delivery – rapid prototyping is trusted for service design
      • Applying design thinking to regulation and policy
      • Legislation as code – rapid testing of policy and legislation, Holidays Act, it is critical if we want to have a chance of ensuring traceable, accountable and trusted decision making by public sectors as we see more automated decision making with the adoption of AI and ML grow.
      • Simultaneous legislation and implementation, to ensure implementation has a chance of meeting the original policy intent.
    • Taiwan – Uber case study, civic deliberation
    • Their Future Matters – data driven insights and outcomes mapping and then co-design of solutions, co-design with Aboriginal NGOs
    • 50 year optimistic future – to collaboratively design what a contextual, cultural and values driven “good” looks like for a society, so we can reverse engineer what we need to put in place to get us there.
  • Final point – if we want people to trust our policies, services and legislation, we need to do open government data, models, traceable and accountable decision making, and representative and transparent public participation in policy.
  • Links:

 

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