By Millie Begovic, UNDP global innovation adviser a.i.
The UNDP’s Innovation Facility has been funded by the Government of Denmark since 2014
Since 2014, UNDP’s Innovation Facility has invested in building organizational muscle for innovation and rapid experimentation. Governments and partners in over 80 countries built up new skills (ethnography, design thinking, foresight), adopted new approaches to complex issues (behavioral insights, new data), crowded in a network of non-traditional partners and generated new investments in doing development differently. Fundamentally, the Facility brought innovation from the margins into the core of what we do and paved the way for UNDP’s largest transformation investment to date — the network of (soon to be) 90 Accelerator Labs covering over 100 countries.
From this year, the Facility has a new focus — helping governments rethink decision making in times of uncertainty and facilitate transformative change that builds resilience in socio economic and political systems (and we’re extremely thankful to the Danish Government for taking the leap of faith with us and investing in this pivot). There is no playbook for countries to deal with the impact of automation on labor markets, to rethink governance in light of the compound effects of climate change and pollution, or to grasp the series of cascading risks triggered by COVID (though admittedly a global pandemic was not at all on our radar screen when we planned the pivot). What is increasingly evident is that mainstream policy practice characterized by predictability, linear and siloed planning, predominantly quantitative analysis, the over reliance on quick fixes and single point solutions is a mismatch with this emerging reality. We need to develop a new set of policy capabilities that acknowledge uncertainty as a feature, not as a bug. The current context, when decision makers have to take 100% responsibility for decisions based on 50% of information and the World Uncertainty index has reached an all-time high, has made this all the more apparent.
So what does it mean to aim for system transformation if we accept the premise that radical uncertainty is a key feature of socio-economic systems? How do we preserve a sense of agency for our development mandate in the face of seemingly intractable problems that defy our traditional planning approaches and create conditions for perpetual self-renewal in the face of uncertainty? What logic should investments in innovation follow in contexts were data-driven approaches provide no certainty on future behaviors?
Working with 7 country offices, we are testing 4 underpinnings of system transformation approaches:
1. Focus on system dynamics (rather than ‘solutions’)
Acknowledging uncertainty upfront means in many ways “reverse engineering” our traditional project documents. Rather than starting from a position of certainty and predictability (inscribed in a log frame) we need to start by acknowledging what we don’t know and focus on the structural elements of a system (rather than “solutions”). In this optic, a development intervention that wants to bring about change, say, in agricultural systems is better seen as a mechanism that gradually resolves/explores uncertainties about system dynamics through learning and adaptation and ongoing sense-making, rather than a series of “fixes” to a well identified set of problems. This might reveal that an agricultural system is a symptom of a larger set of dynamics playing out in the economic system thereby opening up a wider set of entry points and policy options to ‘play’ with. The cultural change required to counteract the almost automatic reflex towards predictability of outcomes and problem solving is one aspect we are delving into. It requires opening up a space for rethinking the logic of our interventions and an enquiry into the structural elements of social systems (rather than jumping into solutions mode).
2. From single point solutions to coherent portfolios.
We learned the hard way that development issues cannot be solved through single point solutions. Even if lazy headlines might suggest otherwise, in isolation, hackathons do not “solve” climate change, education campaigns do not “solve” domestic violence and COVID lockdowns have not “solved’ air pollution. This is where we hit a barrier with our former emphasis on small scale experimentations. Having a bunch of projects (even if conceived as “agile” experiments in governance, social inclusion, etc.) is quite different from designing a set of interventions that learn from each other over time and are coherent with the multidimensional nature of a particular development issue (e.g the drivers of depopulation). Together with other development organisations, we started to explore and codify the emergent practice of portfolio management, where portfolio is meant first and foremost as a learning mechanism (rather than a tool for project tracking). This raises questions about differences in dynamic management and understanding what does a success ‘look’ like in a portfolio (versus a single project) and possibly also changes decision-making dynamics both within the team and in a relationship with partners who we work with. However, it is this new lens that informs the second wave of our innovation facility efforts. Though unforeseen, it has provided a new reference point also for our COVID response framing.
3. From short-termism to deep demonstrations
Deep dive into understanding the system and toggling between a wider spectrum of policy options in face of complexity implies a shift in a time horizon and ambition of our intervention — away from short-termism and toward longer term engagement over transforming deep systemic issues. On moving from addressing small parts of the big development issues (eg. transparency of public sector) to transformative issues (eg. determinants of trust), we draw inspiration from Mariana Mazzucato’s work on policy moonshots, and the 10-in-10 focused on the biggest 10 global issues in 10 years. For a heavily projectized, delivery driven organization this is easier said than done and requires thinking through process and support on how to turn something like policy moonshot into a collaborative engagement with the Government and other partners. Hence the Deep Demonstrations concept, which we (shamelessly) borrowed from Climate Kic.
4. From documents to visual and physical representation of systems
We know that in order to operate in complex systems it is important to build a shared image of that system that can help navigate our course. Shifting our work from word docs and PDFs into visual and physical representations of systems might create new dynamics within the team (more socialized sense-making and strategy design) and uncover non-obvious connections and dynamics that hadn’t been considered. If uncertainty is indeed ‘unmeasurable,’ visual representation of issues might help uncover emergent properties of the system that allow for perpetual learning. Guided by Chora Foundation, we explored the use of 3d representations of our portfolios to better understand, quite literally, our current “positions” in a system. We have a hunch that this might also change the type of discussions we have with our partners away from what is a value of any single project to what their fit within the context of other interventions in the portfolio may be.
For example, in the graph below, it is obvious (without knowing that individual projects are about) that the portfolio tends to focus dominantly on the national and institutional capacities (a place where the plates aggregate). Parts of the portfolio with no current initiatives (gaps?) are also obvious.
We are also inspired by the experiential simulations-as-a-way-to-understand-system-dynamics of both the Synthesis Center at the Arizona University (see their work on simulating heat-scapes in cities and alternative economies) and Roger Kneebone’s Center for Performance Studies at the Imperial College.
Questions moving forward
Ultimately, then, the new focus of the innovation facility is to help test and develop, together with our government counterparts, a new, distinctive set of policy capabilities that allow us to better operate in conditions of uncertainty.
Shifting the Facility’s focus from small scale experimentation to deep systemic change is likely to be anything but straightforward and raise more questions than answers.
- How difficult will it be for our partners (Governments, donors) to take a leap of faith with us on this exploration seeing as though the shift tends to hit against many constraints in the way public policy and development sectors work (siloed organization, short-termism, projectized nature of work)? Are there donors and governments interested in moving beyond projects, and invest in portfolios for system transformation?
- If a starting point is that this is a long-term engagement, what do the interim ‘small wins’ and practical steps in the right direction look like that are necessary to maintain the momentum and keep the faith toward the deep demonstration of change?
- Connected to that, what does the ‘success’ look like when we’re interested in transforming systems? And what incentives can we create for colleagues to move beyond the imperatives of short term delivery to embrace a longer term perspective and assign a premium to learning?
We’d love to compare notes with organizations who are also considering or in the middle of similar transition.