# 7. Taking Collective R\&D Further *In this chapter, we set out an agenda to take the practice of collective R\&D for sustainable development further. After six years across one hundred countries, we've learned it takes an ecosystem to go to scale. We’ve also started to see what sustainable development looks like when you apply a collective R\&D approach, offering leads for others to deepen these hunches.* Once you are up and running, empowering collectives, working from the [bottom up](/undp-accelerator-labs/references/glossary.md#bottom-up) and driven by learning and impact – what comes next? When you've set up a capability that thinks in systems and makes collectives smarter – when and how do you hand over and move on to new uncharted territory? When working in brittle public sectors and communities – how long can you hold space for learning in collectives? These are unanswered questions for the UNDP Accelerator Labs at the time of writing this guide. Amidst these unknowns, one collective mystery stands out: scaling. The next frontier in R\&D for sustainable development needs to address how to take it to scale. Scale, however, is a problematic idea. Here's what we commonly observe: investors usually want it, while many active in the field question whether it's even the right target. Teams new to R\&D, particularly in the public sector, rarely have a clearly defined tolerance for experimental learning – try asking about what failure rate is acceptable or even desirable[\[1\]](#footnote-1) and you might be laughed out of the room. Most social-sector decision makers expect experiments to turn into larger operations – like magic! – whereas scaling glitches are chalked up to poor design.

Figure 58: Mapping how innovations travel through ecosystems and reflecting on which roles drive their adoption and diffusion. At the Experimenter's Codification Fest in Thimphu (July 2024).

But designing for scale does not work on complex problems. The experiences that led us to write this guide point to a different understanding. When working with emergence in complex systems, replication is very difficult, and maybe even undesirable. Often we start small and then get caught up in the growth trap. Our R\&D efforts become less about exploring and creating new worlds and more about getting others (i.e. the collective) to adopt our own original idea and grow it (see Figure 58). This chapter outlines a call for thinking differently about how we move from exploratory probes to systems change, building on what we know and what we need to learn. ## What we’ve learned

Figure 59: How R&D scales in sustainable development.

Scaling R\&D in sustainable development happens: with multiplicity … in waves … through collectives. ### Scaling happens w**ith multiplicity…** Conventional wisdom says scaling means replicating one successful solution across many contexts. Our experience points to something different: scaling is more about the combination of many small ideas than the replication of one big idea. Especially in sustainable development, scaling does not require standardization or uniformity. Sometimes it is even better to maintain many options[\[2\]](#footnote-1) as a hedge against uncertainties. In [Laos](/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices/thinking-in-systems/vignette-1-laos-reframing-of-waste.md), for example, running multiple waste experiments in parallel created space for new patterns to emerge, in [Argentina](/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices/forming-collectives/vignette-3-argentinas-convos-network.md), strengthening multiple community businesses reduced digital exclusion, and in [Uganda](/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices/thinking-in-systems/vignette-2-ugandas-deforestation-challenge.md) a systems learning approach meant that different actors led experiments and learned together. Yet maintaining this multiplicity of options creates a paradox: we need to drive diffusion towards two very different aims: getting at least one tangible innovation off the ground on one hand, and holding space for multiple experiments and thinking in systems on the other. What we have found is that prototypes can be used as entry points towards a systems approach. No one innovation will transform a system, but making tangible R\&D outputs real triggers the system to reveal some of its dynamics and helps us see its key players and connections. See the vignette of [Bosnia and Herzegovina](/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices/finding-out-what-works/vignette-23-bosnia-and-herzegovinas-green-transition-portfolio.md)'s green portfolio for an example of how they used results and insights from early experiments – both successes and challenges – to shape the architecture of system-wide initiatives. ### **…and in waves…** Most scaling frameworks assume scaling is linear and predictable – for example, when we double the invested resources, outputs or reach will double as well. This “scaling by design” approach focuses on identifying decision makers, pitching them R\&D results, and securing resources to multiply outputs. The practices in this guide reveal a different pattern: scaling happens in non-linear ripples through ecosystems (Figure 60). As momentum builds through relationships and networks,[\[3\]](#footnote-2) innovations typically move through three waves of adoption allies. In the first wave, we engage with “movers” who have built-in incentives to get ideas off the ground. In the second, the “provers” bring operational capacity to create tangible results and provide credibility. In the third, “smoothers” can offer their networks to embed R\&D results at scale. Each plays a distinct role in diffusing ideas and catalyzing action. Figure 60: The three waves of R&D scaling. Each wave engages different types of allies (Movers, Provers, and Smoothers) creating ripples of scaling that expand through the ecosystem. **Movers (incentives)**\ The first wave of R\&D diffusion often starts when we find someone with a shared vision or need. These first movers come with built-in incentives and are receptive even when things are still fuzzy. Very likely, they have already been trying to get others in their team, department or community to pursue similar ideas. Their eyes shine when they first hear about these ideas. They are relieved to find collaborators who are ready to try something out and are often eager to lend legitimacy to these initial efforts. They make ideal first allies for developing early prototypes. **Provers (capabilities)**\ When initial experiments show promise, the second wave begins. Provers bring operational capacity to take ideas forward and create tangible results. They build on the proof of concept from first movers and help diffuse results more broadly. Provers might be middle managers in the public sector or private sector entrepreneurs willing to take calculated risks. They help us navigate the ecosystem and make critical connections. They might also advise on whom to influence and which aspects of initial experiments will generate buy-in, visibility, or resources. As we see in [Laos](/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices/thinking-in-systems/vignette-1-laos-reframing-of-waste.md) and other vignettes in this guide, government officials who modelled plastic-free practices shifted perceptions of what is possible, and mandates followed suit. This is where the momentum of a systems approach becomes visible - small experiments create waves. **Smoothers (networks)**\ In the third wave, we look for smoothers who build on the traction created by first movers and the credibility established by provers. Smoothers offer their networks. They hold decision-making power and potentially resources to promote R\&D results at scale. Particularly in the public sector, they are essential allies for embedding insights and shifting policies. While we might imagine we can target these institutional power brokers directly from the start, our experience shows otherwise. When ideas are ahead of demand, decision makers typically come on board only after seeing traction and momentum in the ecosystem. They engage only after movers have taken the first steps and provers have lent their capability to make the results of collective R\&D more than a one-off. Figure 61: The indirect route to smoothers. Building connections through movers and provers to diffuse R&D results at scale. Building allies in ripples establishes credibility and provides initial validation of new ways of problem-solving. These relationships open introductions and establish momentum. When entrepreneurs sense new markets, they create a buzz that something new and real is happening. A movement with momentum opens doors to scale the result of collective R\&D. ### **…through collectives** As we've seen, there is no direct path[\[4\]](#footnote-3) to the actors who can take innovations to scale (see Figure 61). A ripple approach works better: we go where there is momentum, engaging movers first, then provers, before reaching smoothers. Throughout these waves, we weave collectives: groups of actors who combine their diverse knowledge, expertise, and solutions to learn, experiment, and take coordinated action toward a shared ambition or intent. A collective scaling approach helps actors see their connections to others in a way that makes the collective smarter. ![Figure 62: Government officials, employers' and workers' organizations, and informal economy actors seeing and discussing connections within the ecosystem at a UNDP-ILO policy dialogue on the informal economy (Victoria Falls, May 2022).](/files/5721d1e7f453697e25460846c7a13a7303d9dca9) This guide and the stories on which it is based point to a simple but effective tactic: if you want R\&D to scale, make the connections among communities, entrepreneurs, and others in the ecosystem visible (see Figure 62). Helping disconnected actors see the potential and the benefits of working as a collective. What we can see, we can build on, so helping the system see itself is key. Making the system see itself is important because ecosystems with growth potential are by definition not yet fully mature. There likely isn't an already existing platform that supports growth (programs, policies, scalable solutions, or wide-reaching ventures). When we form collectives and co-create from the start, we're already diffusing innovations. And sometimes, what is scaled is the relationship, not the product. Just as the ideas that gain traction may differ from what we originally pitch, ecosystems themselves are not monolithic. They can have complex interactions, including competitive dynamics between players[\[5\]](#footnote-4) over recognition and resources. It takes an ecosystem to go to scale, but incentives are always shifting and power dynamics are real forces at play. ## What R\&D reveals about sustainable development This guide codifies our insights on how to apply R\&D for sustainable development: learning in collectives, thinking in systems, accelerating what is already happening, and opening doors for systems learning. Six years of practice across one hundred countries has revealed something deeper about development itself. What does sustainable development look like when we put these collective R\&D practices in place? Once we apply R\&D, what looks different about the drive to ensure future generations meet their development needs?

Figure 63:What collective R&D reveals about sustainable development and directions for future inquiry and learning.

When we apply collective R\&D, sustainable development looks more open-ended, it taps into the power of the informal, and it faces polarization head-on. In closing, we offer three fuzzy front end directions for the future of sustainable development. In the spirit of collective learning, these offers are posed here as potential directions for future inquiry and learning. ### R\&D reveals the open-endedness in sustainable development The practices outlined in this guide embrace not knowing as a core part of sustainable development. Given current uncertainties, we don't always know what should be and what can be done, and the first step is to admit that. An explorer mindset is required. At the same time, it is important not to undermine the wealth of technical expertise that exists in the sustainable development community. An open-ended R\&D approach can create friction with the best-practice mentality in the development sector.[\[6\]](#footnote-5) This friction should be productively channelled into a research agenda: **How do development investments learn from/with the knowledge of those closest to the problem?**\ More learning is needed to understand how development interventions adapt, based on learning from the people they serve. We've made some humble attempts.[\[7\]](#footnote-6) R\&D elevates opportunity spaces that by definition do not fit into previously defined categories, strategies or budget lines. Once R\&D reveals previously unseen opportunities or problems, how do we steer limited resources to follow these new directions? This points to a need for more learning on how and where development strategies pivot, based on R\&D results, while remaining accountable to those they serve. ### R\&D helps us see the value in the informal Classical development economics has traditionally viewed informal economies as anomalies that will or should be phased out once countries' economies grow to a certain threshold. And innovation was seen as driven by large firms.[\[8\]](#footnote-7) Our experience in collective R\&D reveals something different: there is vibrant and untapped potential among informal innovators, outside of registered economic activity. We, and others[\[9\]](#footnote-8) have learned that millions of informal innovators are solving problems with extraordinary ingenuity and modest means. Sustainable development needs to learn to see individuals who solve problems outside of large, registered institutions and firms. During the pandemic we saw people crowdfunding 3D printed face shields and ventilator parts for public hospitals in Tanzania, more recently informal waste pickers are being incentivized with mirco-insurance. Sustainable development needs to unlock the latent collective power of those innovating in the informal sector. Future collective R\&D needs to amplify informal innovations and build systems to incentivize frugal problem solving. Could tapping into bottom-up production and peer networks become a genuine component of development strategy?A key learning area for sustainable development is understanding: **What does development look like when it recognizes informal innovation systems?**\ More inquiry is needed to connect two fields of inquiry: innovation theory and informal economies. Finding a way to recognize informal, small-scale innovations that occur outside of firms and outside of formal innovation spaces is critical for creating breakthroughs in sustainable development, particularly related to recycling and reusing plastics and other materials, but likely in other areas of sustainable development as well. There is also a gap in understanding which R\&D policies are right for innovators in informal economies, especially as tax incentives are unlikely to drive innovation in the informal economy. Given the staying power of informal innovation, this area needs more focus in how we understand and invest in sustainable development. ### R\&D gives us a way to reckon with polarizing development solutions The deeper we go into complex and wicked problems, the more likely it is that all solutions won’t satisfy everyone all the time. And given how close we are to current climate, technology and inequality tipping points, tradeoffs are likely to be the norm. Consensus is needed, and yet it is often illusive across different constituencies and even over time. What was once considered a development solution can become polarizing in light of new data, changes in technology costs, or evolving social consensus. This means that sustainable development needs to be prepared for situations where there is no one universally and perpetually agreed solution, where consensus evolves and complex problems are only partially solved before they change and morph yet again. Given the tradeoffs entailed in dealing with complex problems, even solutions can be polarizing. Making invisible waste pickers’ contributions a part of recycling systems as was demonstrated in Viet Nam’s vignette, can be seen as progress as it moves this form of labor towards more dignity. Seen from an environmental systems angle however, creating a predictable pipeline of plastic as inputs to value chains might increase demand for plastic waste. This type of conundrum is frequent in our sustainable R\&D experience, and is becoming increasingly common given the limited range of options policy designers face in the current context. Social, economic and environmental objectives are hard to achieve at the same time. Progress for one community might mean setbacks for another. And while this is not new for development economists, development interventions need more learning on how to create common ground among different communities. **How do we collectively deliberate on polarizing solutions?**\ With polarized progress as the emerging new normal, full and continuous consensus is unlikely, particularly among the general public. And this may only grow more difficult in the post-truth age of AI. Sustainable development R\&D needs to find ways to enable different social groups to cross their boundaries, to find ways of cooperating in the absence of consensus.[\[10\]](#footnote-9) Future sustainable development R\&D should be devoted to learning how to constructively transcend divergent perspectives and finding the patterns of which solutions in which moments find traction among communities differently affected by sustainable development problems. *** ## **Notes** 1. As Dave Snowden (2012) and standard R\&D practice suggest, we should design experiments with failure in mind. A certain percentage should actually fail, otherwise we're not pushing the boundaries. [↑](#footnote-ref-0) 2. Inspired many times over by Problematizing Scale in the Social Sector (1): Expanding Conceptions An opinion piece by Gord Tulloch (2018). [↑](#footnote-ref-1) 3. Everett Rogers' (2003) work on diffusion of innovations shows that ideas spread through social systems via interpersonal networks and communication channels, not necessarily through top-down decisions. [↑](#footnote-ref-2) 4. John Kay (2010; 2012) describes this as obliquity: the principle that complex goals are often best achieved indirectly. [↑](#footnote-ref-3) 5. See Moore (1993) [↑](#footnote-ref-4) 6. Best practices work well in ordered domains where cause-and-effect relationships are clear. However, as Snowden and Boone (2007) explain, they have limited value in complex challenges where context determines what works, and copying what succeeded elsewhere rarely produces the same results. See also Andrews, Pritchett, and Woolcock (2017) on how the replication of 'best practice' solutions in development can lead to 'isomorphic mimicry' where organizations look capable without actually building real capability. [↑](#footnote-ref-5) 7. See Lucarelli (2025) [↑](#footnote-ref-6) 8. See Schumpeter, Joseph A. 1942. *Capitalism, Socialism and Democracy*. New York: Harper and Brothers. [↑](#footnote-ref-7) 9. See the work of Anil Gupta (2016), Eric von Hippel (2005). Also see Jeroen de Jong et al. (2023) or Kruse et al. (2019). [↑](#footnote-ref-8) 10. Star & Griesemer (1989) [↑](#footnote-ref-9) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://bas-leurss-personal-organization.gitbook.io/undp-accelerator-labs/advancing-r-and-d/7.-taking-collective-r-and-d-further.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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