Notes on Zahed Yousuf’s Work: A Chronological Overview, 1/5/26

A UNEP report on *Relationships and Resources* applied this relational framework to livelihoods analysis in Darfur, finding that “incidences of good governance prevailed when there were strong relationships.” The framework illuminated why communities make the decisions they do and how they interact with other resource users. It also yielded indicators for capacity-building strategies and the design of programming interventions that build collaboration across institutions.

To operationalise relational analysis at scale, the author points to graph database technology, which is “specifically designed to handle relationships between data points, such as nodes, relationships and their various properties.” This technology provides a more accurate representation of complex real-world networks and offers a more intuitive way to query those structures.

The article opens with a fundamental challenge to conventional practice: “What if a Political Economy Analysis (PEA) could be more than an analytical tool, instead a process of systemic change?” Yousuf argues that PEA holds enormous untapped potential — it can illuminate “the incentives, relationships, power dynamics, and informal rules that hold systems in place” — yet neither the development sector nor the systems-innovation field is deploying it in ways that genuinely shift systems.

Development actors undertake PEA, but in forms that are too superficial or constrained. Organisations are “set up for technical intelligence, not relational intelligence”, and because institutional incentives reward “tidy indicators and logframe discipline, not the messy work of making sense of power”, PEA becomes “a product, a checked box.”

Systems innovators — design labs and governance prototypers — tend to avoid political economy altogether. Their entry point is possibility and design: “imagining alternative institutional forms rather than diagnosing the power dynamics that shape existing ones.” They are fluent in systems language, but “the political realities, the incentives, informal rules, and entrenched interests that determine whether new designs can take root often sit outside their frame.”

The result is a shared failure: “even the most imaginative prototypes struggle to take root; they are resisted, absorbed, or quietly reshaped by the incentives and informal structures already governing the system.”

Yousuf grounds this critique in his direct experience leading a PEA for a governance reform programme in the Democratic Republic of Congo — a context where “weak institutions, entrenched hierarchies, competing incentives, low transparency, and the chronic lack of resources create an environment that resists change at every turn.” The conventional PEA model — gather intelligence, map stakeholders, produce a report — felt “far too narrow, too hierarchical, too extractive, too steeped in the ‘old power’ logic where knowledge is held by the few and delivered downwards.”

Drawing on Geoff Mulgan’s argument in *Big Mind* that societies fail not from lack of expertise but from inability to “connect and mobilise the intelligence that already exists across people, places, and institutions”, Yousuf argues that in fragile contexts like DRC, “what mattered most was not the data we collected, but the relationships that formed, the shared understanding that emerged, and the moments when people could see the system together.” This is because fragile systems are not held together by formal structures — “they are sustained by histories, informal agreements, personal loyalties, and deeply embedded incentives.”

Yousuf’s response is to reimagine PEA as “a collaborative journey, a way for people inside the system to see it together, make sense of it together, and co-create ways through it together. Not another analysis about them, but a process with them.” What is needed, he argues, is “a different kind of PEA: one that is politically honest, deeply relational, and directly connected to collective redesign” — not diagnosis alone, not imagination alone, but “a way of working that brings power and possibility together.”

In the DR Congo, the financial management system behaves not like a bureaucratic machine but “like a complex social ecosystem: shaped by informal incentives, relationships of dependence and loyalty, structural scarcity, fragile controls, and adaptive workarounds that prevent total collapse.” Crucially, it “is held together by patterns of interaction, not simply formal procedures” — meaning it cannot be understood through conventional desk reviews, segmented interviews, or focus groups.

Rather than conducting a political economy analysis *of* individuals, the approach created a process that enabled people to view themselves *as* a system, to examine the patterns and incentives that shape their collective behaviour, and to explore their collaborative capacities to shift those behaviours. The centrepiece was a three-day collective sensemaking workshop — “not as another consultation exercise, but as a shared inquiry into how the system really works, and where opportunities for shifting it might lie.”

By weaving the openness of storytelling with the structure of the Estuarine Framework, the process allowed people to speak across institutional boundaries and see that they were “not facing isolated problems. They were confronting a shared system, one that consistently produced the patterns and behaviours they had each been experiencing alone.”

These were “not technical weaknesses; they were institutional structures or relational configurations that the system repeatedly defaulted to.” Participants were “no longer simply telling stories; they were mapping the system’s behavioural DNA.”

The significance of the work lies not in its diagnostic findings but in “the infrastructure of collaboration that took shape”: a shared analytical language; routines for cross-actor sensemaking; recognition of the informal rules and incentives shaping behaviour; and a network of actors capable of “seeing the system together.” In this sense, political economy is becoming “a platform for collaborative intelligence — a way for people to generate shared insight and discover pathways for moving together through uncertainty.”

The emerging agency is not “the authority to control the system, but the willingness to inquire into it. Not the certainty of a blueprint, but the confidence to experiment into the future, together.”

The author began working on a governance reform programme in the DRC expecting PEA to function as a technical diagnostic tool — a way to “map incentives, power, and interests so programmes could be better designed.” What emerged instead was something more transformative. Working collaboratively with people from across the public finance system revealed that “the core problem is not a lack of insight but the absence of a shared space in which insight can be interpreted, tested, and acted on collectively.” PEA shifted from explaining how the system works to creating the conditions for people to work politically within it.

Through Structured Storytelling and the Estuarine Framework, participants came to see their individual experiences not as isolated failures but as expressions of a shared system. A consistent pattern emerged: the behaviour of the public finance system was not primarily shaped by formal rules, but by “informal incentives, relationships, and workarounds that allowed the system to function under conditions of scarcity, even as it failed to deliver frontline health and education services reliably.”

Collaborative analysis converged on five leverage points that consistently shaped system behaviour: diversion of funds, manual and insecure revenue collection, weak financial controls, absence of expenditure and treasury plans, and unreliable taxpayer registries. These were not abstract governance weaknesses — they were “the points where incentives, norms, and power intersected, where informal authority was exercised and where the system repeatedly defaulted to familiar patterns.”

Crucially, participants did not see these as technical problems awaiting fixes. They recognised them as political bottlenecks. Practices such as fund diversion were “embedded in survival strategies, loyalty networks, and informal compensation mechanisms.” Weak controls were not simply failures of capacity; they “created space for discretion, negotiation, and parallel authority.”

This reframing shifted the conversation toward grounded political questions: why does a bottleneck persist, who benefits, and what informal permissions sustain it? The analysis made visible the “hidden codes and informal infrastructures that quietly determine whether money ever reaches a clinic or a classroom.” The conclusion was that improving frontline outcomes requires more than better execution — it requires “a public finance system that distributes power more widely, expanding who can see, influence, and act within it.”

The next phase centres on small, politically aware experiments connected to the identified leverage points. These are explicitly framed not as pilots to scale or solutions to roll out, but as probes — ways of continuing the political economy analysis through action. By acting at leverage points, the work creates opportunities to observe how the system responds: which actors are willing to collaborate, where resistance surfaces and what it protects, and how new working relationships begin to form.

“Experimentation does not follow political economy analysis; it deepens it.” It is through action that motivations, resistance, and informal power structures become visible — not in theory, but in practice. This is what it means to work politically: “not to apply solutions to a system, but to engage with it as it is, to test, sense, and adapt in response to how power and relationships actually move.”

However, the author recognises a critical limit: acting at leverage points, even carefully, does not automatically lead to institutional redesign. “Shared insight, however powerful, is not enough to reconfigure relationships or change the rules of the game.” What is missing is not intelligence, but infrastructure — “the architectures that allow people to continue making sense together, to negotiate power and difference, and to learn how to act collectively over time.”

PEA is therefore reframed as the foundation of civic infrastructure: not an endpoint, but a shared platform supporting collaborative sense-making, the surfacing of informal power, the gradual building of trust, and politically realistic experimentation. Crucially, this infrastructure “is not neutral. It shapes who gets to participate in sense-making, whose knowledge counts, and how decisions are influenced — quietly redistributing power through how coordination and learning are organised.” To hold, this infrastructure requires networks with purpose — small, cross-system networks drawing people from government, assemblies, civil society, and media, deliberately cutting across institutional hierarchies so that “authority, insight, and initiative circulate rather than pool in a single centre.”

These networks are not bureaucratic structures but shared agreements about how people convene and participate, observe and interpret signals from the system, make decisions under uncertainty, and reflect, adapt, and learn together over time.

Systems change efforts typically begin with diagnosis — and political economy analysis is a powerful tool for that purpose. It can generate shared understanding, revealing “how power, incentives, and informal rules were shaping outcomes.” But this legibility has a ceiling: political economy “made the system legible, but it did not help redesign the pathways through which people could act on that understanding.”

The natural next steps — collaboration and governance design — each carry their own limitations. Collaboration “can generate conditions for trust and learning, but without changes to how authority, resources, and decisions are structured, that momentum struggles to translate into durable shifts.” Over time it risks becoming “a holding space for frustration rather than a driver of change.” Governance design, meanwhile, can produce thoughtful institutional architectures that nonetheless “remain disconnected from the incentives and relationships that shape behaviour in practice.”

The deeper problem is structural: “Systemic change doesn’t fail because any one of these approaches is wrong. It falters because none of them is sufficient on its own, and because we rarely design them to work together, over time.”

When systems begin to genuinely shift, what matters is less any single intervention and more how a small number of underlying capacities interact — capacities “that allow people to see the system more clearly, act together within it, and adapt as power dynamics and conditions change.”

In practice, “these capacities are distributed across various actors and practices” and no single organisation holds all three equally well. But where they begin to interact, “looser constellations of practice can form networks that learn, coordinate, and gradually build legitimacy through action rather than formal mandate.”

Collective intelligence has become one of the most influential ideas in systems change. Across governments, innovation labs, and civic platforms, a common assumption now shapes practice: bring together diverse perspectives, and systems become more legible. Patterns emerge. Decisions improve. But there is a problem. “We are getting better at understanding systems. We are not getting better at changing them.”

Collective intelligence strengthens how systems understand and decide. It makes systems more legible. It reveals patterns, surfaces constraints, and improves judgment. But improved understanding does not, on its own, change how systems behave. Systemic change requires actors with different roles, incentives, and authority to engage in ways that reshape interactions over time. Political economy analysis, for example, can map power, incentives, and informal networks with increasing precision. Yet these insights rarely translate into durable reform. The constraint is not analytical. It is behavioural.

The issue is not only how well systems understand themselves. It is whether actors within them can act together under conditions of uncertainty. This is where we need to move beyond collective intelligence and towards collaborative intelligence.

In complex systems, and particularly in political environments, the central challenge is not only cognitive. It is relational and behavioural. Collaborative intelligence can therefore be understood more precisely as: “the capacity of diverse actors to act within a system together through iterative experimentation, and to learn from how the system responds.”

The work of Reos Partners is particularly instructive here. Their *stretch collaboration* approach shows that actors do not need agreement, prior trust, or shared control to work together. They can begin with action. Collaboration, in this sense, is not alignment. It is joint experimentation. Actors take small, deliberate steps, not to implement predefined solutions, but to test how the system behaves. These actions function as probes, generating signals: which actors engage, and under what conditions; where resistance strengthens or weakens; which informal rules hold, and which begin to shift; what new possibilities emerge through interaction. Actors do not need full agreement in advance. They learn how to move together by acting and adapting as the system responds.

In complex systems, trust is rarely a starting point. It emerges through shared experience — by observing how others act under pressure, how commitments are honoured, and how risks are taken. Over time, repeated interaction builds a different kind of foundation: trust grounded in behaviour, not alignment. Collaborative intelligence is therefore not an extension of collective intelligence. It is a distinct capability: the ability to navigate uncertainty, work across difference, and generate new patterns of behaviour through interaction.

In governance work in the Democratic Republic of Congo, political economy analysis was redesigned as a collaborative sense-making process rather than a diagnostic exercise. Actors from ministries, civil society, and frontline services mapped their experiences using a shared framework. Patterns that had appeared isolated became recognisable as part of a wider system. This was collective intelligence: the system became able to see itself. But the shift that mattered came later. When participants formed small cross-system groups to experiment together, the work changed. The question was no longer whether the system was understood, but whether actors could act within it in ways that generated new information. These groups became “the early architecture of collaborative intelligence — capable of observing, probing, and adapting in response to how the system behaved.”

The systems change community has invested heavily in collective intelligence. This is necessary. No single actor can understand complex systems alone. But understanding is not the endpoint. Once systems become more legible, the central challenge shifts. It is no longer about insight. It is about how actors engage with uncertainty, difference, and power through action. This is not primarily an intelligence problem. It is a problem of interaction. The next frontier in systems change lies in strengthening collaborative intelligence: the capacity to experiment, sense, and adapt within complex systems.

At the most advanced level, it is no longer a question of whether actors can act together. It is whether their interactions begin to shift the incentive structures that govern behaviour.

Durable change requires something more specific. It emerges when interactions produce self-reinforcing patterns of behaviour across actors. In these conditions, expectations shift, norms stabilise, and deviation becomes costly — not because it is centrally enforced, but because it disrupts relationships and practices that actors now depend on. This is what durability looks like in practice: not sustained reform, not institutionalisation, but a self-reinforcing behavioural equilibrium.