For decades, our approach to societal challenges, particularly in policymaking and delivery, has been rooted in a fundamental assumption: that outcomes can be conceived as fixed goal states. This "predicted good" paradigm, as it's often called, underpins linear program design, logic models, and command-and-control management structures, operating on a seemingly simple logic: define the desired end state, chart the pathway, and engineer the system to get there. It's a method that promises certainty and control, where success is measured by hitting pre-set targets. This is a model I have followed for years. I have covered the Delivery Unit approach as a fan over this period. It is important in politics to show ‘delivery’
Below is a curated synopsis of the work around the Cynafin Framework! Obviously not all my own words but with a little help from AI in my NOtebookLM - a wonderful tool!
However, the increasingly complex, multi-faceted, and uncertain systemic landscapes of today—such as climate change, social fragmentation, and, critically, systemic inequality—are fundamentally challenging these traditional approaches. The implicit assumptions behind fixed goals are collapsing under these modern conditions. These include the beliefs that the environment is stable enough to define a desirable end, that "good" is broadly consensual, that centralising goal definition is legitimate, and that the system is sufficiently closed and controllable.
When applied to intricate issues like systemic inequality, the very act of predefining a goal space risks collapsing the diversity, adaptability, and latent intelligence of the system. A predicted outcome, in such contexts, becomes a constraint rather than a guide, reducing the system’s capacity to sense, evolve, and respond. Moreover, predefined outcomes can serve as instruments of projection and control, encoding power asymmetries by dictating whose future is being predicted and whose definition of "desirable" prevails. For genuine systemic change to occur, goal spaces cannot be static, centralised, or fixed; instead, they must be provisional, situated, and subject to continual renegotiation as the system evolves. This understanding necessitates a paradigmatic shift from predictive control to generative emergence, where outcomes are viewed not solely as products of strategic foresight, but as emergent expressions of systemic coherence.
Understanding and Navigating Complexity: The Cynefin and Estuarine Frameworks
To move beyond the limitations of fixed targets, we need frameworks that help us understand the very nature of the problems we are facing.
The Cynefin Framework: Diagnosing the Challenge
Developed by Dave Snowden, the Cynefin framework is a foundational model in modern decision science that assists leaders and decision-makers in categorising situations into different domains: Clear (Simple/Obvious), Complicated, Complex, Chaotic, and Confused (Disorder). This diagnostic tool helps tailor decision strategies to the specific nature of the problem.
Crucially for policymaking and addressing systemic issues, Cynefin counters the "one-size-fits-all" mindset of traditional management, promoting context-specific approaches. Each domain requires a different approach:
In Clear domains, cause-and-effect relationships are straightforward, and best practices work well.
In Complicated domains, cause-and-effect can be identified through expert analysis, allowing for good practices.
However, for complex domains, which characterise problems like systemic inequality where cause and effect can only be seen in retrospect, Cynefin advocates for adaptive experimentation. This involves probing, sensing, and responding to generate emergent practices, signifying that a rigid, predefined plan simply won't work.
Chaotic domains, conversely, demand decisive, stabilising action before any sense-making can occur.
By recognising disorder (confusion) and reframing the problem domain, organisations can avoid critical decision errors that arise from misapplying tools meant for one context (e.g., analytical methods for clear or complicated problems) to complex or chaotic problems. This directly addresses why a series of projects based on linear logic often fail to deliver real change in complex systems.
In essence, Cynefin provides modern decision science with a flexible sense-making structure, equipping practitioners to adapt, learn, and make more resilient decisions in the face of the uncertainty and complexity characteristic of 21st-century problems.
Estuarine Mapping: Guiding Strategic Movement
Building on Cynefin's diagnostic lens, Estuarine Mapping provides practical ways to shape interventions in complex systems. While Cynefin helps you establish where you are (what kind of system or problem you face), Estuarine Mapping guides you in strategic movement and action within these complex contexts.
Its core features are vital for navigating systemic change:
It helps map the possibilities for change and highlights where small "micro-nudges" might shift system patterns.
It accounts for the dynamic, non-linear nature of real-world change, using the metaphor of an estuary where possibilities and constraints flow and shift over time. Like water in an estuary, some elements are stable, while others constantly change and require regular monitoring.
Most importantly, Estuarine Mapping prioritises sensemaking in the "thickness" of the present—the full range of dispositions and possibilities—rather than working toward fixed, abstract end-goals. This emphasis on the present, rather than a predetermined future, is key to adaptive policy.
Together, Cynefin and Estuarine Mapping provide a robust toolkit for organisations dealing with complex, adaptive challenges, enabling both effective diagnosis and adaptive strategic action.
Redefining Success: From Outcome Delivery to Emergent Coherence
The insights from the discussed frameworks fundamentally redefine what success looks like in complex systems, shifting our perspective in several key ways:
1. From Outcome Delivery to Capacity Building The strategic unit of work shifts to "agentic capacity"—the distributed ability of diverse actors to sense, interpret, act, and learn together. This means investment moves from control towards cultivating the conditions for coherent outcomes to emerge from within the system itself. Building agentic capacity involves expanding sensing capacity, fostering fielded intelligence, developing feedback infrastructure, ensuring resource access, enabling decoherence from legacy structures, and facilitating de-territorialisation and recontextualisation. The true goal becomes the development of the conditions for systemic emergence, rather than dictating specific responses.
2. From Static Plans to Rotating Attractors Provisional goals and Key Performance Indicators (KPIs) are still used, but they are explicitly treated as "temporary, revisable orientation points rather than final destinations". These goals will "rotate, shift, dissolve, or deepen as systems evolve," and governance must support this rotation, not resist it. This adaptive approach is termed "Outcome Rotation Governance", which views the goal as mobile and revisable, anchoring effort in growing systemic coherence and agentic infrastructure. This recognises the partiality of any singular outcome and accepts the contingency of all present knowledge.
3. From Vectorised Control to Relational Development Instead of forcing every actor along a single trajectory towards a predefined goal, governance must steward the quality of inter-agent relationships so that coordination, innovation, and course-correction arise organically. This cultivates "relational maturity" within the system—its ability to host diversity without fragmenting, sustain feedback without collapse, and generate internal alignment through dialogue, not enforcement. The system is no longer optimised for a singular output but cultivated for coherence, adaptability, and depth of engagement. This prioritises trust-building infrastructures over control architectures and reciprocal intelligibility over centralised clarity.
4. Success as Relational Maturity and Systemic Emergence Ultimately, success in complex systems is no longer measured by hitting a pre-set target. It is measured by the system’s increasing relational maturity—its capacity to host diversity without fragmentation, to reconfigure in response to feedback, and to generate evolving coherence without external command. Systems are viewed as living fields of interrelations, where value is co-produced and continually negotiated across differences, rather than a machine designed to deliver a target output. The outcome is not a goal to be delivered, but an emergent property of a system’s internal diversity and its capacity to maintain generative interrelation. The true goal becomes the development of the conditions for systemic emergence.
Real-World Embodiments of This Shift
We are already witnessing these shifts in practice through initiatives tackling complex societal challenges:
Local Delivery Pilots (LDPs): Embracing Process Learning and Relationships The LDPs aim to create conditions in places to eliminate inequalities by embedding physical activity in local systems, rather than just delivering isolated programmes. Their evaluation highlights key themes that directly support this new approach:
Process Learning Focus: Evaluations emphasise process learning over strict target hitting due to the inherent complexity and evolving nature of place-based work. This acknowledges that learning what works as you go is more important than rigidly adhering to a plan.
Balancing Measurable Outcomes with "Softer System Changes": LDPs balance traditional measurable outcomes (e.g., increased activity) with "softer system changes like relationships and trust". Building trust among partners and community members is explicitly treated as a critical intermediate outcome leading to sustainability, supporting ongoing system change beyond initial funding cycles.
Flexible Funding and Partnerships: Addressing inequalities requires flexible funding and partnership approaches to tailor interventions to local needs. Successful LDPs depend on multi-agency collaboration, including local authorities, health bodies, and voluntary organisations, co-designing solutions.
GM Moving: A Whole-System, Movement-Building Approach GM Moving explicitly frames inactivity as a complex, place-based problem that needs a whole-system response, not isolated projects. This directly addresses the point that a series of projects often fail to deliver real change in systemic inequality.
Movement-Building and Relationships: Hayley Lever, a key voice behind GM Moving, emphasises that "movement-building (culture + system change) is as important as delivery". A huge emphasis is placed on convening, trust, and relationships as primary levers of change, rather than command-and-control. Influence comes from connecting people across formal boundaries and sustaining conversations over time.
Embedded Evaluation: GM Moving highlights the role of embedded researchers and continuous learning to understand what's working and why, using evidence to iterate strategy and scale successful models. Their evaluation embraces both quantitative measures and "qualitative impact (culture shifts, partner collaboration)" as indicators of success.
Complexity Requires Humility: The approach acknowledges that complexity requires humility, expecting emergence, adapting as evidence appears, and accepting that progress is uneven.
Cultivating Emergent Futures
These examples demonstrate that tackling systemic issues like inequality demands a fundamental shift in how we approach policy and delivery. It's about moving away from the illusion of full control and predictable outcomes, towards cultivating system capacity, fostering deep relationships, and navigating emergent futures with adaptability and continuous learning.
The new goal space is not a static end state, but a living field, a shared sensibility that evolves as the system matures in intelligence, capacity, and relational depth. Goals are not enforced, but arise through emergent coherence; they are contextually situated, plural, and iteratively reframed. This perspective means that transformation is no longer a linear journey to a finish line, but a continuous unfolding of new capacities, meanings, and configurations. The outcome becomes an emergent presence – a state where differences are held generatively, futures remain open, action is meaningful, and sense is co-created.
This is the messy, but vital, frontier of governance and strategy in a complex world: to hold space for coherence without control, for futures without foreclosure, and for agency without domination.