Unfit for uncertainty: Rethinking decision-making for missions
How outdated decision logics are undermining Labour’s ambitions for a mission-driven government.
Given Labour won the last general election on the promise of national renewal and a mission-driven state with a “relentless focus on long-term ends” after years of crisis management — frustration is understandable. After the Budget this week, it feels as if political ambition has met the practical reality of sustaining long-term political direction inside institutions designed around stability and risk management. Add a “black hole” in public finances and sporadic and often contradictory regulation, and it has rarely been harder for public servants to know what to decide — or, critically, how to make decisions in the first place.
Drawing on my work in early-stage policymaking at HMRC and research at UCL’s Institute for Innovation and Public Purpose, this short piece of “writing as thinking” argues that the UK’s public institutions are structurally incapable of navigating uncertainty because they are built on a decision-making logic designed for predictability, control and linear planning — not for complexity, learning or adaptation. Because, if the UK is serious about missions, or radically overhauling how the civil service thinks and works, the everyday practicalities of how decisions are framed, contested and made will need to change.
The issue of managing uncertainty
Across government and beyond, a lot of my work has been at the earliest stages of decision-making — where ideas are half-baked, assumptions untested, and uncertainty is at its highest. In these spaces, one of the things I see teams struggle with the most is the ability to be agile enough to experiment, learn and iterate while being stable enough to maintain direction and meet deadlines. In fact, as Rainer Kattel and James Plunkett have noted, institutions are often both too stable and too agile — highly proficient at producing procedures and frameworks that promise control, yet poor at learning, experimenting, or re-orienting in the midst of uncertainty. The result is an all too familiar blend of paralysis, short-termism, and “everythingism”: needing to respond to everything while changing little.
This dynamic is not accidental. It is a lasting hangover from New Public Management — a model that has compartmentalised and hollowed out the civil service in order to make it measurable, auditable and controllable. Designed for an era of fewer citizens, slower information flows and limited data capacity, our civil service is oriented around prediction and control despite vast improvements in data, research and digital infrastructure. Put simply, we have left ourselves in a situation where we are taking 21st century challenges, evaluating them with 20th century tools, and responding within 19th century institutions built in a different time and for a different time.
In “Seeing Like a State”, James C Scott describes this as the state’s drive for “legibility” — making societies administratively visible by simplifying their complexity into categories that can be planned for. To illustrate this he uses the example of modern scientific forestry in Prussia. In an effort to maximise timber output, foresters cleared mixed woodlands and replanted them in perfectly straight rows of a single fast-growing species. The first generation produced higher yields, fantastic. But, by the second and third generations, yields collapsed. The drive for order — monoculture, straight lines, predictability — had unintentionally erased the complex, interdependent ecology that sustained the forest and made it viable in the first place.
New Public Management simply extends this logic into government, treating institutions as machines to be ordered rather than ecosystems to be stewarded. It rewards prediction over understanding and linear planning over iterative learning in what Funtowicz and Ravetz call a “doctrine reinforced by guesswork,” reliant on cost–benefit analysis and other appraisal tools built for marginal change in predictable environments. These methods make little room for uncertainty or contested values because they flatten complexity, obscure causality and routinely miss the non-linear, cascading effects that dominate policymaking. The result is a higher risk of expensive failures, often harming those most in need.
More fundamentally, this mode of public problem-solving is simply too slow and too distant from the people it is meant to help, offering little opportunity for learning or course correction in the first place. And, for policymaking — a forum directly concerned with people and their behaviour — it is simply not tenable to expect civil servants and ministers to predict, design and justify interventions up-front without addressing these structural constraints. Put as many policy design cycles in front as me as you would like, the “waterfall” model of policy delivery, illustrated in The Radical How, makes this explicit. Planning happens early, implementation happens late, and evaluation tends to arrive long after the window for improvement has closed.
Recent literature on Human Learning Systems pushes this argument further, describing how this creates a “pathological culture of blame and defensiveness,” within the civil service, where ambiguity is treated as error and risk is transferred onto individuals. Even when uncertainty is real and unavoidable, public officials are expected to present polished cases that imply certainty, rather than surface complexity or challenge assumptions — something I see time and time again within HMRC. What this ultimately means is that talented civil servants (of which I work with many) become trapped in an environment where they are punished for being wrong, rewarded for nothing going wrong, and rarely recognised when things go right.
This paralysis and culture of negativity isn’t individualised. I work with and have met so many talented, driven and imaginative civil servants. However, because the institutional grammar, design and hierarchy of decision-making treats uncertainty as a threat to be contained rather than a condition to be worked with, they are not given the time or space to do their best work. We have designed our organisations to reduce risk and cost under the premise of control but failed to appreciate how work actually gets done in complex systems — ultimately treating learning as political performance.
Recent reflections from municipal leaders in China — long associated with rigid target-driven governance — note that obligatory numerical targets distort behaviour, narrow attention and create perverse incentives within institutions.
Ultimately, this manifests as both a bias towards inaction and an inclination to make decisions when the least is known. Jen Pahlka memorably phrases this as “policy vomit” — in which the words of the policy are vomited, essentially undigested, into the administration of a policy, without the benefit of thoughtful, learning and iteration. In a department like HMRC, where impact is traditionally inseparable from cost, this often means using cost-benefit analysis (as suggested by the UK government’s guide to policy appraisal — the ‘Green Book’) as a means to centre on the risk of doing something rather than the opportunity for it to go well — effectively excluding learning from the process altogether and overlooking or under-considering real-world dynamics.
Towards managing missions or muddling through?
Over the last decade, governments and policymakers have been increasingly turning to mission-oriented innovation as a way to break out of incrementalism and tackle the “wicked” problems causing so much trouble for our ways of working today. It signals a shift away from the narrow technocratic logic of correcting market failures towards a more ambitious role for the state in shaping and co-creating markets, mobilising collective action and directing investment towards clearly defined public purposes.
Labour’s commitment to a mission-driven government, not dissimilar to those across Europe as part of a broader “third generation” of innovation and policy frameworks, was meant to embody this shift — a break from short-term crisis management and a renewed focus on long-term ends. Essentially, a recognition that neoliberal and technocratic policy tools are fundamentally unsuited to steering complex socio-technical systems as they privilege efficiency, prediction and control over relevance, learning and adaptation.
There is plenty of noise on mission theory. The problem, however, is that while there are many practical examples of a shift towards achieving mission policy — and more pragmatic, learning centric ways of policymaking — any meaningful practices generated by this new approach to policy have struggled to take hold.
Demos Helsinki’s “Missions for governance” paper reports that a 2022 survey of more than 40 OECD countries found that only one in four mission initiatives had a clearly defined target; only 15% had a dedicated governance structure; and only one in ten had a plan for monitoring and evaluation. In other words, while governments are embracing the language of missions, they are implementing them through business-as-usual structures, inherited from New Public Management, rather than transforming the way we make decisions.
This is perhaps indicative of a larger problem of institutional change. While missions and many other forms of experimental governance look to give us a blueprint for reigniting innovation policy, they do not help shift the entrenched challenges that government faces internally. It makes me wonder — we’ve been practicing systems thinking, futures thinking and experimental forms of governance for decades (with missions as the new synthesis of some of these tools) yet regardless of how good they sound, they haven’t scaled or had the impact any of them promise because they have all failed to address these entrenched challenges (See an interesting expansion of this point here).
To me, this all suggests that regardless of the approach, the need for a reimagined architecture of our institutions has never been more important if we want missions to be actually transformative rather than symbolic. Missions cannot be delivered through a policymaking architecture optimised for certainty, marginal change and after-the-fact evaluation. They require a state capable of working with uncertainty rather than suppressing it — one that builds learning into the fabric of everyday decision-making, rather than treating it as an optional add-on.
Reimagining public value must begin in the texture of everyday decisions.
If missions are to mean anything, the civil service needs to rebuild its capacity to create public value as a practice. That requires a new grammar of decision-making: one that treats uncertainty as navigable, frames choices in terms of risk and opportunity rather than cost and control (See Simon Sharpe), and builds learning into the routine choreography of how problems are defined and acted upon.
This shift depends on everyday practices, not high-level strategies: developing shared strategic alignment; negotiating problem definitions with those affected; testing ideas early; iterating in public; and learning at portfolio scale rather than programme-by-programme. These are the capabilities that make missions possible — and without them, missions will remain symbolic rather than transformative. I will write about those practices elsewhere and be sharing my MPA thesis “(Re)imagining Mission Neighbourhoods: Emerging Practices of Urban Transformation” soon.
For now, the point is simple. If the UK is serious about missions, it must first change how everyday decisions are made.
This post is part of CIVICWORKS; a publication on (re)thinking civic bureaucracy, institutional reform, dynamic capabilities, policymaking and technology.
Unfit for uncertainty: Rethinking decision-making for missions was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.
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