Category Archives: Rationale

Dealing with AI

Dealing with AI

The UK Government publication of its AI Opportunities Action Plan (Clifford & Department for Science Innovation and Technology, 2025) sets out an agenda primarily focussed on the growth opportunities enabled by AI.

Many of the recommendations require translation from the problematic context to implementations using specific AI technologies, such as generative pre-trained transformers (GPT), and are therefore all examples of where problematisation is inevitable. As Callon (1980) observes 

“Problematisation culminates in configurations characterised by their relative singularity. There is not one single way of defining problems, identifying and organising what is certain, repressing what cannot be analysed.”

How then are these decisions to be enacted, the processes of deciding, in the operationalisation of this action plan? This question is difficult to answer as the action plan is just a set of recommendations, a “roadmap for government to capture the opportunities of AI to enhance growth and productivity and create tangible benefits for UK citizens.” 

Roadmaps, like actions and solutions, present static nominalisations of what should be a dynamic process. The actual intervening in problematic areas such as health care, education and provision of services etc. will emerge from the normal processes of deliberating over any technology, not just AI. Restating Callon’s perspective, there will be an “abundance of problematisations” resulting from these deliberating processes leading to options where specific choices will need to be made. It is in the choosing between options, in the different ways in which interventions with AI technologies can be problematised, that the inevitable conflicts between different stakeholder needs, differing policy objectives, will be made clear. It is here that problem structuring as a deliberative process needs to operate. 

“The usefulness of Callon’s work on problematisation is that it draws attention to the choices faced by an operational researcher when investigating a problem, and that there is not a single right answer to what is problematised. Whilst this supports the claims of Churchman, Ackoff and Checkland that these choices exist and that operational researchers are making conscious decisions about what to work on (and therefore what to ignore), it does not provide an answer to the problem of OR becoming practitioner-free, i.e. the problem of dealing with situational logics. The answer is found in the emergence of problem structuring methods (PSMs).” (Yearworth, 2025, pp. 13-14)

I was writing for Operational Researchers in my book, but the point is valid for any analyst or decision maker and their profession. Choices exist and conscious decisions need to be made about what to work on and what to ignore and these choices need to be made visible1 and debatable through formal processes – such as through the use of PSMs.

The situational logic that sits at the heart of the AI opportunities action plan is that choosing AI leads to (economic) growth. However, if we abrogate on our moral responsibility to make ethical choices and fall back on the simple rule-following of situational logics then we may as well hand the deliberation and implementation of an AI action plan to an AI itself2 and wash our hands of the consequences. 

I recognise that the AI opportunities action plan makes specific reference to “[g]lobal leadership on AI safety and governance via the AI Safety Institute, and a proportionate, flexible regulatory approach” and reflects the fact that choices need to be made by overloading the use of “proportionate.” Deliberating and deciding over a flexible regulatory approach will require hard work, these will be (and should be) difficult choices. Given the scale of the challenges and opportunities of AI, apportioning sole agency for this deliberating and deciding to the AI Safety Institute (AISI) just narrows the location and scope of debate around problematisations to, in effect, informing decisions about the boundaries of regulation that are broadly pro-innovation. Deflecting focus away from this concentration of decision making by talking about assurance tools in an AI assurance ecosystem just sounds like marketing i.e., our attention on the situational logic in operation here is being misdirected by the AISI.

For almost all the recommendations in the action plan, problematising should be a diffuse activity across a very broad range of actors, problem contexts, stakeholders, and technologies – putting choice into the hands of people best able to decide for themselves the scope of adoption of AI. By all means give organisations, and individuals, the processes that would enable them to make informed decisions, but these are not imposed ‘flexible regulation’ and ‘assurance tools’ that ultimately disempower.      

  • Callon, M. (1980). Struggles and Negotiations to Define What is Problematic and What is Not. In K. D. Knorr, R. Krohn, & R. Whitley (Eds.), The Social Process of Scientific Investigation (pp. 197-219). Springer Netherlands: Dordrecht. https://doi.org/10.1007/978-94-009-9109-5_8

  1. In effect the models/maps of the structured problem. ↩︎
  2. Checkland introduced the idea of the trap of situational logics in OR practice. However it was Rosenhead, writing in Rational Analysis for a Problematic World, who used the analogy of the sausage machine, which is more than apt here. ↩︎

Not getting lost in process

Not getting lost in process

Debates about political process and endless delays in making decisions threaten to weaken trust in our democratic institutions especially with regard to pressing matters like the provision of adult social care. Politics has become a “prisoner of process” (Bagehot, 2025). 

Bagehot makes a good point and cites Blair in support of the observation that process, rather than being the means to the end, has become an end in itself. Bagehot draws on Stafford Beer’s (not attributed) POSIWID heuristic – the purpose of a system is what it does – to suggest that the system’s purpose has become an endless cycle of debate without action, although, in passing, observing that deliberation is necessary to “ensure that decisions are simply not made” (my emphasis). This is all good, but I think there are a number of conceptual errors that unhelpfully muddy the argument.

Starting with Beer’s POSIWID, it is a simple observation that system is being interpreted here in a narrow sense. We would hope that any system of governance has feedback mechanisms in it. Rather than a simple linear sequence of steps we would expect something like deliberation → action → observation (of effects of actions) → comparison → deliberation → …, where the comparison step derives an error signal based on the difference between what was intended and what happened. This system should operate in a continual cycle of feedback – it is both unlikely that our actions achieve the desired effect and the world keeps changing anyway. While we might conclude that the evident purpose of the system is to endlessly deliberate i.e., deliberation → deliberation → …, we could go a bit further and observe where the system is broken – the action element is missing and therefore the feedback loop is not operating. I think we would both agree that the system needs to be repaired. 

POSIWID is useful and the elicitation of feedback loops, at any desirable level of detail, provides a powerful analytical tool; but I believe there is another way of looking at this problem and the use of a process approach offers some benefits, rather than being consigned in the narrow sense to a trap of deliberation. The key can be found in the way in which we use language in our analyses. I have previously railed against the use of language like ‘solution’ and ‘fix’ in the context of complex problems, but in the analysis of the feedback loop above I rather consciously used ‘deliberation’, ‘action’ and ‘observation’ to emphasise the linearisation of what should be a system and that this arises from the nominalization of elements that should be thought of as verbs.

Getting stuck in a process of deliberation, or an endless sequence of deliberations, is likely when all the actors, including analysts and commentators (expert and otherwise), are constrained by their nominalizations. A better conceptualising of process thinking is to think of governing as a process and that for it to perform it must consist of further processes such as deliberating, acting (or effecting change, or intervening) and observing (or measuring). These processes are all necessary for governing but none are sufficient, by themselves, for properly enacting the process of governing. Note the use of the gerund form of the verb to convey a sense of continual ongoingness of the process. We can decompose this schema (or model) to any level of detail that is required using conditions of necessity and sufficiency as a test on whether a process is required in the model. 

Coming back to Bagehot’s analysis, we can clearly agree that the process of acting is not working well, but it cannot be reduced to a simple intervention that is yet to happen and that will somehow ‘fix’ the problem. The process of deliberating is obviously not working well either, it is clearly not sufficient by itself to enable the process of governing and our measure of its performance should of necessity include its commissioning of useful planning to enable acting. Rather than being prisoners of process, we would be better served by realising that processes are all there are, both in the world and in our ways of intervening in the world. To do this, amongst other things, requires a change in our language, away from nominalizations, especially ones like ‘action’ and ‘solution’, and recognise that acting or intervening is a continuing and ongoing process and may be enacted at any level of scale (socially, temporally, spatially,…).

In the case of adult social care there is clearly a whole lot of process detail that is completely missing between deliberating and intervening and nobody seems to be talking about it. We are left with unedifying analyses and useless solutionist traps. 

There are no solutions to complex problems

There are no solutions to complex problems

This may sound like a rather bleak pronouncement and completely contrary to the way in which complex problems are presented and discussed by, amongst others, politicians, the media, and commentators – expert and otherwise. However, I believe we have a language problem that gets in the way of having informed discussions about complex problems and how we work to do something about them. I want to explain that whenever we hear a statement that articulates a claim to have a solution or fix to a complex problem the speaker is making a category mistake, in effect their claim is logically meaningless. To then discuss and debate such statements, especially by introducing quantitative measures of success such as targets, we compound the category mistake and waste effort – both time and resources – chasing after illusions. 

To support my argument, let’s start with considering the idea of problems that do have a solution. The simplest category is puzzles, and good examples range from crosswords and sudoku through to spatial puzzles like jigsaws and Rubik cubes. There is one solution that everyone can agree is correct, and while it is possible to not always find the solution (i.e., not complete the puzzle), or to only solve part of the puzzle, it is not possible to have an ambiguous solution. The answer is either right, wrong, or perhaps only partially right. Many maths problems fall into this ‘puzzle’ category too. Skills can be developed at solving such problems. We can set tests and examine the performance of the problem solver by not just checking for the right answer but also for them to ‘show working’. 

If we next look at the solution to a puzzle we can see that once we have produced it the problem ceases to exist. By ceases to exist I mean that the problem has a known solution and does not need to be found again. Of course, the solution can be kept secret or not communicated – that is, after all, the enduring property of puzzles as tests or enjoyment. The dictionary definition of solution just reaffirms this straightforward notion as a resolution or answer. In process terms, a process for solving or fixing a problem is a process that is designed to make the problem go away. The problem is solved or fixed and there is nothing else that needs to be done.

From this starting point of problems as puzzles with singular solutions it is not too difficult to extend the argument to problems that have multiple solutions but still nonetheless have the property that whichever solution is chosen to fix the problem, the problem remains fixed (or solved) and ceases to exist. Good examples here are algorithms or computer programs designed to address a specific problem. We could also re-cast the notion of a requirement or a constraint as a problem to be solved and accept that there will be multiple possible solutions that adequately satisfy the requirement/constraint. We might introduce some notion of efficiency – in use of resources and/or time – associated with each solution and therefore have some objective measure to choose between solutions. For example, there are many different algorithms for sorting a data set but for the same available computing resource, some algorithms would take less time to sort the data than others. We can think of this category of problems as well-defined.

By increasing the complexity of the problem, we start to run into difficulties in deciding whether we have a solution. We may be able to formulate the problem quite well in that we can write down a set of requirements and constraints, but solutions might be contested. For example, we may need to improve travel links between communities on either side of a river. There are many different ways of facilitating this ranging from a footbridge, road bridge, ferry, tunnel, rail bridge and so on. Problem formulation requires delving into what improve means and for whom, and deciding which solution to choose is a function of many considerations, not least budgets, timescales, impact on environment etc. After considering all these factors and embarking on a construction project following a well-defined problem formulation (as a specification or a project plan), we may still be in some doubt whether we have achieved a solution to the original problem.

The final class of problems defy both formulation and solution and have been variously labelled as wicked (following Rittel and Webber (1973, pp. 161-167)), or messy (following Ackoff, 1974, pp. 20-21)) or swampy (following Schön (1987, p. 3)). Using the definition of Rittel and Webber, so called wicked problems can be defined as follows (Yearworth, 2025, pp. 18-19):

  1. There is no definitive formulation – formulating a wicked problem is the problem. It is not possible to approach a wicked problem with preconceived notions of how it might be addressed, the only way forward is to start a process of enquiry and develop ways forward from there. 
  2. There are no stopping rules. The process of intervening is also the same as understanding the nature of the problem – the intervention is ‘good enough’ or the best that can be achieved within other limitations external to the process (e.g. time, budget, patience, etc.).
  3. Interventions are not right or wrong, there are no formal decision rules for defining correctness, they can only be viewed as making things better or worse for certain interests. Judgments will depend on personal interests and values. 
  4. There is no immediate or ultimate test for an intervention. Interventions will generate ‘waves of consequences over an extended – virtually an unbounded – period of time’. The consequences of an intervention are thus difficult to evaluate because the consequences will be continually changing. 
  5. Interventions are ‘one-shot operations’, and experiments are difficult to conduct. Every intervention is consequential and effectively irreversible. Interventions are essentially unique in nature – we cannot intervene in the same problem context twice as our interventions change the problem context. 
  6. There is no enumerable, exhaustively describable set of possible interventions. There are no criteria that enable us to judge whether we have found all of the interventions that are possible in a given problem context. It is a matter of realistic judgment about how expansive the process of enquiry should be.
  7. Every wicked problem is essentially unique. ‘Essentially’ implies that aspects may be common, but to think in terms of categories or classes of wicked problems with common ‘solutions’ is misleading. 
  8. Wicked problems can be considered symptoms of other problems, i.e., there is inherent systemicity in the world. ‘The level at which a problem is settled depends upon the self-confidence of the analyst and cannot be decided on logical grounds. There is nothing like a natural level of a wicked problem. Of course, the higher the level of a problem’s formulation, the broader and more general it becomes: and the more difficult it becomes to do something about it.’ 
  9. Can be contested at the level of explanation; there is likely to be conflicting evidence or data. It is not possible to rigorously test hypotheses about interventions due to their unique circumstances.
  10. Whereas scientific progress arises as a consequence of refuted hypotheses (in a sense, being wrong is good), in the area of policy and planning, decisions that have negative consequences are not tolerated (being wrong is not good).

In order to avoid the possibility of confusing the notion of solution with this type of complex problem I have introduced the use of intervention into this definition – the act of intervening in this type of problem context – and it is within this definition that the source of the category mistake can be found. We may intervene in these problem contexts but to say that we will solve or fix the problem is to logically contradict the definition. It would be perfectly reasonable to contest the definitions, but given the messiness of problem contexts that demand our attention I believe it is reasonable to assert that a category (or categories) of problem exist that are not puzzles or well defined/formulated (Also see Pidd (2009, p.44)). Our first action must be to structure the problem.

This category of problem is endemic and we must take heed of these properties. To blithely talk of solutions and fixes in the context of these types of complex problems is misleading. If this arises from a genuine misunderstanding about the nature of wicked/messy problems then perhaps it is understandable. However, journalists, politicians and expert commentators should be aware of these characteristics and to continue with using the language of solutions and fixes is to exhibit partiality or deliberate deceit. 

I write about this extensively in Section I of my book (Yearworth, 2025, pp. 1-43).

Ackoff, R. L. (1974). Redesigning the future: a systems approach to societal problems. Wiley-Interscience: New York; London

Pidd, M. (2009). Tools for thinking: modelling in management science (3rd ed.). John Wiley & Sons: Chichester.

Rittel, H., & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169. https://doi.org/10.1007/BF01405730

Schön, D. A. (1987). Educating the reflective practitioner. Jossey-Bass: San Francisco, CA

Yearworth, M. (2025). Problem Structuring: Methodology in Practice (1st ed.). John Wiley & Sons, Inc.: Hoboken. https://doi.org/10.1002/9781119744856 

Problem Structuring : Methodology in Practice

Problem Structuring : Methodology in Practice

My new book is now available!

Current perspectives on approaches to problem structuring in operational research and engineering and prospects for problem structuring methods applicable to a wide range of practice.

Despite the myriad successes of Operational Research (OR) in government and industry, critique of its continued relevance to complex, wicked problems led to the emergence and evolution of Soft OR as a more humanist orientation of the discipline centred on a methodological framing of techniques known as Problem Structuring Methods (PSMs). These have enabled OR practitioners to broaden the scope of OR to address complex problem contexts that require transforming, planning and strategising interventions for their clients. The original core PSMs of Soft Systems Methodology (SSM), Strategic Options Development and Analysis (SODA) and the Strategic Choice Approach (SCA) are presented using a new analytical framework based on constitutive rules, epistemologies, and affordances of the modelling approach. Practical considerations in PSM based interventions are discussed emphasising trust-building, stakeholder identification, facilitation and ethical practice. A wide range of PSM applications are surveyed demonstrating clear intersections with communities of practice grounded in the applied social sciences. The development of a new PSM based on Hierarchical Process Modelling (HPM) of purpose arising from a processual turn in engineering practice offers additional insights for the practice of Soft OR. New developments in PSM practice built on use of Group Support Systems (GSS) and exploiting developments in machine learning are presented. Prospects for bringing the Soft OR project back into better alignment with mainstream OR are discussed in the context of new education programs and a possible processual turn in OR.

Problem Structuring: Methodology in Practice contains four linked sections that cover:

  1. Problem formulation when dealing with wicked problems, justification for a methodological approach, the emergence of soft OR, the relevance of pragmatic philosophy to OR practice.  
  2. Traces debates and issues in OR leading to the emergence of soft OR, comparative analysis of PSMs leading to a generic framework for soft OR practice, addressing practical considerations in delivering PSM interventions.
  3. Charts the emergence of a problem structuring sensibility in engineering practice, introduces a new PSM based on hierarchical process modelling (HPM) supported by teaching and case studies, makes the case for a processual turn in engineering practice supported by HPM with relevance to OR practice.
  4. Evaluation of PSM interventions, survey of applications, use of group support systems, new developments supported by machine learning, re-contextualising soft OR practice.

Problem Structuring: Methodology in Practice is a thought-provoking and highly valuable resource relevant to all “students of problems.” It is suitable for any UK Level 7 (or equivalent) programme in OR, engineering, or applied social science where a reflective, methodological approach to dealing with wicked problems is an essential requirement for practice.

Note that some institutions may have access via their usual eBook provider (e.g. ProQuest, Perlego), if not by default on the subscription then by a separate order for this specific title.

Wicked problems and category mistakes

Wicked problems and category mistakes

This is a brief introduction to the notion of a wicked problem. It is based on the highly-cited paper by Rittel and Webber (1973). The following characterise wicked problems:

  1. There is no definitive formulation. In a sense, formulating a wicked problem is the problem
  2. There are no stopping rules. The process of intervening is also the same as understanding the nature of the problem – the intervention is “good enough” or the best that can be achieved within other limitations (e.g. of time, budget…)
  3. Interventions are not right or wrong, they can only be viewed as making things better or worse for certain interests i.e. the intervention has made things both better and worse depending on who you ask
  4. There is no immediate or ultimate test of an intervention. Interventions will generate “waves of consequences” over a period of time
  5. Interventions are “one-shot operations”, experiments are difficult to conduct, every intervention counts significantly, they are essentially unique in nature
  6. No enumerable, exhaustively describable, set of possible interventions
  7. Every wicked problem is essentially unique. “Essentially” implies that aspects may be common, but to think in terms of categories or “classes” of wicked problems with common “solutions” is misleading
  8. Wicked problems can be considered as symptoms of other problems i.e. there is inherent systemicity in the world
  9. Can be contested at the level of explanation, there is likely to be conflicting evidence or data

The corollary of this definition is that certain statements about problems are likely to be rendered false or meaningless if it can be shown that the problem is actually wicked, in effect the statement is demonstrating that a category mistake is being made. The following is not an exhaustive list:

  1. ‘Solving’ or ‘curing’ a wicked problem is a contradiction; there are no ‘solutions’, ‘cures’…
  2. Words that suggest an objective point of view used in the context of the problem at the very least need to be debated e.g. words like optimal, best, right, smart, correct, … all suggest the question – for whom? Alternatively, no decision taken should ever be considered wrong.
  3. Any statement of measurable quantity that supports an argument for the problem getting better or worse without acknowledging the dynamic complexity that systemicity implies i.e. “…worse then better…” is a more believable statement given dynamic complexity
  4. Statements that appear to deny the systemic nature of the problem e.g. ignoring requisite variety
  5. Containing irrefutable assertions of fact e.g. “…this proves conclusively that…”
  6. Use of binary choices, any mention of “silver bullets”
  7. Misrepresenting or ignoring plurality e.g. “The public…”
  8. Emphasis on producing plans rather planning as a process

If any of these corollaries are contested e.g. if someone claims to have a solution to a wicked problem, then they are likely to be making a claim about only an aspect of the problem, or only from a certain viewpoint; or their formulation is not that of a wicked problem i.e. they are talking about something ‘tame’. Statements that contain phrases like “…optimal solution…” or “…this proves conclusively that if we do this we will have the best outcome…” in the context of a wicked problem definitely signal a likely category mistake.

Category mistakes are a warning sign – be sceptical of claims being made. They suggest either misunderstanding or partiality.

It’s worth reading the Rittel and Webber paper. Despite its age, it still does an exceptionally good job of reminding us of the characteristics of wicked problems that’s just as relevant today.

The first steps towards a coherent approach to problem formulation can be found in Rosenhead’s (1996) introduction to Problem Structuring Methods.

Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155-169. doi:10.1007/BF01405730
Rosenhead, J. (1996). What’s the problem? An introduction to problem structuring methods. Interfaces, 26(6), 117-131. doi:10.1287/inte.26.6.117

Systems modelling in engineering

Systems Modelling in Engineering

The wider and more pervasive use of appropriate systems modelling techniques would have a beneficial impact on the way in which engineers deal with messy socio-technical problems. This class of problems is commonly defined by the following characteristics; i) difficulty on agreeing the problem, project objectives, or what constitutes success, ii) situations involving many interested parties with different worldviews, iii) many uncertainties and lack of reliable (or any) data, and iv) working across the boundary between human activity systems and engineered artefacts. All systems models attempt to conceptualise, via appropriate abstraction and specialised semantics, the behaviour of complex systems through the notion of interdependent system elements combining and interacting to account for the emergent behavioural phenomena we observe in the world.

Engineers have developed a multitude of approaches to systems modelling such as Causal Loop Diagrams (CLDs) and System Dynamics (SD), Discrete Event Modelling (DEM), Agent Based Modelling and simulation (ABM), and Interpretive Structural Modelling (ISM) and these are all included in my programme of research.   However, despite their extensive use, there still exists a number of research challenges that must be addressed for these systems modelling approaches to be more widely adopted in engineering practice as essential tools for dealing with messy problems. These systems modelling approaches as used in current engineering practice provide little or no account of how the process of modelling relates to the process of intervention (if any). This is in part due to the wider challenge to address the poor awareness and uptake of Problem Structuring Methods (PSMs) in engineering, the current inadequate way of integrating these more engineering-focussed systems modelling approaches into PSMs, and lack of understanding in how to deploy them appropriately in addressing messy problems in specific contexts. There is also the need to interpret the current state of the social-theoretic underpinning to systems modelling into a form that is appropriate for use in engineering. This need arises from the endemic atheoretical pragmatism that exists in engineering practice. The lack of methodology supported by suitable theory to counter this i) hinders the development of understanding why methods work or not, and also what it means for them to work, ii) acts as a barrier to communication between practitioners and disciplines, and iii) has ethical consequences, as pragmatic use of methods raises the problem of instrumentalism.

Addressing this methodological challenge is currently a central core of my work. I believe this research is transformational in that it integrates academically disparate areas of expertise in engineering, management, and social science, into a coherent articulation of systems modelling for engineers.