Judgement under uncertainty: Heuristics and Biases in Organisational Transformation
Cognitive capacity building for transformations in 'post-normal' conditions
In 2018, embedded at Leuphana University for my first PhD case study, I intuited something that contradicted conventional wisdom: organisational transformation failures were fundamentally psychological rather than technical or strategic.
Seven years on, this intuition has been empirically validated through the research of my co-founder, Lily Pepper, whose 2025 master’s thesis ‘The Psyche of Transformation: Deep-Level Resistance of Organisations to Transformation for Sustainability’ provides the empirical backbone the early 2018 propositions lacked. Her work demonstrates that obstacles to transformation stem from unconscious defense mechanisms, collective anxieties, and entrenched assumptions -creating what she terms “immutability of deep leverage points” rooted in the work of Donella Meadows and others.
Today, with this validation, a hybrid intelligence platform in development, and AI reshaping organisational intelligence, that intuition is becoming operational reality through our work at Ozyntel.
What follows is the original essay, with reflections on how Lily’s research and our recent platform development connect to and extend these foundational insights
I wrote this on site at my first case study at Leuphana University Lüneburg, whilst I was reading Kahneman’s Thinking Fast and Slow in 2016. I gave up half way through (I had field word stuff on my mind) and selected Leonard Mlodinow’s ‘Subliminal’ instead; much more tangible in my view. Here is what I noted down and how I integrated it into my thinking, my action inquiry and the ongoing PhD research.
Cognitive Heuristic Bias & Post-Normal Science
One of the foundations of behavioural economics is ‘prospect theory: an analysis of decision under risk’ Kahneman and Tversky (1979). Also, ‘judgement under uncertainty: heuristics and biases’ Tversky and Kahneman (1975). These bear significant implications for the important, and occasionally overlooked aspect of behavioural change in organisations working (or not) towards sustainability.
This is due to the connection to the imperative of the post-normal scientific arena, which is where we can no longer undertake standard, certain, low stakes scientific research, as there is too much not known about what we don’t know in any given social or organisational environment. It also correlates to the context of sustainability science and policy, where complex systems such as organisations experience conditions of high uncertainty and stakes for decision-making are also high (Funtowicz & Ravetz, 1993; Ravetz, 2004, 2006).
In post-normal conditions -high uncertainty, high stakes, values in dispute - traditional expert-driven approaches fail. Organisations facing sustainability transformation operate in precisely this domain, where the consultant as detached expert cannot work; the diagnostician as embedded, reflexive practitioner becomes essential.
The implications of decisions made under these conditions inimitably affect our collective march towards reducing negative environmental impacts, and towards creating circular flows of natural resources through the organisation (i.e. Herman Daly’s concept of throughput), creating more positive societal and environmental impact.
So I asked myself, with these insights in mind, how can the work of behavioural and cognitive psychologists like Kahneman & Tversky (Kahneman & Tversky, 2011; Tversky & Kahneman, 1973), and Mlodinow (2013)[1], with their view of how the mind works, influence, enable and balance research into organisational transformation?
Chomsky’s arguments in his prolific, influential and contentiously political works, spanning decades, connects the responsibility of the public intellectual, and the study of behaviour of individuals, with the need to comprehend the internal processes, the mental models we use, undergirding such behaviour.
Chomsky referred to mind of an individual and that individual’s ensuing behaviour and the effect vice-a-versa. This cross-scalar interaction is similarly used in work understanding and navigating sustainability transformation, whereas the difference is one of ‘fractal’ integration: from individual neurosychology to the external behaviour of individuals in a group responding to each other - to group cognition and sense-making in an organisational environment under conditions of high uncertainty, stakes and conflict, we see an organisational behaviour that is complex, and often unhealthy - hence organisational pathologies.
In search of grounded knowledge of organisational transformation (for sustainability), we can refer to the mental models and collective behaviours of small teams, groups or dyads of individuals – challenging sub-optimal practices as change-agents embedded in their organisation – and the ensuing behaviour and/or pathologies of the organisation itself with its surroundings.
Propositions:
1) That obstacles in organisational sustainability transformations are largely rooted in collective cognitive ‘machinery’ or mental models (‘dissonance’), ‘simple’ or substitution heuristics, and systematic biases.
The establishment of the ‘Leverage Points for Sustainability Transformation’ project at Leuphana might help support this, however this very much depends on the type and outcomes of the research they do, which will likely take a couple of years.
2025 Update: This proposition requires refinement based on recent empirical research. Building on the leverage points framework conceived by Meadows, and updated by Abson et al, we found that obstacles are not merely rooted in collective cognitive machinery but, according to our CSO, Lily Pepper’s research, creates active immutability— the organisational psyche deploys unconscious defence mechanisms specifically to prevent paradigm change. This is not passive resistance but active immune response, positioning the organisational paradigm and unconscious in Schein’s deepest cultural layer where they interact with collective sensemaking to defend against transformation.
2) That the medical or organism analogy in organisational research is no coincidence. That it is prescient and useful towards providing ‘an accurate diagnosis [that] may suggest an intervention to limit the damage that bad judgements and choices often cause’.
As a doctor of a patient, one first uses the empirical evidence at hand to make a deductively reasoned argument for what is going on – the diagnosis. Second, one prescribes a course of action, moving on to what needs to occur to treat the symptoms and remedy the causes and cure the malady if possible.
Only then can an evaluation of the course of action take place so that a prognosis can be made of the future health of the patient relative to the malady. It is here proposed that this sequence of logical investigation rooted in Pragmatism and embedded, compassionate action - analytic, evaluative, then therapeutic – can be applied both to an individual and an organisation.
3) “To be a good diagnostician, a physician needs to acquire a large set of labels for diseases, each of which binds an idea of the ‘illness’ and its symptoms, possible antecedents and causes, possible developments and consequences and possible interventions to cure or mitigate the illness”.
Lily’s research validates this 2018 propositions’ prescience: organisations develop distinct “pathologies”- dissociation, schizophrenia, sociopathy, fever - depending on their psychological dynamics and history. Our evolving diagnostic framework now maps dysfunction types (fear of change, toxic leadership, unresolved trauma, defence mechanisms) to specific therapeutic needs, moving beyond metaphor to actionable intervention design based on the Inner Development Guide of the IDG network.
4) Being an organisational diagnostician requires the same level of experience and expertise as a doctor has: a much deeper, system(at)ic understanding, more than just – in the organisational context, not the medical – a consultant, self-titled visionary, analyst, or saviour fanatic might use or claim to possess.
For, in fact, all our decisions, heuristics and errors come into play, even when we assume, at a profound level, that we are right, rational and objective in our approach, we are always subject to our unconscious biases.
The more aware, willing to learn and to suspend judgement, and less egotistical we are of these influences, the more we have the likelihood of counterbalancing their effects.
This way, we can reduce the risk of over or misdiagnosing an illness or a pathology. In the context of this work, this undergirding proposition is considered paramount for developing useful insights, as if we are to pursue the lens of organisational ecology, then organisations are subject to pathologies also; such as schizophrenia, dissociation and fever.
2025 Update: The AI era adds complexity: organisational diagnosticians must now become “context engineers” - designing systems that encode appropriate cognitive/heuristic biases while preserving human judgment for ontological questions AI cannot (yet!) address. Research shows people doubt their own cognitive abilities when AI enters decision-making, paralleling transformation failures - or stunting - where external consultants provide “answers” without building internal capacity.
This leads us to add a new AI-specific proposition to the original four:
5) AI-powered organisational diagnosis tools risk replicating the very heuristics and biases they should help overcome, unless grounded in lived experience, reflexive practice, and appropriate (not general-purpose) intelligence architectures.
Just as Kahneman and Tversky showed humans substitute easy questions for hard ones; AI systems trained on general corpora substitute pattern-matching for understanding. The bounded rationality theories covered above must now inform not just human decision-making but the design of hybrid intelligence systems. Context is the critical variable; each sector has different sustainability understanding, requiring tailored rather than blueprint approaches.
This explains why, so often, blueprint solutions from major consultancies (€250-500k for 6-12 month roadmaps) achieve poor outcomes: they attempt normal science in post-normal conditions, whether delivered by human consultants or AI systems. Probabilistic methods like Bayesian data analysis can map patterns across transformation contexts while preserving causal understanding that LLMs lack.
From Diagnosis to Healing: Operationalising These Insights
The medical analogy extends beyond diagnosis. Organisations require not just identification of pathologies but therapeutic transformation - interventions addressing organisational trauma, defence mechanisms, and deep cultural assumptions.
Seven years of subsequent research and practice reveal that this demands:
1. Inner Development Goals integration
Building psychological capacities (self-awareness, perspective-taking, sense-making) in change agents at individual and team levels, recognizing that technical skills alone cannot navigate transformation complexity.
2. Fractal agency cultivation
Change propagates across scales (individual → team → organisation → system) when agency is understood as an innately human, emergent property rather than a top-down mandate. The Green Office coordinators mentioned in the footnote intuited this: their thirst for operational knowledge on behavioural and organisational psychology reflected recognition that transformation requires internal capacity, not external prescription.
3. Embedded action research
The extractive consulting model reproduces the cognitive biases this essay critiques. Embedded practitioners co-create knowledge with organisations rather than diagnosing from outside, building local competencies rather than dependency.
4. Hybrid intelligence platforms
AI augments rather than replaces diagnostic judgment when designed with appropriate and ethical intelligence architectures. This means using grassroots lived experience to de-bias systems, employing probabilistic methods for causal understanding, and preserving human judgment for paradigm-level questions.
5. Current context amplifies urgency
EU deregulation has left organisations in limbo - they’ve invested in sustainability infrastructure but lack guidance for next steps. Consumer demand persists despite policy rollback. Thousands of businesses prepared for transformation but have been abandoned mid-process, exemplifying the post-normal conditions we’ve described above: high uncertainty, high stakes, insufficient blueprint solutions.
The organisational diagnostician’s role has never been more critical - or more complex. We’re no longer just understanding human heuristics and biases; we’re ensuring AI systems don’t amplify organisational pathologies. The 2018 insight holds true: a deeper and broader organisational diagnosis leads to better transformation outcomes. The challenge as we end 2025: building that diagnostic capacity across sectors and contexts while maintaining the reflexive, embedded practice that makes it effective.
That’s why, as we move into 2026, developing our organisational diagnostic health checks, we are being rigorous and systematic about pathology, so that we can design the right therapeutic intervention, with embedded expert implementation guided by hybrid action research and AI-supported analytics. Ultimately, empowering further capacity building and ongoing healing from the clustered complexity and damage the polycrisis wreaks.
[1] Incidentally observed to be quite popular on the reading lists of the administrators of the Green Office between 2015-2016. Coupled with their identified request for workshops that could help them understand and navigate their institutional dynamics, the argument that this thirst for operational knowledge on behavioural and organisational psychology present in sustainability coordinators and change leaders is not just coincidence.
References
Pepper, L. (2025). The Psyche of Transformation: Deep-Level Resistance of Organisations to Transformation for Sustainability. [Master’s thesis, Maastricht Sustainability Institute, Maastricht University]. Unpublished manuscript.
Funtowicz, S. O., & Ravetz, J. R. (1993). Science for the Post-Normal Age. Futures(September), 739-755.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the econometric society, 263-291.
Kahneman, D., & Tversky, A. (2011). Thinking, fast and slow: Macmillan.
Mlodinow, L. (2013). Subliminal: How your unconscious mind rules your behavior: Vintage.
Ravetz, J. R. (2004). The post-normal science of precaution. Futures, 36(3), 347-357. doi:http://dx.doi.org/10.1016/S0016-3287(03)00160-5
Ravetz, J. R. (2006). Post-Normal Science and the complexity of transitions towards sustainability. Ecological Complexity, 3(4), 275-284. doi:http://dx.doi.org/10.1016/j.ecocom.2007.02.001
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive psychology, 5(2), 207-232.
Tversky, A., & Kahneman, D. (1975). Judgment under uncertainty: Heuristics and biases Utility, probability, and human decision making (pp. 141-162): Springer.
Kets de Vries, M., et al. (2011). Organizations on the couch: A clinical perspective on organizational dynamics and change. European Management Journal, 29(3), 183-200.
Meadows, D. H. (1999). Leverage points: Places to intervene in a system. Sustainability Institute.
O’Brien, K., & Sygna, L. (2013). Responding to climate change: The three spheres of transformation. In Proceedings of transformation in a changing climate (pp. 16-23). University of Oslo.
Schein, E. H. (2010). Organizational culture and leadership (4th ed.). Jossey-Bass.