By Nanda Wijermans and Bruce Edmonds

“Greenwashing” is when claims are made by organisations (usually companies) to mislead “the public to believe that a company or other entity is doing more to protect the environment than it is” (United Nations 2026). The intent is to put a spin on their practices to give the impression they are ecologically and climatologically responsible. This is different to the situation where the claims give an accurate impression of what they do, allowing the public to make an accurate assessment.

“Theorywashing” [1] is when aspects of a model are misleadingly justified using some existing theory. Associating such aspects with theory, in this way, helps give the impression that a model is academically justified and not merely an ad hoc construction. The intent is to put a spin on existing modelling choices that were really made for other reasons (ease of implementation, habit etc.). This is different to the situation where the claims gave an accurate impression of the sources of model elements – a genuine attempt to implement an existing theory in a model mechanism.

This should also not be confused with the situation when a modelling team is engaging in theorising using a model [2] – in that case, the theory in question (either existing or being formulated) is the focus of the modelling effort and will stand or fall depending on the success of the modelling. “Theorywashing” is aimed at giving credibility to modelling choices that were actually ad hoc.

Why does theorywashing matter? Firstly, it is just another example of academic authors prioritizing the promotion of their paper over the truthfulness of their justification. This can have deleterious effects, from the serious (e.g. a policy actor making a wrong judgement that results are more rigorous than they are and making unfounded decisions as a result) to the more trivial (e.g. wasting other academics’ time as they chase down the reference to understand the reasoning behind a model). Secondly, it maintains the academic norm whereby everything in a model is supported somehow – giving the impression that there are no arbitrary modelling choices. This undermines honesty in modelling, as this norm is at odds with modelling reality, where it is almost always necessary to use some auxiliary assumptions to make a simulation work.

Clearly theorywashing can be a matter of degree. A social science theory might be so vaguely defined that a candidate mechanism might happen to be consistent with it, in which case there is little harm in pointing this out. However, this is different from attributing the reason for that implementation choice. In terms of Antosz et al. (2023), which categorises the different uses of “theory” in agent-based modelling. Theorywashing might come into three of these:

“When a theory is imported from elsewhere to help specify/justify/label a component of a wider ABM”. This is the specific case discussed above.
“The specific ABM is designed within a more abstract framework. (sometimes called a ‘theoretical framework’)”. This is the case where an ABM is deliberately designed to be consistent and/or using the framework. If this is an accurate portrayal of intention of the modellers this is fine. To the extent the ABM does not really fit the framework or the framework was invoked after the design to justify it, it is not.
“When a family of related models results from a body of work, collectively constituting a ‘theory’ in some sense”. This is theory in the sense of (Giere 2010) – think of a physics undergraduate course with a title like “The theory of simple harmonic motion”. Again, as in the previous case, this might well be justified as a particular example (e.g. the set of models and maths about “opinion dynamics”), but that is different to whether it was the motivation for the modelling choices.

To put it simply, what we are asking is for honesty in describing the provenance of assumptions, processes and structures of a simulation. If this is honestly described, then a future modeller might attempt to check the robustness of the claimed results to changes in this assumption or even change this assumption and further develop the model. If some aspect is spuriously ascribed to some theory this chance might be missed.

Notes

1. The term was invented by the first author, however we are not the first to use it, e.g. “James L.” wrote about how the Operational Theory Research Institute “…was crucial in ‘theorywashing’ Israeli military actions through the esoteric language of French theory References” (James L. 2023) and Luis Garicano defined theory washing as: “smart minds put to work trying to make coherent the incoherent and justify the unjustifiable” (Garciano 2025). Our, more specific, usage is consistent with these and retains its nicely pejorative flavour.

2. As in the process leading to a theory, as per (Swedberg 2012).

References

Antosz, P., Birks, D., Edmonds, B., Heppenstall, A., Meyer, R. Polhill, J.G., O’Sullivan, D. & Wijermans, N. (2023) What do you want theory for? – A pragmatic analysis of the roles of “theory” in agent-based modelling. Environmental Modelling & Software, 168, 105802. DOI:10.1016/j.envsoft.2023.105802

Garciano, L (2025) Post on X/Twitter. https://x.com/lugaricano/status/1908543321267433524 (Accessed 26 May 2026)

Giere, R. N. (2010). Explaining science. University of Chicago Press.

James L. (2023) Israeli Deleuzian Forces – Or, the ‘theorywashing’ of occupation. The College Hill Independent, 20^th September 2023. https://www.theindy.org/article/3024 (Accessed 26 May 2026)

Swedberg, R. (2012). Theorizing in sociology and social science: turning to the context of discovery. Theory and Society, 41(1), 1–40. https://doi.org/10.1007/s11186-011-9161-5

United Nations (2026) Greenwashing – the deceptive tactics behind environmental claims. UN website on climate change issues. https://www.un.org/en/climatechange/science/climate-issues/greenwashing (Accessed 26 May 2026)

Wijermans, N. & Edmonds, B. (2026) “Theorywashing” – the practice of misattributing theory to impart academic credibility. Review of Artificial Societies and Social Simulation, 8 Nov 2026. https://rofasss.org/2026/08/11/theorywashing

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

By Andreas Tolk

In 2017, Shermer observed that in cases where moral and epistemological considerations are deeply intertwined, it is human nature to cherry-pick the results and data that support the current world view (Shermer 2017). In other words, we tend to look for data justifying our moral conviction. The same is an inherent challenge for simulations as well: we tend to favour our underlying assumptions and biases – often even unconsciously – when we implement our simulation systems. If now others use this simulation system in support of predictive analysis, we are in danger of philosophical regress: a series of statements in which a logical procedure is continually reapplied to its own result without approaching a useful conclusion. As stated in an earlier paper of mine (Tolk 2017):

“The danger of the simulationist’s regress is that such predictions are made by the theory, and then the implementation of the theory in form of the simulation system is used to conduct a simulation experiment that is then used as supporting evidence. This, however, is exactly the regress we wanted to avoid: we test a hypothesis by implementing it as a simulation, and then use the simulated data in lieu of empirical data as supporting evidence justifying the propositions: we create a series of statements – the theory, the simulation, and the resulting simulated data – in which a logical procedure is continually reapplied to its own result….

In particular in cases where moral and epistemological considerations are deeply intertwined, it is human nature to cherry-pick the results and data that support the current world view (Shermer 2017). Simulationists are not immune to this, and as they can implement their beliefs into a complex simulation system that now can be used by others to gain quasi-empirical numerical insight into the behavior of the described complex system, their implemented world view can easily be confused with a surrogate for real world experiments.“

I am afraid that we may have fallen into such a fallacy in some of our efforts to use simulation to better understand the Covid-19 crisis and what we can do. This is for sure a moral problem, as at the end of our recommendations this is about human lives! And we assumed that the recommendations of the medical community for social distancing and other non pharmaceutical interventions (NPI) is the best we can do, as it saves many lives. So we built our models to clearly demonstrate the benefits of social distancing and other NPIs, which leads to danger of regress: we assume that NPIs are the best action, so we write a simulation to show that NPIs are the best action, and then we use these simulations to prove that NPIs are the best action. But can we actually use empirical data to support these assumptions? Looking closely at the data, the correlation of success – measured as flattening the curves – and the amount and strictness of the NPIs is not always observable. So we may have missed something, as our model-based predictions are not supported as we hope for, which is a problem: do we just collect the wrong data and should use something else to validate the models, or are the models insufficient to explain the data? And how do we ensure that our passion doesn’t interfere with our scientific objectivity?

One way to address this issue is diversity of opinion implemented as a set of orchestrated models, to use a multitude of models instead of just one. In another comment, the idea of using exploratory analysis to support decision making under deep uncertainty is mentioned. I highly recommend to have a look at (Marchau, Bloemen & Popper 2019) Decision Making Under Deep Uncertainty: From Theory to Practice. I am optimistic that if we are inclusive of a diversity of ideas – even if we don’t like them – and allow for computational evaluation of ALL options using exploratory analysis, we may find a way for better supporting the community.

References

Marchau, V. A., Walker, W. E., Bloemen, P. J., & Popper, S. W. (2019). Decision making under deep uncertainty. Springer. doi:10.1007/978-3-030-05252-2

Tolk, A. (2017, April). Bias ex silico: observations on simulationist’s regress. In Proceedings of the 50th Annual Simulation Symposium. Society for Computer Simulation International. ANSS ’17: Proceedings of the 50th Annual Simulation Symposium, April 2017 Article No.: 15 Pages 1–9. https://dl.acm.org/citation.cfm?id=3106403

Shermer, M. (2017) How to Convince Someone When Facts Fail – Why worldview threats undermine evidence. Scientific American, 316, 1, 69 (January 2017). doi:10.1038/scientificamerican0117-69

Tolk, A. (2020) The Danger of too much Compassion - how modellers can easily deceive themselves. Review of Artificial Societies and Social Simulation, 28th April 2020. https://rofasss.org/2020/04/28/self-deception/

Review of Artificial Societies and Social Simulation

Tag Archives: assumptions

“Theorywashing” – the practice of misattributing theory to impart academic credibility

By Nanda Wijermans and Bruce Edmonds

Notes

References

The Danger of too much Compassion – how modellers can easily deceive themselves

By Andreas Tolk

References

For discussion about social simulation research