Community member post by Antonie Jetter
I frequently struggle to explain how participatory modeling deals with uncertainty. I found useful guidance in the management literature.
After all, participatory modeling projects and strategic business planning have one commonality – a group of stakeholders and decision-makers aims to understand and ultimately influence a complex system. They do so in the face of great uncertainty that frequently cannot be resolved – at least not within the required time frame. Businesses, for example, have precise data on customer behavior when their accountants report on annual sales. However, by this time, the very precise data is irrelevant because the opportunity to influence the system has passed.
Two key lessons from the management literature deal with the nature of uncertainty and responding to four major types of uncertainty.
Nature of uncertainty: objective or subjective?
Does the term uncertainty pertain to phenomena that are objectively unknowable? Or is uncertainty subjective because people “feel” uncertain about something? The management literature acknowledges both forms of uncertainty.
Objective uncertainty is not preventable and persists even after all possible efforts are made to collect data and gain insights. This ‘residual’ uncertainty occurs in many planning efforts because much of the future is unknowable. It can result in the subjective experience of “feeling uncertain”, however, subjective uncertainty may also have other root causes, such as a lack of understanding of the system under study or mistrust in the decision-process.
Participatory modeling needs to address both types of uncertainty appropriately: a community’s discomfort with what is unknowable, for example, may lead to requests for more and more data. If modelers give in to this desire, two things can happen: subjective uncertainty can remain unchanged which may make it impossible for the community to agree on a course of action. Or subjective uncertainty decreases as more data is presented, even though the collected data does not address all of the objective uncertainty. As a result, the community may feel increasingly at ease with the now seemingly lower levels of uncertainty, while being unaware of residual objective uncertainty. This is the case of so-called “unknown unknowns” or “unk unks”. They are frequently discussed in the management literature, because one cannot successfully prepare for or manage what one is not aware of.
State, effect, and response uncertainty and unknown unknowns
So how can modelers conceptualize the different types of uncertainty in their models? Building on earlier literature on uncertainty in business environments, Frances Miliken (1987) offers an interesting framework that differentiates state, effect, and response uncertainty.
State uncertainty refers to a situation where the variables that contribute to a problem (ie., the system elements) are well understood, but their value is unknown. For example, a modeling team may know that the dollar exchange rate affects the business in a particular way or how the growth of the population relates to water use. However, they do not know what the exchange rate or the future population count will be.
Computational system models are usually well set up to address this type of uncertainty: the models are run for a range of possible values that the uncertain variables can take. The results of these different input scenarios are interpreted as the range of possible system states.
Effect uncertainty refers to a situation where the variable is understood to be relevant for a particular problem but the nature of its impact is not known. For example, a community may observe that its population is aging or that new modes of transportation, such as ride-sharing services, are emerging. However, it does not know how these trends will impact them specifically: How many housing units with parking spaces will be needed in a neighborhood? How many family homes versus home for singles and couples? Will young people move to the suburbs or live downtown? In system modeling, uncertainty about the model structure – how variables affect each other – is typically considered to be something that needs to be resolved before the model can be useful. Stakeholders can aid this process by offering their perspectives, such as explaining what members of their communities would do.
Increasingly, modelers also use system models in an exploratory fashion to create and test the model outcomes for a range of alternative system structures. Rather than synthesizing knowledge into one model, they thus construct an ensemble of plausible models and explore their impacts.
The third category of uncertainty, response uncertainty, is experienced by planners and community decision makers: it is a lack of response options and the inability to predict the likely consequences of a response choice. Coming up with response options is a creative act: city planners, faced with the possibility that ride-sharing services may reduce the need for downtown parking, for example, cannot rely on tried-and-true ways – as there are none – to respond to this trend. Instead, they need to innovate and think about the robustness of their solutions in various possible scenarios – in some cities, a chosen response is to modify building codes to require that newly built parking garages have street-level entrances (rather than ramps) so that they can be converted into apartments or offices buildings if they become obsolete.
These types of responses are not generated by or within the system model. But a good system model, that creates a deep understanding of the problem at hand, can help the process. It can also help evaluate the impacts, including unintended consequences, of the response options under discussion.
But what about those unk unks? Because the modelers and decision makers are not aware of them, they are not explicitly reflected in the model and not considered in decision making. But that does not mean that participatory system modeling does not address them at all.
Unk unks can be caused by the complexity of the system which results in a lack of understanding of how the system behaves. In response, system modeling provides a toolset for exploring the dynamic behavior of complex systems in a systematic fashion. Unk unks can also be caused by so-called “blind spots” – a lack of awareness for important system elements that are consequently not included in the model. Participatory modeling aims to minimize these blind spots by including diverse stakeholder groups and by systematically pooling their insights and perspectives.
Participatory system modeling thus provides approaches for addressing all of the above types of uncertainty. However, to fully leverage them, modelers and communities need clarity about what uncertainties they are struggling with.
Do you find these frameworks helpful? How else do you conceptualize uncertainty in your projects?
Milliken, F. J. (1987). Three types of perceived uncertainty about the environment: state, effect, and response uncertainty. Academy of Management Review, 12, 1: 133–143.
Biography: Antonie Jetter is an Associate Professor of Engineering and Technology Management and Director of the Innovation Program in the Maseeh College of Engineering and Computer Science at Portland State University. While still in college, she was on the founding team of a venture-backed start-up company in equipment manufacturing. Her research is focused on improving the management of early stage new product development, which results in better product planning methods, simple and efficient decision heuristics and successful project organization. She applies participatory Fuzzy Cognitive Map modeling in scenario planning, product planning and stakeholder engagement. She is member of the Participatory Modeling pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).
This blog post kicks-off a series resulting from the second meeting in October 2016 of the Participatory Modeling pursuit. This pursuit is part of the theme Building Resources for Complex, Action-Oriented Team Science funded by the National Socio-Environmental Synthesis Center (SESYNC).