Unknowns

Lessons for strengthening community-university partnerships

By David D. Hart, Bridie McGreavy, Anthony Sutton, Gabrielle V. Hillyer and Darren J. Ranco

authors_david-hart_bridie-mcgreavy_anthony-sutton_gabrielle-hillyer_darren-ranco
1. David D. Hart; 2. Bridie McGreavy; 3. Anthony Sutton; 4. Gabrielle V. Hillyer; 5. Darren J. Ranco (biographies)

In an increasingly polarized world, how can partnerships between communities and universities strengthen the kinds of deliberative and democratic practices that might help address many local and global challenges? How can such partnerships improve practices that involve listening and responding across differences (the deliberative part)? How can they help find ways to make shared decisions and take joint actions, knowing that complete agreement or mutual understanding may never be possible (the democratic part)?

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Participatory scenario planning

authors_maike-hamann_tanja-hichert_nadia-sitas
1. Maike Hamann (biography)
2. Tanja Hichert (biography)
3. Nadia Sitas (biography)

By Maike Hamann, Tanja Hichert and Nadia Sitas

Within the many different ways of developing scenarios, what are useful general procedures for participatory processes? What resources are required? What are the strengths and weaknesses of involving stakeholders?

Scenarios are vignettes or narratives of possible futures, and when used in a set, usually depict purposefully divergent visions of what the future may hold. The point of scenario planning is not to predict the future, but to explore its uncertainties. Scenario development has a long history in corporate and military strategic planning, and is also commonly used in global environmental assessments to link current decision-making to future impacts. Participatory scenario planning extends scenario development into the realm of stakeholder-engaged research.

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Multidisciplinary perspectives on unknown unknowns

By Gabriele Bammer

gabriele-bammer_nov-2021
Gabriele Bammer (biography)

This is part of a series of occasional “synthesis blog posts” drawing together perspectives on related topics across i2Insights contributions.

How can different disciplines and practitioners enhance the ability to understand and manage unknown unknowns, also referred to as deep uncertainty?

Seventeen blog posts have addressed these issues, covering:

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Ignorance: Vocabulary and taxonomy

By Michael Smithson

Michael Smithson
Michael Smithson (biography)

How can we better understand ignorance? In the 1980s I proposed the view that ignorance is not simply the absence of knowledge, but is socially constructed and comes in different kinds (Smithson, 1989). Here I present a brief overview of that work, along with some key subsequent developments.

Defining ignorance

Let’s begin with a workable definition of ignorance and then work from there to a taxonomy of types of ignorance. Our definition will have to deal both with simple lack of knowledge but also incorrect ideas. It will also have to deal with the fact that if one is attributing ignorance to someone, the ignoramus may be a different person or oneself.

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Five lessons to improve how models serve society

By Andrea Saltelli

andrea-saltelli
Andrea Saltelli (biography)

Models are mathematical constructs better understood by their developers than by users. So should the public trust models? What insights can help society demand the quality it needs from modeling?

Mathematical modelling is a multiverse, where each scientific discipline adopts its own styles of modeling and quality control. Very little in the way of ‘user instructions’ is available to those affected by modeling practices.

This blog post presents five lessons to improve modelling that were developed as a manifesto by a cross-disciplinary group of natural and social scientists (Saltelli et al., 2020).

Lesson 1: Mind the assumptions

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Judgment and decision making with unknown states and outcomes

By Michael Smithson

Michael Smithson
Michael Smithson (biography)

What issues arise for effective judgments, predictions, and decisions when decision makers do not know all the potential starting positions, available alternatives and possible outcomes?

A shorthand term for this collection of possible starting points (also known as prior states), alternatives, and outcomes is “sample space.” Here I elucidate why sample space is important and how judgments and decisions can be influenced when it is incomplete.

Why is sample space important?

When it comes to dealing with unknowns, economists and others traditionally distinguish between “risk” (where probabilities can be assigned to every possible outcome) and “uncertainty” (where the probabilities are vague or unknown). Both of those versions of unknowns assume that decision makers know everything about the sample space.

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A quick guide to post-normal science

By Silvio Funtowicz

silvio-funtowicz
Silvio Funtowicz (biography)

Post-normal science comes into play for decision-making on policy issues where facts are uncertain, values in dispute, stakes high and decisions urgent.

A good example of a problem requiring post-normal science is the actions that need to be taken to mitigate the effects of sea level rise consequent on global climate change. All the causal elements are uncertain in the extreme, at stake is much of the built environment and the settlement patterns of people, what to save and what to sacrifice is in dispute, and the window for decision-making is shrinking. The COVID-19 pandemic is another instance of a post-normal science problem. The behaviour of the current and emerging variants of the virus is uncertain, the values of socially intrusive remedies are in dispute, and obviously stakes are high and decisions urgent.

In such contexts of policy making, normal science (in the Kuhnian sense, see Kuhn 1962) is still necessary, but no longer sufficient.

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Can examining cross-disciplinary interactions illuminate unknown unknowns?

By Rick Szostak

author_rick-szostak
Rick Szostak (biography)

Unknown unknowns are challenges that we will face in future that we do not foresee today.

Here I argue that an important subgroup of unknown unknowns occurs when some phenomenon that we know a lot about has an unexpected effect on another phenomenon that we know a lot about, especially when there are few links between the two silos of knowledge. An example is unanticipated “interactions” between medications prescribed by medical practitioners from different specialities. Here I explore such disciplinary interactions more generally.

Disciplinary scholars focus on interactions among the phenomena that their discipline studies, but usually ignore interactions with phenomena studied in other disciplines.

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Considering uncertainty, awareness and ambiguity as a three-dimensional space

By Fabio Boschetti

author-fabio-boschetti
Fabio Boschetti (biography)

The concept of unknown unknowns highlights the importance of introspection in assessing knowledge. It suggests that finding our way in the set of known-knowns, known-unknowns, unknown-knowns and unknown-unknowns, reduces to asking:

  1. how uncertain are we? and
  2. how aware are we of uncertainty?

When a problem involves a decision-making team, rather than a single individual, we also need to ask:

  1. how do context and perception affect what we know?

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Choosing a suitable transdisciplinary research framework

By Gabriele Bammer

Author - Gabriele Bammer
Gabriele Bammer (biography)

What are some of the key frameworks that can be used for transdisciplinary research? What are their particular strengths? How can you choose one that’s most suitable for your transdisciplinary project?

The nine frameworks described here were highlighted in a series for which I was the commissioning editor. The series was published in the scientific journal GAIA: Ecological Perspectives in Science and Society between mid-2017 and end-2019.

Choosing among them is not a matter of right or wrong, but of each being more or less helpful for a particular problem in a particular context. And, of course, different frameworks can also be used in combination.

The brief descriptions and figures that follow aim to encapsulate each framework’s key strengths.

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Eight grand challenges in socio-environmental systems modeling

By Sondoss Elsawah and Anthony J. Jakeman

authors_sondoss-elsawah_anthony-jakeman
1. Sondoss Elsawah (biography)
2. Anthony Jakeman (biography)

As we enter a new decade with numerous looming social and environmental issues, what are the challenges and opportunities facing the scientific community to unlock the potential of socio-environmental systems modeling?

What is socio-environmental systems modelling?

Socio-environmental systems modelling:

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How can expertise in research integration and implementation help tackle complex problems?

By Gabriele Bammer

author - gabriele bammer
Gabriele Bammer (biography)

What is expertise in research integration and implementation? What is its role in helping tackle complex societal and environmental problems, especially those dimensions that define complexity?

Expertise in research integration and implementation

Addressing complex societal and environmental problems requires specific expertise over and above that contributed by existing disciplines, but there is little formal recognition of what that expertise is or reward for contributing it to a research team’s efforts. In brief, such expertise includes the ability to:

  • identify relevant disciplinary and stakeholder inputs
  • effectively integrate them for a more comprehensive understanding of the problem
  • support more effective actions to ameliorate the problem.

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