Unknowns – Page 4 – Integration and Implementation Insights

Sharing integrated modelling practices – Part 2: How to use “patterns”?

October 9, 2023June 1, 2017

By Sondoss Elsawah and Joseph Guillaume

authors_sondoss-elsawah_joseph-guillaume — 1. Sondoss Elsawah (biography)
2. Joseph Guillaume (biography)

In part 1 of our blog posts on why use patterns, we argued for making unstated, tacit knowledge about integrated modelling practices explicit by identifying patterns, which link solutions to specific problems and their context. We emphasised the importance of differentiating the underlying concept of a pattern and a pattern artefact – the specific form in which the pattern is explicitly described.

In order to actually use patterns to communicate about practices, the artefact takes on greater importance: what form could artefacts describing the patterns take, and what mechanisms and platforms are needed to first create, and then share, maintain, and update these artefacts?

While the concepts of ‘problem, solution and context’ should be discussed in some way, there is no single best way of representing patterns as artefacts. The form of artefacts will differ depending on many factors, including how the users perceive the ease of:

Sharing integrated modelling practices – Part 1: Why use “patterns”?

September 12, 2024May 30, 2017

By Sondoss Elsawah and Joseph Guillaume

How can modellers share the tacit knowledge that accumulates over years of practice?

In this blog post we introduce the concept of patterns and make the case for why patterns are a good candidate for transmitting the ‘know-how’ knowledge about modelling practices. We address the question of how to use patterns in a second blog post.

In broad terms, a pattern links a solution to a problem and its context. As a means of externalizing understanding of practices, the concept has been used productively in various fields, including architecture, computer science, and design science. For a more general introduction to patterns, see Scott Peckham’s blog post. While a “pattern” is ultimately a simple idea, there tends to be disagreement about a precise definition. This poses a problem for this blog post.

Argument-based tools to account for uncertainty in policy analysis and decision support

September 11, 2024February 23, 2017

By Sven Ove Hansson and Gertrude Hirsch Hadorn

authors_sven-ove-hansson_gertrude-hirsch-hadorn — 1. Sven Ove Hansson (biography)
2. Gertrude Hirsch Hadorn (biography)

Scientific uncertainty creates problems in many fields of public policy. Often, it is not possible to satisfy the high demands on the information input for standard methods of policy analysis such as risk analysis or cost-benefit analysis. For instance, this seems to be the case for long-term projections of regional trends in extreme weather and their impacts.

However, we cannot wait until science knows the probabilities and expected values for each of the policy options. Decision-makers often have good reason to act although such information is missing. Uncertainty does not diminish the need for policy advice to help them determine which option it would be best to go for.

Dealing with deep uncertainty: Scenarios

October 25, 2022January 5, 2017

By Laura Schmitt Olabisi

What is deep uncertainty? And how can scenarios help deal with it?

Deep uncertainty refers to ‘unknown unknowns’, which simulation models are fundamentally unsuited to address. Any model is a representation of a system, based on what we know about that system. We can’t model something that nobody knows about—so the capabilities of any model (even a participatory model) are bounded by our collective knowledge.

One of the ways we handle unknown unknowns is by using scenarios. Scenarios are stories about the future, meant to guide our decision-making in the present.

Uncertainty in participatory modeling – What can we learn from management research?

August 24, 2022November 10, 2016

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.

Four things everyone should know about ignorance

April 13, 2021November 1, 2016

By Michael Smithson

“Ignorance” is a topic that sprawls across a grand variety of disciplines, professions and problem domains. Many of these domains have their own perspective on the unknown, but these are generally fragmentary and often unconnected from one another. The topic lacks a home. Until fairly recently, it was a neglected topic in the humanities and human sciences.

I first started writing about it in the 1980’s (e.g., my book-length treatment, Ignorance and Uncertainty: Emerging Paradigms), but it wasn’t until 2015 that the properly compiled interdisciplinary Routledge International Handbook on Ignorance Studies (Gross and McGoey 2015) finally appeared.

Making predictions under uncertainty

August 24, 2022August 25, 2016

By Joseph Guillaume

Prediction under uncertainty is typically seen as a daunting task. It conjures up images of clouded crystal balls and mysterious oracles in shadowy temples. In a modelling context, it might raise concerns about conclusions built on doubtful assumptions about the future, or about the difficulty in making sense of the many sources of uncertainty affecting highly complex models.

However, prediction under uncertainty can be made tractable depending on the type of prediction. Here I describe ways of making predictions under uncertainty for testing which conclusion is correct. Suppose, for example, that you want to predict whether objectives will be met. There are two possible conclusions – Yes and No, so prediction in this case involves testing which of these competing conclusions is plausible.

Why are interdisciplinary research proposals less likely to be funded? (Reblog)

September 5, 2024August 23, 2016

By Gabriele Bammer

The first empirical support for a long-standing complaint by interdisciplinary researchers was recently published in the leading journal Nature. The Australian National University’s Lindell Bromham, Russell Dinnage and Xia Hua showed that interdisciplinary research is less likely to be funded than discipline-based research proposals (Nature, 534, 684–687 (30 June), DOI: 10.1038/nature18315).

They cleverly applied a technique from evolutionary biology that examines relatedness between biological lineages, using a hierarchical classification of research fields rather than an evolutionary tree. The relative representation of different field of research codes and their degree of difference were used as a proxy measure for interdisciplinarity.

The results, based on 5 years of data from the Australian Research Council’s Discovery program, are robust and are unaffected when number of collaborators, primary research field and type of institution are taken into account.

What does it mean?

Ten communication tips for translational scientists

January 3, 2022July 19, 2016

By Sunshine Menezes

As someone who works with scientists, journalists, advocates, regulators, and other types of communication practitioners, I see the need for translational scientists who can navigate productive, start-to-finish collaborations between such groups on a daily basis.

This translation involves the use of new, more integrated approaches toward scientific work to confront wicked environmental problems society faces.

In spite of this need, cross-boundary communication poses a major stumbling block for many researchers. Science communication requires engagement with potential beneficiaries, not just a one-way transfer of information.

Six types of unknowns in interdisciplinary research

October 2, 2024May 10, 2016

By Gabriele Bammer

What types of unknowns are tackled in interdisciplinary research? I draw on my experience directing a program of research on the feasibility of prescribing pharmaceutical heroin as a treatment for heroin dependence. Analysis of this case revealed six different types of unknowns:

Disciplinary unknowns
Unknowns of concern to stakeholders
Unknowns marginalised by power imbalances
Unknowns in the overlap between disciplines
New problem-based unknowns
Intractable unknowns.