What every interdisciplinarian should know about p values

Community member post by Alice Richardson

Alice Richardson (biography)

In interdisciplinary research it’s common for at least some data to be analysed using statistical techniques. Have you been taught to look for ‘p < 0.05’ meaning that there is a less than 5% probability that the finding occurred by chance? Do you look askance at your statistician colleagues when they tell you it’s not so simple? Here’s why you need to believe them.

The whole focus on p < 0.05 to the exclusion of all else is a historical hiccup, based on a throwaway line in a manual for research workers. That manual was produced by none other than R.A. Fisher, giant of statistical inference and inventor of statistical methods ranging from the randomised block design to the analysis of variance. But all he said was that “[p = 0.05] is convenient to take … as a limit in judging whether a deviation is to be considered significant or not.” Convenient, nothing more! Continue reading

Conceptual modelling of complex topics: ConML as an example / Modelado conceptual de temas complejos: ConML como ejemplo

Community member post by Cesar Gonzalez-Perez

cesar-gonzalez-perez
Cesar Gonzalez-Perez (biography)

A Spanish version of this post is available

What are conceptual models? How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

This blog post describes ConML, which stands for “Conceptual Modelling Language”. ConML is a specific modelling language that was designed to allow researchers who are not expert in information technologies to create and develop their own conceptual models. It is useful for the humanities, social sciences and experimental sciences. Continue reading

Using the concept of risk for transdisciplinary assessment

Community member post by Greg Schreiner

greg-schreiner
Greg Schreiner (biography)

Global development aspirations, such as those endorsed within the Sustainable Development Goals, are complex. Sometimes the science is contested, the values are divergent, and the solutions are unclear. How can researchers help stakeholders and policy-makers use credible knowledge for decision-making, which accounts for the full range of trade-off implications?

‘Assessments’ are now commonly used. Following their formal adoption by the Intergovernmental Panel for Climate Change (IPCC) in the early 1990s, they have been used at the science-society-policy interface to tackle global questions relating to biodiversity and ecosystems services, human well-being, ozone depletion, water management, agricultural production, and many more. Continue reading

What makes research transdisciplinary?

Community member post by Liz Clarke

Liz Clarke (biography)

What do we mean by transdisciplinarity and when can we say we are doing transdisciplinary research? There is a broad literature with a range of different meanings and perspectives. There is the focus on real-world problems with multiple stakeholders in the “life-world”, and a sense of throwing open the doors of academia to transcend disciplinary boundaries to address and solve complex problems. But when it comes to the practicalities of work in the field, there is often uncertainty and even disagreement about what is and isn’t transdisciplinarity.

Let me give an example. Continue reading

Three schools of transformation thinking

Community member post by Uwe Schneidewind and Karoline Augenstein

uwe-schneidewind
Uwe Schneidewind (biography)

‘Transformation’ has become a buzzword in debates about sustainable development. But while the term has become very popular, it is often unclear what is meant exactly by ‘transformation’.

The fuzziness of the concept can be seen as a strength, giving it metaphoric power and facilitating inter- and transdisciplinary cooperation. However, this fuzziness means there is also a danger of the transformation debate being co-opted by powerful actors and used strategically to impede societal change towards more sustainable pathways.

karoline-augenstein
Karoline Augenstein (biography)

Thus, issues of power are at stake here and we argue that a better understanding of the underlying assumptions and theories of change shaping the transformation debate is needed. We delineate three schools of transformation thinking and their assumptions about what drives societal change, and summarize them in the first table below. We then examine the relationship of these three schools of thinking to power, summarized in the second table. Continue reading

Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel

jan-kwakkel
Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are. Continue reading