Uncertainty – Page 2 – Integration and Implementation Insights

What every interdisciplinarian should know about p values

April 15, 2022April 30, 2019

By Alice Richardson

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!

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.

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.

Ten dimensions of integration: Guidelines for modellers

September 5, 2024March 8, 2016

By Serena Hamilton and Tony Jakeman

authors_mosaic_serena-hamilton-tony-jakeman — 1. Serena Hamilton (biography)
2. Tony Jakeman (biography)

Why Integrated Assessment and Integrated Modelling? In our highly connected world environmental problems have social or economic causes and consequences, and decisions to assist one segment of a population can have negative repercussions on other parts of the population. It is broadly accepted that we require integrated, rather than piecemeal approaches to resolve environmental or other complex problems.

Integrated Assessment and its inherent platform, Integrated Modelling, bring together diverse knowledge, data, methods and perspectives into one coherent and comprehensive framework. This process of organizing and synthesizing multiple forms of information across disciplinary and conceptual boundaries allows us to explore linkages and feedbacks between different parts of the system, as well as the trade-offs involved with alternative management interventions on different socioeconomic and environmental criteria.