Filed Under #qualitative_modelling

Boolean qualitative modelling on Christmas Island

Boolean qualitative modelling on Christmas Island Conservation managers often want us to predict how species will respond to different management options when we don’t have enough data to parameterise a dynamical model. Previously, we found that a popular Qualitative Modelling technique involving probabilistic analysis has philosophical problems and produces contradictory results. We proposed a new method, based on Boolean analysis, that remedied these problems (Kristensen et al. 2019 Meth Ecol Evol). However, it was still an open question how the Boolean approach would perform in a real-world application. Yi Han’s new paper in Ecological Modelling is the first time the Boolean approach has been applied to...

Typo in qualitative modelling paper

There is a typo in our recent paper to MEE, Dealing with high uncertainty in qualitative network models using Boolean analysis. Example 2 of Box 1 should read “y = water/wine” not “y = wine/water”. Many thanks to Anubhav Gupta at University of Zurich for emailing us to let us know.

Boolean approach to qualitative network modelling

Boolean approach to qualitative network modelling In a new paper in Methods in Ecology and Evolution (draft with supplementary here), we tackled an important question for ecological modellers: how do we predict an ecosystem’s behaviour when the data needed to parameterise a model are lacking? This problem is particularly important when our models are needed for conservation decision-making. For example, managers may be considering different pest-control programmes, which have the potential to lead to negative outcomes for native species, and predictions of these outcomes are needed for sound decisions to be made. These outcomes can in principle be predicted using dynamical models, however experts rarely have...

Why does it matter to conservation decision-making if alternative Qualitative Modelling methods produce contradictory predictions?

Previously, I have written about how the probabilistic approach to Qualitative Modelling (QM) (e.g. Raymond et al. 2011) can lead to contradictory predictions of species response to a management intervention, and how this is similar to the paradoxes of the Principle of Indifference that we find in the philosophy literature. A reviewer of our new manuscript (Kristensen et al. 2019) asked us an interesting and thought-provoking question: why is it that we think these contradictions matter? They did not find the contradictory predictions of the probabilistic QM methods to be a problem because it is interesting to learn how different...

Comic about PI

Comic about PI SMBC has a funny comic that’s also about the Principle of Indifference.

Network structural uncertainty in Qualitative Modelling

Network structural uncertainty in Qualitative Modelling Background Imagine a situation in which we want to model the behaviour of a food web, but we don’t know what the species interaction strengths are, and we’re not 100 percent sure what the structure of the food web is, either. For example, in the figure below, experts are are uncertain about whether or not there is a direct interaction between species 4 and 5, and so both structures 1 and 2 are possibilities. We want to know, which of these candidate structures is more likely to represent the true network structure? An example of network structural uncertainty. Experts are...

Some notes on the Principle of Indifference

Some notes on the Principle of Indifference A classical statement of the Principle of Indifference (PI) is as follows (p. 45 Keynes, 1921): if there is no known reason for predicating of our subject one rather than another of several alternatives, then relatively to such knowledge the assertions of each of these alternatives have an equal probability. Thus equal probabilities must be assigned to each of several arguments, if there is an absence of positive ground for assigning unequal ones. The various forms of PI encompass two ideas (Norton, 2008). First is a truism of evidence: if we have no grounds for distinguishing outcomes, then we should...

Fibonacci numbers and alternating signs in species responses to press perturbation in a food chain

In a paper from 2001, Dambacher and Rossignol made a curious observation: Fibonacci numbers appear in the adjoint and absolute feedback matrices that result from a weighted-predictions matrix type analysis (Dambacher et al. 2003) on food chains. The weighted-predictions matrix analysis is a way of predicting how species in a food web will respond to a the press perturbation of one of the species, so that implies that the pattern of species response to certain kinds of disturbance follows a neat mathematical pattern. To understand where these Fibonacci numbers come from, a paper by Usmani (1994) is useful. First some...

The Principle of Indifference is actually two principles in one

The Principle of Indifference is actually two principles in one In a previous post, I wrote about the philosophical problems caused by the Principle of Indifference. The problems are illustrated with a variety of thought-experiments that create paradoxes, such as Bertrand’s paradox. I also discussed how a problem in ecological modelling for conservation decision-making seems closely related to this philosophical problem. When I realised this connection, it seemed to me that, in order to solve the ecological problem, I would need to solve the philosophical problem first. Reading the philosophy literature, however, I realised that solving the philosophical problem would not be so simple. As Shackel (2007) sums it up,...

"ValueError: expected a DNF expression" when trying espresso_exprs example from pyeda docs

I’ve recently been working on a qualitative modelling project where I am trying to uncover “truths” about the response of species in an ecosystem to control of invasive species. Long story short, I’ve been looking into various boolean minimisation techniques. I’ve been playing with Python EDA, a Python library that I think provides a front-end to the Robert Brayton and Richard Rudell espresso heuristic logic minimiser, developed at University of California, Berkeley. I was trying out the examples on the Two-level Logic Minimisation docs page, and I had no issues with the second ‘Minimise truth tables’ example. However for the...

The Principle of Indifference in ecological modelling

(Update April 2019: a paper on the topic below has now been published in MEE) Qualitative modelling Qualitative modelling (QM) holds the promise of obtaining predictions from dynamical models even when we don’t have all the data needed to parameterise them. How does QM achieve this? In short, the idea is to explore the range of possible parameter values to create an ensemble of possible predictions, and then interpret those predictions probabilistically and/or qualitatively. For example, if 89% of models predict that rabbit control will increase penguin populations on Macquarie Island, then that might be considered moderate support for that...