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. Continue reading Typo in qualitative modelling paper
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 … Continue reading Boolean approach to qualitative network modelling
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 … Continue reading Why does it matter to conservation decision-making if alternative Qualitative Modelling methods produce contradictory predictions?
SMBC has a funny comic that’s also about the Principle of Indifference. Continue reading Comic about PI
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, … Continue reading Network structural uncertainty in Qualitative Modelling
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 … Continue reading Some notes on the Principle of Indifference
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 … Continue reading Fibonacci numbers and alternating signs in species responses to press perturbation in a food chain
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 … Continue reading The Principle of Indifference is actually two principles in one
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 … Continue reading “ValueError: expected a DNF expression” when trying espresso_exprs example from pyeda docs
(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 … Continue reading The Principle of Indifference in ecological modelling
