Abstract
The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the world’s biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex network of theories, each often having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logic’s ability to represent rich mathematics ideas with probability theory’s ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build unified knowledge based of knowledge for ecology and evolution.
Footnotes
↵0 email: philippe.d.proulx{at}gmail.com
Made the article more focused on ecology and evolution (and larger!). Add recents results on Bayesian higher-order probabilistic programming.