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Posts

Future Blog Post

less than 1 minute read

Published: January 01, 2199

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published: August 14, 2015

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published: August 14, 2014

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published: August 14, 2013

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published: August 14, 2012

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

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publications

Gradualism, natural selection, and the randomness of mutation - Fisher, Kimura, and Orr, connecting the dots

Published in Biology & Philosophy, 2023 with Elliott Sober

Evolutionary gradualism, the randomness of mutations, and the hypothesis that natural selection exerts a pervasive and substantial influence on evolutionary outcomes are pair-wise logically independent. Can the claims about selection and mutation be used to formulate an argument for gradualism? In his Genetical Theory of Natural Selection, R.A. Fisher made an important start at this project in his famous “geometric argument” by showing that a random mutation that has a smaller effect on two or more phenotypes will have a higher average fitness than a random mutation that has a larger phenotypic effect. Motoo Kimura’s demonstration that a gene’s probability of fixation depends on both the selection coefficient and the effective population size shows that Fisher’s argument for gradualism was mistaken. Here we analyze Fisher’s argument and explain how Kimura’s theory leads to a conclusion that Fisher did not …

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Fitness, Mutational Load, and Eugenics

Published in Philosophy of Science, 2025

Eugenic arguments are not a thing of the past. In 2016, geneticist Michael Lynch published an article in Genetics arguing that human mental and physical performance are declining at a rate of 1% per generation. This estimate is not based on measurements of performance but on an argument from mutational load: Medical interventions are relaxing selection on the human population, which will lead to a buildup of deleterious mutations. This simple argument from mutational load is invalid. When the argument is made valid, it is not obvious that there are any significant consequences for human population health.

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Comparing the strengths of two causes of an effect - zeroing-out, adding-in, and the need for baselines

Published in Synthese, 2026 with Elliott Sober

Suppose 50-year-old Sue now has lung cancer, due to the fact that C = c & A = g (meaning that Sue smoked c cigarettes and inhaled g grams of asbestos over the previous 30 years), and that neither cause caused the other. Given this, a retrospective question arises – did one of those actual causes have a stronger influence than the other on her getting lung cancer? We propose a “zeroing-out” criterion for making sense of this question; it says that C = c was a stronger causal influence than A = g precisely when Pr(lung cancer | C = c & A = g) – Pr(lung cancer | C = 0 & A = g) > Pr(lung cancer | C = c & A = g) – Pr(lung cancer | C = c & A = 0). Zeroing-out uses Pr(lung cancer | C = c & A = g) as a baseline and relates that baseline to two counterfactual probabilities. We discuss how zeroing-out applies to three evolutionary examples − the influences of selection and drift on the fixation of an allele, the influences of group and individual selection on the evolution of altruism, and the influences of stabilizing selection and ancestral influence (aka “phylogenetic inertia”) on the evolution of tetrapody in land vertebrates. Zeroing-out differs from an “adding-in” criterion, which uses Sue’s probability of having lung cancer at age 50, given her actual state at age 20 (at which time she was cancer free and C = 0 & A = 0) as a baseline and asks whether her risk of having lung cancer at age 50 would be greater if C = c were true of the 30 years in between than it would be if A = g were true of those years. Zeroing-out and adding-in generate identical criteria for comparing causal influences in this example because the probabilities are related “monotonically” (a concept we define). We then describe examples in which monotonicity fails and the two criteria differ. We prove theorems that describe when the two criteria disagree and when they do not. We then consider how zeroing-out and adding-in are related to six quantitative measures of causal strength that have been proposed. Inter alia, we discuss how our framework is related to interventionism.

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A Crisis of Population Genetics Statistics: Noah A. Rosenberg. 2025. Mathematical Properties of Population-Genetic Statistics: Quadratic Forms Most Beautiful

Published in Acta Biotheoretica, 2026

Population genetics relies heavily on homozygosity statistics. Over the past two decades, however, as the number and applications of homozygosity-based statistics has increased, there has been a crisis of confidence. Homozygosity statistics are mathematically constrained by other population genetics statistics, such as maximum allele frequency, creating problems for interpretation and portability. Mathematical investigations into these constraints have been criticized for being irrelevant to biology. Noah Rosenberg’s new book Mathematical Properties of Population-Genetic Statistics is his response to these concerns. While accepting many of the criticisms, Rosenberg shows that careful attention to purely mathematical constraints on homozygosity can be leveraged to design new statistics to achieve new goals.

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Many methods, many aims: evaluating methods in the philosophy of science Sophie J. Veigl and Adrian Currie(eds.): Methods in the philosophy of science: a user’s guide.

Published in Metascience, 2026 with Aja Watkins and Andrew Zeppa

Increasingly, philosophers of science are recognizing the need to reflect on our own methodology, much like we reflect on the methodology of scientists. Explicit, sometimes critical reflection on our methodology has inspired experimentation with new methods and adoption of methods from other science studies disciplines.

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