Dynamic linear modeling to unlock new tests of directionality in fossil lineages

Principal Investigator: Beckett Sterner, Arizona State University

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Overview
Team

This project seeks to make major advances in the modeling frameworks available for paleobiologists to characterize and test hypotheses about the direction and tempo of complex evolutionary changes over long timescales. In particular, they will develop dynamic linear modeling methods that enable multivariate trait models essential to representing teleological phenomena such as goal-directed systems. A key output of this work will be more refined assessments of hypotheses about directional patterns and evolutionary processes, based on error statistics beyond classical Fisherian or Neyman-Pearson hypothesis testing, which will lead to better generalizability and model validation in a computationally efficient platform.

Beckett Sterner

Beckett Sterner

Cluster:
Directionality in Genomics and Macroevolution
Project:
New tests of directionality in fossil lineages
Role:
Subaward Principal Investigator

Beckett Sterner is a philosopher of biology with a background in statistics and computational modeling. His work on evolutionary tempo and mode, biological individuality, and information aims to uncover novel ways of approaching scientific problems and challenges for objectivity given different background assumptions, e.g. about whether models are adequate to represent the phenomena. More broadly, Sterner studies the social epistemology of pluralism: what knowledge do we need to get things done together while differing in fundamental ways? He investigates this question in the context of the life sciences, where globally coordinating data-intensive research has taken on central importance for addressing societal challenges such as biodiversity loss and climate change.

John Fricks

John Fricks

Cluster:
Directionality in Genomics and Macroevolution
Project:
New tests of directionality in fossil lineages

John Fricks is a mathematical biologist who uses tools from probability and statistics to study biological dynamics. He has done significant work on stochastic models of molecular motors, including creating statistical methods to better understand time series emerging from nano-scale experiments on these motors. In addition, he has worked on stochastic models and inference for disease dynamics both at the cellular and populations levels, including studies of RSV and measles.

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