Causation and the Time-Asymmetry of Knowledge

Jan 1, 2024·
Thomas Blanchard
Thomas Blanchard
· 0 min read
Abstract
This paper argues that the knowledge asymmetry (the fact that we know more about the past than the future) can be explained as a consequence of the causal Markov condition. The causal Markov condition implies that causes of a common effect are generally statistically independent, whereas effects of a common cause are generally correlated. I show that together with certain facts about the physics of our world, the statistical independence of causes severely limits our ability to predict the future, whereas correlations between joint effects make it so that no such limitation holds in the reverse temporal direction. Insofar as the fact that our world conforms to the causal Markov condition can itself be explained in terms of the initial conditions of the universe, my view is compatible with Albert’s well-known account of the origins of temporal asymmetries, but also provides a more illuminating way to derive the knowledge asymmetry from those initial conditions.
Type
Publication
Australasian Journal of Philosophy, 102(4), 959-977
publication
Thomas Blanchard
Authors
Maître de Conférences en Philosophie

I am Associate Professor in the philosophy department at the Université Bordeaux Montaigne. Previously, I was a postdoctoral researcher in philosophy and psychology in the Concepts and Cognition Lab at UC-Berkeley for the Varieties of Understanding Project, an Assistant Professor at Illinois Wesleyan University, and an Akademischer Rat (roughly equivalent to assistant professor) at the University of Cologne. I received my Ph.D. from Rutgers University in 2014.

My research is in the philosophy of science, and focuses mainly on causation, causal modeling and causal explanation. I am interested in a wide variety of issues concerning causation including causal asymmetries, levels of causal explanation, the causal exclusion problem, the epistemology of causal inference, causal cognition, and causal decision theory. My work also investigates the use of certain causal concepts and assumptions in particular sciences such as biology, epidemiology and medicine.