When should evidence guide choice over causation, how can meaning emerge from signaling games, and why might utility comparisons quietly break the ethics we build on them?
My links: https://linktr.ee/frictionphilosophy.
1. Guest
Brian Skyrms is Distinguished Professor of Logic and Philosophy of Science and Economics at the University of California, Irvine, and an Emiritus Professor of Philosophy at Stanford University. His work has focused on science, causation, decision theory, game theory, and the foundations of probability.
2. Interview Summary
Brian Skyrms frames the opening around the long-running split between causal and evidential decision theory: both aim at “maximizing expected payoff,” but they compute “expectation” using different probability functions—ordinary degrees of belief for the evidentialist, versus probabilities meant to represent causal efficacy for the causal theorist. He suggests that once you’re clear on that conceptual difference, much of the remaining literature becomes a matter of pushing (and disputing) intuitions about “funny cases,” especially Richard Jeffrey–style “news value” reasoning versus causal evaluation in cases like Newcomb-style problems, which he’s surprised to see repeatedly “rise from the grave” in new philosophical (and AI-adjacent) waves of debate.
From there, the interview broadens into Skyrms’s broader picture of how decision theory connects to a cluster of topics—conditionals, causation, and modal/necessity talk—where he favors a pragmatic, human-centered treatment rather than hunting for extra “facts” supposedly delivered by raw intuition. In that spirit, he discusses Bayesian-friendly ways of handling subjunctive conditionals that preserve some “probability of a conditional” motivations while avoiding classic triviality worries, and he emphasizes that what counts as “rational” can shift with the setting: in correlated evolutionary contexts (where you reliably meet similar agents), evidential/Jeffrey-style reasoning can predict what evolves better than individualistic causal-choice reasoning, which helps motivate his interest in the evolution of cooperation.
In the second half, Skyrms highlights two big research programs. First, he explains how a naturalistic account of meaning can start from information transfer in David Lewis–style signaling games—tracking how learning dynamics can move populations toward equilibria where signals stabilize—and then treating deception as a deviation (strategic or mistaken) from an emergent equilibrium use-pattern. Second, he summarizes his recent work on utilitarianism as largely a measurement-theory project: many familiar philosophical “add up the utils” arguments implicitly assume overly strong scales, and once you respect the legitimate degrees of freedom in utility measurement, celebrated cases (including Derek Parfit-style population examples) can become formally meaningless because permissible rescalings flip the verdicts. He then connects this methodological moral to epistemology: you can’t “answer” the complete skeptic (that’s a fool’s game), but you can make progress with partial skeptics by getting precise about what “the future will be like the past” could even mean—a point associated with Nelson Goodman—and by using Bayesian tools to articulate which inductive inferences are actually supported. He closes on a deflationary note about philosophy’s value: he won’t sell it as for everyone; for him, it’s mainly that it’s fun to question what people accept without thinking.
3. Interview Chapters
00:00 - Introduction
01:19 - Causal and evidential decision theory
04:05 - Maximizing expected payoff
05:27 - Correlation without causal connection
06:17 - Causation
10:41 - Deliberating over the past
13:42 - Common intuitions
16:11 - Philosophical landscape
18:05 - Predicted riches
21:27 - Transparent box case
23:55 - Normative significance of decision theory
28:33 - Subjunctive conditionals
33:33 - Closest possible worlds
35:15 - Backtracking counterfactuals
37:07 - Example
39:47 - Possible worlds
41:38 - Metaphysical possibility
44:05 - Suppositional approach
48:10 - Benefits of the approach
51:19 - Foundations of utility
56:55 - Utilitarianism and measuring utility
1:01:28 - Saving utilitarianism
1:02:43 - Vague preferences and credences
1:06:10 - Content in signalling games
1:13:48 - Not sui generis
1:15:23 - Signalling games with only one person
1:18:18 - Accounting for content more broadly
1:21:01 - Inductive skepticism
1:23:50 - Required assumptions
1:25:55 - Problem of induction
1:31:11 - Contracts and games
1:33:11 - Correlations
1:38:33 - Value of philosophy
1:39:56 - Conclusion
