Complexity scientist David Wolpert joins me to consider the idea of meaning at its most fundamental level. Historically, information theory has helped us quantify information (e.g., bits), but says nothing about the ways information might be useful, significant, relevant, or meaningful. Recently, however, Wolpert and colleagues have filled in what's missing from that account, offering a theory of "semantic" or "meaningful" information by showing how some information actually has causal power to influence the well-being and viability of systems in context. Here we explore this idea and a number of its implications for what's "meaningful" across the complexity stack, from a whirlpool to a bacterium all the way up to us.

0:00 Introduction

0:46 Meaning and Semantic Information

2:17 Background Context: Information Theory, Utility Functions, and Statistical Thermodynamics

14:03 Meaning FOR a System: What Information Helps One Stay Far from Equilibrium

21:54 Meaning: Mutual Information with Causal Power for Viability

27:57 Meaning and Meaurement up the Complexity Stack

33:42 Indirect Meaning, Chains of Significance, and Intelligence

37:20 A Semantic Information Theory of Individuality?

42:03 Relative vs. Absolute Semantic Information Metrics

49:52 The Complexification of Meaningful Information through Evolutionary Transitions

52:30 Layered Meaning through Evolution