Authors create a benchmark across discrete/continuous and static/dynamical systems and introduce the Causal Abstraction Error (CAE) metric that reliably distinguishes valid from invalid causal abstractions when it includes faithfulness testing.
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Derives Boltzmann distributions for 1D spin models as stochastic processes, quantifies structure via excess entropy and statistical complexity, specifies mechanisms with epsilon-machines, and reports agreement with typical configurations.
Canonical functionalism identifies consciousness-relevant functional organization with the minimal state-transition structure obtained by equating internal states that exhibit identical future behavior under all possible continuations.
A constructor theory framework models prebiotic information as physical differences and meaning as functional consequences in Casimir-Lifshitz protocell clusters.
Casimir-stabilized protocell clusters form ε-machines whose attractor states and transitions create emergent prebiotic information through physical memory rather than molecular polymers.
Introduces a defeasible rule-based coaching layer that converts diagnostic failures into policy rule revisions in adaptive agent-based regulatory simulations, demonstrated on an emissions-regulation ABM.
Neural network learning opacity stems from three dynamical complexity properties in training, rendering some sources of opacity irreducible.