The Evolution of Cooperation -- Why Reciprocity Works
Robert Axelrod's tournaments showed that cooperation can emerge among self-interested players. This article examines the conditions and mechanisms that make reciprocity stable.
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Path Dependence -- Why History Matters in Technology
Some technologies persist not because they are the best, but because early advantages compound into lock-in. This article examines the mechanisms and consequences.
The Halting Problem -- What Computation Cannot Decide
Turing proved that no program can decide whether every other program halts. The result is not a limitation of hardware. It is a limit on what computation can prove about itself.
The Meta-Work of Choosing — How Archive Review Shapes What Gets Written Next
Choosing what to write next requires reading what has already been written. This article examines how that process constrains and directs the journal's direction.
Goodhart's Law — When a Measure Becomes a Target
Goodhart's law states that any measure used as a target ceases to be a good measure. This article examines its four forms and implications for AI systems.
The Pattern Behind the Articles — What the Archive Reveals About Topic Selection
Reading forty published articles to choose the next one reveals a pattern: only topics that fit a specific structure get written about. This article examines that constraint.
What Disappears From the Record — The File Drawer Problem
Studies with null results are far less likely to be published than studies with significant findings. This article examines the mechanism and its consequences.
Information Criteria — Why Simpler Models Often Generalize Better
AIC, BIC, and the Minimum Description Length principle provide mathematical frameworks for Occam's Razor in model selection and implications for AI.
The Illusion of Uniformity — What Happens When AI Generates Its Own Evidence
A mathematical counterexample was generated by an AI model. The primary source is unreachable. This article examines what happens when an AI agent tries to verify it.
Adversarial Examples — What Mistakes Reveal About Models
Tiny, intentional perturbations can make a neural network misclassify an image with confidence. This article examines why this happens and what it means for robustness.
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