Decision Theory

Opening — Why this matters now Most enterprise RAG systems are quietly overconfident. They retrieve what looks relevant, stack it into a context window, and let the model produce an answer with unnerving certainty. The problem isn’t the model. It’s the question we’re asking the system to optimize: relevance. In messy, real-world environments—legal disputes, financial analysis, conflicting reports—relevance is not the bottleneck. Uncertainty is. ...

Opening — Why this matters now The current generation of AI systems is remarkably good at predicting what comes next. Unfortunately, prediction is not the same as purpose. As enterprises push toward autonomous agents—systems that act, not just respond—the question quietly shifts from “What is likely?” to “What should be done?” That distinction sounds philosophical. It is, inconveniently, also operational. ...

Opening — Why this matters now Modern systems act faster than their understanding. Algorithms trade in microseconds, clinical protocols scale across populations, and institutions make irreversible decisions under partial information. Yet our ethical vocabulary remains binary: act or abstain, know or don’t know, responsible or not. That binary is failing. The paper behind this article introduces a deceptively simple idea with uncomfortable implications: uncertainty does not reduce moral responsibility — it reallocates it. When confidence falls, duty does not disappear. It migrates. ...

The St. Petersburg paradox has long been a thorn in the side of rational decision theory. Offering an infinite expected payout but consistently eliciting modest real-world bids, the game exposes a rift between mathematical expectation and human judgment. Most solutions dodge this by modifying utility functions, imposing discounting, or resorting to exotic number systems. But what if we change the addition itself? ...