Decision Theory

Evidence is not context. That is the small, expensive misunderstanding behind many enterprise RAG systems. A user asks a question, the system retrieves semantically similar chunks, the model reads them, and the answer arrives with a tone that suggests the matter has been settled. Very reassuring. Sometimes even correct. But in the situations where RAG is supposed to be most useful — compliance reviews, financial analysis, legal memos, medical evidence summaries, internal strategy briefings — the problem is often not that the system has too little relevant material. The problem is that the relevant material disagrees, overlaps, dates badly, or supports several competing interpretations at once. ...

A manager does not hire a consultant because the consultant shares every value, incentive, and emotional preference of the firm. The consultant wants fees. The doctor wants throughput. The lawyer wants billable hours. The cloud provider wants usage. Humanity, somehow, survives this scandal. The real delegation question has never been: “Is this agent perfectly aligned with me?” It is: “Will things go better if I let this agent decide here?” ...

TL;DR for operators The paper is not a magic trick that turns an infinite expected value into a finite one. The ordinary St. Petersburg expectation still diverges. Anyone claiming otherwise has either missed the point or found a very ambitious way to lose a philosophy seminar. What the paper actually does is more interesting. Takashi Izumo defines a coarse-grained version of arithmetic in which numbers are first mapped into finite “grains,” each grain is represented by a selected internal value, and addition is performed through repeated projection to those representatives.1 Under this operation, an increment can become too small to move the current coarse state. That phenomenon is called absorption. Repeated absorption produces inertness: further additions keep arriving, but the represented total stops changing. ...