<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
  <title>Xiao Zhang</title>
  <link href="https://xzhang.art/atom.xml" rel="self"/>
  <link href="https://xzhang.art/blog/"/>
  <id>https://xzhang.art/</id>
  <updated>2026-11-10T13:00:00.000Z</updated>
  <author><name>Xiao Zhang</name><uri>https://xzhang.art/</uri></author>
  <entry>
    <title>Where Objectives Come From: Why AI Needs Normative Modeling</title>
    <link href="https://xzhang.art/NLF/"/>
    <id>https://xzhang.art/NLF/</id>
    <published>2026-05-06T11:23:00.000Z</published>
    <updated>2026-11-10T13:00:00.000Z</updated>
    <summary>A loss function is only one of the four parts of a decision problem. From Wald’s decision theory to normative modeling, this post asks where objectives come from, and why modern AI needs to treat them as hypotheses to be tested.</summary>
  </entry>
</feed>
