<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Drift‑Diffusion Model |</title><link>https://www.khaichiong.com/tags/driftdiffusion-model/</link><atom:link href="https://www.khaichiong.com/tags/driftdiffusion-model/index.xml" rel="self" type="application/rss+xml"/><description>Drift‑Diffusion Model</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Feb 2024 00:00:00 +0000</lastBuildDate><image><url>https://www.khaichiong.com/media/icon_hu_da05098ef60dc2e7.png</url><title>Drift‑Diffusion Model</title><link>https://www.khaichiong.com/tags/driftdiffusion-model/</link></image><item><title>Combining Choices and Response Times in the Field: A Drift‑Diffusion Model of Mobile Advertisements</title><link>https://www.khaichiong.com/publications/drift-diffusion-mobile-ads/</link><pubDate>Thu, 01 Feb 2024 00:00:00 +0000</pubDate><guid>https://www.khaichiong.com/publications/drift-diffusion-mobile-ads/</guid><description>&lt;p&gt;The authors apply the drift‑diffusion model, a staple of neuroeconomics, to
field data from mobile advertising. By jointly modeling consumer choices and
the time taken to make those choices, they shed light on underlying cognitive
processes and the effectiveness of mobile ad campaigns.&lt;/p&gt;</description></item></channel></rss>