Language Models Can Reduce Asymmetry in Information Markets – Slides

Yann HOFFMANN

04 Feb 2025 — 1 min read

Photo by Annie Spratt / Unsplash

Language Models Can Reduce Asymmetry in Information Markets

In this presentation, I explore how language models (LMs) can help address information asymmetry in digital marketplaces. The core challenge in such markets is that buyers need to inspect information before purchasing, while sellers must limit access to prevent unauthorized use. This leads to inefficiencies and market failures.

The slides cover:

The buyer’s inspection paradox and its impact on information markets.
A simulated digital marketplace where LM-powered agents buy and sell information.
Key experiments evaluating the marketplace’s usefulness, biases in LM decision-making, and price sensitivity.

The findings suggest that structured digital marketplaces, powered by AI, can enhance decision-making, reduce inefficiencies, and create fairer economic interactions.

The Shapley Value in Machine Learning

https://docs.google.com/presentation/d/1JaszydwLkLyrHF3rHOMyQjbg-S_zliO-DB9icHpSEXI/edit#slide=id.g337e724fb5c_0_0

Read more

Analyzing Multi-Dimensional Data for Clinical Tests: A Practical Case Study

Analyzing Multi-Dimensional Data for Clinical Tests: A Practical Case Study

Purpose: The purpose of this article is to give a technical overview of the steps that can be taken to analyze temporal, multi-dimensional data to apply classification, clustering, and sequence analysis in the context of a clinical test. Introduction: In the spring of 2023 before starting my PhD, I worked

Reinforcement Learning for Optimizing Compute Clusters

Reinforcement Learning for Optimizing Compute Clusters

Main Contributions * Theoretical generalization of Q-learning to multi-objective environments * Implementation of a proof-of-concept on synthetic examples * Dashboard visualization of performance over time Preview Abstract Hyperparameter tuning of complex systems is a problem that relies on many decision-making algorithms and hand-crafted heuristics. Developers are often called upon to tune these hyperparameters

Using Compute Power to Iterate Faster Through Machine Learning Experiments

Using Compute Power to Iterate Faster Through Machine Learning Experiments

I have created a Python template that makes running ML experiments on AWS convenient so you too can take advantage of massive cloud compute for your projects. This cuts down the experiment cycle time, allows you to batch multiple experiments together, and boosts overall productivity. Motivation Waiting for scripts to

Using compute power to iterate faster through ML experiments

Using compute power to iterate faster through ML experiments

How to scale up Machine Learning using AWS, Docker, WandB, and Python.