Machine learning and its contribution to economic growth
[Artificial intelligence] machines have a new capability that no biological species has: the ability to share knowledge and skills almost instantaneously with others. Specifically, the rise of cloud computing has made it significantly easier to scale up new ideas at much lower cost than before. This is an especially important development for advancing the economic impact of machine learning because it enables cloud robotics: the sharing of knowledge among robots.
Will Artificial Intelligence Recharge Economic Growth?
By: Timothy Taylor – Macalester College
Machine learning and policy
How can machine learning be used within the context of central banking and policy analyses? Chakraborty and Joseph present the machine learning toolbox relevant to economic forecasting and banking supervision. They discuss both the advantages and shortcomings of such an approach.
Machine learning at central banks
Authors: Chiranjit Chakraborty, Andreas Joseph
From: Bank of England
Caution is warranted
Jon Danielsson cites three reasons why machine learning and AI can destabilize markets: a) by focusing on risk that can be measured as opposed to risk that matters; b) by making it easier for economic agents to “optimize against the system and create hidden complexities”; c) by making the system more homogeneous.
Artificial intelligence and the stability of markets
By: Jon Danielsson – LSE
Fuster and al. find that their model using machine learning to predict creditworthiness of US mortgage applications performs worse than logit models.
Predictably Unequal? The Effects of Machine Learning on Credit Markets
Authors: Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, Ansgar Walther
From: Federal Reserve Bank of New York – Imperial College – Warwick Business School
An application
In this paper Victor Duarte proposes a global, nonlinear numerical method to solve a large class of continuous time models in economics and finance using machine learning.
Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning
Author: Victor Duarte
From: MIT
