Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272627 
Authors: 
Year of Publication: 
2023
Series/Report no.: 
IZA Discussion Papers No. 16000
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Artificial Intelligence (AI) scientists are challenged to create intelligent, autonomous agents that can make rational decisions. In this challenge, they confront two questions: what decision theory to follow and how to implement it in AI systems. This paper provides answers to these questions and makes three contributions. The first is to discuss how economic decision theory – Expected Utility Theory (EUT) – can help AI systems with utility functions to deal with the problem of instrumental goals, the possibility of utility function instability, and coordination challenges in multi-actor and human-agent collectives settings. The second contribution is to show that using EUT restricts AI systems to narrow applications, which are "small worlds" where concerns about AI alignment may lose urgency and be better labelled as safety issues. This papers third contribution points to several areas where economists may learn from AI scientists as they implement EUT. These include consideration of procedural rationality, overcoming computational difficulties, and understanding decision-making in disequilibrium situations.
Subjects: 
economics
artificial intelligence
expected utility theory
decision-theory
JEL: 
D01
C60
C45
O33
Document Type: 
Working Paper

Files in This Item:
File
Size
382.6 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.