Artificial Intelligence and the Economics of Decision-Making

Wim Naudé

No 16000, IZA Discussion Papers from IZA Network @ LISER

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.

Keywords: decision-theory; expected utility theory; artificial intelligence; economics (search for similar items in EconPapers)
JEL-codes: C45 C60 D01 O33 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2023-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mic and nep-upt
References: View references in EconPapers View complete reference list from CitEc
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Published - published in: W. Naudé and T. Gries and N. Dimitri (eds.), Artificial Intelligence: Economic Perspectives and Models, Cambridge University Press, Cambridge, 2024

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Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp16000

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