Literature DB >> 26185245

Economic reasoning and artificial intelligence.

David C Parkes1, Michael P Wellman2.   

Abstract

The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.
Copyright © 2015, American Association for the Advancement of Science.

Entities:  

Mesh:

Year:  2015        PMID: 26185245     DOI: 10.1126/science.aaa8403

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  9 in total

Review 1.  Machine behaviour.

Authors:  Iyad Rahwan; Manuel Cebrian; Nick Obradovich; Josh Bongard; Jean-François Bonnefon; Cynthia Breazeal; Jacob W Crandall; Nicholas A Christakis; Iain D Couzin; Matthew O Jackson; Nicholas R Jennings; Ece Kamar; Isabel M Kloumann; Hugo Larochelle; David Lazer; Richard McElreath; Alan Mislove; David C Parkes; Alex 'Sandy' Pentland; Margaret E Roberts; Azim Shariff; Joshua B Tenenbaum; Michael Wellman
Journal:  Nature       Date:  2019-04-24       Impact factor: 49.962

2.  How Humans Solve Complex Problems: The Case of the Knapsack Problem.

Authors:  Carsten Murawski; Peter Bossaerts
Journal:  Sci Rep       Date:  2016-10-07       Impact factor: 4.379

3.  Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

Authors:  G Skoraczyński; P Dittwald; B Miasojedow; S Szymkuć; E P Gajewska; B A Grzybowski; A Gambin
Journal:  Sci Rep       Date:  2017-06-15       Impact factor: 4.379

4.  Quantifying Motor Task Performance by Bounded Rational Decision Theory.

Authors:  Sonja Schach; Sebastian Gottwald; Daniel A Braun
Journal:  Front Neurosci       Date:  2018-12-14       Impact factor: 4.677

5.  Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments.

Authors:  Jordi Grau-Moya; Matthias Krüger; Daniel A Braun
Journal:  Entropy (Basel)       Date:  2017-12-21       Impact factor: 2.524

6.  An adaptive decision-making system supported on user preference predictions for human-robot interactive communication.

Authors:  Marcos Maroto-Gómez; Álvaro Castro-González; José Carlos Castillo; María Malfaz; Miguel Ángel Salichs
Journal:  User Model User-adapt Interact       Date:  2022-04-09       Impact factor: 4.412

7.  3Es for AI: Economics, Explanation, Epistemology.

Authors:  Nitasha Kaul
Journal:  Front Artif Intell       Date:  2022-03-29

Review 8.  Neurocomputations of strategic behavior: From iterated to novel interactions.

Authors:  Yaomin Jiang; Hai-Tao Wu; Qingtian Mi; Lusha Zhu
Journal:  Wiley Interdiscip Rev Cogn Sci       Date:  2022-04-19

Review 9.  Information Theory for Agents in Artificial Intelligence, Psychology, and Economics.

Authors:  Michael S Harré
Journal:  Entropy (Basel)       Date:  2021-03-06       Impact factor: 2.524

  9 in total

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