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Machine behaviour
Open accessAbstract
Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.
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“Such endogenous coordination failures blur the line between attack and accident (Leibo et al., 2017;Rahwan et al., 2019), reinforcing that defenses must account for both malicious swarms and emergent collective behaviors.”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Rahwan et al., 2019
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“Augmentation is therefore a coevolutionary process during which humans learn from machines and machines learn from humans (Amershi, Cakmak, Knox, & Kulesza, 2014;Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“Only such studies can examine the feedback loops between human influence on machine behavior and machine influence on human behavior, which cause the emergent behaviors that are otherwise impossible to predict (Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“Computer scientists may be expert technologists, but they are generally not trained social or behavioral scientists (Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“These differences lead to entirely new machine behaviors (Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“Openly available, robust natural language generation (NLG) algorithms have an ability to generate human-like texts across multiple domains”
From Value attributed to text-based archives generated by artificial intelligence (2023) · cites this paper as [1]
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“This means that research into the beliefs and cognitive processes underlying LLMs need to resort to more indirect and interdisciplinary methods (Rahwan et al., 2019).”
From Exploring the psychology of LLMs’ moral and legal reasoning (2024) · cites this paper as Rahwan et al., 2019
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“First, we demonstrate the effectiveness of experimental economics methods in studying choice behavior of artificial intelligence (43), which adds earlier studies of children (23, 30), monkeys (31), rats, and pigeons (32).”
From The emergence of economic rationality of GPT (2023) · cites this paper as [43]
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“Paraphrasing (Rahwan et al. 2019): “Machine behavior, similarly, cannot be fully understood without …the social environments”, and accordingly “machine behavior” can be characterized as an enigma because of its social milieu, complexity, and unpredictability: Possati’s technological rigor allows…”
Representation-Induced Algorithmic Bias
Stanton, S. C., Dermoudy, J., & Ollington, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_9
Synchronizing Everything to the Digitized World
Shibuya, K. (2022) · Springer eBooks · doi:10.1007/978-981-19-0950-4_7
(2021) · Morals & Machines · doi:10.5771/2747-5182-2021-1-3
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Kirwan, C. G., & Fu, Z. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817024-3.00006-4
Terhoeven, J., Certa, M., & Wischniewski, S. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748927372-185
(2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748927372-1
Regalado, J. R. M., Monteza, A. H., Juárez, J. C. M., Baique, N. A., & Delgado, A. H. C. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06417-3_46
Les robots sociaux et affectifs en santé
Devillers, L. Y. (2022) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/j.banm.2022.07.020
Guliyev, H. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4177241
You Can Love a Robot, But Should You Fight With it?
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A Dataset Generation Framework for Evaluating Megapixel Image Classifiers and Their Explanations
Machiraju, G., Plevritis, S. K., & Mallick, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_25 · Cited by 1
“Conversely, can model explanations reveal learning mechanisms and behaviors [68,81], and if so, what mechanisms are desirable for megapixel imagery?”
BackgroundAristotle on AIPreprint
Tham, E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4451379
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A Major Transition in the Evolution of Human Cooperation Since the Emergence of Agriculture
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Mit Hirnforschung die KI besser verstehen
Krauß, P. (2023) · Springer eBooks · doi:10.1007/978-3-662-67179-5_22
Krauß, P. (2023) · Springer eBooks · doi:10.1007/978-3-662-67179-5_1
Pelatihan Pelajar Hebat Untuk Menurunkan Perilaku Tidak Disiplin Siswa SMP Negeri 1 Yogyakarta
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“…perceived technology security acts as a resistance factor that explains the low usage of FRP. Similar concerns have been raised by Rahwan et al. [8], in which a deep understanding of how users respond to a security issue in FRP is needed, especially as this new-age smart system becomes…”
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“First, the evolution of AI technology has empowered robots with the ability to autonomously adapt to changing scenarios [19, 49].”
Algorithmic trading, what if it is just an illusion? Evidence from experimental asset marketsPreprint
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Polowczyk, J. (2023) · doi:10.18559/978-83-8211-192-7
Metadata
- Authors
- Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. ‘., Roberts, M. E., Shariff, A., Tenenbaum, J. B., & Wellman, M.
- Affiliations
- Max Planck Institute for Human Development (DE)
- MIT Media Lab (US)
- University of Vermont (US)
- Centre National de la Recherche Scientifique (FR)
- Brigham Young University (US)
- Yale University (US)
- Max Planck Society (DE)
- Canadian Institute for Advanced Research (CA)
- Imperial College London (GB)
- Microsoft (United States) (US)
- Meta (United States) (US)
- Northeastern University (US)
- Max Planck Institute for Evolutionary Anthropology (DE)
- Harvard University (US)
- University of California San Diego (US)
- University of British Columbia (CA)
- Massachusetts Institute of Technology (US)
- University of Michigan (US)
- Corresponding author
- Rahwan, Iyad
- ORCID
- Rahwan, Iyad
- Obradovich, Nick
- Bongard, Josh
- Bonnefon, Jean-François
- Breazeal, Cynthia
- Crandall, Jacob W.
- Christakis, Nicholas A.
- Couzin, Iain D.
- Jackson, Matthew O.
- Jennings, Nicholas R.
- Lazer, David
- McElreath, Richard
- Mislove, Alan
- Parkes, David C.
- Pentland, Alex ‘Sandy’
- Roberts, Margaret E.
- Shariff, Azim
- Tenenbaum, Joshua B.
- Wellman, Michael
- Venue
- Nature
- Published
- 2019-04
- Volume
- 568
- Issue
- 7753
- Pages
- 477-486
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4211017739
- Cited by
- 1,364
- References
- 115
- Citations by year
- 2026: 330 · 2025: 258 · 2024: 187 · 2023: 157 · 2022: 142 · 2021: 143 · 2020: 113 · 2019: 31
- FWCI
- 29.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- KOPS (University of Konstanz) (submittedVersion)
- HAL (submittedVersion, cc-by)
- Toulouse 1 Capitole Publications (Université Toulouse I Capitole) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- National Science Foundation (N00014-14-1-0635, 1355061, 1447634, N00014-09-1-1074, 1231216, 1344227)
- Robert Wood Johnson Foundation
- Office of the Director of National Intelligence (2017-17061500006)
- Future of Life Institute
- Deutsche Forschungsgemeinschaft (422037984)
- Agence Nationale de la Recherche (ANR-17-EURE-0010, 2017-)
- Max-Planck-Gesellschaft (W911NG-11-1-0385, N00014-09-1-1074, IOS-1355061, N00014-14-1-0635)
- Companhia Brasileira de Metalurgia e Mineração (CCF-1231216)
- Defense Advanced Research Projects Agency
- Intelligence Advanced Research Projects Activity (2017-17061500006)
- Office of Naval Research (N00014-09-1-1074, N00014-14-1 0635, IOS-1355061, W911NG-11-1-0385, N00014)
- NIH Office of the Director
- Army Research Office (W911NF-14-1-0431, W911NF-16-1-0304, N00014-14-1-0635, W911NG-11-1- 0385, IOS-1355061, W911NF-16-1-, W911NF, N00014-09-1-1074)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Keywords
- artificial intelligence, artificial intelligence systems, AI regulation, interdisciplinary research agenda, institutional constraints, societal impact of AI
- MeSH terms
- Artificial Intelligence, Humans, Motivation, Robotics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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