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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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Zhang, H.-B., Yu, Q., Zhang, X., Zhang, Y., Huang, T., Ding, J., Yan, L., Cao, X., Yin, L., Liu, Y., et al. (2025) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2025.100551 · Cited by 7
“The examination of network features identified through CPM provides updated insights into underlying neurophysiological mechanisms (Rahwan et al., 2019).”
How Dynamic vs. Static Presentation Shapes User Perception and Emotional Connection to Text-Based AI
Bhat, M. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712131 · Cited by 8
Gezgin, A. Y., & Arıcıoğlu, M. A. (2025) · Sustainability · doi:10.3390/su17083601 · Cited by 7
Governance of Smart Service Social Responsibility: A Systematic Literature Review
Nan, H., Lin, J., Xia, Z., & Zeng, S. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3597053 · Cited by 10
Artificial intelligence and economic psychology: toward a theory of algorithmic cognitive influence
Fernández, F. R. (2025) · AI & Society · doi:10.1007/s00146-025-02592-4 · Cited by 13
“For example, if many users start relying on an AI advisor, the AI’s training data (which may include those users’ outcomes) will evolve, which in turn changes its advice—a co-evolving human–AI loop (Rahwan et al. 2019a, b).”
InfluentialDeng, J., Wu, F., & Qi, J. (2025) · Applied Sciences · doi:10.3390/app151910322 · Cited by 9
Özoğul, G. (2025) · Cogent Food & Agriculture · doi:10.1080/23311932.2025.2568199 · Cited by 9
“Ai technologies in agriculture are creating a multitude of new complex interactions with human – machine, machine–machine (Rahwan et al., 2019), and increasingly machine–ecosystem–earth systems (galaz, 2014; Markolf et al., 2018). if Ai becomes widespread enough and plays a significant role in…”
Writing with AI boosts trust-building efficiency
Purcell, Z. A., Jakesch, M., Dong, M., Nußberger, A.-M., & Köbis, N. (2025) · iScience · doi:10.1016/j.isci.2025.114092 · Cited by 11
“17,24 27 In its classic form, the trust game captures the willingness to accept vulnerability, a core feature across disciplinary definitions of trust.”
The AI-powered soft skills renaissance: cultivating human abilities in the digital era
Muthukumar, M., Sitharaj, A., Sundaram, M. M., & Dhananjeiyan, B. (2025) · AI and Ethics · doi:10.1007/s43681-025-00912-4 · Cited by 7
“Establishing a balance in which AI enhances rather than supplants human capabilities is a requirement for assuring ethical and robust technological integration [81].”
Under pressure: how time constraints, task complexity, and AI reliability shape human-AI interaction
Hermanns, L., & Teubner, T. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2587732 · Cited by 12
“As these systems become increasingly integrated into knowledge work and decision-making processes (Rahwan et al. 2019), ensuring that users can effectively calibrate trust and validate AI outputs under varying cognitive conditions becomes paramount for realising the technology’s intended benefits…”
Jarrahi, M. H. (2025) · Big Data & Society · doi:10.1177/20539517251381697 · Cited by 14
“A nascent but growing fi eld of research, often referred to as “ machine behavior, ” has begun to study AI systems not merely as engineering artifacts but as agentic systems with distinct behavioral patterns that unfold within use contexts beyond training phases (Rahwan et al., 2019).”
Korkmaz, G., & Toraman, Ç. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08675-0 · Cited by 9
Service Ecosystem: A Lens of Smart Digital Society
Xue, X., Feng, Z., Chen, S., Zhou, Z., Qin, C., Li, B., Wang, Z., Wang, S., Wu, H., Zhang, L., et al. (2021) · IEEE International Conference on Services Computing · doi:10.1109/scc53864.2021.00039 · Cited by 7
HCI driving alienation: autonomy and involvement as blind spots in digital ethics
Jungtäubl, M., Zirnig, C., & Ruiner, C. (2023) · AI and Ethics · doi:10.1007/s43681-023-00298-1 · Cited by 6
“AI systems are increasingly able to make decisions and take actions (at least partially) autonomously that were previously completed by humans [4, 5].”
Measuring an artificial intelligence language model’s trust in humans using machine incentives
Johnson, T., & Obradovich, N. (2024) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad1c69 · Cited by 13
“To solve this challenge, we draw on conceptual methods from the field of machine behavior [11], a field that has provided insights into the biases [12, 13], errors [14], and decision-making properties [15] of AI models.”
BackgroundMethodSelf-citationSarkadi, Ş. (2023) · IEEE Technology and Society Magazine · doi:10.1109/mts.2023.3340232 · Cited by 8
Reasonable Machines: A Research Manifesto
Benzmüller, C., & Lomfeld, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58285-2_20 · Cited by 9
Active Human Agency in Artificial Intelligence Mediation
Fanni, R., Steinkogler, V., Zampedri, G., & Pierson, J. (2020) · International Conference on Smart Objects and Technologies for Social Good · doi:10.1145/3411170.3411226 · Cited by 6
Improving human collective decision-making through animal and artificial intelligence
Sueur, C., Bousquet, C. A. H., Espinosa, R., & Deneubourg, J. (2021) · Peer Community Journal · doi:10.24072/pcjournal.31 · Cited by 7
The Role of Artificial Intelligence in Healthcare and Medical Negligence
Mehta, D. (2023) · Liverpool Law Review · doi:10.1007/s10991-023-09340-y · Cited by 10
“Sometimes human decisions are dominated by machines, sometimes vice-versa and sometimes there is a densely intertwined human–machine co-behaviour (Rahwan et al. 2019).”
On the Importance of Posture and the Interaction Environment
Hansen, A. K., Nilsson, J., Jochum, E., & Herath, D. C. (2020) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3371382.3378288 · Cited by 7
“This need was also emphasised by Rahwan et al [2].”
BackgroundMessner, C. (2020) · International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique · doi:10.1007/s11196-020-09735-4 · Cited by 7
Using edge cases to disentangle fairness and solidarity in AI ethics
Brusseau, J. (2021) · AI and Ethics · doi:10.1007/s43681-021-00090-z · Cited by 7
Fang, J., Schooler, L. J., & Luan, S. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01828-1 · Cited by 7
“Rahwan et al. (2019) argue that the behavior of ML algorithms can be difficult to analyze formally; therefore, they call for investigating ML models with experimental and behavioral methods, much in the same way that these methods are used to study the behavior of humans and other animals.”
BackgroundThe Opportunities and Costs of AI in Behavioural SciencePreprint
Mills, S., Costa, S., & Sunstein, C. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4490597 · Cited by 7
Vartiainen, H., Liukkonen, P., & Tedre, M. (2023) · doi:10.35542/osf.io/g5fb8 · Cited by 7
Cognitive phantoms in large language models through the lens of latent variables
Peereboom, S., Schwabe, I., & Kleinberg, B. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100161 · Cited by 8
“As an alternative to analytic assessment of the underlying processes in LLMs, adopting a machine behaviourist perspective can be useful [7].”
BackgroundTomada de decisão nas organizações: o que muda com a Inteligência Artificial?
Yu, A. S. O., Nardy, A., Hirano, H. I., de Oliveira, J. F. A., Ribeiro, N., & Grando, N. (2024) · Estudos Avançados · doi:10.1590/s0103-4014.202438111.017 · Cited by 6
Imitation and Large Language Models
Boisseau, É. (2024) · Minds and Machines · doi:10.1007/s11023-024-09698-6 · Cited by 8
“In an article entitled ‘machine behaviour’, Rahwan et al. (2019) draw analogies between the study of animal behaviour and the study of algorithmic functioning and operations, bearing in mind the ‘fundamental differences between machines and animals’.”
AI in terrorism sentencing: evaluating predictive accuracy and ethical implications
Farber, S. (2024) · Criminal Justice Studies · doi:10.1080/1478601x.2024.2415558 · Cited by 6
“The use of multiple AI models allows for a comparative analysis of performance and consistency, helps mitigate potential biases inherent in any single model, and enhances the robustness and reliability of the findings (Kroll, 2015; Rahwan et al., 2019).”
Artificial Intelligences: A Bridge Toward Diverse Intelligence and Humanity's Future
Levin, M. G. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202401034 · Cited by 11
Frimpong, V. (2025) · Administrative Sciences · doi:10.3390/admsci15070263 · Cited by 9
Deepfakes and the crisis of digital authenticity: ethical challenges in the age of synthetic media
Verma, A. (2025) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-04-2025-0083 · Cited by 6
Perception vs reality: a multi-level framework for analyzing user perceptions of LLM-based chatbots
Liu, Y.-K., & Rau, P.-L. P. (2025) · Information Technology & People · doi:10.1108/itp-03-2024-0403 · Cited by 5
Wang, Z., Cheng, Q., Gu, Z., Liu, C., Cun, D., Shi, X., Wu, X., Zhou, Z. M., He, X., Vlacic, L., et al. (2025) · Research · doi:10.34133/research.0982 · Cited by 6
“Algorithmic biases embedded in model training or objective functions may result in unequal service distribution, amplifying existing urban inequalities [ 89 ].”
Catta-Preta, M. C., Omeñaca, A. T., Picó, J. F. I., & Fierro, J. M. M. (2025) · Applied Sciences · doi:10.3390/app152211951 · Cited by 6
Herdability of Laplacian Game Control Systems
Guo, J., Ji, Z., YunGang, L. I. U., & Lin, C. (2026) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2026.3651237 · Cited by 6
“Gametheoryhasbeenappliedacrossvariousdomains,including cooperative intelligence [10], machine intelligence [11], AI ethics [12], and control systems [13], [14], [15], [16].”
Meng, X., Wang, X., & Peng, T. (2026) · Communication Methods and Measures · doi:10.1080/19312458.2026.2613829 · Cited by 7
“The coordination dimension distinguishes whether bots operate autonomously or in collaboration with other bots (Grimme et al., 2017; Rahwan et al., 2019).”
Agentic AI in Branding: Driving Hyper-personalization and Real-time Adaptation
Puri, S., Pradhan, J. A., Bernabe, B. G., & Ray, A. (2026) · Global Business Review · doi:10.1177/09721509251414773 · Cited by 8
General scales unlock AI evaluation with explanatory and predictive power
Zhou, L., Pacchiardi, L., Martínez‐Plumed, F., Collins, K. M., Moros-Daval, Y., Zhang, S., Zhao, Q., Huang, Y., Sun, L., Prunty, J. E., et al. (2026) · Nature · doi:10.1038/s41586-026-10303-2 · Cited by 21
Grigaliūnas, Š., Brūzgienė, R., & Venčkauskas, A. (2023) · IEEE Access · doi:10.1109/access.2023.3322162 · Cited by 6
“Typically, machine learning and artificial intelligence algorithms are trained on specific categories of data and may not be able to account for broader contextual factors that may influence the interpretation of digital evidence [11].”
Background
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.
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