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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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Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection?
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Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
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Lee, S., & Kim, E. (2025) · Korean Journal of Journalism & Communication Studies · doi:10.20879/kjjcs.2025.69.1.001 · Cited by 2
Gazi, S. E., & Ibrahimi, A. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3373-2262-9.ch007 · Cited by 2
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Yan, X., Xuan, X., Ono, J. P., Guo, J., Mohanty, V., Shekar, A. K., Gou, L., Wang, B., & Ren, L. (2025) · Computer Graphics Forum · doi:10.1111/cgf.70125 · Cited by 2
“To ensure safety, robustness, and fairness, AI developers must efficiently identify, understand, and address such slices before deployment [RCO ∗ 19].”
Workforce Transformation in the Age of AI: Augmentation Over Automation
Shoib, E. (2025) · The Artificial Intelligence Business Review · doi:10.64044/x96dx491 · Cited by 2
Holistic Ethical Commons: A Dynamic Framework for Ethical Decision Making in Multi-Agent AI Systems
Perumal, R. K. L. (2025) · Intelligent Cybersecurity Conference (ICSC) · doi:10.1109/icsc65596.2025.11140371 · Cited by 2
“To understand the ethical quandaries unique to multi-agent systems, consider three illustrative scenarios [4], [5]: 1) Cooperative Coordination: A team of autonomous delivery drones organizes flight paths in dense urban areas.”
Humans and LLMs rate deliberation as superior to intuition on complex reasoning tasks
Neys, W. D., & Raoelison, M. (2025) · Communications Psychology · doi:10.1038/s44271-025-00320-8 · Cited by 4
“This intuitive link between deliberation and perceived reliability has signi fi cant broader implications, as it suggests that public trust in decision-making could be readily cultivated, but it also runs the risk of exploitation 55 .”
Do AI agents trump human agency?
Astobiza, A. M. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00608-y · Cited by 2
“The study integrates insights from machine behaviour [30], behavioral modeling [34], complexity science [20] and socio-technical systems theory [5] to address key questions about the future of AI agents and their societal roles.”
Ribeiro, J. L., Plangger, K. A., & Montecchi, M. (2025) · British Journal of Management · doi:10.1111/1467-8551.70027 · Cited by 5
Building Trust in the Age of Intelligent Machines
Subrahmanyam, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3404-2.ch008 · Cited by 2
From Counsel to Code: Implications of AI Dependency for Authentic Human Bonds – A Systematic Review
Kamener, K. (2025) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100020 · Cited by 3
A large-scale evaluation of commonsense knowledge in humans and large language models
Nguyen, T. D., Watts, D. J., & Whiting, M. E. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag029 · Cited by 6
“Designing machines that exhibit human-like tendencies in both individual and social environments is a central objective of AI research (61 – 63).”
Planning with ComPlanAI: Comparative insights from a human–AI evaluation of comprehensive plans
Sung, S., Jung, C., & Park, Y. (2026) · Cities · doi:10.1016/j.cities.2026.106898 · Cited by 2
Algorithmic ethics in corporate contexts: Knowledge mapping for responsible management
Barreno-Alcalde, S., Delso-Vicente, A.-T., & Rivera-Heredia, A. (2026) · Journal of Management & Organization · doi:10.1017/jmo.2026.10089 · Cited by 4
“Hence, the need for an interdisciplinary approach integrating sociological, economic, philosophical, and psychological perspectives (Boyd & Holton, 2018; Campolo et al., 2017; Crawford & Calo, 2016; Rahwan et al., 2019).”
Can adversarial attacks by large language models be attributed?
Cebrián, M., Abeliuk, A., & Telle, J. A. (2026) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000085 · Cited by 3
Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
Qian, C., Zhu, K., Horton, J. J., Manning, B. S., Tsai, V., Wexler, J., & Thain, N. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789078 · Cited by 13
Chen, A., & Lu, Y. (2026) · Journal of Management Information Systems · doi:10.1080/07421222.2025.2602395 · Cited by 2
“The intelligence level of AI diagnostics, therefore, plays a crucial role in shaping patients’ perceptions and feelings toward AI systems [68].”
Measuring agentic AI adoption and control frameworks in finance
Mustafa, A. U., & Aysan, A. F. (2026) · Modern Finance · doi:10.61351/mf.v4i1.557 · Cited by 2
“Conceptually, the phenomenon aligns with a broader perspective in which autonomous computational entities can be treated as decision-making “actors” whose behavior must be anticipated and constrained, much like other complex socio-technical agents (Rahwan et al., 2019).”
University–industry collaboration for AI-driven service innovation
Kriz, A., Cao, Z., Hartley, N., Verreynne, M., Indulska, M., & Vegh, V. (2026) · Journal of service management · doi:10.1108/josm-12-2024-0545 · Cited by 4
Designing generative AI chatbots for decentralized finance: deriving insights from DeFAI
Bonnet, S., Hannemann, J.-G. A., & Teuteberg, F. (2026) · Electronic Commerce Research · doi:10.1007/s10660-026-10116-5 · Cited by 3
Wang, Z.-H., Xu, X., & Zhao, C. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3681214 · Cited by 2
“Due to the complexity and opacity of most AI technologies, it is inherently di ffi cult to identify errors by AI systems [13].”
Hermanns, L., Degen, K., & Teubner, T. (2026) · Government Information Quarterly · doi:10.1016/j.giq.2026.102142 · Cited by 3
Bridging the gap: Explainable AI for advancing Earth System Science
Huang, F., Jiang, S., Shangguan, W., Li, L., Zhang, Y., Zhang, R., Li, Q., Li, D., & Dai, Y. (2026) · Science China Earth Sciences · doi:10.1007/s11430-025-1898-3 · Cited by 2
“The approaches involve canonical correlation analysis (Burges, 2010; Raghu et al., 2017), clustering (Raghu and Schmidt, 2020), differential analysis (Motteler et al., 1995; Aires et al., 2004; Rahwan et al., 2019), linear (Lees et al., 2022) or symbolic approximations (Liu et al., 2023a).”
Garbage in, garbage forever: why data needs a cold-chain mentality
Toscani, G. (2026) · Transforming Government People Process and Policy · doi:10.1108/tg-01-2026-0021 · Cited by 2
Knowledge Co‐Creation by Human–Machine Social Interaction: Based on Decision Field Theory
Ren, Z., Shen, X., & Xia, S. (2026) · Systems Research and Behavioral Science · doi:10.1002/sres.70103 · Cited by 2
Kinana, O. B. B. (2026) · Horizons Intermediary Journal of Business Research · doi:10.67150/hijbr.v1i1.6 · Cited by 2
AI for science: Progress, challenges, and perspectives
Xu, Y., Shao, Z., Liu, X., Li, Y., An, Z., Fu, C., Huang, C., Zhang, L., Gao, F., Li, J., et al. (2026) · The Innovation · doi:10.1016/j.xinn.2026.101530 · Cited by 6
Price of Anarchy in Algorithmic Matching of Romantic Partners
Abeliuk, A., Elbassioni, K., Rahwan, T., Cebrián, M., & Rahwan, I. (2023) · ACM Transactions on Economics and Computation · doi:10.1145/3627985 · Cited by 2
Self-citationDifferences of Training Structures on Stimulus Class Formation in Computational Agents
Carrillo, A., & Betancort, M. (2023) · Multimodal Technologies and Interaction · doi:10.3390/mti7040039 · Cited by 2
Toward Behavioral AI: Cognitive Factors Underlying the Public Psychology of Artificial Intelligence
Mukherjee, S., Senapati, D., & Mahajan, I. (2023) · Springer eBooks · doi:10.1007/978-981-99-3966-4_1 · Cited by 3
THE RISK OF POLITICAL MANIPULATION TURNING INTO A POLITICAL INFODEMIC: PRECAUTIONS FOR GENERATION Z
Orhan, E. B. (2023) · Akademik Yaklaşımlar Dergisi · doi:10.54688/ayd.1380473 · Cited by 2
The contingent animal: does artificial innateness misrepresent behavioral development?
Kohn, G. M., & Kostecki, M. (2024) · Adaptive Behavior · doi:10.1177/10597123241232170 · Cited by 2
“Some even proposed that standard ethological methods — once solely used to research organisms — can be extended to understanding the behavior of arti fi cial systems (Rahwan et al., 2019).”
On the (Non-)Reliance on Algorithms - A Decision-Theoretic Account
Sinclair‐Désgagné, B. (2021) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2024.102844 · Cited by 2
Die digitale Verantwortungslücke: Vorschläge zur Haftung für algorithmisches Fehlverhalten
Beckers, A., & Teubner, G. (2024) · Springer eBooks · doi:10.1007/978-3-658-43521-9_7 · Cited by 2
Investigating the Impact Factors for Trust Analysis of Autonomous Vehicle
Wang, T., Xu, M., Liu, L., Chen, J., & Wang, Y. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60477-5_14 · Cited by 2
A Socio-Legal Perspective on Gender-Based Discrimination in Machine Learning Algorithm
Roy, R., Dixit, A. K., Chaturvedi, S. K., Saxena, S., Kaur, G., & Kant, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3594-5_36 · Cited by 2
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System
Roshdi, M., Petzold, J., Wahby, M., Ebrahim, H., Bereković, M., & Hamann, H. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00187 · Cited by 2
“V2X approaches might also pose different challenges for XAI, for example, in the domain of machine behavior [18].”
BackgroundNew Perspectives on the Future of Computing Education: Teaching and Learning Explanatory Models
Höper, L., & Schulte, C. (2024) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3699538.3699558 · Cited by 2
“Similarly, Rahwan et al. [42] discuss using interdisciplinary approaches and methods from other disciplines to examine and explain AI systems behaviors.”
InfluentialInterface design for visual blind spots in cooperative driving
Liu, B., Zhang, J., & Luximon, Y. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2500448 · Cited by 2
“…formation and maintenance of relationships between team members and determines whether team members choose to rely on each other during tasks (Chen and Barnes2014; Hancock, Billings, and Schaefer 2011; Hancock, Nourbakhsh, and Stewart 2019; Phillips, Ososky, and Jentsch 2014; Rahwan et al. 2019).”
Küçükkaya, B., & Özoğlu, M. (2025) · BMC Nursing · doi:10.1186/s12912-025-03438-6 · Cited by 2
“This developing situation can cause misinformation and tension between the general population and technology industries [46, 47].”
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.
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