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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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“A key aspect in human-centric paradigms such as HAI or Hybrid Intelligence is that the behavior of the human and the AI are assumed to impact each other [117].”
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Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs
Rao, A., Khandelwal, A., Tanmay, K., Agarwal, U., & Choudhury, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.892 · Cited by 79
Machines vs. humans: The evolving role of artificial intelligence in livestreaming e-commerce
Yuan, H., Lü, K., & Fang, W. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.115077 · Cited by 41
“In the iterative process, machines constantly learn new rules or create models and improve them over time (Rahwan et al., 2019).”
Model Talk: Calculative Cultures in Quantitative Finance
Hansen, K. B. (2020) · Science Technology & Human Values · doi:10.1177/0162243920944225 · Cited by 33
“Influence is mutual in these socio-material practices: human influence feeds into and shapes models, while, in return, models affect perceptions and practices (Rahwan et al. 2019, 483).”
BackgroundVisual behavior modelling for robotic theory of mind
Chen, B., Vondrick, C., & Lipson, H. (2021) · Scientific Reports · doi:10.1038/s41598-020-77918-x · Cited by 32
Li, L., Li, Y., Song, B., Shi, Z., & Wang, C. (2022) · Behavioral Sciences · doi:10.3390/bs12070205 · Cited by 32
Innovation, Technology Development and Transfer
Change, I. P. O. C. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009157926.018 · Cited by 36
Liao, X., Zheng, Y.-H., Shi, G. J., & Bu, H. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123377 · Cited by 35
Li, H., & Tian, F. (2026) · Group Decision and Negotiation · doi:10.1007/s10726-026-09980-1 · Cited by 78
“At the stage of decision implementation, AI functions not only as an information processor but also as the executor of decisions, excelling in instantaneous responses and consistent execution (Awad et al. 2018; Rahwan et al. 2019).”
Group-level personality detection based on text generated networks
Sun, X., Liu, B., Meng, Q., Cao, J., Luo, J., & Yin, H. (2019) · World Wide Web · doi:10.1007/s11280-019-00729-2 · Cited by 30
“Therefore it has broad applications [49] such as human-computer interaction [39, 54], mental healthcare [23], business analysis [29] and human resource management [7].”
BackgroundTactile Internet with Human-in-the-Loop: New frontiers of transdisciplinary research
Fitzek, F. H. P., Li, S., Speidel, S., & Strufe, T. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821343-8.00010-1 · Cited by 31
Ethical stakes of Industry 4.0
Trentesaux, D., & Caillaud, E. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.1486 · Cited by 31
Gao, Z., Wen-min, L., Liang, J., Hanxi, P. A. N., Xu, W., & Shen, M. (2021) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2021.02172 · Cited by 34
Effects of human–machine interaction on employee’s learning: A contingent perspective
Wang, S., Zhao, H., & Xiaomei, Z. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.876933 · Cited by 38
von Schenk, A., Klockmann, V., & Köbis, N. (2025) · Perspectives on Psychological Science · doi:10.1177/17456916231194949 · Cited by 30
Perceived authenticity of virtual characters makes the difference
Huang, J., & Jung, Y. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.1033709 · Cited by 36
“…some social rules in human-to-human interactions can be applied to people ’ s interaction with computers, including virtual characters (Reeves and Nass, 1996), studies also suggest that people treat technology and humans as different kinds of communicators (Shechtman and Horowitz, 2003; de…”
BackgroundInfluentialToscani, G. (2022) · Journal of Knowledge Management · doi:10.1108/jkm-07-2022-0574 · Cited by 30
Artificial intelligence and skills in the workplace: An integrative research agenda
Margaryan, A. (2023) · Big Data & Society · doi:10.1177/20539517231206804 · Cited by 30
“In addition, causal designs that could help shed light on the causes and consequences of skill transformation on the uptake of AI in the workplace would be required (Rahwan et al., 2019).”
BackgroundRobertson, R. E., Jiang, S., Lazer, D. M. J., & Wilson, C. (2019) · ACM Web Science Conference (WebSci) · doi:10.1145/3292522.3326047 · Cited by 30
“That is, our audit is not focused on human behavior, but machine behavior: the algorithm is our subject [41].”
BackgroundSelf-citationEvaluating the impact of artificial intelligence technologies in public services
van Noordt, C., & Misuraca, G. C. (2020) · International Conference on Theory and Practice of Electronic Governance · doi:10.1145/3428502.3428504 · Cited by 35
“Any form of assessment of AI impacts should thus research the AI-system ‘in the wild’ to really understand the effects [18].”
BackgroundEthical Design of Conversational Agents: Towards Principles for a Value-Sensitive Design
Wambsganß, T., Höch, A., Zierau, N., & Söllner, M. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86790-4_37 · Cited by 31
“For example, they can lead to inaccurate predictions for specific subgroups or may carry the implicit values of programmers and organizations [14].”
BackgroundAn Ensemble Learning–Online Semi-Supervised Approach for Vehicle Behavior Recognition
Zhang, H., & Fu, R. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3095053 · Cited by 31
Algorithms as partners in crime: A lesson in ethics by design
Krügel, S., Ostermaier, A., & Uhl, M. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107483 · Cited by 30
La inteligencia artificial y la toma de decisiones gerenciales
Quinto, N. M. D., Villodas, A. J. C., Montero, C. P. C., Cueva, D. L. E., & Vera, S. A. N. (2021) · Revista de Investigación Valor Agregado · doi:10.17162/riva.v8i1.1631 · Cited by 67
Interdependence in Vehicle-Pedestrian Encounters and its Implications for Vehicle Automation
Domeyer, J. E., Lee, J. D., Toyoda, H., Mehler, B. L., & Reimer, B. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3041562 · Cited by 33
Machine learning and social theory: Collective machine behaviour in algorithmic trading
Borch, C. (2021) · European Journal of Social Theory · doi:10.1177/13684310211056010 · Cited by 33
“Recognizing this entails calls for a sociological discussion of inter-algorithmic relations, or what Rahwan et al. (2019) refer to as collective machine behaviour.”
BackgroundInfluentialGeneral intelligence disentangled via a generality metric for natural and artificial intelligence
Hernández‐Orallo, J., Loe, B. S., Cheke, L. G., Martínez‐Plumed, F., & hÉigeartaigh, S. Ó. (2021) · Scientific Reports · doi:10.1038/s41598-021-01997-7 · Cited by 33
We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine InteractionPreprint
Chugunova, M., & Sele, D. (2020) · SSRN Electronic Journal · doi:10.3929/ethz-b-000442053 · Cited by 30
Emerging human-technology relationships in a co-design process with generative AI
Vartiainen, H., Liukkonen, P., & Tedre, M. (2025) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101742 · Cited by 28
Machine learning and human‐machine trust in healthcare: A systematic survey
Lin, H., Han, J., Wu, P., Wang, J., Tu, J., Tang, H., & Zhu, L. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12268 · Cited by 33
Myers, S., & Everett, J. A. C. (2024) · Cognition · doi:10.1016/j.cognition.2024.106028 · Cited by 31
“…use any kind of algorithm or statistical model to perform tasks that usually require human intelligence - are changing the world around us, modeling functions typical of the human mind such as visual and speech recognition, reasoning, and problem solving (Rahwan, Cebrian, Obradovich, et al., 2019).”
Beyond Anthropomorphism: Social Presence in Human– AI Collaboration Processes
Siemon, D., Elshan, E., de Vreede, T., Ebel, P., & de Vreede, G. (2025) · Journal of Management Studies · doi:10.1111/joms.70000 · Cited by 36
“Such systems seek to replace or augment human labour beyond traditional information systems (IS) that simply support human activities (Berente et al., 2021; Rahwan et al., 2019).”
Instruments, agents, and artificial intelligence: novel epistemic categories of reliability
Duede, E. (2022) · Synthese · doi:10.1007/s11229-022-03975-6 · Cited by 37
“…begun theorizing how best to situate human efforts alongside AIs conceived as ‘alien’ collaborators or interlocutors in scientific investigations (Bommasani et al., 2021; Sourati & Evans, 2021) or to develop a new science dedicated to studying the behavior (or actions) of AI (Rahwan et al., 2019).”
BackgroundLearning machine learning: On the political economy of big tech's online AI courses
Luchs, I., Apprich, C., & Broersma, M. (2023) · Big Data & Society · doi:10.1177/20539517231153806 · Cited by 28
“Scholars of machine behavior apply methods such as “ randomized experiments, observational inference and population-based descriptive statistics-methods, ” to estimate the societal impact of algorithms (Rahwan et al., 2019: 479).”
MethodRequirements Engineering in Machine Learning Projects
Gjorgjevikj, A., Mishev, K., Antovski, L., & Trajanov, D. (2023) · IEEE Access · doi:10.1109/access.2023.3294840 · Cited by 33
“…precisely because of their complexity, susceptibility to imperfections of the data they learn from, the difficulty in interpreting the functional processes that generate their output, as well as any new behavior arising from their interactions with the world or changes in their environment [8].”
BackgroundInfluentialThe positioning of biofuel cells-based biobatteries for net-zero energy future
Patra, S., Verma, J., Mishra, Y. K., Kurinec, S. K., Wang, Q., Syväjärvi, M., & Tiwari, A. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107919 · Cited by 29
van Zoelen, E. M., van den Bosch, K., & Neerincx, M. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.692811 · Cited by 31
“We are interested in how the behavior of the human ánd the behavior of the robot changes as a result of this process, making our study fi t within a new area of research in which both human and machine behavior are assessed [with their mutual dependencies; cf. (Rahwan et al., 2019)].”
BackgroundMethodBehavioral Science and Education for Sustainable Development: Towards Metacognitive Competency
Hanisch, S., & Eirdosh, D. (2023) · Sustainability · doi:10.3390/su15097413 · Cited by 43
“This set of four questions is still influential today in how behavioral and evolutionary scientists attempt to explore the causes of human and animal behavior (e.g., [108–110]) and even the behaviors of machine intelligences [111].”
BackgroundSong, X., Dong, M., Feng, K., Li, J., Hu, X., & Liu, T. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120473 · Cited by 27
A longitudinal approach for understanding algorithm use
Chacon, A., Kausel, E. E., & Reyes, T. H. (2022) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2275 · Cited by 32
Artificial intelligence, ethics, and intergenerational responsibility
Klockmann, V., von Schenk, A., & Villeval, M. C. (2022) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2022.09.010 · Cited by 36
The Effect of Bots on Human Interaction in Online Communities
Safadi, H., Lalor, J. P., & Berente, N. (2024) · MIS Quarterly · doi:10.25300/misq/2023/17901 · Cited by 29
“This contributes to the ongoing discourse on the unintended impacts of technology (Aanestad et al., 2021; Rahwan et al., 2019).”
Computed compatibility: examining user perceptions of AI and matchmaking algorithms
Paul, A., & Ahmed, S. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2196579 · Cited by 35
“Arti fi cial intelligence (AI) has been touted as a signi fi - cant driver of the fourth industrial revolution as it continues to transform the operations of di ff erent sectors of society (Schwab 2017; Rahwan et al. 2019).”
BackgroundTorkamaan, H., Tahaei, M., Buijsman, S., Xiao, Z., Wilkinson, D., & Knijnenburg, B. P. (2024) · Human-computer interaction series · doi:10.1007/978-3-031-55109-3_2 · Cited by 28
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.
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