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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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Chai, Y., Liang, R., Samtani, S., Zhu, H., Wang, M., Liu, Y., & Jiang, Y. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3270581 · Cited by 23
“DL models are often criticized for being black boxes, where the results of the model lack interpretability [31], [32].”
BackgroundAI, Behavioural Science, and Consumer Welfare
Mills, S., Costa, S., & Sunstein, C. R. (2023) · Journal of Consumer Policy · doi:10.1007/s10603-023-09547-6 · Cited by 35
Hybrid Intelligence Systems Combining Human Expertise and AI/RPA for Complex Problem Solving
Venigandla, K., Vemuri, N., & Vemuri, N. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24mar2039 · Cited by 20
“Human experts provide contextual understanding, ethical reasoning, and subjective judgment, while AI/RPA systems offer data-driven insights, pattern recognition, and computational power (Rahwan et al., 2019) [5].”
BackgroundArtificial intelligence and unemployment dynamics: an econometric analysis in high-income economies
Masoud, N. (2024) · Technological Sustainability · doi:10.1108/techs-04-2024-0033 · Cited by 26
Emergence of cooperation in the one-shot Prisoner’s dilemma through Discriminatory and Samaritan AIs
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“The emergent consequences of these hybrid human– AI interaction dynamics remain largely obscure and under-researched [9,10,21–26].”
Robotics and Algorithmic Trading: A New Era in Stock Market Trend Analysis
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“ML algorithms may enhance trading tactics by evaluating large datasets and finding patterns that older approaches may miss (Rahwan et al., 2019).”
BackgroundDigitalität, Mediatisierung und Bildung – Megatrends aus medienpädagogischer Perspektive
Herzig, B. (2023) · Springer eBooks · doi:10.1007/978-3-658-30766-0_5 · Cited by 19
Collective cooperative intelligence
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Company Analysis of OpenAI with Special Emphasis on its Future Strategies
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Lui, T. K.-L., Wong, K. K., Mak, L. L., To, E. W.-P., Tsui, V. W. M., Deng, Z., Guo, J., Li, N., Cheung, M. K. S., & Leung, W. K. (2020) · Endoscopy International Open · doi:10.1055/a-1036-6114 · Cited by 19
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Claure, H., Kim, S., Kizilcec, R. F., & Jung, M. F. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107628 · Cited by 25
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An ecological approach to structural flexibility in online communication systems
Palazzi, M. J., Solé‐Ribalta, A., Calleja-Solanas, V., Meloni, S., Plata, C. A., Suweis, S. S., & Borge‐Holthoefer, J. (2021) · Nature Communications · doi:10.1038/s41467-021-22184-2 · Cited by 23
A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?
Abubshait, A., Beatty, P. J., McDonald, C. G., Hassall, C. D., Krigolson, O. E., & Wiese, E. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00895-9 · Cited by 21
“This question is of particular importance given that robots are already used (and will be even more so in the future; Rahwan et al., 2019) in educational and therapeutic settings where a reduced response to feedback could have measurable negative effects on learning outcomes.”
MethodKey points for an ethnography of AI: an approach towards crucial data
van Voorst, R., & Ahlin, T. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02854-4 · Cited by 18
“However, scholars from various disciplines recognizing the limitations of this approach (Rahwan et al, 2019; Adadi and Berrada, 2018; Afnan et al., 2021; Bathaee, 2017; Carabantes, 2020).”
AI as a Team Member: Redefining Collaboration
Tummala, V., Burris-Melville, T. S., & Eskridge, T. C. (2025) · Journal of Leadership Studies · doi:10.1002/jls.70003 · Cited by 25
Humanizing AI for trust: the critical role of social presence in adoption
Ngo, V. M. (2025) · AI & Society · doi:10.1007/s00146-025-02506-4 · Cited by 24
Learn to Machine Learn via Games in the Classroom
Zammit, M., Voulgari, I., Liapis, A., & Yannakakis, G. N. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.913530 · Cited by 19
“Through the use of the world-wide web and access to applications such as social media, games, Google search, YouTube, and the Internet of Things, digital technologies are mediating children’s learning, entertainment and social interactions, and are shaping their everyday lives (Rahwan et al., 2019).”
BackgroundEthics in Information Systems and Design Science Research: Five Perspectives
Herwix, A., Haj-Bolouri, A., Rossi, M., Tremblay, M. C., Purao, S., & Gregor, S. D. (2022) · Communications of the Association for Information Systems · doi:10.17705/1cais.05028 · Cited by 18
Employee frustration with information systems: appraisals and resources
González‐Gómez, H. V., & Hudson, S. A. (2023) · European Management Journal · doi:10.1016/j.emj.2023.03.004 · Cited by 20
Beyond RMSE: Do Machine-Learned Models of Road User Interaction Produce Human-Like Behavior?
Srinivasan, A. R., Lin, Y.-S., Antonello, M., Knittel, A., Hasan, M., Hawasly, M., Redford, J., Ramamoorthy, S., Leonetti, M., Billington, J., et al. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3263358 · Cited by 22
“The importance of understanding the behaviors of the artificial intelligence (AI) including machine learning (ML) algorithms since they are becoming ubiquitous was argued in [23].”
BackgroundSocially intelligent machines that learn from humans and help humans learn
Gweon, H., Fan, J. E., & Kim, B. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0048 · Cited by 24
“To achieve this, we need to go beyond the engineering-centric approach, towards a science-centric approach [145–147], and study machines as targets of scientific study.”
BackgroundMethodChallenges in Understanding Human-Algorithm Entanglement During Online Information Consumption
Lewandowsky, S., Robertson, R. E., & DiResta, R. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231180809 · Cited by 18
“…their central role in directing people to online information and in contributing to the construction of the substrate, relatively little is known about the algorithms used by major platforms and their psychological and societal impacts (Jarsulic, 2022; Rahwan et al., 2019; Wagner et al., 2021).”
In bot we trust? Personality traits and reciprocity in human-bot trust games
Upadhyaya, N., & Galizzi, M. M. (2023) · Frontiers in Behavioral Economics · doi:10.3389/frbhe.2023.1164259 · Cited by 22
Emotion-driven reasoning model for agent-based human–computer negotiation
Wu, J., Zhang, T., Li, Y., & Zhou, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122448 · Cited by 18
Learning from artificial intelligence researchers about international business implications
Ratten, V., Hasan, R., Kumar, D., Bustard, J., Ojala, A., & Salamzadeh, Y. (2024) · Thunderbird International Business Review · doi:10.1002/tie.22374 · Cited by 19
“Therefore, we maintain that it enables entrepreneurs to modify the structure of AI systems and their original functions (Ojala et al., 2023; Rahwan et al., 2019).”
BackgroundImpact of Artificial Intelligence on E-Commerce Development
Areiqat, A. Y., Hamdan, A., Alheet, A. F., & Alareeni, B. A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-69221-6_43 · Cited by 22
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The Autonomous Choice Architect
Mills, S., & Sætra, H. S. (2021) · AI & Society · doi:10.1007/s00146-022-01486-z · Cited by 26
“As machines become more complex, at some point complexity surpasses a human’s ability to understand the exact workings of the machine (Rahwan et al. 2019; Pedersen and Johansen 2020).”
BackgroundDynamics of cooperative excavation in ant and robot collectives
Prasath, S. G., Mandal, S., Giardina, F., Kennedy, J., Murthy, V. N., & Mahadevan, L. (2022) · eLife · doi:10.7554/elife.79638 · Cited by 19
“And finally, given the ability to engineer min-57 imally responsive biomimetic agents such as robots (Rahwan et al., 2019), a question that suggests58 itself is that of the synthesis of effective behaviors using these agents.”
BackgroundAI as Social Actor: A Lacanian Investigation into Social Technology
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“With psychoanalysis as our guide, we can approach not only the behavior of non-human social actors (Rahwan et al., 2019) but more importantly highlight a distinct techno-social interaction within the implicit organization of social realities.”
Human and machine drivers: Sharing control, sharing responsibility
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An interdisciplinary perspective on AI-supported decision making in medicine
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“Team pattern design solutions should be (1) simple enough to provide an intuitive way to facilitate discussions about human-machine individual AI systems to whole human-AI societies can be regarded as one of the great research challenges for the coming decades [17].”
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“Attempts to audit decision-making systems can also advance scientific understanding of society, algorithms, and how they interact in the field [6, 38, 57].”
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“With the emergence of new forms of accessing information online, concerns have emerged about the impact that online platforms have on society, because they guide individuals to algorithmically chosen content (Rahwan et al., 2019; Wagner et al., 2021). previous beliefs.”
The scientific events that shaped the decade
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Could humans and AI become a new evolutionary individual?
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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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