کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند

Abson, D. J., Fischer, J., Leventon, J., Newig, J., Schomerus, T., Vilsmaier, U., …, & Lang, D. J. (2017). Leverage points for sustainable transformation. Ambio, 46, 30–39. https://doi.org/10.1007/s13280-016-0800-y

Adomavicius, G. & Yang, M. (2022). Integrating behavioral, economic, and technical insights to understand and address algorithmic bias: A human-centric perspective. ACM Transactions on Management Information Systems, 13(3), 1-27.

Aggarwal, C. C. (2016). Recommender systems. Cham: Springer International Publishing.

Ahuja, A. (2023, March 1). Generative AI is sowing the seeds of doubt in serious science. The Financial Times. https://www.ft.com/content/e34c24f6-1159-4b88-8d92-a4bda685a73c

Alasubramanian, G. (2020). When artificial intelligence meets behavioural economics. NHRD Network Journal, 14(2), 216-277. https://doi.org/10.1177/2631454120974810

Ali, S., Abuhmed, T., El-Sappagh, S., Khan Muhammad, J. M., Alonso-Moral, R., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N. & Herrera, F. (2023). Explainable artificial intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence. Information Fusion, 99, 101805. https://doi.org/10.1016/j.inffus.2023. 101805

Aonghusa, P. M. & Michie, S. (2020). Artificial intelligence and behavioral science through the looking glass: Challenges for real-world application. Annals of Behavioural Medicine, 54, 942–947. https:// doi.org/10.1093/abm/kaaa095

Balasubramanian, G. (2021). When artificial intelligence meets behavioural economics. NHRD Network Journal, 14(2), 216-277.

Bar-Gill, O., Sunstein, C. R. & Talgam-Cohen, I. (2023). Algorithmic harm in consumer markets. SSRN at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4321763. Accessed 23 June 2023.

Bechtel, W., Abrahamsen, A. & Graham, G. (2017). The life of cognitive science. In The Blackwell Companion to Cognitive Science. https://doi.org/10.1002/9781405164535. part1.

Beer, S. (1993). Designing freedom. Anansi: Canada.

Beshears, J. & Kosowsky, H. (2020). Nudging: Progress to date and future directions. Organizational Behavior and Human Decision Processes, 161(Suppl.), 3–19. https://doi.org/10.1016/j.obhdp.2020.09.001

Bommasani, R., Creel, K. A., Kumar, A., Jurafsky, D. & Liang, P. (2022). Picking on the same person: Does algorithmic monoculture lead to outcome homogenization? Advances in Neural Information Processing Systems, 35, 3663-3678. https://arxiv.org/abs/2211.13972

Brooks, R., Nguyen, D., Bhatti, A., Allender, S., Johnstone, M., Lim, C. P. & Backholer, K. (2022). Use of artificial intelligence to enable dark nudges by transnational food and beverage companies: Analysis of company documents. Public Health Nutrition, 25(5), 1–23. https://doi.org/10.1017/S1368980022000490

Bryan, C. J., Tipton, E., & Yeager, D. S. (2021). Behavioural science is unlikely to change the world without a heterogeneity revolution. Nature human behaviour, 5(8), 980-989.

Burr, C., Cristianini, N. & Ladyman, J. (2018). An analysis of the interaction between intelligent software agents and human users. Minds and Machines, 28(4), 735–774. https://doi.org/10.1007/s11023-018-9479-0

Butenko, A. & Larouche, P. (2017). Regulation for innovativeness or regulation of innovation? TILEC Discussion Paper No. 2015-007. https://ssrn.com/abstract=2584863

Buyalskaya, A., Ho, H., Milkman, K. L., Li, X., Duckworth, A. L., & Camerer, C. (2023). What can machine learning teach us about habit formation? Evidence from exercise and hygiene. Proceedings of the National Academy of Sciences, 120(17), e2216115120.

Chater, N. & Loewenstein, G. (2022). The i-frame and the s-frame: How focusing on individual-level solutions has led behavioural public policy astray. Behavioral and Brain Sciences, 46, e147. https://doi.org/10.1017/S0140525X22002023

De Marcellis-Warn, N., Marty, F., Thelisson, E. & Warin, T. (2022). Artificial intelligence and consumer manipulations: From consumer’s counter algorithms to firm’s self-regulation tools. AI and Ethics, 2, 239–268. https://doi.org/10.1007/s43681-022-00149-5

DellaVigna, S. & Linos, E. (2022). RCTs to scale: Comprehensive evidence from two nudge units. Econo- metrica, 90(1), 81–116. https://doi.org/10.3982/ECTA18709

Duckworth, A. L. & Milkman, K. L. (2022). A guide to megastudies. PNAS Nexus, 1(5), 1–5. https://doi. org/10.1093/pnasnexus/pgac214

Forrester, J. W. (1971). Counterintuitive behavior of social systems. Technological Forecasting and Social Change, 3, 109–140. https://doi.org/10.1016/S0040-1625(71)80001-X

Hacker, P. (2021). Manipulation by algorithmics: Exploring the triangle of unfair commercial practice, data protection, and privacy law. European Law Journal, 1–34. Advanced online publication. https://doi.org/10.1111/eulj.12389

Hagendorff, T. (2022). A virtue-based framework to support putting AI ethics into practice. Philosophy & Technology, 35(3), 55.Hagman, W., Andersson, D., Västfjäll, D. & Tinghög, G. (2015). Public views on policies involving nudges. Review of Philosophy and Psychology, 6(3), 439-453. https://doi.org/10.1007/s13164-015-0263-2

Hallsworth, M. (2023). A manifesto for applying behavioural science. Nature Human Behaviour, 7, 310–323. https://doi.org/10.1038/s41562-023-01555-3

Halpern, D. (2015). ’Inside the Nudge Unit’ W. H. Allen.

Hansen, P.G., Jespersen, A.M. (2013). Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy. European Journal of Risk Regulation, 31 (4), 1-28, 10.1017/S1867299X00002762

Jesse, N. (2018). Internet of things and big data: The disruption of the value chain and the rise of new software ecosystems. AI & Society, 33(2), 229-239. https://doi.org/10.1007/s00146-018-0807-y

Johnson, E. J., Shu, S. B., Dellaert, B. G., Fox, C., Goldstein, D. G., ... & Weber, E. U. (2012). Beyond nudges: Tools of a choice architecture. Marketing letters, 23(2), 487-504.

Kahneman, D. & Tversky, A. (1979). ‘Prospect Theory: An Analysis of Decision Under Risk’. Econometrica, 47(2), 263–291.

Kahneman, D. (2011). Thinking, fast and slow. Penguin Books: UK.

Karlsen, R. & Andersen, A. (2019). Recommendations with a nudge. Technologies, 7(2), 45. https://doi.org/10.3390/technologies7020045

Kleinberg, J., Ludwig, J., Mullainathan, S. & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5), 491–495. https://doi.org/10.1257/aer.p20151023

Komaki, A., Kodaka, A., Nakamura, E., Ohno, Y. & Kohtake, N. (2021). System design canvas for identify- ing leverage points in complex systems: A case study of the agricultural system models, Cambodia. Proceedings of the Design Society, 1, 2901–2910. https://doi.org/10.1017/pds.2021.551

Leventon, J., Abson, D. J. & Lang, D. J. (2021). Leverage points for sustainability transformations: Nine guiding questions for sustainability science and practice. Sustainability Science, 16, 721–726. https:// doi.org/10.1007/s11625-021-00961-8

Ludwig, J. & Mullainathan, S. (2021). Fragile algorithms and fallible decision-makers: Lessons from the justice system. Journal of Economic Perspectives, 35(4), 71–96. https://doi.org/10.1257/jep.35.4.71

Mac Aonghusa, P. & Michie, S. (2020). Artificial intelligence and behavioral science through the looking glass: Challenges for real-world application. Annals of Behavioral Medicine, 54(12), 942–947. https://doi.org/10.1093/abm/kaaa095

Maier, M., Bartoš, F., Stanley, T. D. & Wagenmakers, E. (2022). No evidence for nudging after adjusting for publication bias. Proceedings of the National Academy of Science, 119(31), 2200300119. https:// doi.org/10.1073/pnas.2200300119

Matz, S., Kosinski, M., Nave, G. & Stillwell, D. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714–12719. https://doi.org/10.1073/pnas.1710966114

McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence [Online] [Date accessed: 23/03/2021]: http://www-formal.stanford.edu/jmc/history/dartmouth/ dartmouth.html

Meadows, D. (1997). Leverage points: Places to intervene in a system. Whole Earth, 91(1), 78–84.

Meadows, D. (2001). Dancing with systems. Whole Earth, 106(3), 58–63.

Mele, C., Spena, T. R., Kaartemo, V. & Marzullo, M. L. (2021). Smart nudging: How cognitive technologies enable choice architectures for value co-creation. Journal of Business Research, 129, 949–960. https://doi.org/10.1016/j.jbusres.2020.09.004

Michalek, G., Meran, G., Schwarze, R. & Yildiz, Ö. (2016). Nudging as a new ‘soft’ tool in environmental policy – An analysis based on insights from cognitive and social psychology. Zeitschrift für Umweltpolitik & Umweltrecht, 39, 169–207.

Michie, S., Thomas, J., Johnston, M., Aonghusa, P. M., Shawe-Taylor, J…, & West, R. (2017). The Human Behaviour-Change Project: Harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation. Implementation Science, 12(121). https://doi.org/10.1186/ s13012-017-0641-5

Mills, S. & Sætra, H. S. (2022). The autonomous choice architect. AI and Society, 1–13. https://doi.org/10.1007/s00146-022-01352-0

Mills, S. (2022, August 2). Autonomous nudges and AI choice architects – Where does responsibility lie in computer mediated decision making? Impact of Social Sciences Blog.

Mills, S. (2023). AI for behavioural science. London: Taylor & Francis Group.

Mills, S., Costa, S. & Sunstein, C. R. (2023). AI, behavioural science, and consumer welfare. Journal of Consumer Policy, 46(3), 387–400. https://doi.org/10.1007/s10603-023-09526-0

Mills, S., Costa, S., & Sunstein, C. R. (2023). AI, behavioural science, and consumer welfare. Journal of Consumer Policy, 46(3), 387-400.

Mont, O., Neuvonen, A. & Lahteenoja, S. (2014). Sustainable lifestyles 2050: Stakeholder visions, emerging practices and future research. Journal of Cleaner Production, 63(2), 24–32. https://doi.org/10.1016/j.jclepro.2013.09.007.

Newland, C., & Argyriades, D. (2019). Reclaiming Public Space: Drawing Lessons from the Past as We Confront the Future: Sustainable Development Goal 16. In Public service excellence in the 21st century (pp. 1-30). Singapore: Springer Singapore.

Ng, C. F. (2016). Behavioral mapping and tracking. In R. Gifford (Ed.), Research methods for environmental psychology (pp. xx–xx). https://doi.org/10.1002/9781119162124.ch3

Okeke, F., Sobolev, M., Dell, N., and Estrin. D. (2018). Good vibrations: can a digital nudge reduce digital overload? In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '18). Association for Computing Machinery, New York, NY, USA, Article 4, 1–12. https://doi.org/10.1145/3229434.3229463

Park, J. S., O’Brien, J. C., Cai, C. J., Morris, M. R., Liang, P. & Bernstein, M. S. (2023). Genera- tive agents: Interactive simulacra of human behavior. ArXiv at https://arxiv.org/pdf/2304.03442. pdf. Accessed 20 Apr 2023.

Peterson, J. C., Bourgin, D. D., Agrawal, M., Reichman, D. & Griffiths, T. L. (2021). Using large-scale experiments and machine learning to discover theories of human decision-making. Science, 372, 1209–1214. https://doi.org/10.1126/science.abe2629

Rauthmann, J. F. (2020). A (more) behavioural science of personality in the age of multi-modal sensing, big data, machine learning, and artificial intelligence. European Journal of Personality, 34, 593–598.

Robila, M. & Robila, S. (2020). Applications of artificial intelligence methodologies to behavioral and social sciences. Journal of Child and Family Studies, 29, 1–13. https://doi.org/10.1007/s10826-019-01689-x

Ruggeri, K., Benzerga, A., Verra, S., Folke, T. (2020). A behavioral approach to personalizing public health. Behav Public Policy. https://doi. org/10.1017/bpp.2020.31

Sætra, H. S. (2020). Privacy as an aggregate public good. Technology in Society, 63, 101422. https://doi. org/10.1016/j.techsoc.2020.101422

Sætra, H. S. (2022). AI for the sustainable development goals. CRC Press.

Saheb, T. (2022). Ethically contentious aspects of artificial intelligence surveillance: A social science perspective. Advance online publication. https://doi.org/10.1007/s43681-022-00196-y

Saura, J. R., Ribeiro-Soriano, D. & Zegarra Saldaña, P. (2022). Exploring the challenges of remote work on Twitter users' sentiments: From digital technology development to a post-pandemic era. Journal of Business Research, 142, 242–254.

Schmauder, C., Karpus, J., Moll, M., Bahrami, B., & Deroy, O. (2023). Algorithmic nudging: The need for an interdisciplinary oversight. Topoi, 42(3), 799-807. https://doi.org/10.1007/s11245-023-09907-4

Schmidt, R., & Stenger, K. (2021). (Dis) Embodied Rationality and ‘Choice Posture’: Addressing Behavioral Science's Mind-Body Problem. Available at SSRN 4185086.

Sharbek, N. (2022, August). How traditional financial institutions have adapted to artificial intelligence, machine learning and FinTech? In Proceedings of the International Conference on Business Excellence, 16(1), 837-848.

Shin, D. & Ahmad, N. (2023). Algorithmic nudge: An approach to designing human-centered generative artificial intelligence. ZU Scholars All Works, 5980. https://zuscholars.zu.ac.ae/works/5980

Simon, H. A. (1981). The sciences of the artificial (2nd ed.). MIT Press.

Smith, J. & de Villiers-Botha, T. (2021). Hey, Google, leave those kids alone: Against hypernudging children in the age of big data. AI and Society, 38(4), 1639-1649.

Stone, P. (2016). Artificial intelligence and life in 2030 (Report No. 52). Stanford University.

Sunstein, C. R. (2015). The ethics of influence. Cambridge University Press: USA.

Szaszi, B., Higney, A., Charlton, A., Gelman, A., Ziano, I., Aczél, B., Goldstein, D. G., Yeager, D. S. & Tipton, E. (2022). No reason to expect large and consistent effects of nudge interventions. Proceedings of the National Academy of Sciences, 119(31), e2200732119. https://doi.org/10.1073/pnas.2200732119

Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Penguin Books.

Vanderelst, D. & Winfield, A. (2018). An architecture for ethical robots inspired by the simulation theory of cognition. Cognitive Systems Research, 48, 56–66. https://doi.org/10.1016/j.cogsys.2017.04.002

Vuong, Q. H., Ho, T. M., Nguyen, H. K. & Vuong, T. T. (2018). Healthcare consumers’ sensitivity to costs: A reflection on behavioural economics from an emerging market. Palgrave Communications, 4(1), 1–10. https://doi.org/10.1057/s41599-018-0127-3.

Weinmann, M., Schneider, C. & vom Brocke, J. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433–436. https://doi.org/10.2139/ ssrn.2708250.

West, R., Michie, S., Chadwick, P., Atkins, L. & Lorencatto, F. (2020). Achieving behaviour change: A guide for national government. Public Health England. https://assets.publishing.service.gov.uk/gover nment/uploads/system/uploads/attachment_data/file/933328/UFG_National_Guide_v04.00 1 1_.pdf. Accessed 24 Apr 2023.

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179

Yeung, K. (2017). Hypernudge: Big data as a mode of regulation by design. Information Communication and Society, 1, 118–136. https://doi.org/10.1080/1369118X.2016.1186713