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Personalized nudging

Published online by Cambridge University Press:  13 April 2020

STUART MILLS Open the ORCID record for STUART MILLS [Opens in a new window]
STUART MILLS*
Affiliation:
Future Economies Research Centre, Manchester Metropolitan University, Manchester, UK
*
*Correspondence to: Stuart Mills, PhD Researcher in Behavioural Economics, Future Economies Research Centre, Manchester Metropolitan University, Manchester, UK. E-mail: s.mills@mmu.ac.uk
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Abstract

A criticism of behavioural nudges is that they lack precision, sometimes nudging people who – had their personal circumstances been known – would have benefitted from being nudged differently. This problem may be solved through a programme of personalized nudging. This paper proposes a two-component framework for personalization that suggests choice architects can personalize both the choices being nudged towards (choice personalization) and the method of nudging itself (delivery personalization). To do so, choice architects will require access to heterogeneous data. This paper argues that such data need not take the form of big data, but agrees with previous authors that the opportunities to personalize nudges increase as data become more accessible. Finally, this paper considers two challenges that a personalized nudging programme must consider, namely the risk personalization poses to the universality of laws, regulation and social experiences, and the data access challenges policy-makers may encounter.

Type
New Voices
Information
Behavioural Public Policy , Volume 6 , Issue 1 , January 2022 , pp. 150 - 159
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Beer, D. (2017), ‘The Social Power of Algorithms’, Information, Communication and Society, 20(1): 1–13.CrossRefGoogle Scholar
Beshears, J., Choi, J., Laibson, D., Madrian, B. and Wang, S. (2015), ‘Who is Easier to Nudge?’, NBER Working Paper, [Online] [Date Accessed: 11/01/2020]: https://www.nber.org/programs/ag/rrc/rrc2015/papers/7.3%20-%20Beshears,%20Choi,%20Laibson,%20Madrian,%20Wang.pdf.Google Scholar
Beshears, J., Milkman, K., Dai, H. and Benartzi, S. (2016), ‘Framing the Future: The Risks of Pre-Commitment Nudges and Potential of Fresh-start Messaging’, Working Paper, [Online] [Date Accessed: 09/04/2019]: https://static1.squarespace.com/static/5353b838e4b0e68461b517cf/t/583ca5acd2b8571174b28e40/1480369581625/48-Beshears_et_al_2016.pdf.Google Scholar
Carroll, G., Choi, J., Laibson, D., Madrian, B., Metrick, A. (2009), ‘Optimal Defaults and Active Decisions’, Quarterly Journal of Economics, 124(4): 1639–1674.CrossRefGoogle ScholarPubMed
Data Commons Project (2017), ‘Data Commons Blueprint: A high-trust, lower-cost alternative to enabling data integration and reuse’, [Online] [Date accessed: 24/11/2019]: https://static1.squarespace.com/static/57ad3087579fb3721152748f/t/58aea6b217bffc1271903b2e/1487840951842/Data+Commons+Blueprint+V1.3.pdf.Google Scholar
Hall, W. and Pesenti, J. (2017), ‘Growing the Artificial Intelligence Industry in the UK’, UK Government. [Online] [Date accessed: 13/08/2019]: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/652097/Growing_the_artificial_intelligence_industry_in_the_UK.pdf.Google Scholar
Halpern, D. (2015), Inside the Nudge Unit, WH Allen: London.Google Scholar
Hardinges, J., Wells, P., Blandford, A., Tennison, J. and Scott, A., (2019), ‘Data trusts: lessons from three pilots’, Open Data Institute. [Online] [Date accessed: 11/08/2019]: https://theodi.org/article/odi-data-trusts-report.Google Scholar
Lawrence, M. and Laybourn-Langton, L. (2018), ‘The Digital Commonwealth: From private enclosure to collective benefit’, IPPR Commission on Economic Justice. [Online] [Date accessed: 14/08/2019]: https://www.ippr.org/files/2018-08/cej-platforms-sept18.pdf.Google Scholar
Luckerson, V. (2015), ‘Here's How Facebook's News Feed Actually Works’, Time Magazine. [Online] [Date accesed: 12/07/2019]: https://time.com/collection-post/3950525/facebook-news-feed-algorithm/.Google Scholar
Lundy-Bryan, L. (2018), ‘A Real Use Case for Blockchains: A Global Data Commons’, Outlier Ventures. [Online] [Date accessed: 14/08/2019]: https://outlierventures.io/research/a-real-use-case-for-blockchains-a-global-data-commons/.Google Scholar
O'Hara, K. (2019), ‘Data Trusts: Ethics, Architecture and Governance for Trustworthy Data Stewardship’, WSI White Paper #1. [Online] [Date accessed: 11/08/2019]: https://eprints.soton.ac.uk/428276/1/WSI_White_Paper_1.pdf.Google Scholar
O'Shea, L. (2019), Future Histories, London: Verso Books.Google Scholar
Peer, E., Egelman, S., Harbach, M., Malkin, N, Mathur, A. and Frik, A. (2019), ‘Nudge me right: Personalizing online nudges to people's decision-making styles’, SSRN. [Online] [Date accessed: 10/07/2019]: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3324907.CrossRefGoogle Scholar
Porat, A. and Strahilevitz, L. J. (2014), ‘Personalizing Default Rules and Disclosure with Big Data’, Michigan Law Review, 112(8): 1417–1478.Google Scholar
Rawls, J. (1971), A Theory of Justice, Oxford: Oxford University Press.Google Scholar
Sanders, M, Snijders, V. and Hallsworth, M. (2018), ‘Behavioural science and policy: where are we now and where are we going?’, Behavioural Public Policy, 2(2):144–167.CrossRefGoogle Scholar
Schöning, C., Matt, C. and Hess, T. (2019), ‘Personalised Nudging for more Data Disclosure? On the Adaption of Data Usage Policies Format to Cognitive Styles’, Proceedings of the 52nd Hawaii International Conference on System Sciences, 4395–4404.CrossRefGoogle Scholar
Service, O. (2015), ‘Automatic Enrolment and Pensions: a behavioural success story’, BIT. [Online] [Date accessed: 09/01/2019]: https://www.behaviouralinsights.co.uk/trial-results/automatic-enrolment-and-pensions-a-behavioural-success-story/.Google Scholar
Sunstein, C. (2012), ‘Impersonal Default Rules vs. Active Choices vs. Personalized Default Rules: A Triptych’, SSRN. [Online] [Date accessed: 10/07/2019]: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2171343.Google Scholar
Sunstein, C. (2013), ‘Behavioral Economics and Paternalism’, Yale Law Journal, 122(7): 1867–1899.Google Scholar
Thaler, R. and Benartzi, S. (2004), ‘Save More Tomorrow: Using Behavioural Economics to Increase Employee Saving’, Journal of Political Economy, 112(1): 164–187.CrossRefGoogle Scholar
Thaler, R. and Sunstein, C. (2008), Nudge, London: Penguin Books.Google Scholar
Thaler, R. and Tucker, W. (2013),‘Smarter Information, Smarter Consumers’, Harvard Business Review, 91(1–2): 44–54.Google Scholar
Thunström, L., Gilbert, B. and Jones, C. (2018), ‘Nudges that hurt those already hurting – distributional and unintended effects of salience nudges’, Journal of Economic Behaviour and Organisation, 153(1): 267–282.CrossRefGoogle Scholar
Verbeek, P. (2006), ‘Materialising Morality: Design Ethics and Technological Mediation’, Science Technology & Human Values, 31(3): 361–380.CrossRefGoogle Scholar
Yeung, K. (2017), ‘Hypernudge: Big Data as a Mode of Regulation by Design’, Information Communication and Society, 20(1): 118–136.CrossRefGoogle Scholar
Young, M., Rodriguez, L., Keller, E., Sun, F., Sa, B., Whittington, J. and Howe, B. (2019), ‘Beyond Open vs. Closed: Balancing Individual Privacy and Public Accountability in Data Sharing’, FAT’19 Conference Paper. [Online] [Date accessed: 14/08/2019]: https://faculty.washington.edu/billhowe/publications/pdfs/young_open_v_closed_semi_synthetic_data.pdfCrossRefGoogle Scholar