Glossary of Platform Law and Policy Terms


Cite this article as:
Nicolo Zingales (17/12/2021). Nudging. In Belli, L.; Zingales, N. & Curzi, Y. (Eds.), Glossary of Platform Law and Policy Terms (online). FGV Direito Rio.

Author: Nicolo Zingales

Nudging refers to the use of choice architecture (the nudge) to influence the behavior of an individual or group of individuals (nudgees) without depriving them of the ability to choose a different course of action. The term was coined by Richard Thaler and Cass Sunstein with their book ‘Nudge: Improving Decisions About Wealth, Heath and Happiness’ (2008)1, which offered the first conceptualization of a theory of regulation based on positive reinforcement and indirect suggestions as ways to influence the behavior and decision making of groups or individuals. The book was very impactful, leading to the rise of nudging regulation and even to the creation in 2010 of a ‘nudge’ unit (also called behavioral insights team) within the UK government in order to generate and apply behavioral insights to inform policy, improve public services, and deliver positive results for people and communities.

Thaler and Sunstein propose to formulate public policies in a way that addresses the cognitive biases and helps improve decisions through what they call ‘choice architecture’, i.e., the set of constraints surrounding individuals’ choices. For instance, in one of their early papers, they map the letters of ‘NUDGES’ onto six different types of design interventions:

  1. i[N]centives: leveraging the choosers’ incentives can be a powerful mechanism to direct people. Think, for instance, about providing more salience to information that is relevant to make a decision that is otherwise underestimated, such as emphasizing the opportunity costs of buying a car.
  2. Understand mappings: this evokes a similar concept to the above but referring to specific situations where the information is complex and therefore it is helpful to provide to choosers a map of possible options (for instance, in choosing the best possible cure for a disease).
  3. Defaults: this is probably the most commonly cited type of nudging and refers to pre- selecting an option for the chooser while maintaining the possibility to reverse that choice.
  4. Give feedback: sometimes simply informing whether something is going wrong (or well) helps people redirect.
  5. Expect error: designing the choice architecture in a way that minimizes errors is also a nudge. The reason why this category is not subsumed within the notion of defaults is not apparent.
  6. Structure complex choices: sometimes choices are difficult to make if the choice set is too large. For this reason, giving choosers the ability to structure their choice process (for instance through a filtering system that helps identifying useful ranges) can be a powerful nudge.

In a separate paper, Sunstein (2015)2 lists the different tools that can be used to obtain nudging effects, which appears to be an expansion (and to some extent a correction) of the previous list:

  • Default Rules;
  • Simplification;
  • Use of social norms (e.g., illustrating examples of expected behaviour);
  • Increase in ease and convenience (e.g., making low-cost option for healthy food visible);
  • Disclosure;
  • Warnings (graphic or otherwise);
  • Reminders;
  • Precommitment strategies (by which people commit to a certain course of action);
  • Eliciting implementation intentions (e.g., “do you plan to vote?”);
  • Informing people of the nature and consequences of their own past choices (“smart disclosure”). 

Although the above is a repetition of largely rehearsed concepts, it is important to revisit these for two reasons. First, a constant theme running through these lists is the formulation of design choices to ‘de-bias’ human decision-makers. This is somewhat different from the work of the fast & frugal school of behavioral economics, which endeavored to help decision-makers by offering the best heuristics; and there is no reason in principle why heuristics could not be used in nudging to reach desired outcomes – for example, by framing options in a more visible and appealing fashion. However, the key point of criticism is that nudging tools may not always be used in a way that de-biases individuals: in fact, it can be used in a way that nurtures known biases and, on that basis, elicits choices that are not necessarily in the best interest of individuals. Second, the entire discussion by Thaler and Sunstein refers either explicitly or implicitly to nudging as a choice of public regulation, where the nudger can be trusted (or is at least assumed to be trusted) to pursue the general interest. But insofar as nudging is done by private entities that are not subject to the transparency and accountability safeguards that apply in the governmental context, a discussion about its boundaries and about ways in which compliance can be scrutinized becomes paramount.

It can also be noted that the definition provided by Thaler and Sunstein in their writings on the topic is not always consistent: in their book, they define nudging as “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentive”3. In doing so, they rule out the admissibility in this category of traditional regulatory tools such as bans, fines, taxes, or other economic incentives (or disincentives). However, the line between a nudge and some of those categories is blurred, as the alternative course of action in all those situations remains available to the nudgee (in some instances, at the cost of violating the law) and the authors explicitly admit the possibility that nudges moderately alter one’s economic incentives.

They also argue that nudges are omnipresent in society (as every design choice has potential effects on individuals’ behavior), and therefore the anti-nudging position is a “literal non-starter” – because at least deliberate nudges allow us to appreciate their rationale and operation. However, it is not clear that nudges can always be transparent and intelligible, and serendipity may be a value worth protecting- to let people determine their own path through random, or at least non-deliberate, nudges.

Finally, Thaler and Sunstein only mention examples (such as the GPS, the retirement plan, the narrowing road design) where choice architects design, construct, or organize context without changing the original choice sets or fiddling with incentives. Yet nudges will often have a substantial impact on the range of choices or the incentives of the choosers, and not apparent how the nudger can abstain from the latter scenario. It is also not clear why Thaler and Sunstein separate economic incentives from other forms of incentives, including the prospect of pain and penalties, as that would more accurately incorporate the breadth of the endeavor of behavioral economics.

There is an extensive philosophical discussion about the differences between nudging and manipulation. This discussion has been especially pronounced after the realization that the online world introduces a new type of nudge, one based on algorithmic real-time personalization and reconfiguration of choice architectures based on large aggregates of personal data: the so-called “hyper-nudging” (Yeung, 2016)4. In this context, where the transparency of nudges is hindered by the personalization of the nudges, the accountability of manipulative nudges increases.

There is a range of definitions that can be used to identify manipulation, for instance:

  • an intentional act that successfully influences a person to belief or behavior by causing changes in the mental processes other than those involved in understanding (Faden; Beauchamp, 2017)5;
  • a kind of influence that bypasses or subverts the target’s rational capabilities, in a way that treats its objects as “tools and fools” (Wilkinson, 2014)6;
  • directly influencing someone’s beliefs, desires or emotions, such that she falls short of ideals for belief, desire or emotion in ways typically not in her self-interest or likely not in her self-interest in the present context (Barnhill, 2014)7;
  • a statement or action that does not sufficiently engage or appeal to people’s capacity for reflective and deliberative choice (Sunstein, 2015)8;
  • non-rational influence (Noggle, 1996)9;
  • pressure, but not irresistible pressure amounting to coercion (Raz, 198510; Noggle, 199611);
  • trickery to induce behavior (Noggle, 1996)12;
  • hidden influence: intentionally and covertly influencing decision-making, by targeting and exploiting one’s decision-making vulnerabilities (Susser et al., 2019)13;

We can imagine clear cases of manipulation (subliminal advertising), cases that clearly fall outside of the category (for example, a warning about deer crossings in a remote area), and cases that can be taken as borderline (a vivid presentation about the advantages of a particular mortgage, or a redesign of a website to attract customers to the most expensive products).

In order to distinguish nudging from manipulation, various authors propose criteria to set limits on the acceptability of nudges. For instance, Sunstein requires them to be de-biasing (market failure correcting), educative and non-exploitative (Sunstein, 2015)14.

Thaler, in turn (2015)15, uses the following criteria to distinguish acceptable nudging (or ‘nudging for good’):

  • First, the nudge is transparent and not misleading.
  • Second, it is as easy as possible to opt out.
  • Third, they must increase welfare.

Baldwin (2014)16 focuses on the proportionality of the nudge to scale of the problem, considering evidence of effectiveness and the moral considerations at stake. He then distinguishes between simple nudges, that only engage system 1 thinking (1st-degree nudge), more intrusive nudges that exploit behavioral or volitional limitations so as to bias a decision in the desired direction (2nd-degree nudge), and a yet more intrusive nudge (3rd degree) where there is no ability to appreciate the influence.

He concludes that nudges will have different effects depending on who the targets are, where the following characteristics are relevant: whether the individual´s objective aligns with that of the nudger, and whether their capacity to absorb the nudging information is high or low.


  1. Sunstein, Cass R.; Thaler, Richard H. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.
  2. Sunstein, C. R. (2015). The Ethics of Nudging, 32, Yale J. on Reg., 413, 414. Available at:
  3. Sunstein, Cass R.; Thaler, Richard H. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.
  4. Yeung, K. (2016). “‘Hypernudge’: Big Data as a Nodue if Regulation by Design’ ” TLI Think! Paper 28/2016.
  5. Faden, R., Beauchamp. (2017). A History of Informed Consent (OUP, 1986). In: Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118-136.
  6. Wilkinson, T. M. (2013). Nudging and manipulation. Political Studies, 61(2), 341-355.
  7. Barnhill, A. What is Manipulation? (2014). In: Coons, C., Weber, M. (2014) Manipulation: theory and practice.Oxford University Press.
  8. Sunstein, C. R. (2015). The Ethics of Nudging, 32, Yale J. on Reg., 413, 414. Available at:
  9. Noggle, R. (1996). Manipulative actions: A conceptual and moral analysis. American Philosophical Quarterly, 33(1), 43-55.
  10. Raz, J. The Morality of Freedom. Oxford: Oxford University Press 1986. Canadian Journal of Philosophy, 19(3), 477-490.
  11. Noggle, R. (1996). Manipulative actions: A conceptual and moral analysis. American Philosophical Quarterly, 33(1), 43-55.
  12. Noggle, R. (1996). Manipulative actions: A conceptual and moral analysis. American Philosophical Quarterly, 33(1), 43-55.
  13. Susser, D., Roessler, B., & Nissenbaum, H. (2019). Online manipulation: Hidden influences in a digital world. Geo. L. Tech. Rev.4, 1.
  14. Sunstein, C. R. (2015). The Ethics of Nudging, 32, Yale J. on Reg., 413, 414. Available at:
  15. Thaler, R. H. (2015). The power of nudges, for good and bad. The New York Times, 31, 2015. Available at
  16. Baldwin, R. (2014). From regulation to behaviour change: giving nudge the third degree. The Modern Law Review, 77(6), 831-857.
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By Nicolo Zingales

Nicolo Zingales is Professor of Information Law and Regulation at the law school of the Fundação Getulio Vargas in Rio de Janeiro, and coordinator of its E-commerce research group. He is also an affiliated researcher at the Stanford Center for Internet and Society, the Tilburg Law & Economics Center and the Tilburg Institute for Law and Technology, co-founder and co-chair of the Internet Governance Forum’s Dynamic Coalition on Platform Responsibility.


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