Choice architecture

Choice architecture is a term that is used to describe the different ways in which choices can be presented to consumers, and the impact of that presentation on consumer decision-making. For example, the number of choices presented,[1] the manner in which attributes are described,[2] and the presence of a “default”[3][4] can all influence consumer choice. As a result, advocates of libertarian paternalism and asymmetric paternalism have endorsed the deliberate design of choice architecture to nudge consumers toward personally and socially desirable behaviors like saving for retirement, choosing healthier foods, or registering as an organ donor. These interventions are often justified by the fact that well-designed choice architectures can compensate for irrational decision-making biases to improve consumer welfare.[5] These techniques have consequently become popular among policymakers, leading to the creation of the UK's Behavioural Insights Team and White House "Nudge Unit" for example.[6] While many behavioral scientists stress that there is no neutral choice architecture and that consumers maintain autonomy and freedom of choice despite manipulations of choice architecture,[7] critics of libertarian paternalism often argue that choice architectures designed to overcome irrational decision biases may impose costs on rational agents, for example by limiting choice.[8]

Background

The choice architecture term was originally coined by Richard Thaler and Cass Sunstein in their 2008 book Nudge: Improving Decisions about Health, Wealth, and Happiness.[9] Thaler and Sunstein have endorsed thoughtful design of choice architecture as a means to improve consumer decision-making by minimizing biases and errors that arise as the result of bounded rationality. This approach is an example of “libertarian paternalism”, a philosophy endorsed by Thaler and Sunstein that aims to “nudge” individuals toward choices that are in their best interest without limiting choice. Libertarian paternalism may also be described as soft paternalism.

Behavioral scientists have grouped the elements of choice architecture in different ways. For example Thaler, Sunstein, and John P. Balz have focused on the following “tools” of choice architecture: defaults, expecting error, understanding mappings (which involves exploring the different ways that information presentation affects option comparisons), giving feedback, structuring complex choices, and creating incentives.[5] Another group of leading behavioral scientists has created a typology of choice architecture elements dividing them into those that structure the choice set and those that describe the choice. Examples of choice set structuring include: the number of alternatives, decision aids, defaults, and choice over time. Describing choice options include: partitioning options and attributes, and designing attributes.[7]

Elements of Choice Architecture

Research from the field of behavioral economics has shown that individuals tend to be subject to predictable biases that may lead to decision errors. The following sections describe these biases and describe the ways that they can be minimized by changing decision context through choice architecture.

Reducing Choice Overload

Classical economics predicts that providing more options will generally improve consumer utility, or at least leave it unchanged. However, each additional choice demands additional time and consideration to evaluate, potentially outweighing the benefits of greater choice. Behavioral economists have shown that in some instances presenting consumers with many choices can lead to reduced motivation to make a choice and decreased satisfaction with choices once they are made.[7] This phenomenon is often referred to as choice overload,[10] or the tyranny of choice.[11] However, the importance of this effect appears to vary significantly across situations.[7] Choice architects can reduce choice overload by either limiting alternatives or providing decision support tools.

Choice architects may choose to limit choice options, however limits to choice may lead to reductions of consumer welfare. This is because the greater the number of choices, the greater the likelihood that the choice set will include the optimal choice for any given consumer. As a result, the ideal number of alternatives will depend upon the cognitive effort required to evaluate each option and the heterogeneity of needs and preferences across consumers.[7] There are examples of consumers faring worse with many options rather than fewer in social-security investments[4] and Medicare drug plans [12]

As consumption decisions increasingly move online, consumers are relying upon search engines and product recommendation systems to find and evaluate products and services. These types of search and decision aids both reduce the time and effort associated with information search, but also have the power to subtly shape decisions dependent upon what products are presented, the context of the presentation, and the way that they are ranked and ordered. For example, research on consumer goods like wine has shown that the expansion of online retailing has made it simpler for consumers to gather information on products and compare alternatives, making them more responsive to price and quality information.[13]

Defaults

A large body of research has shown that, all things being equal, consumers are more likely to choose options that are presented as a default.[14] A default is defined as a choice frame in which one selection is pre-selected so that individuals must take active steps to select another option.[15] Defaults can take many forms ranging from the automatic enrollment of college students in university health insurance plans to forms which default to a specific option unless changed.

Several mechanisms have been proposed to explain the influence of defaults. For example, individuals may interpret defaults as policymaker recommendations, cognitive biases related to loss aversion like the status quo bias or endowment effect might be at work, or consumers may fail to opt out of the default due to associated effort.[14] It is important to note that these mechanisms are not mutually exclusive and their relative influence will likely differ across decision contexts.

Types of default include simple defaults where one choice is automatically selected for all consumers, forced choice in which a product or service is denied until the consumer makes a proactive selection, and sensory defaults in which the choice is pre-selected based upon other information that was gathered about specific consumers. Choices that are made repeatedly may also be affected by defaults, for instance persistent defaults may be continually reset regardless of past decisions, whereas reoccurring defaults “remember” past decisions for use as the default, and predictive defaults use algorithms to set defaults based upon other related behavior.[7]

One of the most commonly cited studies on the power of defaults is the example of organ donation. One study found that donor registration rates were twice as high when potential donors had to opt out versus opt in to donor registration.[3] However, the influence of defaults has been demonstrated across a range of domains including investment[4][16] and insurance[17]

Choice Over Time

Choices with outcomes that manifest in the future will be influenced by several biases. For example, individuals tend to be myopic, preferring positive outcomes in the present often at the expense of future outcomes. This may lead to behaviors like overeating or overspending in the short-term at the expense of longer term health and financial security outcomes. In addition, individual projections about the future tend to be inaccurate. When the future is uncertain they may overestimate the likelihood of salient or desirable outcomes,[18][19] and are generally overly optimistic about the future, for example assuming that they will have more time and money in the future than they will in actuality.[20][21]

However research indicates that there are several ways to structure choice architecture to compensate for or reduce these biases. For example researchers demonstrated improved decision-making by drawing attention to the future outcomes of decisions[22] or by emphasizing second best options.[19] In addition, limited time offers can be successful in reducing procrastination.[7]

Partitioning Options and Attributes

The ways in which options and attributes are grouped influence the choices that are made. Examples of such partitioning of options include the division of a household budget into categories (e.g. rent, food, utilities, transportation etc.), or categories of investments within a portfolio (e.g. real estate, stocks, bonds, etc.), while examples of partitioning attributes include the manner in which attributes are grouped together for example a label may group several related attributes together (e.g. convenient) or list them individually (e.g. short running time, little cleanup, low maintenance). The number and type of these categories is important because individuals have a tendency to allocate scarce resources equally across them. People tend to divide investments over the options listed in 401K plans[23] they favor equal allocation of resources and costs across individuals (all else being equal),[24] and are biased to assign equal probabilities to all events that could occur.[25][26] As a result, aggregate consumption can be changed by the number and types of categorizations. For instance, car buyers can be nudged toward more responsible purchases by itemizing practical attributes (gas mileage, safety, warranty etc.) and aggregating less practical attributes (i.e. speed, radio, and design are grouped together as “stylishness”).[27]

Avoiding Attribute Overload

Consumers would optimally consider all of a products attributes when deciding between options. However, due to cognitive constraints, consumers may face similar challenges in weighing many attributes to those of evaluating many choices. As a result choice architects may choose to limit the number of attributes, weighing the cognitive effort required to consider multiple attributes[28] against the value of improved information. This may present challenges if consumers care about different attributes, but online forms that allow consumers to sort by different attributes should minimize the cognitive effort to evaluate many options without losing choice.

Translating Attributes

The presentation of information about attributes can also reduce the cognitive effort associated with processing and reduce errors. This can generally be accomplished by increasing evaluability and comparability of attributes.[7] One example is to convert commonly used metrics into those that consumers can be assumed to care about. For example choice architects might translate non-linear metrics (including monthly credit payments or miles per gallon) into relevant linear metrics (in this case the payback period associated with a credit payment or the gallons per 100 miles).[2] Choice architects can also influence decisions by adding evaluative labels (e.g. good versus bad or high versus low) to numerical metrics,[29] explicitly calculating consequences(for instance translating energy consumption into greenhouse gas emissions), or by changing the scale of a metric (for instance listing monthly cost versus yearly cost).[30]

Examples of Choice Architecture

The concept of choice architecture exists in a number of fields. See for example the work of B. J. Fogg on computers as persuasive technologies; the concept of permission marketing as described by Seth Godin; and as shaping operations[31] in military science. Choice Architecture is also similar to the concept of "heresthetics," or manipulation that changes outcomes without changing people's underlying preferences, described by political scientist William H. Riker. Choice architecture has been implemented in several public and private policy domains. Variants of the Save More Tomorrow Plan (conceived by Richard Thaler and Shlomo Benartzi), which has individuals commit in advance to allocate a portion of future salary increases to savings, have been adopted by companies to increase employee retirement savings.[32]

Lev Virine and Michael Trumper applied choice architecture concept to project management.[33] They proposed Choice Engineering as a choice architecture related framework for improving project decisions. Project managers make predictable, repeated mental mistakes which could lead to project failures. Choice Engineering is a creating of processes or environment in which project managers would be steered towards making better choices rather than mandating these choices. The examples of such processes would be using checklists and templates, introducing project audit rather than direct control, providing full disclosure of information for project team members, improving project management education, and other processes. Virine and Trumper argued that in many cases, especially for smaller projects, it would be more beneficial to use Choice Engineering rather than strict and complex project management processes.

Choice Architecture Challenges

Choice architecture interventions may fail to produce their desired result for several reasons. First, individual differences may lead consumers to respond differently to information. For example, liberals and conservatives have been shown to respond differently to information about the environmental consequences of energy-related behaviors,[34] while individual numeracy has also been linked to different responses to choice architectures.[7] A second major challenge is assessing whether choice architectures are, in fact, improving decision-making. One way of assessing this is to evaluate consumer experiences after the choice has been made both in the short and long-term.[7]

Terminology

See also

References

  1. ↑ Scheibehenne, Benjamin; Greifeneder, Rainer; Todd, Peter (2010). "Can there ever be too many options? A meta-analytic review of choice overload". Journal of Consumer Research 37 (3): 409–25. doi:10.1086/651235.
  2. ↑ 2.0 2.1 Larrick, R.P.; Soll, J.B (2008). "The MPG Illusion". Science 320: 1593–4. doi:10.1126/science.1154983.
  3. ↑ 3.0 3.1 Johnson, E.J.; Goldstein, D.G. (2003). "Do Defaults Save Lives?". Science 302: 1338–9. doi:10.1126/science.1091721. PMID 14631022.
  4. ↑ 4.0 4.1 4.2 Cronqvist, H; Thaler, R (2004). "Design choices in privatized social security systems: Learning from the Swedish experience". American Economic Review 94: 424–8. doi:10.1257/0002828041301632.
  5. ↑ 5.0 5.1 Thaler, Richard H.; Sunstein, Cass R.; Balz, John P. (2013). Shafir, Eldar, ed. The Behavioral Foundations of Public Policy. Princeton, New Jersey: Princeton University Press. pp. 428–39.
  6. ↑ Nesterak, Evan. "Head of White House "Nudge Unit" Maya Shankar Speaks about Newly Formed Social and Behavioral Sciences Team". thepsychreport. Retrieved 13 December 2014.
  7. ↑ 7.0 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 Johnson, Eric J.; Shu, S.B.; Dellaert, B.G.C.; Fox, C.; Goldstein, D.G.; Haeubl, G.; Larrick, R.P.; Payne, J.W.; Schkade, D.; Wansink, B.; Weber, E.U. (2012). "Beyond Nudges: Tools of a choice architecture". Marketing Letters 23: 487–504. doi:10.1007/s11002-012-9186-1.
  8. ↑ Mitchell, Gregory (2005). "Libertarian Paternalism is an Oxymoron". Northwestern University Law Review 99.
  9. ↑ http://www.latimes.com/news/opinion/la-oe-thalerandsunstein2apr02,0,3730262.story
  10. ↑ Iyengar, S.S.; Lepper, M.R. (2000). "When choice is demotivating: can one desire too much of a good thing?". Journal of Personality and Social Psychology 79: 995–1006. doi:10.1037/0022-3514.79.6.995.
  11. ↑ Schwartz, B. (2004). The paradox of choice: why more is less. New York: Harper.
  12. ↑ Kling, J. et al. (2012). "Comparison Friction: Experimental Evidence from Medicare Drug Plans". Quarterly Journal of Economics.
  13. ↑ Lynch, John G.; Ariely, Dan (2000). "Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution". Marketing Science.
  14. ↑ 14.0 14.1 Smith, N.C; Goldstein, D.; Johnson, E. (2013). "Choice without awareness: Ethical and policy implications of defaults". Journal of Public Policy and Marketing.
  15. ↑ Brown, C.L.; Krishna, A. (2004). "The skeptical shopper: a metacognitive account for the effects of default options on choice". Journal of Consumer Research 31: 529–39. doi:10.1086/425087.
  16. ↑ Madrian, B.C.; Shea, D.F. (2001). "The power of suggestion: inertia in 401(k) participation and savings behavior". Quarterly Journal of Economics 116.
  17. ↑ Johnson, E.J. et al. (1993). "Framing, probability distortions, and insurance decisions". Journal of Risk and Uncertainty 7.
  18. ↑ Koehler, D.J. (1991). "Explanation, imagination, and confidence in judgement". Psychological Bulletin 110.
  19. ↑ 19.0 19.1 Shu, S.B. (2008). "Future-biased search: the quest for the ideal". Journal of Behavioral Decision Making 21.
  20. ↑ Kahneman, D.; Lovallo, D. (1993). "Timid choices and bold forecasts: a cognitive perspective on risk taking". Management Science 39.
  21. ↑ Zauberman, G.; Lynch, J.G. (2005). "Resource slack and propensity to discount delayed investments of time versus money". Journal of Experimental Psychology 134.
  22. ↑ Weber, E.U. et al. (2007). "Asymmetric discounting in intertemporal choice: a query theory account". Psychological Science 18.
  23. ↑ Benartzi, S.; Thaler, R. (2001). "Naive Diversification Strategies in Defined Contribution Saving Plans". American Economic Review.
  24. ↑ Messick, D.M. (1993). "Equality as a decision heuristic". In Psychological perspectives on justice.
  25. ↑ Fox, C.R.; Clemen, R.T. (2005). "Subjective probability assessment in decision analysis: partition dependence and bias toward the ignorance prior". Management Science 51.
  26. ↑ Fox, C.R.; Rottenstreich, Y. (2003). "Partition priming in judgement in judgement under uncertainty". Psychological Science 14.
  27. ↑ Martin, J.M.; Norton, M.I. (2009). "Shaping online consumer choice by partitioning the web". Psychology and Marketing 26.
  28. ↑ Peters, E. et al. (2007). "Numeracy skill and the communication, comprehension, and use of risk and benefit information". Health Affairs 26.
  29. ↑ Peters, E. et al. (2009). "Bringing meaning to numbers: the impact of evaluative categories on decisions". Journal of Experimental Psychology.
  30. ↑ Burson, K.A.; Larrick, R.P.; Lynch, J.G. (2009). "Six of one, half dozen of the other: expanding and contracting numerical dimensions produces preference reversals". Psychological Science.
  31. ↑ Headquarters, Department of the Army (22 February 2011) [27 February 2008]. FM 3–0, Operations (with included Change 1). Washington, DC: GPO. p. 5–9. ("www" (PDF). Retrieved 31 August 2013.)
  32. ↑ http://money.cnn.com/2008/07/24/magazines/moneymag/105711588.moneymag/
  33. ↑ Virine, Lev; Trumper, Michael (2013). ProjectThink: Why Good Managers Make Poor Project Choices. Gower Pub.Co.
  34. ↑ Gromet, D.; Kunreuther, H.; Larrick, R. (2013). "Political Ideology Affects Energy-Efficiency Attitudes and Choices". Proceedings National Academy of Sciences.
  35. ↑ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=405940

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