Behavioral Science and Policy

Rapid advances in digital interfaces, the quality of predictive algorithms, and the availability of high-speed internet are allowing firms to offer robo-advice: automated online guidance about which financial products are suitable for a consumer. At their best, robo-advisors feature algorithms that accurately predict which financial products are the optimal match for a given consumer’s needs and use digital choice architectures (that is, ways of presenting the product recommendations online) that are designed to strongly guide consumers to those best-fit products. Yet when algorithms lack great accuracy, strong guidance in favor of products incorrectly predicted to have the best fit could lead consumers to select options that do not meet their needs well. The possibility that strong guidance results in different financial outcomes depending on the quality of the algorithms has important implications for regulating robo-advice. We propose that, consistent with current regulatory practice for human advisors, regulators should require any firm wishing to implement a robo-advisor for its clients to demonstrate both that the robo-advisor is honest (that is, it works in the best interests of the client) and that its algorithms meet some established level of accuracy. In addition, regulators should require any firm offering a robo-advisor to demonstrate that the strength of the guidance provided by the digital choice architecture aligns with the predictive accuracy of the advisor’s algorithms. We offer several practical suggestions for implementing such a regulatory strategy. The need for regulations is becoming increasingly urgent because many robo-advisors now use artificial intelligence (AI) to make predictions. AI has made robo-advisors more powerful and easier to use, which is likely to expand their adoption—and, by extension, the harm that would be caused if subpar robo-advisors are allowed on the market.

Benedict G. C. Dellaert

Tom Baker

Eric J. Johnson

Behavioral Science & Policy

Regulating robo-advice for consumers’ financial decisions: The interplay between algorithm quality &amp; digital choice architecture

监管机器人顾问对消费者财务决策的影响：算法质量与数字选择架构的相互作用

The field of behavioral public policy (BPP) has grown rapidly in the past 10 years, as is indicated by the rising numbers of BPP bodies that routinely apply insights and methods from behavioral science to improving the effectiveness and efficiency of public policy. This article reports on a count showing that between 2018 and 2024, the number of BPP bodies grew worldwide from 201 to 631 and increased on all continents. The article also summarizes results from a survey that asked representatives from 10 BPP bodies that varied by locale, political and cultural environment, and longevity to describe their group’s mission, functions, policy areas, challenges, and lessons for others. Common challenges include setting expectations, sustainability, and bridging the research–policy gap. Results from both the global analysis and the survey highlight that being in an “ecosystem” that includes outside advisors with experience in applying behavioral science and running local programs can enhance efficacy. Other lessons from the survey include the importance of focusing on solving problems rather than on applying a particular kind of intervention, being multidisciplinary (for example, by involving people with expertise in project management), designing for scale, presenting data in the context of a larger narrative, and formalizing operating procedures.

Faisal Naru

Behavioral public policy bodies: New developments &amp; lessons

行为公共政策机构：新发展与经验教训

Craig R. Fox

Sim B Sitkin

Editors’ Note

编者按

Many workplaces and insurers sponsor programs to increase employees’ physical activity, but these programs often fail to create healthy behaviors or else work only temporarily. They typically offer financial incentives without considering cognitive biases that influence whether people will join the programs and remain committed to exercising. We argue that interventions should leverage both insights from behavioral economics and the availability of remote-monitoring technologies, such as automatic step trackers, to be more effective. In this article, we summarize relevant insights from behavioral economics, highlight research findings that show the value of combining behaviorally informed program design with remote monitoring, and suggest strategies for selecting interventions and remote-monitoring devices.

Sujatha Changolkar

Kevin G. Volpp

Mitesh S. Patel

Using Behavioral Economic Interventions with Remote-monitoring Technologies to Increase Physical Activity

利用行为经济学干预结合远程监控技术提高身体活动水平

Cash transfer programs aim to lessen the harmful effects of economic deprivation by giving cash or its equivalent directly to people in need. In this article, we combine insights from three areas of behavioral science-economics, child development, and cognitive psychology (including behavioral economics and the psychology of poverty)—to shed light on the logic behind providing cash transfers to families with children and to identify specific design features that policymakers should consider when creating these programs. We also summarize key research findings on the outcomes of such programs and present case studies of projects that have been evaluated in randomized controlled studies. We argue that unconditional cash transfers (which provide the money with no strings attached) are preferable to conditional cash transfers (which require recipients to meet specified conditions) for providing economic security and improving children's life outcomes. Conditional cash transfers can achieve similar goals, however, if they impose little administrative burden on parents and if infrastructure is in place to support meeting the conditions for receiving the cash. We end with recommendations for how best to design cash transfer programs for families with children.

Lisa A. Gennetian

Eldar Shafir

J. Lawrence Aber

Jacobus De Hoop

Behavioral Insights into Cash Transfers to Families with Children

儿童家庭现金转移的行为洞察

The U.S. government administers many public programs and services. Creating programs that work requires an understanding of the psychological processes that influence behavior. To this end, policymakers may collaborate with academics who have expertise in behavioral science to generate ideas for improving existing programs, procedures, or policies; to test existing programs; or to design wholly new programs that address societal problems. Such collaborations also enable academics to test new or established theories in real-world settings. In this article, we draw on our collective experience in the U.S. Office of Evaluation Sciences, where we have worked on studies that evaluate various federal programs, to outline some of the core issues that make research collaborations between academics and government agents challenging. We also offer tips for making these partnerships productive and mutually beneficial.

Shibeal O’ Flaherty

Lizzie Martin

Syon Bhanot

Crystal Hall

Sebastian Jilke

Tyler Simko

Mary Steffel

How academics and policymakers can collaborate effectively: Lessons from using behavioral science to improve U.S. federal government policies

学术界与政策制定者如何有效合作：运用行为科学改善美国联邦政府政策的经验教训

This article explores why private sector adoption of behavioral science has been limited and shares strategies for expanding its integration into businesses. After outlining three core barriers to adoption, the author proposes solutions for more effectively communicating the field’s value to business leaders—solutions rooted in his personal experience and supported by observations shared by other successful practitioners. He then focuses on actions that the broader behavioral science community (including academics, policymakers, and practitioners) can take to promote the private sector’s long-term adoption of behavioral science.

Scott Young

Bridging the divide between the potential &amp; practice of behavioral science in the private sector

弥合私营部门行为科学潜力与实践之间的鸿沟

Evidence-based decision-making (EBDM)—using the best available evidence from multiple sources to make informed decisions—is critical to the successful functioning of any organization. Much of the literature on EBDM has focused on the decision-maker as an individual. Yet to be most efficient and effective, EBDM requires extensive communication with others. Our review of the body of research on EBDM shows that it is typical for several individuals and groups to contribute to evidence-based decisions. These varied participants provide the skills and knowledge needed to incorporate diverse types of evidence into the decision process. We have found that conversations and interactions through three distinct networks contribute to successful EBDM. These networks consist of decision-makers and their staff and either (a) researchers in a relevant field; (b) stakeholders in the community or organization; or (c) colleagues with similar responsibilities and challenges, which we call communities of practice. Building social connections with people in each of these networks enhances a person’s ability to make good decisions for an organization. It is therefore imperative that organizations engaging in EBDM have ongoing programs and policies geared toward creating and maintaining these three critical connections.

Farimah HakemZadeh

Denise M. Rousseau

Evidence-based decision-making is a social endeavor

循证决策是一项社会事业

ISSN	2379-4607, 2379-4615
h-index	10

涉及主题	心理学政治学业务医学经济法学计算机科学生物哲学社会学物理
出版信息	出版商: Brookings Institution Press，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2018，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%

Behavioral Science and Policy

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Regulating robo-advice for consumers’ financial decisions: The interplay between algorithm quality & digital choice architecture

Behavioral public policy bodies: New developments & lessons