An inconvenient truth: Difficult problems rarely have easy solutions

Abstract

Individual-level interventions are often interesting and easy to implement, but are unfortunately ill-equipped to solve most major global problems (e.g., climate change, financial insecurity, unhealthy eating). Resources spent developing, pursuing, and touting relatively ineffective i-frame interventions draw resources away from the development and implementation of more effective s-frame solutions. Behavioral scientists who want to develop solutions to the world's biggest problems should focus their efforts on s-frame solutions.

Chater & Loewenstein's (C&L's) target article has generated an invaluable conversation about behavioral science's future. They make two incisive observations. First, behavioral scientists’ i-frame interventions to tackle global challenges have been oversold. They will not move the needle on issues like climate change, income inequality, and unhealthy diets. Second, although well-intentioned, behavioral scientists studying i-frame interventions have inadvertently advanced corporate interests that vehemently oppose far more effective structural solutions. For these reasons, I agree that behavioral scientists should shift their focus away from i-frame interventions because doing so is at best having limited impact and at worst causing harm.

Some disagree with C&L on the grounds that we need both i-frame and s-frame solutions. But first, how do the two differ? In my view, an i-frame intervention is designed to shift the behavior of individuals, whereas an s-frame intervention is designed to shift the behavior of populations. s-Frame interventions are typically light touch or heavy-handed. For example, an energy company sending mailers comparing people's energy use to their neighbors is an i-frame intervention designed to address climate change. Governments mandating that companies do so is “s-frame-light.” In contrast, a government carbon tax to curb fossil-fuel emissions is s-frame and will produce larger effects than the other approaches. So why not pursue all three? Can't we push for a carbon tax, while identifying behavioral interventions to get people to use less household energy? And given that many structural solutions may never be realized, shouldn't we focus on doable i-frame interventions?

Obviously, it is possible to pursue all three paths. But it is not possible to focus on or emphasize all three paths. Attentional and physical resources are limited. A researcher spending time investigating or promoting an i-frame solution is not spending that time investigating or promoting an s-frame solution. Funding dollars spent on i-frame research is not spent on s-frame work.

The focus on i-frame solutions is even more harmful than it sounds. The TED talk, op-ed, or podcast offering quick, sexy fixes to major societal problems is exciting. The truth, that many of our biggest problems require unsexy and politically difficult solutions, is less exciting. As a result, funders, policymakers, aspiring celebrity scientists, and concerned citizens are more motivated to believe in the promise of i-frame solutions than s-frame ones. Corporations that help drive our biggest societal problems (e.g., food companies that make and market unhealthy foods) are also more likely to promote i-frame solutions because they do not threaten their bottom lines. Further, they delight in the popularity of research that encourages people to view issues like unhealthy diets, climate disasters, and financial insecurity as matters of personal responsibility that must be dealt with by empowering individuals to exert greater self-control.

So where does this leave behavioral scientists who want to help solve global challenges? Typically, scientists ask questions they are curious about and that other scientists find interesting. This approach works well if you want to learn something about human psychology or offer self-help ideas or treatments for people. But if your goal is to contribute population-level solutions (which are required for most big challenges), a scientist must begin the research process by asking: (1) What is known about the problem drivers, (2) what has been tried, and (3) what solutions are most promising? Answering these questions will often require engaging content experts. If many i-frame interventions have proven unsuccessful and structural forces drive the problem, it becomes hard to justify continued pursuit of those interventions. For example, for the past 50 years, researchers have tested many i-frame solutions for unhealthy dietary habits that contribute to cardiovascular diseases and type 2 diabetes (Brownell, 2010). During that time, the problems have only gotten worse. There is now consensus across major health organizations and governments that structural changes to food environments are needed to improve diets. Therefore, if behavioral scientists want to generate solutions to this public health crisis, they should not invest deeply in i-frame interventions. There are cases where i-frame solutions may work for specific and well-defined behavioral challenges. For example, changing an electronic health record default from the prescription of costly brand name drugs to generic ones increased prescribing of more affordable generic drugs from 75 to 98% (Olshan, Rareshide, & Patel, 2019). If institutions widely adopt such practices it can positively influence a population, but scalability is hard to achieve.

Although pressing societal problems are rarely solved with easy-to-implement design changes, there is a clear role for behavioral scientists to advance knowledge on s-frame solutions. Experiments can simulate structural policies to understand whether they work and how they can be altered to increase impact. For example, randomized controlled experiments with individuals can test whether a guaranteed income policy might improve financial security and health or have potential unintended consequences.

It is also important to recognize that some i-frame approaches can produce or undergird more significant s-frame change. C&L view interventions like conflict of interest disclosures and information provision strategies (e.g., restaurant menu calorie labeling) as i-frame. But this view is incomplete. For example, Chile has a law requiring foods high in salt, sugar, and fat to display stop-sign-shaped warning labels on their packaging. Although these labels can help individuals change their eating habits, this policy mandate dramatically altered the food choice context by making the toxicity of the food supply highly salient. This mass education effort might increase support for other s-frame policies. Further, the labeling system facilities the implementation of s-frame policies. Foods displaying warning labels in Chile cannot be sold in schools or advertised during children's television programing, and these policies have produced meaningful reductions in sugary drink and unhealthy food sales (Taillie et al., 2021; Taillie, Reyes, Colchero, Popkin, & Corvalán, 2020).

C&L have prompted valuable self-reflection. The behavioral sciences can and should inform the design of s-frame solutions for global challenges. But it requires a deep understanding of the problems, partnering with change agents (e.g., policymakers, advocates) who have policy knowledge, and committing to using our powerful tools to advance s-frame solutions instead of i-frame ones.