There will be different perspectives on the definitions of these two terms depending on context. Put simply, behavioral economics lies at the intersection of economics and psychology. Behavioral science is a somewhat broader term than behavioral economics as it is more inclusive of things that do not lie at the intersection (e.g., pure social psychology or neuroscience).
While the boundaries are evolving due to the nature of knowledge discovery, some things that lie at the intersection (i.e., behavioral economics as I’ve depicted) include choice under risk and uncertainty, prospect theory and loss aversion, myopic loss aversion, behavioral time discounting, heuristics, biases, mental accounting, behavioral game theory, and neuroeconomics. You will find that some people have a perspective that behavioral economists have foundational training in economics (i.e., come from economics departments) whereas other use the term more loosely and include those in the behavioral science area.
Note that when when you visit the intersection of these two circles you will inevitably see the distinction between how people behave versus how people should behave. You may also see the concept of nudges and behavioral solutions that try to address issues that people face.
Behavioral science is inclusive of the intersection and may also include things beyond that like memory processes, empathy, emotions, learning, moral foundations theory, group decision making, neuroscience, psychology of aging, etc.
Just wanted to share a Harvard Business Review post by my colleague, Shlomo Benartzi. In a nutshell, the digital world opens up many possibilities to apply behavioral economics to help people. Shlomo mentions some research that he, Hal Hershfield, and I recently did with Acorns, a robo-saving app. How Digital Tools and Behavioral Economics Will Save Retirement
During a recent conversation about user interface design and the differences in approach compared to behavioral science, the topic quickly turned to a question about what is the best organizational model for implementing behavioral science?
While behavioral science has been on the rise worldwide, the organizational model is still an important, unresolved question. Should the function sit within marketing? Within the user experience team? As a separate Behavioral Science Officer or Office of Behavioral Science to make the quality high and initiatives vivid? Or perhaps the behavioral science function should lie within the product team? Maybe within the digital strategy group?
In my work with Digitai in the past year, I’ve done work with companies in countries like Australia, Germany, Spain, UK, and the US. Although anecdotal, I’ve seen significant (albeit still emerging) activity with setting up behavioral science initiatives which go beyond pure marketing and are attached to innovation. This inherently requires more cross-functional integration of behavioral science with other existing functions within a company. Furthermore, this sometimes means helping to elevate the sophistication of the innovation ecosystem. This might include new technology partnerships, partnerships with researchers from the scientific community, and upgrades to a company’s testing and production platforms.
Yet while I have worked across many types of companies in the behavioral science area, it’s been somewhat skewed toward large companies with some increasing activity in the middle-market company space (e.g., which see the potential to disrupt the market by leveraging behavioral science principles). What about other companies that have more modest aspirations or resources compared to the large companies that are committed to more substantial investments?
The answer to that would need to be addressed separately to be responsible, but it does brings me back to the original question, which is “what is the best organizational structure for implementing behavioral science?” The key to answering this question is to think about strategy and goals first and then to design the organizational structure to fit the strategy. If your strategy is to innovate, then you may need a model that allows for a lot of cross-function interactions both within and outside of the company. You might want a behavioral science officer and an advisory board. If your efforts are focused mostly on marketing, then you might be fine with a simpler model and hiring or assigning some specialists to the department. If resources are even more limited, then perhaps the solution could include occasional use of outside resources, some training, or use of some do-it-yourself thinking tools (e.g., checklists and things to think about for behavioral science). A key to implementing behavioral science initiatives is to really think about strategy and goals first. Then you can think about the predominant organizational model that you’d like to follow plus any elements that might help with implementation.
Think strategy first, then tactics.
Readers of this post might also be interested in the following short video on implementing behavioral science initiatives
I was recently asked on Quora, “Should one use heuristics at all as they are prone to cognitive biases?” What follows are my thoughts.
Heuristics usually allow people to make quicker decisions. For example, when catching a fly baseball, sometimes people use a gaze heuristic to keep the ball at roughly the same inbound angle by adjusting their position and moving forward or backward. This gaze heuristic allows us to quickly and dynamically make judgments to catch the ball as opposed to having to pull out the slide rulers, calculators, and physics books to figure out where the ball is going to land.
While heuristics can help with speed, sometimes we have the benefit of time to slow down to think about the consequences of potential biases and where the heuristics might lead us astray. For example, people often talk about a 4% drawdown rule/heuristic for using your wealth at retirement (e.g., use up 4% of your wealth each year). But one has to remember that using up your wealth is essentially an irreversible process. What type of risk analysis have you done? Have you assessed your longevity and potential variances both positive and negative? What happens if you live 40 more years? Have you accounted for negative health events? Have you thought about maximizing your happiness in retirement as opposed to economic numbers?
So heuristics can be used with some success. But heurisrics should be audited for their benefits and shortcomings, especially if the consequences of an error are significant enough.
The balancing act is tricky, and I think context and desired outcomes matter. For example:
A thirty-year old might have problems saving for retirement because they think of savings as being for stranger. The solution might be to increase emotional connection between the thirty-year old and their future self so that the right behavior of saving can be achieved.
A person might be emotionally attached to their home and as a result, they might try to sell their home at too high of a price. It might be better if they can loosen their emotional attachment and feelings of endowment. Getting 3rd party perspectives might be helpful to the seller in terms of distancing themselves so they can set a reasonable market price.
Sometimes it’s hard to control emotions and desire, and people may try to precommit to a state so that proper decisions are more likely to be made in spite of the situation. I have heard of behavioral economists pouring salt over desserts at dinner (after they’ve had a few bites to get the taste) so they are less inclined to eat the whole thing.
The main takeaways are that there are essentially “two minds” at work, and they work in concert in different ways. Sometimes you need emotion. Sometimes you want less of it. Sometimes you can’t really change your emotions so you need self-control devices and external perspectives. Other times you need to try to slow down thinking. There are many different approaches.
One concept that I describe in my recent book, Inside Nudging: Implementing Behavioral Science Initiatives, draws from Roberto Verganti. He uses the term, “Design-Driven Innovation.” I re-coin the concept as “Meaning-Driven Innovation” to ease the explanation a bit. The concept is that in order to innovate under such a framework, one needs to change the relationship between the product or service and the end user. In this framework, the designer must address the question, “what does the product or service mean to the end user?”
In my book, I describe how colleagues and I created an app to help retirees plan for their retirement journey with guidance from a financial advisor. This effort involved equipping financial advisors with some software tools (informed by the behavioral sciences) that they could use with retirees to help the retirees discover blindspots, form priorities and deal with cognitive/emotional difficulties, and reflect on risks more thoroughly. The upshot of our design approach was to try to change the relationship between the advisor and retiree. We wanted the advisor to mean more to the retiree than just a person involved with fees, funds, and fiduciary responsibility. We wanted advisors to evolve to become trusted financial and life advisors. See a figure from my book below:
The meta meaning that we played to one was about connection, creating a new connection between the advisor and retiree. There are other meta meanings to describe relationships with products and services though. For example, there can be products that help to transform people. Or there can be products whose design and meaning are to protect. Or products can be designed to make a person feel more in control.
In summary, one possible relationship between behavioral science, design, and innovation is about changing the meaning between products/services and people through use of behavioral science principles (whether these principles come from psychology, behavioral economics, or the like).