I was invited by the Behavioural Insights Team (BIT) to speak about cultural behavioral science and its applications in public policy. I explain how, by using cultural evolution as a theory of human behavior can help some of the challenges in behavioral science in terms of long term change and contextual factors that affect whether an intervention will work. This figure from a key paper presents the history of behavioral science that has led to cultural evolutionary behavioral science as an obvious next step.
The most relevant papers are:
Muthukrishna, M. (2019). Cultural Evolutionary Public Policy. Nature Human Behaviour, 4, 12-13. [Download] [Publisher]
Schimmelpfennig, R. & Muthukrishna, M. (2023). Cultural Evolutionary Behavioural Science in Public Policy. Behavioural Public Policy. [Publisher] [Download] [Twitter] [LinkedIn]
My talk begins with an overview of the problem plaguing behavioral economics – the lack of a theoretical foundation that can guide policy interventions. I introduce cultural evolution as a possible solution to bridge the theoretical gap. By using cultural evolution as a theory of human behavior, improvements can be made in policy efficiency. For example, studying how social norms change and evolve over time will provide a foundation for implementing effective policy interventions in multicultural societies. Some of this history is captured in this figure from the paper:
Considering the historical path dependence of norms provides crucial in understanding why certain populations hold certain beliefs, like vaccine hesitancy and a distrust in healthcare systems. Identifying how people acquire cultural norms, and narrowing down the ultimate causes for behavior (through cultural distance tools like world.culturalytics.com) could provide insights into designing interventions that work.
Understanding cultural evolution and behavioral science can help reanalyze the literature on public policy, providing insights into why some approaches are successful while others are not. I explain how studying universal cognitive capabilities will provide a deeper understanding of norm change, and thus, improve policy design.
Diversity is a double edged sword. Governments and organizations often push for greater diversity and tolerance for diversity, because the human tendency is toward squashing difference and selecting others like ourselves. But diversity can both help and harm innovation.
On the one hand, there’s intellectual arbitrage: discoveries and technologies situated in one discipline that draw on a key insight from another. Here diversity is a fuel for the engine of innovation.
On the other hand, diversity is, by definition, divisive. Without a common understanding, common goals, and common language, the flow of ideas in social networks is stymied, preventing recombination and reducing innovation. How do we reap the benefits without paying the costs?
Consider the challenge of collaborations between scientists and humanities scholars (or even between scientists in different disciplines). The key is to find common ground through strategies such as optimal assimilation, translators and bridges, or division into subgroups.
Resolving the tension between diversity and selection is at the core of a successful innovation strategy. And there are many possible solutions.
Some dimensions of diversity matter more than others—without a common language, communication is difficult. On the other hand, food preferences create little more than an easily solved coordination challenge for lunch.
But between these are many dimensions where optimal assimilation may be desirable and traits can be optimized, such as psychological safety so people feel free to share unorthodox ideas.
Other strategies include interdisciplinary translators. In my role at the Database of Religious History (DRH)—a large science and humanities collaboration—we have benefited from a few scholars trained in both to bridge the gap.
Innovation can also be divided into independent groups, coordinating within the group but competing against others trying different strategies (e.g. competition between firms).
Check out the full issue here: https://www.nae.edu/244665/Winter-Issue-of-The-Bridge-on-Complex-Unifiable-Systems