Ants, Traders, or Drivers: An Inquiry into Complex Behavior Systems
Applied behavioral science and the scaling problem
Over the last decade, applied behavioral science has demonstrated its ability to add a ton of value. However, as with all new emerging fields, it comes with its own set of problems. In this case, it’s a problem that’s tied to its core focus on the individual: scaling.
Fundamentally, applied behavioral science relies on bringing tools and insights from fields related to psychology and using them to challenge classic notions about how people behave. This approach is enticing, but not without limitations. Yes, it allows us to step in and say, “Oh hey, nice model. Too bad it’s based on a completely fictional idea of what a human is.” But, even when we identify the right issues, we still hit some significant roadblocks.
While behavioral science can tell us a lot about how individuals might differ from classical economics assumptions (like our rational friend, Homo Economicus), they are, by and large, still describing individual behavior or limited to small groups. For example, we can get all sorts of insights from studies on how small nudges or tweaks in choice architecture influence individual decisions,2 but those findings are often derived from controlled experiments or pilot programs involving relatively small groups. Applied behavioral scientists have developed a rigorous approach to take insights like these, pilot them in real-world environments, and use an iterative approach until they get to a solution that works and even (somewhat) scales.
References
- Bar-Yam, Y. (1997). Dynamics of Complex Systems. Addison-Wesley.
- Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
- Holland, J. H. (2014). Complexity: A Very Short Introduction. Oxford University Press.
- Miller, J. H., & Page, S. E. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.
- Mitchell, M. (2009). Complexity: A Guided Tour. Oxford University Press.
- Ostrom, E. (2010). Beyond markets and states: Polycentric governance of complex economic systems. American Economic Review, 100(3), 641-672.
- Room, G. (2011). Complexity, Institutions and Public Policy: Agile Decision-Making in a Turbulent World. Edward Elgar Publishing.
- Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill.
- Allcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95(9–10), 1082–1095.
- Kettle, S., Hernandez, M., Ruda, S., & Sanders, M. (2016). Behavioral interventions in tax compliance: Evidence from Guatemala. The World Bank Economic Review, 30(suppl_1), S46–S57.
- Mertens, S., Herberz, M., Hahnel, U. J. J., & Brosch, T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119(1), e2107346118.
- Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685–686.
- Hölldobler, B., & Wilson, E. O. (1990). The ants. Harvard University Press
- Helbing, D. (2001). Traffic and related self-driven many-particle systems. Reviews of Modern Physics, 73(4), 1067–1141.
- Johansson, R. (2009). Vision Zero – Implementing a policy for traffic safety. Safety Science, 47(6), 826–831.
About the Author
Dr. Sekoul Krastev
Dr. Sekoul Krastev is a decision scientist and Co-Founder of The Decision Lab, one of the world's leading behavioral science consultancies. His team works with large organizations—Fortune 500 companies, governments, foundations and supernationals—to apply behavioral science and decision theory for social good. He holds a PhD in neuroscience from McGill University and is currently a visiting scholar at NYU. His work has been featured in academic journals as well as in The New York Times, Forbes, and Bloomberg. He is also the author of Intention (Wiley, 2024), a bestselling book on the science of human agency. Before founding The Decision Lab, he worked at the Boston Consulting Group and Google.
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