Behavioural economics


To discuss: the pointlessness of behavioural economics as modified classical economics, when large data applications make this a predictive science

A new paradigm for the introductory course in economics:

Our intro courses fail to reflect the dramatic advances in economics – concerning information problems and strategic interactions, for example – since Samuelson’s paradigm-setting 1948 textbook. Missing, too, is any sustained engagement with new problems we now confront and on which economics has important insights for public policy – climate change, innovation, instability and growing inequality amongst them. This column introduces a free online interactive text – now used as the standard intro at UCL, Sciences Po, and Toulouse School of Economics – which responds.

Collective behavioural economics

In financial markets we have some elegant models of collective human behaviour in, e.g. Black-Scholes formulae etc.

In more general contexts, what do we do? A population of mis-specified Bayesian learners? (Shalizi 2009) A bunch of partially informed voters? Distributed learners?

Things to think about here: Bounded rationality, rational inattention, institutions as stable orbits in behavioural systems, devious negotiation strategies

Individual models

See marketing psychology.

Risk Perception

See the risk perception page.

Charness, Gary, and Matthias Sutter. 2012. “Groups Make Better Self-Interested Decisions.” Journal of Economic Perspectives 26 (3): 157–76. https://doi.org/10.1257/jep.26.3.157.

Hausman, Daniel M. 2011. “Mistakes About Preferences in the Social Sciences.” Philosophy of the Social Sciences 41 (1): 3–25. https://doi.org/10.1177/0048393110387885.

Peters, Ole. 2019. “The Ergodicity Problem in Economics.” Nature Physics 15 (12, 12). Nature Publishing Group: 1216–21. https://doi.org/10.1038/s41567-019-0732-0.

Roughgarden, Tim. 2018. “Complexity Theory, Game Theory, and Economics,” January. http://arxiv.org/abs/1801.00734.

Shalizi, Cosma Rohilla. 2009. “Dynamics of Bayesian Updating with Dependent Data and Misspecified Models.” Electronic Journal of Statistics 3: 1039–74. https://doi.org/10.1214/09-EJS485.

Sharpe, Keiran. 2015. “On the Ellsberg Paradox and Its Extension by Machina.” SSRN Scholarly Paper ID 2630471. Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract=2630471.

Valentine, Melissa A, Daniela Retelny, Alexandra To, Negar Rahmati, Tulsee Doshi, and Michael S Bernstein. 2017. “Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds as Organizations.” In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 3523–37. ACM. http://hci.stanford.edu/publications/2017/flashorgs/flash-orgs-chi-2017.pdf.