View by Day

Since December 1st till March 31 you can apply to our programs:
Master in Economic Research and PhD in Economics

Entry requirements are:
- BA or MA degree or equivalent
- Proficiency in spoken and written English
- Solid background in mathematics
- Previous education in economics is recommended

Your online application must content following documents:
- Curriculum vitae
- Statement of motivation
- Copies of your diplomas and transcripts
- Proof of English proficiency level
- Contact details for two (or max. three) referees

For more information please see sections: How to apply to MAER or How to apply to PhD
In case of any question, please do not hesitate to contact us at This email address is being protected from spambots. You need JavaScript enabled to view it. or This email address is being protected from spambots. You need JavaScript enabled to view it.
or see the FAQ sections for MAER or Phd

Daniel N Hauser, Ph.D.

Aalto University, Finland

Authors: Daniel N. Hauser, J. Aislinn Bohren

Abstract: A growing literature in economics seeks to model how agents process information and update beliefs. In this paper, we link two common approaches: (i) defining an updating rule that specifies a mapping from prior beliefs and the signal to the agent’s subjective posterior, and (ii) modeling an agent as a Bayesian learner with a misspecified model. The updating rule approach has a more transparent conceptual link to the underlying bias being modeled, while the misspecified model approach is ‘complete’, in that no further assumptions on belief-updating are necessary to analyze the model, and has well-developed solution concepts and convergence results. We show that any misspecified model can be decomposed into two objects that summarize the biases it introduces: the updating rule captures how the agent interprets realized information, while the forecast captures how the agent anticipates future information. We derive necessary and sufficient conditions for a forecast and updating rule pair to be represented by a misspecified model. This provides conceptual guidance for which model to select to represent a given bias. Finally, we consider two natural ways to select forecasts: introspection-proofness and naive consistency. We demonstrate how introspection-proofness places a natural bound on the magnitude of bias in an application with motivated reasoning, and how naive consistency impacts a firm’s ability to screen consumers in a credit market application.

Keywords: Model misspecification, belief formation, non-Bayesian updating, heuristics

Full Text: The Behavioral Foundations of Model Misspecification: A Decomposition