Template-Type: ReDIF-Paper 1.0
Title: Model Uncertainty
Author-Name:  Robin Musolff
Author-Email: robin.musolff@uni-bonn.de
Author-Name:  Florian Zimmermann
Author-Email: florian.zimmermann@iza.org
Classification-JEL: D01, D83
Keywords: Mental Models, Beliefs, Attention, Confidence, Representations


Abstract: Mental models help people navigate complex environments. This paper studies how people deal with model uncertainty. In an experiment,
participants estimate a company’s value, facing uncertainty about which one of two models correctly determines its true value. Using a between subjects design, 
we vary the degree of model complexity. Results show that in high-complexity conditions people fully neglect model uncertainty in
their actions. However, their beliefs continue to reflect model uncertainty. This disconnect between beliefs and actions suggests that complexity
leads to biased decision-making, while beliefs remain more nuanced. Furthermore, we show that complexity, via full uncertainty neglect, leads to
higher confidence in the optimality of own actions.
  
Note: 
Length: 112
Creation-Date: 2025-08
Revision-Date:
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp697
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2025_697