Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/313107 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
ECONtribute Discussion Paper No. 355
Verlag: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
Zusammenfassung: 
People often form mental models based on incomplete information, revising them as new relevant data becomes available. In this paper, we experimentally investigate how individuals update their models when data on predictive variables are gradually revealed. We find that people's models tend to be 'sticky,' as their final models remain strongly influenced by earlier models formed using a subset of variables. Guided by a simple framework highlighting the role of attention in shaping model revisions, we document that only participants who exert lower cognitive effort during the revising stage, relative to the initial model formation stage - as proxied by time spent - exhibit significant model stickiness. Additionally, subjects' final models are strongly predicted by their reasoning type - their self-described approach to extracting models from multidimensional data. While model stickiness varies across reasoning types, effort allocation across stages remains a strong predictor of stickiness even when accounting for reasoning.
Schlagwörter: 
mental models
learning dynamics
attention
mental representation
bounded rationality
JEL: 
D83
D91
Dokumentart: 
Working Paper

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