STRATEGICALLY BIASED LEARNING IN MARKET INTERACTIONS

Giulio Bottazzi and Daniele Giachini ()
Additional contact information
Daniele Giachini: Institute of Economics & Department EMbeDS, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56127 Pisa, Italy

Advances in Complex Systems (ACS), 2022, vol. 25, issue 02n03, 1-18

Abstract: We consider a market economy where two rational agents are able to learn the distribution of future events. In this context, we study whether moving away from the standard Bayesian belief updating, in the sense of under-reaction to some degree to new information, may be strategically convenient for traders. We show that, in equilibrium, strong under-reaction occurs, thus rational agents may strategically want to bias their learning process. Our analysis points out that the underlying mechanism driving ex-ante strategical decisions is diversity seeking. Finally, we show that, even if robust with respect to strategy selection, strong under-reaction can generate low realized welfare levels because of a long transient phase in which the agent makes poor predictions.

Keywords: Learning; strategic interaction; behavioral bias; financial markets (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525922500047
Access to full text is restricted to subscribers

Related works:
Working Paper: Strategically biased learning in market interactions (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:25:y:2022:i:02n03:n:s0219525922500047

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525922500047

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().