Information unraveling and limited depth of reasoning [Dataset]

DOI

Information unraveling is an elegant theoretical argument suggesting that private information is voluntarily and fully revealed in many circumstances. However, the experimental literature has documented many cases of incomplete unraveling and has suggested limited depth of reasoning on the part of senders as a behavioral explanation. To test this explanation, we modify the design of existing unraveling games along two dimensions. In contrast to the baseline setting with simultaneous moves, we introduce a variant where decision-making is essentially sequential. Second, we vary the cost of disclosure, resulting in a 2×2 treatment design. Both sequential decision-making and low disclosure costs are suitable for reducing the demands on subjects' level-k reasoning. The data confirm that sequential decision-making and low disclosure costs lead to more disclosure, and there is virtually full disclosure in the treatment that combines both. A calibrated level-k model makes quantitative predictions, including precise treatment level and player-specific revelation rates, and these predictions organize the data well. The timing of decisions provides further insights into the treatment-specific unraveling process.

Identifier
DOI https://doi.org/10.11588/DATA/YMANSW
Related Identifier IsSupplementTo https://doi.org/10.1016/j.geb.2025.09.004.
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/DATA/YMANSW
Provenance
Creator Benndorf, Volker ORCID logo; Kübler, Dorothea ORCID logo; Normann, Hans-Theo ORCID logo
Publisher heiDATA
Contributor Normann, Hans-Theo; heiDATA: Heidelberg Research Data Repository
Publication Year 2025
Funding Reference Deutsche Forschungsgemeinschaft (DFG) CRC TRR 190 ; Deutsche Forschungsgemeinschaft (DFG) FOR 5392
Rights ODC-By v1.0; info:eu-repo/semantics/openAccess; https://opendatacommons.org/licenses/by/1-0/
OpenAccess true
Contact Normann, Hans-Theo (Heinrich-Heine University Duesseldorf, DICE, Germany)
Representation
Resource Type Dataset
Format text/tab-separated-values; application/x-stata-syntax; text/plain
Size 34350; 1174; 4726
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Economics; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences