Data and Code for: Mistakes, Overconfidence and the Effect of Sharing on Detecting Lies

Principal Investigator(s): View help for Principal Investigator(s) Marta Serra-Garcia, UC San Diego; Uri Gneezy, UC San Diego

Version: View help for Version V1

Name	File Type	Size	Last Modified
code			08/26/2021 05:27:PM
data			08/26/2021 05:27:PM
packages			08/26/2021 05:28:PM
qualtrics			08/26/2021 05:28:PM
results			08/26/2021 05:27:PM
README.pdf	application/pdf	150.1 KB	09/27/2021 08:07:PM

Project Citation:

Serra-Garcia, Marta, and Gneezy, Uri . Data and Code for: Mistakes, Overconfidence and the Effect of Sharing on Detecting Lies. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-09-28. https://doi.org/10.3886/E140961V1

Project Description

Summary: Mistakes and overconfidence in detecting lies could help lies spread. Participants in our experiments observe videos in which senders either tell the truth or lie, and are incentivized to distinguish between them. We find that participants fail to detect lies, but are overconfident about their ability to do so. We use these findings to study the determinants of sharing and its effect on lie detection, finding that even when incentivized to share truthful videos, participants are more likely to share lies. Moreover, the receivers are more likely to believe shared videos. Combined, the tendency to believe lies increases with sharing.

Scope of Project

Subject Terms: Detecting lies; overconfidence; sharing behavior; fake news

JEL Classification:
      C72 Noncooperative Games
      C91 Design of Experiments: Laboratory, Individual
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

Geographic Coverage: United States

Time Period(s): 10/22/2018 – 6/5/2020 (Fall 2018, Winter-Spring 2019, Spring 2020)

Data Type(s): experimental data

Methodology

Data Source: Laboratory experiment conducted at UC San Diego and online experiments conducted on Amazon Mechanical Turk.

Collection Mode(s): other

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Published Versions

V1 [2021-09-28]

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