Mistakes, Overconfidence, and the Effect of Sharing on Detecting Lies
Author
Suggested Citation
DOI: 10.1257/aer.20191295
Download full text from publisher
References listed on IDEAS
- Rabin, Matthew & Eyster, Erik & Weizsäcker, Georg, 2015.
"An Experiment on Social Mislearning,"
CEPR Discussion Papers
11020, C.E.P.R. Discussion Papers.
- Eyster, Erik & Rabin, Matthew & Weizsäcker, Georg, 2018. "An Experiment On Social Mislearning," Rationality and Competition Discussion Paper Series 73, CRC TRR 190 Rationality and Competition.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Lohse, Tim & Qari, Salmai, 2021.
"Gender differences in face-to-face deceptive behavior,"
Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 1-15.
- Tim Lohse & Salmai Qari, 2019. "Gender Differences in Face-to-Face Deceptive Behavior," CESifo Working Paper Series 7995, CESifo.
- Tim Lohse & Salmai Qari, 2020. "Gender Differences in Face-to-Face Deceptive Behavior," Discussion Papers of DIW Berlin 1922, DIW Berlin, German Institute for Economic Research.
- In'acio B'o & Li Chen & Rustamdjan Hakimov, 2023. "Strategic Responses to Personalized Pricing and Demand for Privacy: An Experiment," Papers 2304.11415, arXiv.org, revised Nov 2024.
- Pedro Gonzalez-Fernandez, 2024. "Belief Bias Identification," Papers 2404.09297, arXiv.org, revised Nov 2024.
- Gonzalo Cisternas & Jorge Vásquez, 2022. "Misinformation in Social Media: The Role of Verification Incentives," Staff Reports 1028, Federal Reserve Bank of New York.
- Konstantinos Ioannidis, 2022. "Habitual Communication," Tinbergen Institute Discussion Papers 22-016/I, Tinbergen Institute.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Asanov, Igor, 2021. "Bandit cascade: A test of observational learning in the bandit problem," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 150-171.
- Cheng, Ing-Haw & Hsiaw, Alice, 2022.
"Distrust in experts and the origins of disagreement,"
Journal of Economic Theory, Elsevier, vol. 200(C).
- Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110, Brandeis University, Department of Economics and International Business School.
- Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R2, Brandeis University, Department of Economics and International Business School, revised Jan 2017.
- Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R3, Brandeis University, Department of Economics and International Business School, revised Mar 2018.
- Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R, Brandeis University, Department of Economics and International Business School, revised Nov 2016.
- Astier, Nicolas, 2018.
"Comparative feedbacks under incomplete information,"
Resource and Energy Economics, Elsevier, vol. 54(C), pages 90-108.
- Nicolas Astier, 2018. "Comparative Feedbacks under Incomplete Information," Post-Print hal-01465189, HAL.
- Cary Frydman & Ian Krajbich, 2022. "Using Response Times to Infer Others’ Private Information: An Application to Information Cascades," Management Science, INFORMS, vol. 68(4), pages 2970-2986, April.
- Nicolas Astier, 2016. "Comparative Feedbacks under Incomplete Information," Working Papers hal-01465189, HAL.
- Dasaratha, Krishna & He, Kevin, 2020.
"Network structure and naive sequential learning,"
Theoretical Economics, Econometric Society, vol. 15(2), May.
- Krishna Dasaratha & Kevin He, 2017. "Network Structure and Naive Sequential Learning," Papers 1703.02105, arXiv.org, revised May 2020.
- Krishna Dasaratha & Kevin He, 2019.
"Aggregative Efficiency of Bayesian Learning in Networks,"
Papers
1911.10116, arXiv.org, revised Sep 2024.
- Krishna Dasaratha & Kevin He, 2021. "Aggregative Efficiency of Bayesian Learning in Networks," PIER Working Paper Archive 21-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Theo Offerman & Giorgia Romagnoli & Andreas Ziegler, 2022.
"Why are open ascending auctions popular? The role of information aggregation and behavioral biases,"
Quantitative Economics, Econometric Society, vol. 13(2), pages 787-823, May.
- Theo Offerman & Giorgia Romagnoli & Andreas Ziegler, 2020. "Why are open ascending auctions popular? The role of information aggregation and behavioral biases," Tinbergen Institute Discussion Papers 20-071/I, Tinbergen Institute.
- March, Christoph & Ziegelmeyer, Anthony, 2020.
"Altruistic observational learning,"
Journal of Economic Theory, Elsevier, vol. 190(C).
- Christoph March & Anthony Ziegelmeyer, 2016. "Altruistic Observational Learning," CESifo Working Paper Series 5792, CESifo.
- Duffy, John & Hopkins, Ed & Kornienko, Tatiana & Ma, Mingye, 2019. "Information choice in a social learning experiment," Games and Economic Behavior, Elsevier, vol. 118(C), pages 295-315.
- Mueller-Frank, Manuel, 2024. "As strong as the weakest node: The impact of misinformation in social networks," Journal of Economic Theory, Elsevier, vol. 215(C).
- Bolte, Lukas & Fan, Tony Q., 2024. "Motivated mislearning: The case of correlation neglect," Journal of Economic Behavior & Organization, Elsevier, vol. 217(C), pages 647-663.
- Duffy, John & Hopkins, Ed & Kornienko, Tatiana, 2021. "Lone wolf or herd animal? Information choice and learning from others," European Economic Review, Elsevier, vol. 134(C).
- Dasaratha, Krishna & He, Kevin, 2021.
"An experiment on network density and sequential learning,"
Games and Economic Behavior, Elsevier, vol. 128(C), pages 182-192.
- Krishna Dasaratha & Kevin He, 2019. "An Experiment on Network Density and Sequential Learning," Papers 1909.02220, arXiv.org, revised May 2021.
- Sadler, Evan, 2020. "Innovation adoption and collective experimentation," Games and Economic Behavior, Elsevier, vol. 120(C), pages 121-131.
- Khandelwal, Vatsal, 2024. "Learning in networks with idiosyncratic agents," Games and Economic Behavior, Elsevier, vol. 144(C), pages 225-249.
More about this item
JEL classification:
- C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
- L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aecrev:v:111:y:2021:i:10:p:3160-83. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.