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Games
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The Economics of Privacy: Privacy and Data Protection in a...
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The Economics of Privacy: Privacy and Data Protection in a Digital World

A special issue of Games (ISSN 2073-4336).

Deadline for manuscript submissions: closed (31 March 2018) | Viewed by 22859

Special Issue Editors

Prof. Dr. Dorothea Kübler

E-Mail Website
Guest Editor
1. WZB Social Science Center Berlin, D-10785 Berlin, Germany
2. Technical University Berlin, Faculty of Economics and Management, 10623 Berlin, Germany
Interests: experimental and behavioral economics; market design; economics of privacy; education and labor economics
Prof. Dr. Hans-Theo Normann

E-Mail Website
Guest Editor
Duesseldorf Institute for Competition Economics (DICE), Universitaetsstr. 1, 40225 Duesseldorf, Germany
Interests: industrial organization; game theory; experimental economics; behavioral economics; more specifically: cooperation and collusion; communication in experimental games; social preferences; economics of privacy

Special Issue Information

Dear Colleagues,

This Special Issue of Games is dedicated to the economics of privacy. In a digital world, topics like online privacy and the protection of personal data have become increasingly important. We invite game theoretical and experimental contributions on the economics of privacy. Potential topics include, but are by no means limited to, the valuation of personal data, online search, data security, information aggregation, targeted advertising, price discrimination, as well behavioral effects in digital markets. We list below further keywords that may help to identify suitable topics for the Special Issue.

Prof. Dr. Dorothea Kübler
Prof. Dr. Hans-Theo Normann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Games is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • privacy
  • digitization
  • information
  • algorithms
  • data mining
  • fairness
  • privacy paradox

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Published Papers (4 papers)

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Research

14 pages, 1042 KiB  
Article
Personal-Data Disclosure in a Field Experiment: Evidence on Explicit Prices, Political Attitudes, and Privacy Preferences
by Joachim Plesch and Irenaeus Wolff
Games 2018, 9(2), 24; https://doi.org/10.3390/g9020024 - 10 May 2018
Cited by 2 | Viewed by 4813
Abstract
Many people implicitly sell or give away their data when using online services and participating in loyalty programmes—despite growing concerns about company’s use of private data. Our paper studies potential reasons and co-variates that contribute to resolving this apparent paradox, which has not [...] Read more.
Many people implicitly sell or give away their data when using online services and participating in loyalty programmes—despite growing concerns about company’s use of private data. Our paper studies potential reasons and co-variates that contribute to resolving this apparent paradox, which has not been studied previously. We ask customers of a bakery delivery service for their consent to disclose their personal data to a third party in exchange for a monetary rebate on their past orders. We study the role of implicitly and explicitly stated prices and add new determinants such as political orientation, income proxies and membership in loyalty programmes to the analysis of privacy decision. We document large heterogeneity in privacy valuations, and that the offered monetary benefits have less predictive power for data-disclosure decisions than expected. However, we find significant predictors of such decisions, such as political orientation towards liberal democrats (FDP) and membership in loyalty programmes. We also find suggestive evidence that loyalty programmes are successful in disguising their “money for data” exchange mechanism. Full article
(This article belongs to the Special Issue The Economics of Privacy: Privacy and Data Protection in a Digital World)
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17 pages, 390 KiB  
Article
Voluntary Disclosure of Private Information and Unraveling in the Market for Lemons: An Experiment
by Volker Benndorf
Games 2018, 9(2), 23; https://doi.org/10.3390/g9020023 - 10 May 2018
Cited by 4 | Viewed by 5440
Abstract
We experimentally analyze a lemons market with a labor-market framing. Sellers are referred to as “workers” and have the possibility to provide “employers” with costly but credible information about their “productivity”. Economic theory suggests that in this setup, unraveling takes place and a [...] Read more.
We experimentally analyze a lemons market with a labor-market framing. Sellers are referred to as “workers” and have the possibility to provide “employers” with costly but credible information about their “productivity”. Economic theory suggests that in this setup, unraveling takes place and a number of different types are correctly identified in equilibrium. While we do observe a substantial degree of information disclosure, we also find that unraveling is typically not as complete as predicted by economic theory. The behavior of both workers and employers impedes unraveling in that there is too little disclosure. Workers are generally reluctant to disclose their private information, and employers enforce this behavior by bidding less competitively if workers reveal compared to the case where they conceal information. Full article
(This article belongs to the Special Issue The Economics of Privacy: Privacy and Data Protection in a Digital World)
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18 pages, 281 KiB  
Article
Generalized Trust, Need for Cognitive Closure, and the Perceived Acceptability of Personal Data Collection
by David Chavanne
Games 2018, 9(2), 18; https://doi.org/10.3390/g9020018 - 13 Apr 2018
Cited by 3 | Viewed by 5537
Abstract
This vignette-based study examines how generalized trust and the need for cognitive closure relate to the perceived acceptability of contemporary business methods of personal data collection. Subjects are exposed to four scenarios that describe a method of personal data collection, involving either brand-name [...] Read more.
This vignette-based study examines how generalized trust and the need for cognitive closure relate to the perceived acceptability of contemporary business methods of personal data collection. Subjects are exposed to four scenarios that describe a method of personal data collection, involving either brand-name companies or generic descriptors of companies. After each scenario, subjects rate how acceptable they find the practice of data collection, along with the frequency and quality of experiences that they have had with the company (for brand names) or type of company (for generic descriptors). Judgments of perceived acceptability are analyzed, both across the portfolio of judgments and within each separate scenario. While analyses of each separate scenario point to the context-dependency of the perceived acceptability of data collection, several results stand out when analyzing the subjects’ portfolios of responses in the aggregate. Higher generalized trust is linked to a higher average acceptability rating, and the effect is stronger when companies are described with brand names rather than generic descriptors. Uniformly, however, no relationship is found between need for cognitive closure and perceived acceptability. Additionally, positive experiences are found to be a stronger predictor of perceived acceptability of data collection than frequency of use. Full article
(This article belongs to the Special Issue The Economics of Privacy: Privacy and Data Protection in a Digital World)
23 pages, 3202 KiB  
Article
Does Imperfect Data Privacy Stop People from Collecting Personal Data?
by Simeon Schudy and Verena Utikal
Games 2018, 9(1), 14; https://doi.org/10.3390/g9010014 - 5 Mar 2018
Cited by 3 | Viewed by 6331
Abstract
Many companies try to access personal information to discriminate among consumers. We analyse how privacy regulations affect the acquisition and disclosure of information in a simple game of persuasion. Theory predicts that no data will be acquired with Disclosure Duty of collected data [...] Read more.
Many companies try to access personal information to discriminate among consumers. We analyse how privacy regulations affect the acquisition and disclosure of information in a simple game of persuasion. Theory predicts that no data will be acquired with Disclosure Duty of collected data whereas Consent Law with perfect privacy results in complete information acquisition. Imperfect privacy, i.e., an environment in which leaks of collected data are possible, gives rise to multiple equilibria. Results from a laboratory experiment confirm the qualitative differences between Consent Law and Disclosure Duty and show that imperfect privacy does not stop people from collecting personal information. Full article
(This article belongs to the Special Issue The Economics of Privacy: Privacy and Data Protection in a Digital World)
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