Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/32049
Appears in Collections: | Economics Journal Articles |
Peer Review Status: | Refereed |
Title: | Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence |
Author(s): | Chorus, Caspar van Cranenburgh, Sander Daniel, Aemiro Melkamu Sandorf, Erlend Dancke Sobhani, Anae Szép, Teodora |
Keywords: | Obfuscation Signaling Choice behavior Preferences Hiding |
Issue Date: | Jan-2021 |
Date Deposited: | 3-Dec-2020 |
Citation: | Chorus C, van Cranenburgh S, Daniel AM, Sandorf ED, Sobhani A & Szép T (2021) Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence. Mathematical Social Sciences, 109, pp. 28-44. https://doi.org/10.1016/j.mathsocsci.2020.10.002 |
Abstract: | Theories of decision-making are routinely based on the notion that decision-makers choose alternatives which align with their underlying preferences—and hence that their preferences can be inferred from their choices. In some situations, however, a decision-maker may wish to hide his or her preferences from an onlooker. This paper argues that such obfuscation-based choice behavior is likely to be relevant in various situations, such as political decision-making. This paper puts forward a simple and tractable discrete choice model of obfuscation-based choice behavior, by combining the well-known concepts of Bayesian inference and information entropy. After deriving the model and illustrating some key properties, the paper presents the results of an obfuscation game that was designed to explore whether decision-makers, when properly incentivized, would be able to obfuscate effectively, and which heuristics they employ to do so. Together, the analyses presented in this paper provide stepping stones towards a more profound understanding of obfuscation-based decision-making. |
DOI Link: | 10.1016/j.mathsocsci.2020.10.002 |
Rights: | This is an open access article distributed under the terms of the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article. |
Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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1-s2.0-S0165489620300913-main.pdf | Fulltext - Published Version | 1.49 MB | Adobe PDF | View/Open |
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