The evolution of lying in well-mixed populations

Capraro, Valerio ORCID: https://orcid.org/0000-0002-0579-0166, Perc, Matjaz and Vilone, Daniele ORCID: https://orcid.org/0000-0002-3485-9249 (2019) The evolution of lying in well-mixed populations. Journal of The Royal Society Interface, 16 (156). p. 20190211. ISSN 1742-5689 (doi:10.1098/rsif.2019.0211)

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Abstract

Lies can have profoundly negative consequences for individuals, groups and even for societies. Understanding how lying evolves and when it proliferates is therefore of significant importance for our personal and societal well-being. To that effect, we here study the sender–receiver game in well-mixed populations with methods of statistical physics. We use the Monte Carlo method to determine the stationary frequencies of liars and believers for four different lie types. We consider altruistic white lies that favour the receiver at a cost to the sender, black lies that favour the sender at a cost to the receiver, spiteful lies that harm both the sender and the receiver, and Pareto white lies that favour both the sender and the receiver. We find that spiteful lies give rise to trivial behaviour, where senders quickly learn that their best strategy is to send a truthful message, while receivers likewise quickly learn that their best strategy is to believe the sender’s message. For altruistic white lies and black lies, we find that most senders lie while most receivers do not believe the sender’s message, but the exact frequencies of liars and non-believers depend significantly on the payoffs, and they also evolve non-monotonically before reaching the stationary state. Lastly, for Pareto white lies we observe the most complex dynamics, with the possibility of both lying and believing evolving with all frequencies between 0 and 1 in dependence on the payoffs. We discuss the implications of these results for moral behaviour in human experiments.

Item Type: Article
Additional Information: Online ISSN: 1742-5662
Keywords (uncontrolled): Biotechnology, Biophysics, Biochemistry, Bioengineering, Biomaterials, Biomedical Engineering
Research Areas: A. > Business School > Economics
Item ID: 27340
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 12 Aug 2019 09:45
Last Modified: 15 Sep 2019 00:21
URI: https://eprints.mdx.ac.uk/id/eprint/27340

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