Abstract

In this paper, we present an evolutionary trust game, taking punishment and protection into consideration, to investigate the formation of trust in the so-called sharing economy from a population perspective. This sharing economy trust model comprises four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Punishment in the form of penalty for untrustworthy providers and protection in the form of insurance for consumers are mechanisms adopted to prevent untrustworthy behaviour. Through comprehensive simulation experiments, we evaluate dynamics of the population for different initial population setups and effects of having penalty and insurance in place. Our results show that each player type influences the ‘existence’ and ‘survival’ of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Additionally, we observe that imposing a heavier penalty or having insurance for all consumers (trustworthy and untrustworthy) can be counterproductive for promoting trustworthiness in the population and increasing the global net wealth. Our findings have important implications for understanding trust in the context of the sharing economy, and for clarifying the usefulness of protection policies within it.

Highlights

  • Background and MotivationIn the past decades, evolutionary game theory (EGT) has been used extensively as a standard framework for understanding the emergence and maintenance of cooperation

  • Our sharing economy trust model consists of a finite set of agents occupying the nodes of a real network, and the edges denote interactions or ‘transactions’ between them

  • We carried out simulation experiments to analyse evolutionary dynamics of the population and effects of different initial configurations, punishment for untrustworthy providers, and insurance for consumers

Read more

Summary

Background and Motivation

EGT has been used extensively as a standard framework for understanding the emergence and maintenance of cooperation. They found that trust can be promoted if players are connected via a social network, and more interestingly, they showed that the heterogeneity of a network topology influences trust evolution depending on the level of difficulty of the game They studied the effects of different update rules based on the extended N-player trust game[26]. Airbnb’s policy, for instance, protects customers against accommodation listings that are unclean, unsafe, not adequately accessible, or misrepresented online by offering a refund or assisting with alternative accommodation[36] The former ride sharing platform Carpooling ( BlaBlaCar) used to offer compensatory train tickets for stranded passengers, for instance, if a ride was canceled on a short notice or when the driver did not show up[37]. Other (quasi-insurance) measures include fiduciary payment services and the promotion of codes of conduct (such as Airbnb’s non-discrimination appeal)

Methods
Results
Discussion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call