Does players’ identification affect trust and reciprocity in the lab?

Dubois, Dimitri; Willinger, Marc; Blayac, Thierry

doi:10.1016/j.joep.2011.09.012

Cited by 18 publications

(27 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Allowing reputation formation increases trust (Berg et al 1995;Boero et al 2009;Charness et al 2011;Dubois et al 2012);  Third party punishment increases trust (Charness et al 2008);  Reduction in social distance increases trust, as shown by the effect of unbinding communication in Bracht and Feltovich (2009);  Fair procedures, such as consultative voting on the preferred outcome, increase trust in Bogliacino, Jiménez and Grimalda (2015).…”

Section: Trustmentioning

confidence: 99%

Microfoundations, behaviour, and evolution: Evidence from experiments

Bogliacino

Codagnone

2021

Structural Change and Economic Dynamics

View full text Add to dashboard Cite

The article discusses whether and to what extent experiments can contribute to a research paradigm based on the study of human behaviour in complex evolving environments and on the problem of asymmetric adjustment among different components of economic system along certain trajectories, focusing on the possibility that experimental evidence may represent an external consistency check on this type of heterodox modelling. It considers the evidence on rationality of human agents, and the possibility to identify a microfoundation alternative to homo oeconomicus, discussing the evidence on humans as strong reciprocators, as trusting individuals and as embedded in social norms.

show abstract

Section: Trustmentioning

confidence: 99%

Microfoundations, behaviour, and evolution: Evidence from experiments

Bogliacino

Codagnone

2021

Structural Change and Economic Dynamics

View full text Add to dashboard Cite

show abstract

“…This trust metric as average of previous sending amounts was used later on by other research works [8], [3], [25]. However, this average trust metric can not deal with user fluctuating behavior, as we discuss in Section IV.…”

Section: Related Workmentioning

confidence: 99%

“…Our trust calculation will take into account this strategy that we call fluctuate strategy. In general, for trust games, trustworthiness of a user depends on the amount sent to her partners [8], [24]. A higher sending amount should lead to higher trustworthiness.…”

Section: Trust Calculationmentioning

confidence: 99%

“…it lasts several rounds [4], [5], [6]. The pairs of users could be fixed [7] or re-assigned before each round [8]. The total number of rounds in the experiment can be reported to users [9], [10] or not [6].…”

Section: Introductionmentioning

confidence: 99%

“…The total number of rounds in the experiment can be reported to users [9], [10] or not [6]. These games provide different kinds of partner information to players, such as their gender, age and income [11], or their past interaction history [12], [8]. Some of these games allow communication between players [9], [13] or specification of user contracts [14], [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computational Trust Model for Repeated Trust Games

Dang

Ignat²

2016

2016 IEEE Trustcom/BigDataSE/Ispa

View full text Add to dashboard Cite

To cite this version:Abstract-Trust game is a money exchange game that has been widely used in behavioral economics for studying trust and collaboration between humans. In this game, exchange of money is entirely attributable to the existence of trust between users. The trust game could be one-shot, i.e. the game ends after one round of money exchange, or repeated, i.e. it lasts several rounds. Predicting user behavior in the repeated trust game is of critical importance for the next movement of the partners. However, existing behavior prediction approaches uniquely rely on players personal information such as their age, gender and income and do not consider their past behavior in the game.In this paper, we propose a computational trust metric that is uniquely based on users past behavior and can predict the future behavior in repeated trust game. Our trust metric can distinguish between users having different behavioral profiles and is resistant to fluctuating user behavior. We validate our model by using an empirical approach against data sets collected from several trust game experiments. We show that our model is consistent with rating opinions of users, and our model can provide higher accuracy on predicting users' behavior compared with other naive models.

show abstract

Trust spillovers in the sharing economy: Does international Airbnb experience foster cross‐national trust?

Kas

Delnoij

Corten

et al. 2022

J of Consumer Behaviour

View full text Add to dashboard Cite

Sharing economy platforms commonly claim to bring about positive social impacts, such as facilitating contact between individuals that would not have met otherwise.According to contact theory, such intergroup contact would change the stereotypes that individuals hold of outgroup members, such as people with a different nationality or ethnicity. We use a large-scale online Investment Game experiment among Airbnb users to study the effect of Airbnb interactions on cross-national trust. In contrast with common claims about the positive impact of the sharing economy, we did not find that individuals who had prior experience with a nationality as a host or a guest on Airbnb trusted persons of that nationality more. This may be because monetization, institutionalization and professionalization of Airbnb limits the intensity of contact, or because Airbnb mostly establishes contact between individuals with similar backgrounds.

show abstract

Does players’ identification affect trust and reciprocity in the lab?

Cited by 18 publications

References 16 publications

Microfoundations, behaviour, and evolution: Evidence from experiments

Microfoundations, behaviour, and evolution: Evidence from experiments

Computational Trust Model for Repeated Trust Games

Trust spillovers in the sharing economy: Does international Airbnb experience foster cross‐national trust?

Contact Info

Product

Resources

About