Investigating the Evolution of Ingroup Favoritism Using a Minimal Group Interaction Paradigm: The Effects of Inter- and Intragroup Interdependence

Durrheim, Kevin; Quayle, Michael; Tredoux, Colin; Titlestad, Kim Nicole; Tooke, Larry

doi:10.1371/journal.pone.0165974

Cited by 18 publications

(21 citation statements)

References 48 publications

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“…There have been a number of studies which have used dictator games to study in-group favouritism behaviour [23, 24, 25, 26, 27, 28]. Some of the earlier economic studies have compared other-regarding behaviour in naturally occurring and artificial groups using public good games [29, 30, 31] and trust games [32, 33, 34, 35].…”

Section: Introductionmentioning

confidence: 99%

In-group favouritism and out-group discrimination in naturally occurring groups

Abbink

Harris

2019

PLoS ONE

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We study in-group favouritism and out-group discrimination in a multiplayer dictator game in a naturally occuring group setting. An allocator divides a large sum of money among three groups of around 20 recipients each and also to themselves. The groups are supporters of two rival political movements in Thailand and politically neutral subjects. The non-rival out-group acts as a reference point and allows us to measure in-group favouritism and out-group discrimination. A treatment with artificial groups serves as a control. We find both in-group favouritism and out-group discrimination among the naturally occurring groups. In artificial groups, favouritism is observed, but not discrimination. Our results suggest that the two behaviours are not driven by the same motive, and only when groups are in conflict that out-group discrimination is likely to occur.

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Section: Introductionmentioning

confidence: 99%

In-group favouritism and out-group discrimination in naturally occurring groups

Abbink

Harris

2019

PLoS ONE

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“…VIAPPL, the Virtual Interaction APPLication, is a software platform for running experiments to study how social norms and identities emerge through social interaction. VIAPPL data has been analysed previously, but in an aggregated form which did not previously allow the effect of individual interactions to be studied [9]. We have introduced a modelling framework that includes this individual level detail.…”

Section: Discussionmentioning

confidence: 99%

A modelling methodology for social interaction experiments

Fennell,

Gleeson,

Quayle

et al. 2019

Preprint

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Analysis of temporal network data arising from online interactive social experiments is not possible with standard statistical methods because the assumptions of these models, such as independence of observations, are not satisfied. In this paper, we outline a modelling methodology for such experiments where, as an example, we analyse data collected using the Virtual Interaction Application (VIAPPL)a software platform for conducting experiments that reveal how social norms and identities emerge through social interaction. We apply our model to show that ingroup favouritism and reciprocity are present in the experiments, and to quantify the strengthening of these behaviours over time. Our method enables us to identify participants whose behaviour is markedly different from the norm. We use the method to provide a visualisation of the data that highlights the level of ingroup favouritism, the strong reciprocal relationships, and the different behaviour of participants in the game. While our methodology was developed with VIAPPL in mind, its usage extends to any type of social interaction data.

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“…That being said, these approaches can be seen as being complementary to each other, rather than as competing explanations. As a corollary, the position of some advocates of the reciprocity explanation have reframed their perspective such that it becomes highly congruent with the normative perspective (see Durrheim, Quayle, Tredoux, Titlestad, & Tooke, 2016). For instance, Yamagishi and Mifune (2008) stated that:…”

Section: Theoretical and Methodological Implicationsmentioning

confidence: 99%