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The cultural dynamics of copycat suicide.

Mesoudi A - PLoS ONE (2009)

Bottom Line: Here, agent-based simulations, in combination with scan statistic methods for detecting clusters of rare events, were used to clarify the social learning processes underlying point and mass clusters.It was found that social learning between neighbouring agents did generate point clusters as predicted, although this effect was partially mimicked by homophily (individuals preferentially assorting with similar others).The one-to-many transmission dynamics characterised by the mass media were shown to generate mass clusters, but only where social learning was weak, perhaps due to prestige bias (only copying prestigious celebrities) and similarity bias (only copying similar models) acting to reduce the subset of available models.

View Article: PubMed Central - PubMed

Affiliation: Biological and Experimental Psychology Group, School of Biological and Chemical Sciences, Queen Mary, University of London, London, United Kingdom. a.mesoudi@qmul.ac.uk

ABSTRACT
The observation that suicides sometimes cluster in space and/or time has led to suggestions that these clusters are caused by the social learning of suicide-related behaviours, or "copycat suicides". Point clusters are clusters of suicides localised in both time and space, and have been attributed to direct social learning from nearby individuals. Mass clusters are clusters of suicides localised in time but not space, and have been attributed to the dissemination of information concerning celebrity suicides via the mass media. Here, agent-based simulations, in combination with scan statistic methods for detecting clusters of rare events, were used to clarify the social learning processes underlying point and mass clusters. It was found that social learning between neighbouring agents did generate point clusters as predicted, although this effect was partially mimicked by homophily (individuals preferentially assorting with similar others). The one-to-many transmission dynamics characterised by the mass media were shown to generate mass clusters, but only where social learning was weak, perhaps due to prestige bias (only copying prestigious celebrities) and similarity bias (only copying similar models) acting to reduce the subset of available models. These findings can help to clarify and formalise existing hypotheses and to guide future empirical work relating to real-life copycat suicides.

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Three time series indicating (A) baseline suicide occurrences with no clustering, (B) a spatiotemporal cluster resulting from social learning, and (C) a spatial cluster resulting from homophily.Each square within the 10×10 grid indicates one 10-agent sub-group, with the colour of the square indicating the frequency of suicide from green (0%) to red (100%). In A, randomly distributed suicide events can be observed due to the non-copycat probability of suicide (p0 = 0.005). No clustering is detected under these conditions. In B, a spatiotemporal point cluster generated by social learning (s = 5) is marked with a red circle, and can be seen persisting over a period of three generations from t = 73 to t = 75 inclusive, thus showing localisation in both time and space. In C there is no social learning (s = 0), but homophily (h = 1) and large inter-group differences (q = 0.4) causes one sub-group, marked with a red circle, to be composed entirely of high suicide risk agents. This group repeatedly features suicides throughout the simulation run, forming a spatial (but not temporal) cluster despite the lack of social learning.
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pone-0007252-g001: Three time series indicating (A) baseline suicide occurrences with no clustering, (B) a spatiotemporal cluster resulting from social learning, and (C) a spatial cluster resulting from homophily.Each square within the 10×10 grid indicates one 10-agent sub-group, with the colour of the square indicating the frequency of suicide from green (0%) to red (100%). In A, randomly distributed suicide events can be observed due to the non-copycat probability of suicide (p0 = 0.005). No clustering is detected under these conditions. In B, a spatiotemporal point cluster generated by social learning (s = 5) is marked with a red circle, and can be seen persisting over a period of three generations from t = 73 to t = 75 inclusive, thus showing localisation in both time and space. In C there is no social learning (s = 0), but homophily (h = 1) and large inter-group differences (q = 0.4) causes one sub-group, marked with a red circle, to be composed entirely of high suicide risk agents. This group repeatedly features suicides throughout the simulation run, forming a spatial (but not temporal) cluster despite the lack of social learning.

Mentions: The magnitude of q thus determines the individual variation in p1 within the population. Except where indicated otherwise, in the simulations below q = 0.2; six risk factors with q = 0.2 gave a suitable range of individual variation across the population, from p0 (1−q)6 = 0.26p0 to p0 (1+q)6 = 2.99p0. Obviously risk factors in the real world are much more complex than this (e.g. age is continuous not dichotomous and there may be more or less than six factors that may interact non-independently). However, the above implementation captures the essential phenomena of individual differences in risk factors in an abstract, simplified way that is easily implemented in silico. An example time series with a small baseline risk of suicide of p0 = 0.005 and individual differences of q = 0.2 is provided in Figure 1A, which shows rare suicide events distributed randomly in time and space.


The cultural dynamics of copycat suicide.

Mesoudi A - PLoS ONE (2009)

Three time series indicating (A) baseline suicide occurrences with no clustering, (B) a spatiotemporal cluster resulting from social learning, and (C) a spatial cluster resulting from homophily.Each square within the 10×10 grid indicates one 10-agent sub-group, with the colour of the square indicating the frequency of suicide from green (0%) to red (100%). In A, randomly distributed suicide events can be observed due to the non-copycat probability of suicide (p0 = 0.005). No clustering is detected under these conditions. In B, a spatiotemporal point cluster generated by social learning (s = 5) is marked with a red circle, and can be seen persisting over a period of three generations from t = 73 to t = 75 inclusive, thus showing localisation in both time and space. In C there is no social learning (s = 0), but homophily (h = 1) and large inter-group differences (q = 0.4) causes one sub-group, marked with a red circle, to be composed entirely of high suicide risk agents. This group repeatedly features suicides throughout the simulation run, forming a spatial (but not temporal) cluster despite the lack of social learning.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2748702&req=5

pone-0007252-g001: Three time series indicating (A) baseline suicide occurrences with no clustering, (B) a spatiotemporal cluster resulting from social learning, and (C) a spatial cluster resulting from homophily.Each square within the 10×10 grid indicates one 10-agent sub-group, with the colour of the square indicating the frequency of suicide from green (0%) to red (100%). In A, randomly distributed suicide events can be observed due to the non-copycat probability of suicide (p0 = 0.005). No clustering is detected under these conditions. In B, a spatiotemporal point cluster generated by social learning (s = 5) is marked with a red circle, and can be seen persisting over a period of three generations from t = 73 to t = 75 inclusive, thus showing localisation in both time and space. In C there is no social learning (s = 0), but homophily (h = 1) and large inter-group differences (q = 0.4) causes one sub-group, marked with a red circle, to be composed entirely of high suicide risk agents. This group repeatedly features suicides throughout the simulation run, forming a spatial (but not temporal) cluster despite the lack of social learning.
Mentions: The magnitude of q thus determines the individual variation in p1 within the population. Except where indicated otherwise, in the simulations below q = 0.2; six risk factors with q = 0.2 gave a suitable range of individual variation across the population, from p0 (1−q)6 = 0.26p0 to p0 (1+q)6 = 2.99p0. Obviously risk factors in the real world are much more complex than this (e.g. age is continuous not dichotomous and there may be more or less than six factors that may interact non-independently). However, the above implementation captures the essential phenomena of individual differences in risk factors in an abstract, simplified way that is easily implemented in silico. An example time series with a small baseline risk of suicide of p0 = 0.005 and individual differences of q = 0.2 is provided in Figure 1A, which shows rare suicide events distributed randomly in time and space.

Bottom Line: Here, agent-based simulations, in combination with scan statistic methods for detecting clusters of rare events, were used to clarify the social learning processes underlying point and mass clusters.It was found that social learning between neighbouring agents did generate point clusters as predicted, although this effect was partially mimicked by homophily (individuals preferentially assorting with similar others).The one-to-many transmission dynamics characterised by the mass media were shown to generate mass clusters, but only where social learning was weak, perhaps due to prestige bias (only copying prestigious celebrities) and similarity bias (only copying similar models) acting to reduce the subset of available models.

View Article: PubMed Central - PubMed

Affiliation: Biological and Experimental Psychology Group, School of Biological and Chemical Sciences, Queen Mary, University of London, London, United Kingdom. a.mesoudi@qmul.ac.uk

ABSTRACT
The observation that suicides sometimes cluster in space and/or time has led to suggestions that these clusters are caused by the social learning of suicide-related behaviours, or "copycat suicides". Point clusters are clusters of suicides localised in both time and space, and have been attributed to direct social learning from nearby individuals. Mass clusters are clusters of suicides localised in time but not space, and have been attributed to the dissemination of information concerning celebrity suicides via the mass media. Here, agent-based simulations, in combination with scan statistic methods for detecting clusters of rare events, were used to clarify the social learning processes underlying point and mass clusters. It was found that social learning between neighbouring agents did generate point clusters as predicted, although this effect was partially mimicked by homophily (individuals preferentially assorting with similar others). The one-to-many transmission dynamics characterised by the mass media were shown to generate mass clusters, but only where social learning was weak, perhaps due to prestige bias (only copying prestigious celebrities) and similarity bias (only copying similar models) acting to reduce the subset of available models. These findings can help to clarify and formalise existing hypotheses and to guide future empirical work relating to real-life copycat suicides.

Show MeSH
Related in: MedlinePlus