Fig 1.
Illustrative samples of individuals with different levels of richness and evenness.
Four hypothetical examples with low-to-high levels of richness and evenness are shown in (A). The calculated identity diversity with regards to each sample’s richness, evenness, and their influence on the level of diversity is shown in (B). Samples’ diversity was calculated using an information theoretic measure built on Shannon’s entropy formula.
Fig 2.
Seven micro-motifs and their corresponding network structure (Graphlet).
These micro-motifs exemplifying common patterns of perceived causation in cognitive maps.
Fig 3.
Comparison of intra-group (i.e., within social groups of stakeholders) versus inter-group (i.e., between social groups of stakeholders) pairwise cognitive distances.
Independent sample t-tests were used to compare the means, and p-values demonstrate significance of their difference. (A-E) show the distribution of inter-group versus intra-group cognitive distances for five social groups of stakeholders. Overall results for all individuals is shown in (F). Note that in (F) the nonparametric Wilcoxon-Mann-Whitney U test is also significant (p = 0.04).
Fig 4.
Correlation between identity and cognitive diversity.
The figure shows the result of 100 randomly generated samples of size N = 33 individuals. The shaded area represents 95% confidence interval estimated by bootstrapping the sample 1000 times.
Fig 5.
The proximity of identity and cognitive diversities based on micro-motifs in cognitive maps.
Principle component analysis was performed on the seven dimensions of micro-motif frequencies in 1000 random cognitive maps (with 200 individual maps were re-produced form the probability distribution of cognitive maps of each of the five social groups). Two principle components were retained, cumulatively explaining about 85% of variance. Individual maps are illustrated by points in a 2-dimensional principle component scatter plot where points are clustered by their predefined social identities in (A) and by a K-Mean clustering algorithm using the Euclidian distances between points in (B). Black triangles in (A) and (B) illustrate the center of the clusters based on K-nearest neighborhood. The probability of concurrencies of social identities and cognitive clusters is shown in (C).