Identifying discrete behavioural types: a re-analysis of public goods game contributions by hierarchical clustering
Francesco Fallucchi (),
R. Andrew Luccasen and
Theodore Turocy ()
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R. Andrew Luccasen: Mississippi University for Women
Journal of the Economic Science Association, 2019, vol. 5, issue 2, No 8, 238-254
Abstract We propose a framework for identifying discrete behavioural types in experimental data. We re-analyse data from six previous studies of public goods voluntary contribution games. Using hierarchical clustering analysis, we construct a typology of behaviour based on a similarity measure between strategies. We identify four types with distinct stereotypical behaviours, which together account for about 90% of participants. Compared to the previous approaches, our method produces a classification in which different types are more clearly distinguished in terms of strategic behaviour and the resulting economic implications.
Keywords: Behavioural types; Cluster analysis; Machine learning; Cooperation; Public goods; C65; C71; H41 (search for similar items in EconPapers)
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