Identifying discrete behavioural types: a re-analysis of public goods game contributions by hierarchical clustering

Francesco Fallucchi, Andrew R. Luccasen, Theodore L. Turocy

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    Pages (from-to)238-254
    JournalJournal of the Economic Science Association
    Volume5
    Issue number2
    Early online date24 Nov 2018
    DOIs
    Publication statusPublished - Dec 2019

    Keywords

    • behavioural types
    • cluster analysis
    • machine learning
    • cooperation
    • public goods

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