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 language | English |
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Pages (from-to) | 238-254 |
Journal | Journal of the Economic Science Association |
Volume | 5 |
Issue number | 2 |
Early online date | 24 Nov 2018 |
DOIs | |
Publication status | Published - Dec 2019 |
Keywords
- behavioural types
- cluster analysis
- machine learning
- cooperation
- public goods