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Stable Randomisation
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AI
DOI：10.1093/ej/uead039.png)
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Abstract
En 中文
We design a laboratory experiment to identify whether a preference for randomisation defines a stable type across different choice environments. In games and individual decisions, subjects face 20 simultaneous repetitions of the same choice. Subjects can randomise by making different choices across the repetitions. We find that randomisation does define a type that is predictable across domains. A sizeable fraction of individuals randomise in all domains, even in questions that offer a stochastically dominant option. For some mixers, dominated randomisation is responsive to intervention. We explore theoretical foundations for mixing, and find that most preference-based models are unable to accommodate our results.
Keywords:
GROUP DECISION-MAKING
STOCHASTIC CHOICE
SOCIAL PREFERENCES
IRRATIONAL DIVERSIFICATION
EXPECTED UTILITY
RATIONAL CHOICE
PROSPECT-THEORY
RISK-TAKING
STABILITY
CONSISTENCY
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