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Abstract This chapter highlights the importance of simplicity, but in a different way — it argues that, where people sample statistical information from experience, they tend to rely on surprisingly small samples (e.g. focusing on a few recent events). It presents a formal analysis of possible reasons behind this reliance on small samples. Three key results emerge. First, small samples amplify the difference between the average earnings associated with the payoff distributions, thus rendering choices simpler. Second, people who use a simple choice heuristic or choose in accordance with prospect theory benefit from this amplification effect, whereas Bayesian updates do not. Third, although not providing a strictly veridical portrayal of the world, small samples can give rise to surprisingly competitive choices.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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