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Alessandro Stringhi

Publications and source records attributed to Alessandro Stringhi.

3 recordsLinked to original sources

Fooling Yourself: how narratives shape beliefs

Decision-makers often receive information through narratives combining diagnostic evidence with details that carry no information useful for inference. We study whether such nondiagnostic details embedded in a narrative affect belief updating. In a laboratory experiment, participants repeatedly report incentivized beliefs in a Bayesian inference task. We implement three conditions with the same statistical structure: an urn problem with diagnostic colored balls and nondiagnostic white balls; the same problem presented as an investigation narrative with diagnostic clues pointing to one suspect and nondiagnostic clues pointing to neither; and the same narrative context with nondiagnostic clues replaced by no-information messages. We find that nondiagnostic details systematically pull participants' beliefs toward 0.5, the point of maximal uncertainty, despite Bayes' rule prescribing no revision. This response is strongest when nondiagnostic clues are embedded in the investigation narrative, although it also occurs in the urn condition. Conversely, it disappears when no-information messages replace nondiagnostic clues.

econ.GN

Museums as Policy Tools: The Behavioral Impact of Cultural Experiences

Museums can serve as policy tools when their content is purposefully curated. We designed a framed field experiment at the Santa Maria della Scala museum in Siena that leveraged the site's historical role offering care and hospitality.Student visitors randomly assigned to a tour emphasizing this function later donated more to an NGO supporting refugee than those who followed a standard artistic itinerary, with effects concentrated among female participants. These results show that thematically targeted museum experiences can measurably boost charitable behavior toward vulnerable groups, underscoring the untapped potential of cultural institutions in behavioral public policy.

econ.GN

How to improve accuracy for DFA technique

This paper extends the existing literature on empirical estimation of the confidence intervals associated to the Detrended Fluctuation Analysis (DFA). We used Montecarlo simulation to evaluate the confidence intervals. Varying the parameters in DFA technique, we point out the relationship between those and the standard deviation of H. The parameters considered are the finite time length L, the number of divisors d used and the values of those. We found that all these parameters play a crucial role, determining the accuracy of the estimation of H.

q-fin.ST