Inference by Believers in the Law of Small Numbers

By Matthew Rabin. 2000

675 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/574138537790b5b816d799e27a6126837e29804c

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Citations in the corpus

Maps of Bounded Rationality: Psychology for Behavioral Economics (2003, background): “There has also been some discussion of the role of speci c judgment biases in economic phenomena, especially in nance (e.g., Werner F. M. De Bondt and Thaler, 1985; Robert J. Shiller, 2000; Andrei Shleifer, 2000; Matthew Rabin, 2002).”
Modeling the Change of Paradigm: Non-Bayesian Reactions to Unexpected News † (2012, background): “…for example, Barberis, Shleifer, and Vishny (1998); Daniel, Hirshleifer, and Subrahmanyam (1998); Rabin and Schrag (1999); Kahneman and Frederick (2002); Mullainathan (2002); Rabin (2002); Mullainathan, Schwartzstein, and Shleifer (2008); Gennaioli and Shleifer (2010); and Rabin and Vayanos (2010).”
Psychology-based Models of Asset Prices and Trading Volume (2018, background): “Rabin (2002) presents a model of under- and over-reaction based on an incorrect belief in a law of small numbers.”
Errors in Probabilistic Reasoning and Judgment Biases (2018, positive): “This model is useful for straightforwardly elucidating this and other basic implications of belief in the LSN.”
The Bias Bias in Behavioral Economics (2018, background): “The belief in the law of small numbers has been seen as a key example of how people depart from perfect rationality, posing a radical challenge to neoclassical economic theory (Rabin, 1998), and was modeled as mistaking an urn with replacement for one with limited replacement (Rabin, 2002).”
Predicting the Next Step of a Random Walk: Experimental Evidence of Regime-Shifting Beliefs (2002, neutral): “For example, Rabin (2001), who draws on much of the same psychology literature, constructs a formal model of inference based on the law of small numbers and “local representativeness.” His model can also explain shortterm underreaction and long-term overreaction, but makes predictions based on outco”
Financial Markets Where Traders Neglect the Informational Content of Prices (2018, positive): “To compute this quantity, we note that for τηr = τηc = τη, Nr = 1, Nc = N −1 and τξ = (N −1)τη, (36) implies that”
Trading incentives to meet the analyst forecast (2006, background): “This theoretical result obtains both when a) investors have behavioral biases (e.g., Rabin, 2002), and (b) when investors are rational, but have incomplete information about the firm’s future prospects (Hart & Kreps, 1986; Stein, 1987).”