Investigating the effect of changes in Signals, Noise and Agent Types in Cascade Models
Smith and SÃ¸renson (2002) depart from Bikhchandani, Hirshleifer and Welshâ€™s (1999) herding framework in three ways; non-discrete signals, noise and multiple rational agent types. They refer to the standard model of a single type with no noise as a â€œherdingâ€ model, and differentiate themselves by referring to the more general model as a â€œcascade.â€ They find that differing preferences lead to â€œconfounded learningâ€ whereby an incorrect equilibrium can arise regardless of the accuracy of the signal space, due to alternate actions taken due to preferences. In this paper we break down the three alterations from the BHW (1999) model, and analyze through agent based techniques the effect that each one has on the outcome, in an effort to determine which of the three different specifications has the greatest impact on herding and learning in the cascade model.
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