Information Cascades in Multi-Agent Models by Arthur De Vany & Cassey Lee has a section with a useful summary of the relevant economic literature up to 1999. (For more recent overviews, see my other comment.) I copy it below, with links to the works cited (with the exception of Chen (1978) and Lee (1999), both unpublished doctoral dissertations, and De Vany and Walls (1999b), an unpublished working paper):
A seminal paper by Bikhchandani et al (1992) explains the conformity and fragility of mass behavior in terms of informational cascades. In a closely related paper Banerjee (1992) models optimizing agents who engage in herd behavior which results in an inefficient equilibrium. Anderson and Holt (1997) are able to induce information cascades in a laboratory setting by implementing a version of Bikhchandani et al (1992) model.
The second strand of literature examines the relationship between information cascades and large fluctuations. Lee (1998) shows how failures in information aggregation in a security market under sequential trading result in market volatility. Lee advances the notion of “informational avalanches” which occurs when hidden information (e.g. quality) is revealed during an informational cascade thus reversing the direction of information cascades.
The third strand explores the link between information cascades and heavy tailed distributions. Cont and Bouchaud (1998) put forward a model with random groups of imitators that gives rise to stock price variations that are heavy-tailed distributed. De Vany and Walls (1996) use a Bose-Einstein allocation model to model the box office revenue distribution in the motion picture industry. The authors describe how supply adapts dynamically to an evolving demand that is driven by an information cascade (via word-of-mouth) and show that the distribution converges to a Pareto-Lévy distribution. The ability of the Bose-Einstein allocation model to generate the Pareto size distribution of rank and revenue has been proven by Hill (1974) and Chen (1978). De Vany and Walls (1996) present empirical evidence that the size distribution of box office revenues is Pareto. Subsequent work by Walls (1997), De Vany and Walls (1999a), and Lee (1999) has verified this finding for other markets, periods and larger data sets. De Vany and Walls (1999a) show that the tail weight parameter of the Pareto-Levy distribution implies that the second moment may not be finite. Lastly, De Vany and Walls (1999b) have shown that motion picture information cascades begin as action-based, noninformative cascades, but undergo a transition to an informative cascade after enough people have seen it to exchange “word of mouth” information. At the point of transition from an uninformed to an informed cascade, there is loss of correlation and an onset of turbulence, followed by a recovery of week to week correlation among high quality movies.
It compiles a useful summary of the literature (we learnt a lot from going through on of the papers linked), and it attaches handy links to everything, which is a task which is on the one hand very helpful to other people, and on the other tedious and without many marginal benefits for the writer, and so likely to be under-incentivised.
Information Cascades in Multi-Agent Models by Arthur De Vany & Cassey Lee has a section with a useful summary of the relevant economic literature up to 1999. (For more recent overviews, see my other comment.) I copy it below, with links to the works cited (with the exception of Chen (1978) and Lee (1999), both unpublished doctoral dissertations, and De Vany and Walls (1999b), an unpublished working paper):
We (jacobjacob and Ben Pace) decided to award $100 (out of the total bounty of $800) to this answer.
It compiles a useful summary of the literature (we learnt a lot from going through on of the papers linked), and it attaches handy links to everything, which is a task which is on the one hand very helpful to other people, and on the other tedious and without many marginal benefits for the writer, and so likely to be under-incentivised.
I’ll PM you for payment details.