Information Cascade

An information cascade occurs when people abandon their private signal and copy the observed actions of others, so that later actors learn nothing from the choices of those before them — only from the pattern of choices. Each person rationally ignores what they know and follows the crowd. The result: a few early movers can lock in a belief or behaviour that is wrong, and the cascade perpetuates even when most individuals would have chosen differently with access to their own information alone.

The mechanism is sequential observation. Person 1 acts on private information. Person 2 sees 1's action; if 2's private signal is weak, 2 may rationally imitate 1. Person 3 sees 1 and 2; if both chose A, 3 may choose A even when 3's signal says B — because the public history outweighs 3's single observation. Once a run of identical choices appears, later players optimally ignore their own signals and follow. The cascade is informationally inefficient: the pool of private information is never aggregated. One bad early draw can send everyone off a cliff.

Cascades show up in adoption (everyone uses the same tech because others do), fashion, financial bubbles (buy because others are buying), and corporate consensus (the board nods because the first speaker nodded). The key insight from Bikhchandani, Hirshleifer and Welch (1992) is that cascades are fragile. A single contradictory public signal can shatter them. They also tend to be wrong when the early movers were unlucky or when the environment rewards conformity over accuracy. Defending against cascades means either breaking the observational sequence (reveal private info before others act) or injecting a cost to copying (skin in the game, contrarian incentives).

In strategy, the takeaway is to separate information-based convergence from cascade-based convergence. When everyone agrees because the evidence points one way, that's healthy. When everyone agrees because the first few agreed and later actors rationally imitated, the consensus is fragile and may be wrong. The same observable outcome — unanimity — can have different causes. Diagnosing which you're in determines whether to follow the crowd or break the cascade.