Following the Crowd
Imagine choosing between two restaurants. You have a slight preference for Restaurant A, but you see a long queue at Restaurant B. You reason: all those people probably know something I don't. So you join the queue at B. The next person sees an even longer queue at B and does the same. This is an information cascade — a chain of rational imitation that can lead everyone to the same choice, even if that choice is wrong. The queue at B might have started with just two people who happened to arrive first.
The Mechanics of Cascading
Each agent in the simulation receives a private signal about which option (A or B) is truly better. The signal is accurate with probability 'signal_quality' (0.7 = 70% correct). Agents also observe all previous choices. Using Bayesian reasoning, each agent combines their private signal with the social evidence. Once the social evidence becomes strong enough (exceeding the cascade threshold), agents ignore their private signals entirely and follow the crowd. This is individually rational but collectively fragile.
Correct vs Wrong Cascades
The simulation color-codes outcomes: cyan for correct cascades (the herd chose the truly better option) and red for wrong cascades (the herd converged on the inferior option). Run it many times to see that wrong cascades are disturbingly common — sometimes 20-30% of runs produce incorrect herds. The first 2-3 agents' random signals determine the fate of all subsequent agents. This is the fundamental fragility that Bikhchandani, Hirshleifer, and Welch identified in their seminal 1992 paper.
Breaking the Cascade
Information cascades are fragile precisely because they are based on observed behavior, not deep conviction. A single piece of high-quality contrary information can shatter a cascade, causing rapid reversal. In financial markets, this explains both bubbles (wrong cascades forming) and crashes (cascades breaking). The social weight parameter controls how much agents defer to social information versus their own signals. Reduce it to see more independent thinking — and more accurate aggregate outcomes. Increase it to see faster but more fragile cascades.