An information cascade or informational cascade is a phenomenon described in behavioral economics and network theory in which a number of people make the same decision in a sequential fashion. It is similar to, but distinct from herd behavior.

An information cascade is generally accepted as a two-step process. For a cascade to begin an individual must encounter a scenario with a decision, typically a binary one. Second, outside factors can influence this decision, such as the individual observing others' choices and the apparent outcomes.

The two-step process of an informational cascade can be broken down into five basic components:

Social perspectives of cascades, which suggest that agents may act irrationally (e.g., against what they think is optimal) when social pressures are great, exist as complements to the concept of information cascades. More often the problem is that the concept of an information cascade is confused with ideas that do not match the two key conditions of the process, such as social proof, information diffusion, and social influence. Indeed, the term information cascade has even been used to refer to such processes.

This section provides some basic examples of information cascades, as originally described by Bikchandani et al. (1992). The basic model has since been developed in a variety of directions to examine its robustness and better understand its implications.

Information cascades occur when external information obtained from previous participants in an event overrides one's own private signal, irrespective of the correctness of the former over the latter. The experiment conducted by Anderson is a useful example of this process. The experiment consisted of two urns labeled A and B. Urn A contains two balls labeled "a" and one labeled "b". Urn B contains one ball labeled "a" and two labeled "b". The urn from which a ball must be drawn during each run is determined randomly and with equal probabilities (from the throw of a dice). The contents of the chosen urn are emptied into a neutral container. The participants are then asked in random order to draw a marble from this container. This entire process may be termed a "run", and a number of such runs are performed.

Each time a participant picks up a marble, he is to decide which urn it belongs to. His decision is then announced for the benefit of the remaining participants in the room. Thus, the (n+1)th participant has information about the decisions made by all the n participants preceding him, and also his private signal which is the label on the ball that he draws during his turn. The experimenters observed that an information cascade was observed in 41 of 56 such runs. This means, in the runs where the cascade occurred, at least one participant gave precedence to earlier decisions over his own private signal. It is possible for such an occurrence to produce the wrong result. This phenomenon is known as "Reverse Cascade".

A person's signal telling them to accept is denoted as H (a high signal, where high signifies he should accept), and a signal telling them not to accept is L (a low signal). The model assumes that when the correct decision is to accept, individuals will be more likely to see an H, and conversely, when the correct decision is to reject, individuals are more likely to see an L signal. This is essentially a conditional probability – the probability of H when the correct action is to accept, or ${\displaystyle P[H|A]}$. Similarly ${\displaystyle P[L|R]}$ is the probability that an agent gets an L signal when the correct action is reject. If these likelihoods are represented by q, then q > 0.5. This is summarized in the table below.