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:
There is a decision to be made – for example; whether to adopt a new technology, wear a new style of clothing, eat in a new restaurant, or support a particular political position A limited action space exists (e.g. an adopt/reject decision) People make the decision sequentially, and each person can observe the choices made by those who acted earlier Each person has some information aside from their own that helps guide their decision A person can't directly observe the outside information that other people know, but he or she can make inferences about this information from what they do 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.
Basic model 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.
Qualitative example 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".
Quantitative description 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 P [ H | A ] {\displaystyle P[H|A]} . Similarly P [ L | R ] {\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.
The first agent determines whether or not to accept solely based on his own signal. As the model assumes that all agents act rationally, the action (accept or reject) the agent feels is more likely is the action he will choose to take. This decision can be explained using Bayes' rule:
… excerpt ends here. Continue reading the full article.
