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.
[6] This section provides some basic examples of information cascades, as originally described by Bikchandani et al.
[7] The basic model has since been developed in a variety of directions to examine its robustness and better understand its implications.
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).
This means, in the runs where the cascade occurred, at least one participant gave precedence to earlier decisions over his own private signal.
Similarly, it can be shown that an agent will always decrease his expectation of p when he receives a low signal.
He makes a decision based on Bayesian reasoning to determine the most rational choice.
The decision is based on how the value on the right hand side of the equation compares with p.[11] The original model makes several assumptions about human behavior and the world in which humans act,[7] some of which are relaxed in later versions[11] or in alternate definitions of similar problems, such as the diffusion of innovations.
Daniel Sgroi (2002)[14] shows that firms might use "guinea pigs" who are given the opportunity to buy early to kick-start an informational cascade through their early and public purchasing decisions, and work by David Gill and Daniel Sgroi (2008)[15] show that early public tests might have a similar effect (and in particular that passing a "tough test" which is biased against the seller can instigate a cascade all by itself).
Information cascades occur in situations where seeing many people make the same choice provides evidence that outweighs one's own judgment.
This is most effective if these later consumers are able to observe the adoption decisions, but not how satisfied the early customers actually were with the choice.
If test screenings suggest a big-budget movie might be a flop, studios often decide to spend more on initial marketing rather than less, with the aim of making as much money as possible on the opening weekend, before word gets around that it's a turkey.
[19] The social influence model, then, relaxes the assumption of information cascades that people are acting only on observable actions taken by others.
Finally, the social influence model relaxes the assumption of the information cascade model that people will either complete an action or not by allowing for a continuous scale of the "strength" of an agents belief that an action should be completed.
For example, while there is a constant low level of churn in social ties on Twitter—in any given month, about 9% of all social connections change—there is often a spike in follow and unfollow activity following an information cascade, such as the sharing of a viral tweet.
[20] As the tweet-sharing cascade passes through the network, users adjust their social ties, particularly those connected to the original author of the viral tweet: the author of a viral tweet will see both a sudden loss in previous followers and a sudden increase in new followers.
As a part of this cascade-driven reorganization process, information cascades can also create assortative social networks, where people tend to be connected to others who are similar in some characteristic.
[20] Information cascades created by news coverage in the media may also foster political polarization by sorting social networks along political lines: Twitter users who follow and share more polarized news coverage tend to lose social ties to users of the opposite ideology.
[21] In addition to the examples above, Information Cascades have been shown to exist in several empirical studies.
The agent then voices their opinion of which color of balls (red or blue) there is a majority of in the urn for the rest of the participants to hear.
Participants get a monetary reward if they guess correctly, forcing the concept of rationality.
Other examples include The negative effects of informational cascades sometimes become a legal concern and laws have been enacted to neutralize them.
Ward Farnsworth, a law professor, analyzed the legal aspects of informational cascades and gave several examples in his book The Legal Analyst: in many military courts, the officers voting to decide a case vote in reverse rank order (the officer of the lowest rank votes first), and he suggested it may be done so the lower-ranked officers would not be tempted by the cascade to vote with the more senior officers, who are believed to have more accurate judgement; another example is that countries such as Israel and France have laws that prohibit polling days or weeks before elections to prevent the effect of informational cascade that may influence the election results.
[27] One informational cascade study compared thought processes between Greek and German organic farmers, suggesting discrepancies based upon cultural and socioeconomic differences.
In 2004 Helmut Wagner and Wolfram Berger suggested cascades as an analytical vehicle to examine changes to the financial market as it became more globalized.
Wagner and Berger noticed structural changes to the framework of understanding financial markets due to globalization; giving rise to volatility in capital flow and spawning uncertainty which affected central banks.
When the attack by Black September occurred in 1972 it was hard not to see the similarities between their tactics and the Baader-Meinhof group (also known as the Red Army Faction [RAF]).
Establishing a foundation to understanding the passage of information through transnational and multinational organizations, and even more, is critical to the arising modern society.
[31] Summing up all of these points, cascades, as a general term, encompass a spectrum of different concepts.