Editing Information cascade (section)

==Examples and fields of application==
Information cascades occur in situations where seeing many people make the same choice provides evidence that outweighs one's own judgment.  That is, one thinks:  "It's more likely that I'm wrong than that all those other people are wrong.  Therefore, I will do as they do."

In what has been termed a [[reputational cascade]], late responders sometimes go along with the decisions of early responders, not just because the late responders think the early responders are right, but also because they perceive their reputation will be damaged if they dissent from the early responders.<ref>{{cite journal |last1=Lemieux |first1=Pierre |title=Following the herd: why do some ideas suddenly become popular, and then die out just as quickly? |journal=Regulation |date=22 December 2003 |volume=26 |issue=4 |pages=16–22 |id={{Gale|A113304115}} |ssrn=505764 }}</ref>

=== Market cascades ===
Information cascades have become one of the topics of [[behavioral economics]], as they are often seen in financial markets where they can feed speculation and create cumulative and excessive [[market price|price moves]], either for the whole market ([[bubble (economics)|market bubble]]) or a specific asset, like a stock that becomes overly popular among investors.{{fact|date=March 2023}}

Marketers also use the idea of cascades to attempt to get a buying cascade started for a new product. If they can induce an initial set of people to adopt the new product, then those who make purchasing decisions later on may also adopt the product even if it is no better than, or perhaps even worse than, competing products. 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. This is consistent with the idea that cascades arise naturally when people can see what others do but not what they know.{{fact|date=March 2023}}

An example is Hollywood movies. 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.{{fact|date=March 2023}}

Information cascades are usually considered by economists:{{fact|date=March 2023}}
* as products of [[rational expectations]] at their start,
* as irrational [[herd behavior]] if they persist for too long, which signals that collective emotions come also into play to feed the cascade.

=== Social networks and social media ===
{{see|social network analysis}}
Dotey et al.<ref>{{cite web|author=Dotey, A., Rom, H. and Vaca C.|title=Information Diffusion in Social Media|year=2011|publisher=Stanford University|url=https://snap.stanford.edu/class/cs224w-2011/proj/mrom_Finalwriteup_v1.pdf}}</ref> state that information flows in the form of cascades on the [[social network]]. According to the authors, analysis of [[viral phenomenon|virality]] of information cascades on a social network may lead to many useful applications like determining the most influential individuals within a network. This information can be used for ''maximizing market effectiveness'' or ''influencing [[public opinion]]''. Various structural and temporal features of a network affect cascade virality. Additionally, these models are widely exploited in the problem of [[Rumor spread in social network]] to investigate it and reduce its influence in online social networks.

In contrast to work on information cascades in social networks, the [[social influence]] model of [[belief spread]] argues that people have some notion of the private beliefs of those in their network.<ref name="Friedkin Johnsen 2009 p. ">{{cite book | last1=Friedkin | first1=Noah E. | last2=Johnsen | first2=Eugene C. | title=Social Influence Network Theory | publisher=Cambridge University Press | location=Cambridge | year=2009 | isbn=978-0-511-97673-5 | doi=10.1017/cbo9780511976735 }}</ref>  The social influence model, then, relaxes the assumption of information cascades that people are acting only on observable actions taken by others.  In addition, the social influence model focuses on embedding people within a social network, as opposed to a queue.  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.

Information cascades can also restructure the social networks that they pass through. 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.<ref name=":0">{{cite book |doi=10.1145/2566486.2568043 |arxiv=1403.2732 |s2cid=6353961 |chapter=The bursty dynamics of the Twitter information network |title=Proceedings of the 23rd international conference on World wide web |year=2014 |last1=Myers |first1=Seth A. |last2=Leskovec |first2=Jure |pages=913–924 |isbn=978-1-4503-2744-2 }}</ref> 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 mixing|assortative social networks]], where people tend to be connected to others who are similar in some characteristic. Tweet cascades increase in the similarity between connected users, as users lose ties to more dissimilar users and add new ties to similar users.<ref name=":0" /> Information cascades created by news coverage in the media may also foster [[political polarization]] by [[Echo chamber (media)|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.<ref>{{cite journal |last1=Tokita |first1=Christopher K. |last2=Guess |first2=Andrew M. |last3=Tarnita |first3=Corina E. |title=Polarized information ecosystems can reorganize social networks via information cascades |journal=Proceedings of the National Academy of Sciences |date=14 December 2021 |volume=118 |issue=50 |pages=e2102147118 |doi=10.1073/pnas.2102147118 |pmc=8685718 |pmid=34876511 |bibcode=2021PNAS..11802147T |doi-access=free }}</ref>

=== Historical examples ===
* Small protests began in [[Leipzig]], Germany in 1989 with just a handful of activists challenging the [[German Democratic Republic]].<ref name=shirky>{{cite book
  | last = Shirky
  | first = Clay
  | author-link = Clay Shirky
  | title = Here Comes Everybody: The Power of Organizing Without Organizations
  | publisher = [[Penguin Press]]
  | location = New York
  | year = 2008
  | pages = [https://archive.org/details/herecomeseverybo0000shir/page/161 161–164]
  | isbn = 978-1-59420-153-0
  | title-link = Here Comes Everybody: The Power of Organizing Without Organizations
  }}</ref> For almost a year, protesters met every Monday growing by a few people each time.<ref name=shirky/> By the time the government attempted to address it in September 1989, it was too big to quash.<ref name=shirky/> In October, the number of protesters reached 100,000 and by the first Monday in November, over 400,000 people marched the streets of Leipzig. Two days later the [[Berlin Wall]] was dismantled.<ref name=shirky/>

* The adoption rate of drought-resistant hybrid seed corn during the [[Great Depression]] and [[Dust Bowl]] was slow despite its significant improvement over the previously available seed corn. Researchers at [[Iowa State University]] were interested in understanding the public's hesitation to the adoption of this significantly improved technology. After conducting 259 interviews with farmers<ref name=carboneau>{{cite journal
| last = Carboneau
| first = Clark
| title = Using Diffusion of Innovations and Academic Detailing to Spread Evidence-based Practices
| doi = 10.1111/j.1945-1474.2005.tb01117.x
| pmid = 16190312
| journal = [[Journal for Healthcare Quality]]
| volume=27
| issue = 2
| year=2005
| pages=48–52| s2cid = 6946662
}}</ref> it was observed that the slow rate of adoption was due to how the farmers valued the opinion of their friends and neighbors instead of the word of a salesman. See<ref name=difproc>{{cite journal
 |title       = The Diffusion Process
 |author      = Beal, George M.
 |author2     = Bohlen, Joe M.
 |url         = http://www.soc.iastate.edu/extension/presentations/publications/comm/Diffusion%20Process.pdf
 |journal = Special Report No. 18
 |date = November 1981
 |access-date  = 2008-11-11
 |publisher   = Iowa State University of Science and Technology of Ames, Iowa
 |url-status     = dead
 |archive-url  = https://web.archive.org/web/20090408212658/http://www.soc.iastate.edu/extension/presentations/publications/comm/Diffusion%20Process.pdf
 |archive-date = 2009-04-08
}}</ref> for the original report.

=== Empirical studies ===
In addition to the examples above, Information Cascades have been shown to exist in several empirical studies.  Perhaps the best example, given above, is.<ref name="Anderson"/> Participants stood in a line behind an urn which had balls of different colors.  Sequentially, participants would pick a ball out of the urn, look at it, and place it back into the urn.  The agent then voices their opinion of which color of ball (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
* De Vany and Walls<ref>{{cite journal|last=De Vany|first=A.|author2=D. Walls|title=Uncertainty in the movie industry: does star power reduce the terror of the box office?|journal=Journal of Cultural Economics|year=1999|volume=23|issue=4|pages=285–318|doi=10.1023/a:1007608125988|s2cid=54614446}}</ref>  create a statistical model of information cascades where an action is required.  They apply this model to the actions people take to go see a movie that has come out at the theatre.  De Vany and Walls validate their model on this data, finding a similar [[Pareto distribution]] of revenue for different movies.
* Walden and Browne also adopt the original Information Cascade model, here into an operational model more practical for real world studies, which allows for analysis based on observed variables.  Walden and Browne test their model on data about adoption of new technologies by businesses, finding support for their hypothesis that information cascades play a role in this adoption<ref>{{cite journal|last1=Walden|first1=Eric|last2=Browne|first2=Glenn|title=Information Cascades in the Adoption of New Technology|journal=ICIS Proceedings|year=2002|url=http://aisel.aisnet.org/icis2002/40/}}</ref>