An informational cascades is a situation in which every subsequent
actor, based on the observations of others, makes the same choice
independent of his/her private signal.
Behavior: In an informational cascades everyone is individually
acting rationally. Still, even if all participants as a collective
have overwhelming information in favor of the correct action,
each and every participant may take the wrong action. The probability
that everyone is taking the wrong action is less than 50%, but
it is easy to construct examples in which everyone is wrong with
A little bit of public information (or an unusual signal) can
overturn a long-standing informational cascades. That is, even
though a million people may have chosen one action, seemingly
little information can induce the next million people to choose
the opposite action. Fragility is an integral component of the
Informational cascades theory!
A Simple Example:
Let us presume that you and a lot of other people have to find
your way to a new destination, and you come to a crossway where
you can only either go left or right. Everyone has a private imperfect
signal (call it "judgment" or "opinion").
For simplicity, let everyone have a private signal "left"
("right") with probability 2/3 if the true best choice
is to go left (right). So, the signal helps but it is not perfect.
Everyone's signal is equally good. Now, assume that you are the
third person to choose, and you first saw a man and then a woman
go left. I claim that it is optimal for you to go "left"
even if your private signal/intuition says "right".
Why? You know that the man must have had an "l" signal,
because he went left. The woman saw the man go "left."
She would have figured out that the first individual's signal
was "left". If her private signal was "left",
she would have surely walked left, too. If her signal was "right",
she would have been aware of one right and one left signal. She
might have walked either way. Now it is your turn. Having seen
both the man and the woman walk "left," you know that
the man had a "left" signal and the woman had a better
than even chance of having had a "left" signal. Loosely
speaking, the actions of your predecessors give you more than
1 "left" signal. Even if your private information is
1 "right" signal, net-in-net you should choose "left"
if you are acting rationally---and so will everyone choosing after
you. Now, everyone after you will know that what you did had nothing
to do with your private information---but they will be in the
same boat. The optimal decision will be to do the same thing and
go left. One major consequence of informational cascades is that
you may get a million rational individuals walking "left"
just because the first two individuals walked "left",
even if the true best choice was "right." (This will
happen with more than 1/3*1/3=1/9 probability [first two individuals
got incorrect "left" signals].)
So, what does this
mean for society? Cascades predict that you can get massive social
imitation, occasionally leading everyone (the "herd")
to the incorrect choice. (Because everyone knows that there is
very little information in a cascade, cascades are "fragile";
a little bit of new public information can make a big difference).
Informational Cascades from Herd Behavior in the Laboratory,
Celen and Kariv, 2003
Two phenomena that have elicited particular interest are informational
cascades and herd behavior, which can arise in a wide variety of
economic circumstances. These phenomena have been deemed pathological
because erroneous outcomes may occur despite individual rationality,
and they may in fact be the norm in certain circumstances. While
the terms informational cascade and herd behavior are used interchangeably
in the literature, Smith and Sorensen (2000) emphasize that there
is a significant difference between them. An informational cascade
is said to occur when an infinite sequence of individuals ignore
their private information when making a decision, whereas herd behavior
occurs when an infinite sequence of individuals make an identical
decision, not necessarily ignoring their private information. In
other words, when acting in a herd, individuals choose the same
action, but they may have acted differently from one another if
the realization of their private signals had been different. In
an informational cascade, an individual considers it optimal to
follow the behavior of her predecessors without regard to her private
signal since her belief is so strongly held that no signal can outweigh
it. Thus, an informational cascade implies a herd but a herd is
not necessarily the result of an informational cascade. The practical
importance of the distinction between herds and cascades is that
in a cascade social learning ceases since individual behavior becomes
purely imitative and hence is uninformative. In a herd, in contrast,
individuals become more and more likely to imitate but their actions
still may provide information.
Cascades, SKERRATT, 2000
"There is now substantial evidence that financial markets do
not react to information exactly as suggested by the efficient market
hypothesis. Consequently, a number of papers have asked the question
"how can this be explained?" Initially, one of the main
explanations was that exogenous institutional imperfections, such
as transactions costs, are the cause. This type of explanation is
now being replaced by behavioural ones, which focus on exactly how
agents process information. The idea in this paper is that agents
respond not only to their own private information, but also to the
information which they infer other agents have. The inference is
made on the basis of the actions which other agents are seen to
Crashes without External Shocks, Hart and Tauman, 2002
Market crash is usually considered an indication that the fundamentals
of the economy have changed and recession is around the corner.
This however need not be so. For instance, in October 1987 Wall
Street lost over 20% of its value in one day, but this was not followed
by a recession. Moreover, in the days preceding the crash, there
were no significant external events or bad news" that could
justify the dramatic price fall. We argue here that market crashes
(and, similarly, market bubbles) may well be the result of information
processing by the participants|and nothing else. Moreover, in terms
of market observables, it looks as if nothing is really changing.
Still, underneath the surface, there is a gradual updating of information
by the participants. Then, at a certain point in time, this causes
a sudden change of behavior.
Cascades and Rational Conformity, Anderson and Holt
"An information cascade is a pattern of matching decisions.
A cascade can occur when people observe and follow "the crowd"'
which can be rational if the information revealed in other's earlier
decisions outweights one's own private information".
Observational Learning, and Informational Cascades, CAO, Henry
"We offer a model to explain why groups of people sometimes
converge upon poor decisions and are prone to fads, even though
they can discuss the outcomes of their choices. Models of informational
herding or cascades have examined how rational individuals learn
by observing predecessors’ actions, and show that when individuals
stop using their own private signals, improvements in decision quality
cease. A literature on word-of-mouth learning shows how observation
of outcomes as well as actions can cause convergence upon correct
decisions. However, the assumptions of these models differ considerably
from those of the cascades/herding literature. In a setting which
adds ‘conversational’ learning about both the payoff
outcomes of predecessors to a basic cascades model, we describe
conditions under which (1) cascades/herding occurs with probability
one; (2) once started there is a positive probability (generally
less than one) that a cascade lasts forever; (3) cascades aggregate
information ine±ciently and are fragile; (4) the ability
to observe past payoffs can reduce average decision accuracy and
welfare; and (5) delay in observation of payoffs can improve average
accuracy and welfare".
Behavior and Cascading in Capital Markets: A Review and Synthesis,
Hirshleifer and Teoh, 2001
"We review theory and evidence relating to herd behavior, payoff
and reputational interactions, social learning, and informational
cascades in capital markets. We offer a simple taxonomy of effects,
and evaluate how alternative theories may help explain evidence
on the behavior of investors, firms, and analysts. We consider both
incentives for parties to engage in herding or cascading, and the
incentives for parties to protect against or take advantage of herding
or cascading by others".
Decomposition of Global Linkages in Financial Markets over Time,
Forbes and Chinn, 2003
"This paper tests if real and financial linkages between countries
can explain why movements in the world's largest markets often have
such large effects on other financial markets, and how these cross-market
linkages have changed over time. It estimates a factor model in
which a country's market returns are determined by: global, sectoral,
and cross-country factors (returns in large financial markets),
and country-specific effects. Then it uses a new data set on bilateral
linkages between the world's 5 largest economies and about 40 other
markets to decompose the cross-country factor loadings into: direct
trade flows, competition in third markets, bank lending, and foreign