Informational cascades: Meaning, Criticisms & Real-World Uses

What Is Informational Cascades?

An informational cascade occurs when individuals make decisions primarily by observing the actions of others, often disregarding their own private information or signals. This concept is a significant area within behavioral economics, highlighting how collective behavior can override individual rationality. When an informational cascade forms, later individuals may simply imitate predecessors, even if their private knowledge suggests a different course of action. The phenomenon underscores how social learning can lead to conformity, potentially resulting in widespread adoption of an action based on limited initial information.²¹

History and Origin

The concept of informational cascades was formally introduced in a series of influential papers in the early 1990s by economists Sushil Bikhchandani, David Hirshleifer, and Ivo Welch. Their seminal 1992 paper, "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades," published in The Journal of Political Economy, laid the groundwork for understanding how individuals rationally choose to ignore their own private information and instead follow the observed actions of others.²⁰ This research built upon earlier observations of imitative behavior and conformity, providing a formal model to explain how such phenomena could arise even among rational actors.¹⁹ The theory has since been widely applied across various fields, including finance, sociology, and political science, to explain collective behaviors ranging from investment trends to the adoption of new technologies.¹⁸

Key Takeaways

Informational cascades describe situations where individuals make decisions by imitating previous actions, often ignoring their own private data.
They are a core concept in behavioral economics, illustrating how social learning can lead to widespread conformity.
Cascades can result in collectively irrational outcomes, such as market bubbles or crashes, even when individuals are rational.
The phenomenon highlights the fragility of mass behavior, as cascades can be dislodged by new public information.¹⁷
Understanding informational cascades is crucial for effective risk management and policy-making in areas susceptible to collective decision-making.

Formula and Calculation

Informational cascades do not have a direct mathematical formula or calculation in the traditional sense, as they describe a qualitative process of sequential decision-making under uncertainty. However, the theoretical models used to analyze informational cascades often involve Bayesian probability and game theory to determine the conditions under which a cascade will form.

In a simplified model, imagine a sequence of individuals deciding whether to adopt (A) or reject (R) a particular action. Each individual has a private signal about the true state of the world (e.g., whether adopting is good or bad). They also observe the actions of all preceding individuals.

Let's denote:

(p) = probability that a private signal is correct.
(s_i \in {S_A, S_R}) = private signal of individual (i), indicating a preference for adoption or rejection.
(a_i \in {A, R}) = action taken by individual (i).
(H_i = {a_1, a_2, ..., a_{i-1}}) = history of observed actions up to individual (i-1).

Individual (i) makes their investment decision based on their private signal (s_i) and the observed history (H_i). An informational cascade occurs when an individual's optimal action is to follow the observed behavior of predecessors, regardless of their own private signal. This typically happens when the public information conveyed by the observed actions becomes so strong that it outweighs any single private signal.

For instance, if two individuals before you have adopted, and your private signal suggests rejection, you might still choose to adopt if the strength of the public signal (two adoptions) is greater than the strength of your private signal. This can be conceptualized as comparing posterior probabilities:

P(\text{Value is High} | s_i, H_i) \text{ vs. } P(\text{Value is Low} | s_i, H_i)

If, for example, two consecutive "adopt" decisions are observed, the belief in the "adopt" outcome might become so strong that even a "reject" private signal from the next individual is insufficient to sway them from adopting. The probability of a correct cascade forming in a simple binary model, as calculated in Bikhchandani, Hirshleifer, and Welch (1992), involves the accuracy of the private signal (p): Pr[correct cascade] = (\frac{p(p+1)}{2(1-p+p^2)}).¹⁶

Interpreting Informational Cascades

Informational cascades are interpreted as a powerful illustration of how social learning can lead to collective behaviors that may or may not be efficient or accurate. In practical terms, the presence of an informational cascade suggests that individual rationality can paradoxically lead to collective irrationality. When evaluating situations where informational cascades might be at play, it's important to consider:

Fragility: Cascades are often described as fragile. This means they can be easily broken or reversed by a new, strong piece of public information or the actions of a sufficiently informed individual who deviates from the cascade.¹⁵ This fragility stems from the fact that individuals in a cascade are not fully incorporating their private information, making the collective decision less robust than it appears.
Idiosyncrasy: The specific outcome of an informational cascade can be highly dependent on the initial sequence of signals or decisions, meaning that minor early events can disproportionately influence the final outcome.¹⁴ This makes cascades somewhat unpredictable and potentially arbitrary in their direction.
Social Welfare Implications: While cascades can sometimes lead to the correct outcome, they often fail to aggregate dispersed information efficiently, meaning society may not utilize all available knowledge. This can result in suboptimal collective decisions and missed opportunities for better outcomes.

Recognizing these characteristics helps in understanding why behaviors in financial markets or other social contexts can appear to shift suddenly and unexpectedly.

Hypothetical Example

Consider a new, unproven technology company, "QuantumLeap Inc.," whose stock has just gone public. There's considerable uncertainty about its long-term viability.

Investor A is the first to decide whether to buy (Invest) or not to buy (Pass) QuantumLeap shares. Based on their independent, in-depth research (private information), Investor A concludes the company has strong potential and decides to Invest.
Investor B observes Investor A's action. Investor B also has their own private research, which is somewhat ambiguous—leaning slightly towards "Pass." However, seeing Investor A (who is known to be a savvy investor) choose "Invest," Investor B gives more weight to this public signal than their own weak private signal and decides to Invest as well.
Investor C observes both Investor A and Investor B investing. Investor C's private research strongly suggests "Pass." Yet, the combined public signal of two reputable investors choosing "Invest" is very compelling. Investor C rationalizes that A and B must have superior information, or that their combined signal outweighs C's individual assessment. Despite their private signal, Investor C decides to Invest. At this point, an informational cascade has begun.
Investor D, E, and subsequent investors observe a growing number of people investing in QuantumLeap Inc. Their private signals become increasingly irrelevant compared to the overwhelming public signal of previous investors. Even if their own due diligence points to significant risks, they may choose to Invest simply because "everyone else is doing it."

In this scenario, the initial actions of a few individuals create a strong public signal that snowballs, leading others to follow suit, even if their own insights suggest caution. This demonstrates how an informational cascade can drive widespread adoption based on limited actual information, potentially leading to an overvalued asset if the initial assessments were flawed or incomplete. The collective market psychology takes over.

Practical Applications

Informational cascades manifest in various real-world scenarios, particularly within financial markets and investment decisions:

Stock Market Trends: The rapid rise or fall of stock prices can sometimes be attributed to informational cascades. Investors observe early movers buying or selling a particular stock, and even with their own limited or contradictory information asymmetry, they follow suit, amplifying the trend. This can contribute to asset pricing anomalies.
*¹³ Product Adoption and Fads: The success of certain products or technologies often hinges on initial adoption. If a few prominent early adopters embrace a new offering, it can trigger an informational cascade, leading to widespread consumer acceptance, even if the product's underlying quality is not superior.
Bank Runs: A classic example of an informational cascade is a bank run. If a few depositors withdraw their money, others, fearing the bank's insolvency, may follow suit regardless of their personal belief about the bank's stability, leading to a self-fulfilling prophecy.
Real Estate Bubbles: Similar to stock markets, real estate booms can be fueled by cascades. As property values rise, more buyers enter the market, not necessarily due to thorough analysis but because they observe others making profits, creating upward pressure that eventually becomes unsustainable.
Regulatory Scrutiny: Regulators, such as the U.S. Securities and Exchange Commission (SEC), are aware of the impact of informational cascades and herd behavior on market stability. Former SEC Commissioner Kara M. Stein has highlighted the importance of robust data and disclosure to counteract the potential for informed trading and informational cascades to create market fragility.

¹¹, ¹²## Limitations and Criticisms

While informational cascades provide a powerful framework for understanding collective behavior, the theory has several limitations and criticisms:

Assumptions of Rationality: A key premise of informational cascade theory is that individuals are rational. However, in reality, people are subject to numerous cognitive biases and often act irrationally, which could lead to similar outcomes without the strict informational-cascade mechanism. The pure informational cascade model may not fully capture the complexity of human behavior in dynamic environments.
Lack of Communication: The basic models often assume no direct communication between individuals, only observation of actions. I¹⁰n many real-world scenarios, individuals communicate and exchange information, which could either amplify or break a cascade. The presence of verbal communication or direct feedback loops can significantly alter the dynamics of information flow.
Overemphasis on Early Movers: While early movers do have a disproportional influence in the model, the real world often sees instances where later, more substantial information or a highly influential individual can reverse a trend, even if a cascade has seemingly formed. The theory sometimes struggles to account for such reversals without external shocks.
Distinction from Herd Behavior: Critics argue that informational cascades are often conflated with broader "herd behavior." While a cascade implies a herd (individuals taking the same action), a herd does not necessarily imply a cascade. In herd behavior, individuals might act alike due to shared external stimuli, payoff externalities, or common underlying information, rather than purely ignoring their private signals.
*⁸, ⁹ Empirical Difficulty: Empirically distinguishing pure informational cascades from other forms of imitative behavior or common responses to public information can be challenging, as it requires observing individuals' private information and beliefs, which are often unobservable in market settings. Experimental studies have been crucial in this area.

⁷## Informational Cascades vs. Herd Behavior

While the terms "informational cascade" and "herd behavior" are often used interchangeably, there is a distinct difference between them in academic literature. Understanding this difference is crucial for accurately analyzing collective financial actions.

Informational Cascade: An informational cascade occurs when individuals, observing the actions of those who came before them, rationally choose to disregard their own private information and instead follow the observed actions of their predecessors. T⁶he key element is the disregard of private information because the public signal from previous actions becomes overwhelming. In a cascade, social learning effectively ceases, as new individual actions no longer convey unique private information. T⁵his phenomenon is stable in the sense that no individual signal can change the pattern of behavior once the cascade has begun.

⁴Herd Behavior: Herd behavior, in a broader sense, refers to a situation where a group of individuals makes the same decision, or engages in similar actions. U³nlike an informational cascade, herd behavior does not necessarily imply that individuals are ignoring their private information. They might be acting identically because they have similar private information, because of shared external factors (e.g., a common market report), or due to payoff externalities (where the benefit of an action increases with the number of people taking that action). W²hile a cascade is a form of herd behavior, a herd is not always a cascade; in a herd, individuals' actions might still provide some information, and the behavior can be more fragile and prone to sudden shifts if a strong counter-signal emerges. T¹he portfolio management strategies of investors are influenced by both.

FAQs

What causes an informational cascade?

An informational cascade is caused by sequential decision-making under uncertainty where individuals observe the actions of predecessors but not their private information. When the public signal from observed actions becomes sufficiently strong, it outweighs an individual's own private information, leading them to conform.

Are informational cascades always irrational?

No, individuals within an informational cascade are acting rationally given the information available to them. They rationally infer that the accumulated public information from observing previous actions is more reliable than their own private, often imprecise, signal. However, the collective outcome of an informational cascade can be suboptimal or incorrect, as valuable private information from later individuals is suppressed.

How do informational cascades affect financial markets?

Informational cascades can significantly impact financial markets by contributing to rapid price movements, fads, and the formation of speculative market bubbles or crashes. When investors observe others buying or selling an asset, they may follow suit, amplifying trends even without complete information, which can lead to volatility or mispricing. This highlights the importance of independent analysis in investment decision-making.

Can informational cascades be prevented?

Complete prevention of informational cascades is difficult due to their basis in rational social learning. However, mechanisms that increase the transparency of private information (e.g., more robust public disclosure), encourage independent thought, or introduce diverse signals can mitigate their impact. Diversifying sources of information and critical thinking are crucial.

What is the difference between an informational cascade and a fad?

A fad is a short-lived, often irrational, widespread enthusiasm for something. An informational cascade is a specific mechanism that can explain how fads (and other forms of collective behavior) emerge and spread. While all informational cascades result in a form of mass behavior that might appear as a fad, not all fads are necessarily driven purely by informational cascades; other factors like network effects or market psychology can also play a role.