A Simple Story to Start

Imagine a dark tunnel. At the end, there’s either treasure or a trap. People enter one by one, each with a candle. Some candles are dim; others shine brightly, showing what’s ahead.

Those outside can’t see the path. They only hear voices echoing back. Here’s the catch: people shout only when they’re really happy or very upset. If their experience was just okay, they stay quiet.

As more people enter, the crowd hears:

• “Amazing!”
• (silence)
• “Terrible!”
• (more silence)

Silence is unclear. Did the person not enter the tunnel? Or did they have an average experience? This uncertainty changes what the crowd hears. Even if someone found treasure, we might never know.

Online Reviews and What We Learn from Them

This story helps explain recent research on online ratings and reviews. Today, people rely on user-generated content to make smart choices. Online reviews promise to share knowledge, especially for things like books, movies, and restaurants.

According to a 2021 Forrester Survey, 71% of online shoppers in the U.S. read reviews before buying. In India, only 3% never look at ratings (Statista Survey, 2022).

Do Reviews Show True Quality?

Does the “wisdom of crowds” work for online reviews? Can voluntary feedback show the real quality of a product? Reviews should help, but their voluntary nature creates bias.

People are more likely to write reviews when they are very happy or very unhappy. Most users stay silent. This self-selection creates a strong informational bias.

A product with mostly average reactions might seem polarizing based on reviews. Or it might be ignored altogether. This distortion doesn’t go away over time.

The Issue with the Silent Majority

Even with accurate private information, the crowd might not learn the truth. The issue isn’t the crowd’s intelligence but the filtering of voices they hear.

Our research builds on “social learning” models in economics. In these models, society can learn the truth from visible insights. But if only extreme reactions are visible, the truth gets lost.

This has a concerning implication: even with rational people, fake narratives can spread. Not because of lies, but because silent, moderate experiences are unheard.

Real-Life Examples

This isn’t just theory. It happens every day:

• A restaurant seems polarizing because only very happy or very angry diners post reviews.
• A product seems too good or suspiciously bad based on a few extreme reviews.

Consumers can’t tell if a lack of feedback means “no one bought it” or “many bought it and didn’t care enough to say anything.”

Solving the Learning Problem

Interestingly, our research shows a surprising fix. If sellers add fake positive reviews when buyers don’t leave feedback, it restores learning. Why? Because silence is no longer unclear.

Bad-quality products already get more negative reviews. Sellers can’t hide these signals. But for high-quality products, too many satisfied customers stay silent. Fake positive reviews can restore balance.

What This Means for You

These findings offer a warning:

• Consumers should read online reviews with care, especially when feedback is extreme or sparse.
• Platforms should encourage more users to leave reviews, not just the very happy or very angry.
• Sellers should think about how to be transparent without being dishonest.

Often, the crowd is wise. But sometimes, it’s just loud.

Shraman Banerjee teaches economics at Shiv Nadar University.

Swagata Bhattacharjee is a faculty member in economics at OP Jindal Global University.

Originally published under Creative Commons by 360info™.