A social welfare approach to data

What's wrong with online review platforms, asks Nicos Savva, associate professor of management science and operations at London Business School.

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More data leads to better decision-making across the board, from policymakers to shoppers, company managers to house-hunters – which subsequently generates greater public benefits. Such an attitude was prevalent before the digital revolution but the internet triggered an explosion in the amount of data available to anyone with a computer or a mobile phone.

In fact, the digital revolution has led to a large amount of information being crowdsourced – a concept that is now ubiquitous, so it’s easy to forget that it emerged only about 10 years ago. Online platforms such as holiday booking and review site TripAdvisor and RateMDs, where people can review doctors, create a huge amount of knowledge that should, in principle, allow fellow community members to make better choices.

The phenomenon of crowdsourcing is to confirm the suspicion of holding controversial, even maverick, views. Is this not the era of The Wisdom of Crowds, to borrow the title of the influential 2004 book by James Surowiecki? Yet my research, jointly with Yiangos Papanastasiou (Berkeley) and Kostas Bimpikis (Stanford), which develops a mathematical description of information crowdsourcing, shows that granting full access to crowdsourced information may be problematic.

By definition, the relationship among members of these online communities is dynamic. For example, a favourable hotel review on TripAdvisor will encourage others in the community to visit the establishment and write their own critique.

Some believe you get more accurate views in these communities when you have many different and varied voices. But this Panglossian view of crowdsourcing is undermined by the process through which the information is generated.

So a guest stays at a hotel and posts a review based on their own experience. Imagine that the hotel is fairly decent and that the review is favourable. Those reading the review will be encouraged to visit the establishment and, in turn, write their own favourable critique. In this way the hotel’s success becomes self-reinforcing.

One may say the hotel is rightly reaping the rewards of offering good service, which leads to satisfied customers who write complimentary remarks on the website. The problem is that this outcome is inefficient for society, because the self-reinforcing nature of the hotel’s success means that the less-explored, but potentially superior, establishments get less attention. As a result, they have less chance, if any, to prove their worth to customers.

From a utilitarian perspective, where the aim is for the platform to pool vast amounts of user-generated data to deliver the greatest possible public benefit, this is not ideal. This state of affairs is the result of a fundamental difference between the actions that maximise total benefit for society, or social welfare and the individual incentives of the consumers using the platform.

From a social-welfare perspective, we would like consumers to write a positive review about a particular product or service for the benefit of the people reading those critiques. Social welfare is often best served by choosing a completely different producer that has been getting little or no attention. By doing so, consumers would provide fresh information rather than writing another rave review about a highly rated product or service. This would benefit future consumers, widening the information at their disposal and diminishing the self-reinforcing nature of the platform’s rankings.

How can we improve things? The answer lies in consciously restricting the flow of information – a “less-than-fully-informative” policy – which is bound to annoy some. However, we show that this approach can provide much greater public benefit by maximising consumer welfare in the long run.

This approach sees the platform designed deliberately and carefully to give more vague and nuanced recommendations, for example by featuring more prominently providers that are less explored at the expense of highly rated providers that are very much explored.

This encourages consumers to explore lesser-known businesses, giving these enterprises a chance to show their worth to future consumers. The trick is for the platform not to overdo it – recommendations need to be generally informative, so that customers continue to find it desirable and useful to follow them.

Nicos Savva is an associate professor of management science and operations at London Business School.


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