Benefits and challenges of ambiguous product information

The research article by Matthias Lang and Cédric Wasser is now published in Theoretical Economics! We asked Matthias Lang to share their key insights.

Discover why ambiguous information can make you better off. Depending on how uncertainty affects prices and purchases, ambiguous information can benefit or hurt buyers and sellers.

This is a publication that belongs to the CRC Project B02.

We are surrounded by online reviews, social media endorsements, and product information, yet it is often difficult to know what to trust. In this context, what does your research aim to explore?

That’s true, we’ve all been there: scrolling through Amazon reviews, browsing product descriptions, or checking social media to decide if a product is actually worth the hype. You probably hoped the information was accurate. Yet in many markets, consumers face information both online and offline that is incomplete, hard to verify, or potentially biased. Online reviews may be manipulated, influencers’ endorsements may be sponsored, and technical descriptions may leave out important details. Is that five-star review genuine, or paid for? Is the product description telling the whole story?

Our research looks at how such ambiguous information affects trade between a seller and a buyer who dislikes uncertainty. We ask how this uncertainty changes how much buyers are willing to pay, how sellers set their prices, and how it ultimately affects the benefits that consumers and sellers receive.

What do you mean by ambiguous information in the context of your research, and how does it differ from risk?

By ambiguity, we mean uncertainty that arises when information is incomplete, unclear, or difficult to interpret. This is different from risk, where the possible outcomes and their probabilities are known with certainty, such as when rolling a die or spinning a roulette wheel. With ambiguity, the buyer may not know all possible outcomes, how likely they are, or how to interpret the information they receive. This may happen because they lack experience, circumstances have changed, or the available information is limited or too costly.

For example, a product review may be positive, but the consumer may be unsure whether it was written by an unbiased customer, a paid influencer, or a manipulated source. In our model, the buyer reacts to this uncertainty when deciding whether to buy.

Speaking about the model, how does it capture a consumer’s purchase decision?

There are two stages in the model. First is the ex ante stage, before buyers have seen any product information. At this point, buyers consider how they might respond to the information they could receive later. This shapes how attractive it is for them to enter the market.

Then comes the interim stage. The buyers receive information about how much they value the product by, for example, reading product reviews or seeing some endorsement. The seller then sets a price, knowing how the buyer’s information is generated, but not what information the buyer has received. In other words, the seller does not know which particular product information or review the consumer has seen. Buyers compare the price with how much they are willing to pay and decide whether to buy.

Because the information may be ambiguous, buyers may have several plausible interpretations of what it tells them about the product. This is realistic. A vague product description could mean the item is amazing or it could be hiding flaws. Buyers naturally wonder about both possibilities. We use α-max-min preferences to describe how they evaluate these different possibilities.

Could you elaborate on what you mean by α-max-min preferences and how you apply them in your model?

Put simply, the consumer doesn’t just settle on a single interpretation when faced with mixed information. Instead, she considers both a pessimistic interpretation (the product is junk) and an optimistic interpretation (the product is life-changing). The parameter α determines how much weight she places on the pessimistic interpretation. A high α makes her a heavy skeptic, while a lower α makes her more optimistic.

And what do you find?

We demonstrate that, surprisingly, both the seller and the buyer can benefit from ambiguous information. Using information design theory, we describe all the market outcomes that can arise under different types of information. Our results show that ambiguity expands the range of possible outcomes: some combinations of benefits for consumers and sellers can be achieved with ambiguous information that cannot be achieved without it, as Figure 1 illustrates.

In particular, access to ambiguous information shifts the Pareto frontier up so that both the buyer and the seller can be better off at the same time. We are the first to identify three new mechanisms through which information affects trade: interim pessimism, interim optimism, and ex ante optimism.

How can a buyer’s pessimism about ambiguous information actually work to their advantage?

Ambiguity can make buyers cautious. When product information is ambiguous, buyers also consider pessimistic interpretations and start imagining the downside too, which naturally lowers the most they are willing to pay. Sellers see that coming. Anticipating this drop in demand, they face a choice: keep prices high and risk losing the sale, or cut prices to lure skeptical buyers back. Through this mechanism ambiguity softens the seller’s pricing power – resulting in lower prices and revenues than would prevail under any unambiguous information. We call this mechanism interim pessimism. The striking part is that buyers do not suffer any (ex ante or ex post) losses from ambiguity because they will always buy the product.

On the other hand, you show that sellers can also benefit from ambiguity. How does the seller take advantage of the buyer’s optimism?

Ambiguity can also work in the seller’s favor. Optimistic interpretations of ambiguous information can raise the most buyers are willing to pay. This strengthens the seller’s pricing power, allowing the seller to charge a higher price. We call this mechanism interim optimism.

But there is a catch: high prices may discourage buyers from entering the market. This is where ex ante optimism matters. When buyers are optimistic about their ability to avoid bad purchases, they are more willing to enter the market. In other words, ex ante optimism relaxes the buyer’s participation constraint, making it easier for the seller to charge a higher price without driving buyers away.

Together, these two mechanisms allow the seller to charge higher prices and potentially generate revenues exceeding the buyer’s initial expected valuation. When buyers have clear, unambiguous information, such high revenues are impossible.

This research challenges conventional wisdom. What does this imply for policymakers or regulators?

Our findings certainly suggest that the relationship between information and market outcomes is more complicated than it might first appear. Transparency is often seen as an obvious way to protect consumers, and for good reason. Clear, reliable information can help people make better-informed decisions. But our findings suggest that we should carefully consider how people respond to information and how businesses adjust their pricing strategies. Making information clearer may change buyers’ willingness to pay, their willingness to enter a market, and the prices sellers choose. These effects can interact in unexpected ways.

For regulators and online platforms, the broader lesson is that better information does not automatically translate into better market outcomes for everyone. Forcing complete transparency might sound protective, but it could actually backfire by hurting the very people they are trying to protect. The effects depend on how buyers and sellers respond. Ultimately, our findings can help policymakers better understand these responses to design rules and platforms that support better decisions and more beneficial trade.

Read the article: Matthias Lang and Cédric Wasser: Benefits and challenges of ambiguous product information In: Theoretical Economics 2026