Same Product, Different Price: How Do Algorithms Shape Our Sense of Fairness?

You are looking at a hotel room.

The price is 4,500 lira.

You think about it, browse a few other options, and return to the same room a few hours later.

The price has risen to 5,200 lira.

Your first reaction would probably be frustration, but you might still find a way to explain it:

“Demand must have increased.”

“Maybe the cheaper rooms sold out.”

“The weekend is getting closer.”

But what if the person next to you is looking at the same room at the same time and still sees a price of 4,500 lira?

That changes things.

Because the issue is no longer simply that the price went up.

It is why the price went up for me.

And if you learn that the difference was produced by a system evaluating your purchase history, your location, the products you previously viewed, or similar behavioral data, the question becomes even more interesting:

Is it fair for different people to pay different prices for the same product?

Perhaps more importantly:

Is it really the price itself that makes a price fair or unfair?

This is not the same pricing system

Prices changing over time is nothing new.

We have seen it for years with airline tickets.

We see it with hotels.

We see it during holiday periods.

Demand rises, supply falls, time passes, and the price changes.

Personalized pricing, however, introduces something else.

The price can vary not only according to market conditions but also according to what is known or inferred about you.

Two people may look at the same product at the same time, yet if the system assesses their likelihood of purchasing, willingness to pay, or past behavior differently, it may produce two different prices.

From a technical perspective, the logic is understandable.

If a company can estimate consumers’ willingness to pay more accurately, why should it offer everyone the same price?

From a marketing perspective, that is a very appealing question.

From a consumer’s perspective, the same question sounds a little different:

“Are you charging me more because you know how much I am willing to pay?”

This is where economic logic and psychological fairness begin to part ways.

Is the problem that it is expensive, or that it feels unfair?

In consumer behavior, price is never just a number.

A product may cost 1,000 lira.

But whether we see that price as expensive or cheap depends on the comparisons we make.

How much did it cost last week?

How much is it at a competing store?

How much does a similar product cost?

And there is another comparison that is becoming increasingly important:

What did someone else pay?

Experimental research on personalized dynamic pricing shows that consumers may judge individually tailored prices as less fair than prices applied to a broader group of consumers.

The type of data used also matters. Location data and past purchasing behavior may not be evaluated in the same way.

I think this tells us something important.

Consumers are not only asking:

“How much am I paying?”

They are also asking:

“Why am I paying this much?”

And if the answer to the second question is not convincing, even a price that makes economic sense can feel psychologically unfair.

Is it fairer when an algorithm sets the price?

This is where the story takes a slightly unexpected turn.

The role of algorithms in consumer decisions is often met with suspicion.

“A black box.”

“It is tracking me.”

“It is using my data.”

We might therefore expect people to feel even more uncomfortable when they learn that an algorithm determined the price.

But research does not always show that.

A series of experiments in the tourism sector found that some negative reactions declined when consumers facing a disadvantageous price were told that an algorithm had set it.

At first glance, that seems a little strange.

The algorithm is pricing us, yet knowing about the algorithm may make us less angry.

One possible explanation is the way we attribute intention.

If I think a person gave me a higher price, I may assume there is deliberate self-interest or opportunism behind it.

I may see an algorithm as more mechanical.

“It is not targeting me out of malice.”

“That is simply how the system calculated it.”

More interestingly, this effect weakens when the algorithm is made too human-like.

In other words, the more human qualities we give an algorithm, the more we may begin attributing intention to it again.

This reminds me of a broader issue in human–technology relations:

Sometimes humanizing technology increases trust; at other times, it makes responsibility more visible.

But is the algorithm neutral?

The fact that an algorithm makes a decision does not automatically make that decision neutral.

An algorithm is designed to pursue an objective.

Someone decides which data it will use.

Someone determines what it will optimize.

Profit?

The likelihood of a sale?

Reducing customer churn?

The highest possible price that can be charged to a particular consumer?

So when we say, “The algorithm decided,” we are not ending the discussion.

We are only moving it one step back.

What was the algorithm told to do?

That may be the real question.

Field research shows that personalized pricing can increase company revenue.

The picture for consumer welfare, however, is more complicated.

Some consumers may benefit from lower prices, while others face higher ones.

Personalization is therefore not automatically bad for every consumer.

But it is not automatically good for everyone either.

The central issue is not the existence of the technology.

It is whose interests the system optimizes, with which data, and within what limits.

Should a system that knows us also know how much we will pay?

Personalization has long been one of marketing’s favorite words.

A movie suited to you.

Music suited to you.

A product suited to you.

An advertisement suited to you.

Many of these things can genuinely be useful.

But when personalization enters the domain of price, the psychological threshold changes.

Because:

“We found the product that suits you.”

and

“We found the highest price we think you can afford.”

are not the same thing.

This is where the side of personalization that serves the consumer separates from the side that turns knowledge about the consumer into an economic advantage.

Privacy enters the discussion at precisely this point.

When consumers discover that a price differs, they may be disturbed by more than simply having paid extra.

They may also wonder:

“How did you know that about me?”

The more a company knows about us, the better it may understand us.

Yet the same information may also reduce our bargaining power.

Both outcomes can emerge from the same technology.

Does transparency solve everything?

The first solution that comes to mind is simple:

“This price was determined algorithmically.”

“This offer is personalized for you.”

Transparency is certainly important.

But knowing that a system exists is not the same as understanding how it works.

What does the statement

“This price was determined algorithmically.”

actually tell you?

Which of your data was used?

Your past purchases?

The area where you live?

How many times you viewed the product?

How many times you abandoned your cart?

If transparency means only saying, “We used an algorithm,” it is difficult to claim that the consumer is truly informed.

This is where understandable transparency becomes more important.

Users should be able to understand the basic logic behind the decision.

And, if possible, challenge it.

Is there such a thing as a fair price?

It is often impossible to say that a product has one single “fair price.”

We pay one price for the same coffee at an airport and another in our neighborhood.

Even two people sitting next to each other on the same plane may have paid different fares.

We accept many of these differences.

Because there is a story that explains the price gap.

They booked early.

They caught a promotion.

Demand increased.

But the story

“The system predicted that you would pay more.”

does not offer the same comfort.

I do not think the reason is only money.

We sense a power asymmetry.

The platform knows many things about me.

I do not fully know what the platform knows about me.

The platform knows how it determined the price.

I do not.

So the issue is not simply paying a different price.

It is that one side of the price negotiation knows the other far better.

In the end, the issue is still not the algorithm

When we talk about algorithms, we sometimes place technology at the center of the discussion too quickly.

As though the algorithm itself were the problem.

But an algorithm is a tool.

The real issues in a pricing decision return us to much older questions:

Fairness.

Power.

Information.

Transparency.

Trust.

It may be economically rational for a business to offer different prices to different consumers.

It may also be entirely psychologically rational for consumers to feel uncomfortable about it.

Both can be true at the same time.

Perhaps that is why, when we look at a price tag in the future, we will not ask only:

“How much does this product cost?”

We will also wonder:

“Why does it cost this much for me?”

And, in my view, this is where the true ethical boundary of algorithmic pricing will begin.

For a price to be fair, is it enough to know how it was calculated, or do we also have the right to know why it was offered specifically to us?