The article “Why Are We Paying Different Prices? Hint: Artificial Intelligence is Learning What You’re Willing to Pay” published on LinkedIn by Ted Gaubert, discusses how the progress of Artificial Intelligence (AI) pricing engines are allowing them to influence the functioning of markets. AI is defined as the ability of machines to mimic human behaviour so that they can solve problems. Because of our vast cognitive skills, there are many problems where human beings have traditionally performed relatively well. Developments in AI allowed it to be able to mimic such behaviour, and firms are testing AI pricing mechanisms’ readiness for implementation and trying to understand what kind of role it may have in modern markets.
There is no simple answer to this question. However, as the author argues, one thing that is clear is that AI’s solution to the revenue maximisation problem of the firm is better than auctions. It has become a very attractive way to pricing sales items for companies in the e-commerce sector. However, this new technique has become a big concern for regulatory authorities. In fact, auctions that have long been viewed as optimal mechanisms to maximise revenue are currently failing at getting the attention of most experienced e-commerce firms. So, does it mean that it is time to let AI pricing mechanisms take over, for instance, for the items sold in eBay?
Consider a hypothetical first-price auction on eBay of Jean Tirole’s book The Theory of Industrial Organisation. All online participants, including you, submit their bids and the individual with the highest bid gets the book. I am sure you know the result! You are a TSE student with a high valuation for it – we all are – so I suppose you won. However, I can certainly tell you that you did not pay what you are really willing to pay. Why? Because you are a rational individual who realises that bidding a little more than your rival, instead of bidding the value of your actual willingness to pay, will get you the book and you will go back home with some money left in your pockets.
Now, imagine a similar scenario. This time, instead of you personally going online, you bought an option on the platform beforehand, which allows eBay to participate on your behalf in this auction. Suppose eBay will use AI for this purpose. Well, if you are an active customer, the platform has data on your preferences and your willingness to pay for certain types of goods. The AI will start solving the revenue maximisation problem of the retailer, coming up, most probably, with the same result as previously (i.e. you as a winner). These are intelligent algorithms that know that your valuation is highest among the bidders and will therefore sell the book to you. But, guess what? You will not go back home with some money in your pockets! Why? Because the algorithm finds your true valuation of the book, but it does not bid tactically on your behalf. You had to pay a premium to eBay for not having to allocate time to participate in it.
In the end, the AI pricing mechanism does its job for both the seller and the buyer. It reveals your true willingness to pay for the book, creating revenue for the seller. Additionally, it helps you to avoid having to dedicate your time to participate in the purchase. This is the first reason why AI could currently be more attractive than auctions for experienced e-commerce companies such as eBay or Amazon.
Moreover, if one considers the case of airline operators, one can easily reach the same conclusion. As Gaubert (2017) writes, AI is able to “learn about local events that are happening in real time on a global basis far more economically that what could ever be achieved by a host of humans”. It comes up with an optimal solution for the revenue maximisation problem of the firm. In such a case, it may consist of setting high prices for flights going to a city where a big festival is taking place. Therefore, uninformed competitors serve individuals with low valuations, while the informed competitor serves the bucket of individuals with high valuations or the residual demand.
Let’s go back to our hypothetical auction for Jean Tirole’s book, and suppose that the book has a special value for bidders because it is the only book that has been personally signed by him. I guess you will win again, it is the signed copy after all! But, were you tempted to bid more than what you are really willing to pay? Let me answer this question for you, YES! It is the only one on earth that has been signed by him, which means that if you get it, you will be able to sell it for thousands of dollars in the future at the pawn shop. So, you started bidding and you could not stop, right? Congratulations, you are another example of what is called ‘escalating commitments’: you committed to get it and therefore you could not stop bidding.
Nevertheless, if you had allowed the platform to participate for you in this auction, eBay would have avoided this issue. How? Its AI pricing mechanism is good at estimating your willingness to pay because it constantly collects data on your behaviour. AI is so efficient that it could have made a good prediction of your behaviour when participating in an auction. Thus, when solving the revenue maximisation problem of the firm, AI would have stopped bidding on your behalf once it had realised that you would start behaving irrationally. Once again, why? Because it is at that point that revenue is maximised without giving you the disutility you would have received from your escalating commitments.
AI did an excellent job once more: it solved the problem under different conditions, which is the second reason to believe that it is very attractive for the e-commerce industry. Several other reasons can support this fact. For instance, naive consumers – unable to bargain – would also have chosen AI to act on their behalf, because even though they are naive they know that buying the book at a fixed price comes with a premium for the convenience of an immediate purchase, a higher price. This means that consumers also find it attractive.
Ultimately, the examples above and the ones provided by the author support the idea that an AI pricing machine is a very attractive mechanism for the e-commerce sector. It reveals consumers’ willingness to pay accurately, avoids escalating commitments, constantly collects asymmetric information (that is not available to competitors), attracts consumers, and maximises revenue. The concern it is imposing on regulatory authorities, if anything, is just a display of its supremacy and its potential in the near future.
by Maria Alejandra Arenas