Tag: algorithms

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Artificial intelligence in pricing: a double-edged sword?

Artificial Intelligence (AI) is transforming the way we live and work, with many of us knowingly or unknowingly using some form of AI daily. Businesses are also adopting AI in increasingly innovative ways. One example of this is the use of pricing algorithms, which use large datasets on market conditions to set prices.

While these tools can drive innovation and efficiency, they can also raise significant competition concerns. Subsequently, competition authorities around the world are dedicating efforts to understanding how businesses are using AI and, importantly, the potential risks its use may pose to competition.

How AI pricing tools can enhance competition

The use of AI pricing tools offers some clear potential efficiencies for firms, with the potential to reduce costs that can potentially translate into lower prices for consumers.

Take, for instance, industries with highly fluctuating demand, such as airlines or hotels. Algorithms can enable businesses to monitor demand and supply in real time and respond more quickly, which could help firms to respond more effectively to changing consumer preferences. Similarly, in industries which have extensive product ranges, like supermarkets, algorithms can significantly reduce costs and save resources that are usually required to manage pricing strategies across a large range of products.

Furthermore, as pricing algorithms can monitor competitors’ prices, firms can more quickly respond to their rivals. This could promote competition by helping prices to reach the competitive level more quickly, to the benefit of consumers.

How AI pricing tools can undermine competition

However, some of the very features that make algorithms effective can also facilitate anti-competitive behaviour that can harm consumers. In economic terms, collusion occurs when firms co-ordinate their actions to reduce competition, often leading to higher prices. This can happen both explicitly or implicitly. Explicit collusion, commonly referred to as illegal cartels, involves firms agreeing to co-ordinate their prices instead of competing. On the other hand, tacit collusion occurs when firms’ pricing strategies are aligned without a formal agreement.

The ability for these algorithms to monitor competitors’ prices and react to changes quickly could work to facilitate collusion, by learning to avoid price wars to maximise long-term profits. This could result in harm to consumers through sustained higher prices.

Furthermore, there may be additional risks if competitors use the same algorithmic software to set prices. This can facilitate the sharing of confidential information (such as pricing strategies) and, as the algorithms may be able to predict the response of their competitors, can facilitate co-ordination to achieve higher prices to the detriment of consumers.

This situation may resemble what is known as a ‘hub and spoke’ cartel, in which competing firms (the ‘spokes’) use the assistance of another firm at a different level of the supply chain (e.g. a buyer or supplier that acts as a ‘hub’) to help them co-ordinate their actions. In this case, a shared artificial pricing tool can act as the ‘hub’ to enable co-ordination amongst the firms, even without any direct communication between the firms.

In 2015 the CMA investigated a cartel involving two companies, Trod Limited and GB Eye Limited, which were selling posters and frames through Amazon (see linked CMA Press release below). These firms used pricing algorithms, similar to those described above, to monitor and adjust their prices, ensuring that neither undercut the other. In this case, there was also an explicit agreement between the two firms to carry out this strategy.

What does this mean for competition policy?

Detecting collusion has always been a significant challenge for the competition authorities, especially when no formal agreement exists between firms. The adoption of algorithmic pricing adds another layer of complexity to detection of cartels and could raise questions about accountability when algorithms inadvertently facilitate collusion.

In the posters and frames case, the CMA was able to act because one of the firms involved reported the cartel itself. Authorities like the CMA depend heavily on the firms involved to ‘whistle blow’ and report cartel involvement. They incentivise firms to do this through leniency policies that can offer firms reduced penalties or even complete immunity if they provide evidence and co-operate with the investigation. For example, GB eye reported the cartel to the CMA and therefore, under the CMA’s leniency policy, was not fined.

But it’s not all doom and gloom for competition authorities. Developments in Artificial Intelligence could also open doors to improved detection tools, which may have come a long way since the discussion in a blog on this topic several years ago. Competition Authorities around the world are working diligently to expand their understanding of AI and develop effective regulations for these rapidly evolving markets.

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Questions

  1. In what types of markets might it be more likely that artificial intelligence can facilitate collusion?
  2. How could AI pricing tools impact the factors that make collusion more or less sustainable in a market?
  3. What can competition authorities do to prevent AI-assisted collusion taking place?
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A stock market beauty contest of the machines

Each day many investors anxiously watch the stock market to see if their shares have gone up or down. They may also speculate: buying if they think share prices are likely to go up; selling if they think their shares will fall. But what drives these expectations?

To some extent, people will look at real factors, such as company sales and profits or macroeconomic indicators, such as the rate of economic growth or changes in public-sector borrowing. But to a large extent people are trying to predict what other people will do: how other people will react to changes in various indicators.

John Maynard Keynes observed this phenomenon in Chapter 12 of his General Theory of Employment, Interest and Money of 1936. He likened this process of anticipating what other people will do to a newspaper beauty contest, popular at the time. In fact, behaviour of this kind has become known as a Keynesian beauty contest (see also).

Keynes wrote that:

professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those which, to the best of one’s judgement, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees.

When investors focus on people’s likely reactions, it can make markets very unstable. A relatively minor piece of news can cause people to buy or sell in anticipation that others will do the same and that others will realise this and do the same themselves. Markets can overshoot, until, when prices have got out of line with fundamentals, buying can turn into selling, or vice versa. Prices can then move rapidly in the other direction, again driven by what people think other people will do. Sometimes, markets can react to very trivial news indeed. As the New York Times article below states:

On days without much news, the market is simply reacting to itself. And because anxiety is running high, investors make quick, sometimes impulsive, responses to relatively minor events.

The rise of the machine

In recent years there is a new factor to account for growing stock market volatility. The Keynesian beauty contest is increasingly being played by computers. They are programmed to buy and sell when certain conditions are met. The hundreds of human traders of the past who packed trading floors of stock markets, have been largely replaced by just a few programmers, trained to adjust the algorithms of the computers their finance companies use as trading conditions change.

And these computers react in milliseconds to what other computers are doing, which in turn react to what others are doing. Markets can, as a result, suddenly soar or plummet, until the algorithms kick the market into reverse as computers sell over-priced stock or buy under-priced stock, which triggers other computers to do the same.

Robot trading is here to stay. The articles and podcast consider the implications of the ‘games’ they are playing – for savers, companies and the economy.

Articles

Questions

  1. Give some other examples of human behaviour which is in the form of a Keynesian beauty contest.
  2. Why may playing a Keynesian beauty contest lead to an undesirable Nash equilibrium?
  3. Does robot trading do anything other than simply increase the speed at which markets adjust?
  4. Can destabilising speculation continue indefinitely? Explain.
  5. Explain what is meant by ‘overshooting’? Why is overshooting likely to occur in stock markets and foreign exchange markets?
  6. In what ways does robot trading (a) benefit and (b) damage the interests of savers?