{"id":4623,"date":"2024-10-08T09:52:15","date_gmt":"2024-10-08T07:52:15","guid":{"rendered":"https:\/\/www.rivistaeco.com\/?p=4623"},"modified":"2024-10-08T09:52:15","modified_gmt":"2024-10-08T07:52:15","slug":"how-algorithms-drive-our-choices","status":"publish","type":"post","link":"https:\/\/www.rivistaeco.com\/en\/2024\/10\/08\/how-algorithms-drive-our-choices\/","title":{"rendered":"How Algorithms Drive Our Choices"},"content":{"rendered":"

Recommendation systems are those artificial intelligence algorithms that make our lives easier by facilitating our online searches. However, they also have their downside. For example, they push sellers to raise prices. There is also the risk of homogenisation because something that appeals to many will end up being recommended to many. Research gives us an excellent understanding of this.<\/i><\/span><\/p>\n

In the digital age, the invisible hand guiding our online choices is often virtual. Many decisions made while browsing are directed by a recommendation system powered by artificial intelligence. AI algorithms have revolutionised the way we discover products, music, movies, and more. We navigate an overwhelming immense ocean with an overwhelming volume of options: 353 million products on Amazon, 90 million songs on Spotify, 26 billion videos on YouTube, and billions of news articles on social networks. Recommendation systems are modern compasses in this digital ocean, guiding our decisions and shaping our tastes.<\/span><\/p>\n

Algorithms with Great Influence on Our Lives<\/b><\/h3>\n

Recommendation systems are much more than just digital assistants. They are sophisticated algorithms that learn from our behaviours and preferences, as well as those of others. They are the actors behind the scenes creating our digital experiences in a personalised and efficient way. Today, the impact of recommendation systems is astonishing: 75% of movies watched on Netflix, 35% of products viewed on Amazon, and 60% of videos watched on YouTube come from recommendations. No sector is completely immune: they shape choices of financial assets (with “robo-advising”), vacation destinations, and which crops to plant on farms. Recommendation systems also appear in more niche applications, such as suggesting to university students which courses to take, helping scholars decide which articles or books to read, and advising academic journal editors which reviewers to involve. It’s not just about convenience. It is a fundamental change in how we interact with the digital world and behind it lies a crucial and inescapable part of our lives. As always, with great power comes great responsibility and also potential problems. The influence of recommendation systems goes beyond aiding and simplifying choices; it touches deeper issues. Democracy, for example, if recommendation systems curate the news we read. From an economic perspective, they influence market outcomes, competition, and consumer well-being. They are undoubtedly beneficial in many ways, especially in vast digital markets; they provide suggestions that are much more suitable than we could get without the support of artificial intelligence. But they also raise growing concerns, sparking fears that they may lead to unintended consequences such as price inflation, market monopolisation, and the amplification of existing biases.<\/span><\/p>\n

A Digital Word of Mouth<\/b><\/h3>\n

Let’s delve deeper into this intricate web. When we access a site like Amazon or Netflix, the recommendations we see are not simply random selections. They are the result of algorithms processing enormous amounts of data to predict what we, as individuals, might like, and ultimately providing personalised recommendations. The process known as collaborative filtering is similar to a digital form of word of mouth, where the system learns from the collective preferences of users to make predictions. The difference with human word of mouth, however, is that these systems rely on a huge amount of data from other users and other items or products and can very effectively predict individual preferences for unknown products or items. Here, however, the plot thickens: the data used to train the algorithms are a product of our interactions, which are in turn influenced by previous recommendations. This creates a continuous feedback loop where the algorithm’s suggestions can reinforce existing preferences, potentially leading to a narrower field of choices and exacerbating market outcomes. For example, a popular song on Spotify could become even more popular simply because it is recommended more often\u2014a phenomenon known as the “rich get richer” effect.<\/span><\/p>\n

A Study on the Risks and Benefits of Recommendation Systems<\/b><\/h3>\n

All this has led to heated debates among politicians, economists, and computer scientists. For example, the European Digital Service Act (DSA) requires mitigating of the “negative effects of personalised recommendations.” The concern is that if unchecked, systems could increase the market power of already dominant players, altering competition and potentially harming consumer welfare (because choice is reduced). Our recent research seeks to understand the benefits and risks that can arise from recommendation systems. To do so, we adopted a somewhat uncommon approach by simulating artificial intelligence systems in controlled and synthetic environments that reflect real economic scenarios. This method allows us to analyse the complex interaction between artificial intelligence algorithms and market forces in very precise ways that would be difficult to achieve with empirical research (in these cases, much of the data is not available to researchers) and theoretical investigations (modelling complex artificial intelligence algorithms is impractical). The results are interesting and nuanced. On the one hand, recommendation systems undoubtedly simplify life by helping us filter out digital clutter to find products or content that align with our interests. This saves us time (and search costs), and our experience is improved by connecting us with items we would otherwise never have discovered. In more technical terms, the expected utility that a user can obtain by participating in a market with the help of a recommendation system can increase by up to 6% compared to a benchmark where the consumer must independently perform a costly search for preferred items. Recommendation systems are more effective at making decisions for people than people themselves are. On the other hand, we cannot ignore the impact of recommendation systems on markets. They can significantly increase market concentration, turning certain products into “superstars,” sometimes leading to price increases of up to 16%. In our research, we demonstrate that sellers of items in digital markets raise their prices once they realise that consumer behaviour is mediated by a recommendation system. Therefore, the increased utility has a real cost. These developments raise legitimate concerns about consumer welfare, but we also demonstrate that the picture is not entirely negative. The better match guaranteed by recommendation systems can lead to an overall increase in consumer welfare even when considering higher prices. Our simulation approach allows us to delve deeper into the underlying causes of the significant effects produced by recommendation systems. For example, we identified that the demand expressed by consumers assisted by recommendation systems\u2014what we define as “algorithmic demand”\u2014is markedly different from the “human demand” not assisted as seen in the figure. Analysing the changes and fluctuations in algorithmic demand gives us a deeper understanding of how personalised recommendations work and impact markets.<\/span><\/p>\n

Algorithmic Demand vs. Human Demand<\/b><\/p>\n

\"\"
Note: The red line represents “algorithmic demand” mediated by the recommendation system; the blue line represents “human demand,” and the points indicate equilibrium prices for the item considered. As shown, the equilibrium price is higher when algorithms are involved.\u00a0 Source: Authors’ calculations.<\/figcaption><\/figure>\n

Through our simulation method, we also discovered that the dependence of artificial intelligence algorithms on market data they help generate increases market concentration, although this is a secondary effect. It is also interesting to note that the relationship between the amount of information used by recommendation systems and consumer welfare is not linear. Initially, as recommendation systems access more data, welfare increases due to a better match between products and consumer preferences. But beyond a certain point, the negative effects\u2014such as price increases\u2014begin to outweigh the benefits. This indicates the need for a balanced approach in the use of data by recommendation systems and suggests areas for possible regulatory intervention. As with most regulations in the digital economy, finding the right balance between protecting consumer welfare and promoting innovation is crucial.<\/span><\/p>\n

Risks of Manipulation and the Need for Regulation<\/b><\/h3>\n

Our research also examines the darker aspects of AI systems, such as the possibility of platforms manipulating recommendations for profit. This becomes particularly concerning when the platform using the recommendation system also sells products that consumers might choose, creating a conflict of interest. Although manipulation can have negative effects, our research indicates that it also tends to reduce prices for favoured and over-recommended products, thereby mitigating some of the negative effects on consumer welfare. This result, however, opens new avenues for understanding the strategic behaviours of platforms and their implications for market competition. Recommendation systems are a double-edged sword. They are powerful tools that enhance our digital experiences, but their broader implications for markets and consumer welfare require careful examination. We asked ChatGPT what it thinks about recommendation systems and this is the response: “As we venture into this digital ocean, understanding and modelling the role of recommendation systems in our lives becomes both a technological question and a social duty. This research is only the first step in a long expedition to decode the intricate influence of AI on our existence. Who knows, maybe AI and recommendation systems will ultimately be wise enough to suggest the best course for our journey, ensuring we don’t sail off the map of the digital world.”<\/span><\/p>\n

Bio<\/b><\/em><\/p>\n

Emilio Calvano is a full professor of economic policy at the LUISS Guido Carli University and a research fellow at the Einaudi Institute for Economics and Finance.<\/span><\/em><\/p>\n

Giacomo Calzolari is a professor of economics at the European University Institute in Florence and a research fellow at the Centre for Economic Policy Research (CEPR).<\/span><\/em><\/p>\n

Vincenzo Denicol\u00f2 is a full professor of political economy at the University of Bologna.<\/span><\/em><\/p>\n

Sergio Pastorelli is a full professor of econometrics at the University of Bologna.<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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