Project description
Understanding the changes in competition in the digital economy
The recent increase in digital use has led to vast quantities of data on consumers and has dramatically transformed marketing practices and the dynamics of market competition. These changes have altered corporate competition, the level at which corporations compete, and the complexity of their strategies. Companies can now delegate many aspects of their marketing activities to algorithms, enabling them to provide both broadly targeted and highly personalised offers. The EU-funded DMPDE project aims to research and increase our knowledge of this change and how policies must affect it. It will study digital ecosystems, algorithms, data collection, personalised marketing and possible future developments.
Objective
Improvements in technology are enabling firms to collect and process increasingly large amounts of detailed information about their customers and their competitors. This growing use of data is fundamentally changing the nature of competition. For example, it is affecting i) how firms compete, as they offer collections of services, ii) the level at which firms compete, as they make personalised offers to individual consumers, and iii) the complexity of firms strategies, as they increasingly delegate decisions to algorithms.
The research in this proposal will deepen our understanding of how data is changing market power, and how this should be reflected in antitrust and competition policy. There are four key themes:
1. Ecosystems. Many online services are grouped together in ecosystems. These services interoperate with each other and share data. The research will examine how data affects the boundaries of ecosystems. It will also analyse the welfare implications of policies that limit how ecosystems can use data.
2. Personalisation. Data can be used to give consumers a personalised experience. The research will look at how the ability to offer personalised products changes competition and efforts to acquire data. The research will also examine how personalisation affects the incentives to engage in anti-competitive practices like predation.
3. Consumer Search. Consumers often need to search in order to learn about firms product offerings. The research will explore how a consumers search behaviour generates data. It will also examine a firms incentive to manipulate the search process in order to learn more about a consumers preferences
4. Algorithms. Data is a crucial input into algorithms. The research will examine whether platform algorithms can use certain types of data to prevent sellers from charging less on other sales channels. The research will also investigate whether seller algorithms can autonomously learn anti-competitive behaviours like predation
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesecologyecosystems
- natural sciencesbiological sciencesbiological behavioural sciencesethologybiological interactions
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
31080 Toulouse
France