Competition Law and Attention Allocation Infrastructure Dominance

1. Introduction

Attention allocation infrastructure refers to the technological and commercial systems through which a platform determines what information, products, services, advertisements, applications, creators, or businesses receive users' attention.

Examples include:

search-engine ranking systems;

social-media feeds;

recommendation algorithms;

app-store rankings;

online marketplaces;

digital advertising exchanges;

news feeds;

video recommendations;

AI assistants;

content-discovery systems; and

platform notification systems.

The competition-law concern arises when an undertaking with substantial market power controls a critical attention-allocation system and uses that control to exclude, disadvantage, or discriminate against competing businesses.

This can be described as attention-infrastructure dominance.

The fundamental economic idea is that in digital markets, user attention can function as a scarce competitive input. A business may have an excellent product but still struggle to compete if a dominant platform controls whether consumers can discover it.

2. Why Attention Is a Competition-Law Resource

Traditional markets generally focus on inputs such as:

raw materials;

transportation;

physical premises;

distribution networks;

capital.

Digital markets introduce another important resource:

access to consumer attention.

For example, a search engine decides which results users see first. A marketplace determines which sellers appear prominently. A social-media platform determines which posts are recommended.

Consequently:

Platform control → ranking/recommendation → consumer visibility → customer acquisition → competitive success

This makes attention allocation potentially important to market competition.

3. Meaning of Attention Allocation Infrastructure

Attention allocation infrastructure can be divided into several categories.

A. Search ranking

Search engines determine:

ranking;

visibility;

advertisements;

recommendations;

featured results.

B. Recommendation systems

Platforms use algorithms to determine:

videos shown to users;

products recommended;

music promoted;

news presented;

accounts suggested.

C. Marketplace ranking

E-commerce platforms can determine:

seller ranking;

product placement;

"featured" products;

recommendation visibility;

default purchasing options.

D. App-store discovery

App stores control:

search ranking;

featured applications;

editorial recommendations;

default placement.

E. Digital advertising

Advertising platforms allocate users' attention through:

ad auctions;

targeting;

placement;

recommendation;

bidding algorithms.

4. Attention Infrastructure as a Bottleneck

A platform may become a bottleneck between businesses and consumers.

For example:

Consumer → search platform → business

or:

Consumer → marketplace → seller

or:

Consumer → app store → application developer

If the intermediary controls access to consumers, businesses may become dependent upon it.

The competition-law issue is not simply that the intermediary is successful.

The issue is whether the intermediary uses its control over the bottleneck to foreclose competitors or distort competition.

5. Relevant Market Definition

Competition authorities must first identify the relevant market.

Possible markets include:

general search services;

online marketplace services;

digital advertising;

app distribution;

social-network services;

online video platforms;

news aggregation;

comparison-shopping services.

Attention allocation itself may not always constitute a separate relevant market.

Instead, the allocation mechanism may form part of the competitive conditions within another relevant market.

6. Dominance

Dominance may arise where a platform possesses:

substantial market share;

strong network effects;

large user base;

data advantages;

high switching costs;

control over distribution;

economies of scale;

significant barriers to entry.

However, market share alone does not establish abusive conduct.

The authority must examine the broader competitive structure.

7. Network Effects and Attention

Attention-allocation platforms frequently benefit from network effects.

For example:

More users → more advertisers → more revenue → greater investment → better platform → more users

Similarly:

More users → more sellers → greater product variety → more users

This can make established platforms difficult to challenge.

A dominant platform can therefore acquire an important strategic advantage by controlling the mechanism through which users discover competing products.

8. Data and Attention Allocation

Recommendation systems often depend upon data.

The platform may collect:

searches;

clicks;

purchases;

viewing behaviour;

engagement;

location;

browsing patterns.

More data can improve recommendation accuracy.

This creates a feedback loop:

More users → more behavioural data → better recommendations → more engagement → more users.

If the same undertaking also operates competing services, this information advantage can create competition concerns.

9. Self-Preferencing

One of the most important concerns is self-preferencing.

Suppose:

Platform controls ranking + platform owns competing product.

It may place its own product:

at the top;

in a preferred position;

in default settings;

in recommendation panels;

in search suggestions.

Competitors may consequently receive less consumer attention.

The competition-law question is whether such preferential treatment constitutes an abuse of dominance or another prohibited practice.

10. Case Law: Google Shopping

Google and Alphabet v Commission — Case T-612/17

Google Shopping is perhaps the most directly relevant EU competition case for attention allocation.

The European Commission found that Google systematically favoured its own comparison-shopping service in its general search results compared with competing comparison-shopping services.

The General Court substantially upheld the Commission's decision.

Importance

The case demonstrates that a dominant digital platform's control over visibility and ranking can have competition-law significance.

The important mechanism is:

Search dominance → control over visibility → preferential treatment → reduced opportunities for rivals

This provides a powerful analytical framework for attention-allocation infrastructure.

11. Case Law: Microsoft v Commission

Microsoft v Commission — Case T-201/04

Microsoft involved interoperability restrictions and tying involving Windows.

The case demonstrated the competition significance of control over a technological platform.

Relevance

Attention infrastructure may similarly depend upon a dominant technological environment.

A platform that controls:

operating-system access;

application distribution;

default settings;

interfaces;

discovery mechanisms

may influence which competing services consumers encounter.

The case therefore illustrates how control over an important technological layer can be leveraged into adjacent competitive environments.

12. Case Law: Google Android

Google and Alphabet v Commission — Case T-604/18

The Android case involved Google's contractual practices concerning its mobile ecosystem, including arrangements related to search, application distribution and browsers.

Relevance

Mobile ecosystems control significant amounts of consumer attention.

Default settings can influence:

which search engine users employ;

which applications they discover;

which services they access.

The Android litigation therefore demonstrates how defaults and ecosystem architecture can influence competitive opportunities.

13. Case Law: Bronner v Mediaprint

Oscar Bronner GmbH v Mediaprint — Case C-7/97

Bronner concerned access to a newspaper distribution system controlled by another undertaking.

The Court established stringent conditions for treating refusal to provide access to infrastructure as abusive.

Relevance

Attention-allocation infrastructure can resemble distribution infrastructure.

A platform may provide the route through which businesses reach consumers.

However, Bronner establishes an important limitation:

Not every commercially valuable distribution or attention channel must automatically be opened to competitors.

The infrastructure must satisfy the demanding requirements associated with compulsory access.

14. Case Law: Commercial Solvents

Commercial Solvents v Commission — Joined Cases 6/73 and 7/73

Commercial Solvents established important principles concerning refusal by a dominant undertaking to supply an input to a downstream competitor.

Relevance

Suppose a dominant digital platform controls an indispensable attention-distribution mechanism and also competes downstream.

If the platform selectively restricts competitors' access while preserving its own downstream access, the Commercial Solvents principles may become relevant.

15. Case Law: United Brands

United Brands v Commission — Case 27/76

United Brands remains a foundational authority concerning dominance and abusive conduct.

The Court examined the relationship between a powerful undertaking and its customers and competitors.

Relevance

Attention platforms can create substantial commercial dependency.

A business may depend upon:

search traffic;

marketplace traffic;

advertising exposure;

app-store visibility;

recommendation systems.

United Brands supports the broader principle that dominance must be assessed in its economic context.

16. Case Law: Hoffmann-La Roche

Hoffmann-La Roche v Commission — Case 85/76

Hoffmann-La Roche is a leading authority concerning exclusionary conduct by dominant undertakings.

The Court emphasized the special responsibility of a dominant firm not to undermine effective competition.

Relevance

An attention platform could potentially use:

preferential ranking;

loyalty incentives;

exclusive promotional arrangements;

preferential recommendations

to reinforce user and business dependence.

Where such conduct substantially forecloses rivals, the principles of Hoffmann-La Roche may become relevant.

17. Case Law: Intel

Intel v Commission — Case C-413/14 P

Intel concerned exclusivity-inducing rebates and the assessment of their foreclosure effects.

Relevance

Attention platforms can use economic incentives to influence visibility.

For example:

lower commissions for exclusive sellers;

preferential advertising rates;

promotional credits;

preferred placement for businesses agreeing to exclusivity.

Intel demonstrates the importance of assessing whether such practices are capable of restricting competition.

18. Case Law: Amazon Marketplace

European competition authorities have investigated Amazon's treatment of marketplace sellers and its use of non-public seller data.

Relevance to attention infrastructure

Amazon simultaneously operates:

marketplace infrastructure;

ranking systems;

advertising infrastructure;

retail activities.

That creates a potential structural conflict.

If a platform controls seller visibility while also competing with those sellers, it may possess the ability to influence competitive outcomes through:

ranking;

recommendations;

advertising;

featured-product placement.

The broader competition concern is therefore:

Marketplace control + attention allocation + downstream competition.

19. Self-Preferencing as an Attention Problem

Self-preferencing can take many forms.

Search

A platform's own service appears first.

Marketplace

The platform's own products receive prominent placement.

App store

The platform's applications receive preferential discovery.

Social media

The platform promotes affiliated services.

AI assistant

An AI assistant may preferentially recommend the platform's own services.

The key issue is whether preferential treatment materially restricts competitive opportunities.

20. Algorithmic Discrimination

Attention allocation is increasingly automated.

Algorithms can determine:

ranking;

recommendations;

advertising;

product visibility;

search results.

A dominant platform could theoretically discriminate against rivals without any employee manually deciding which competitor should be disadvantaged.

The legal analysis should therefore focus on:

the platform's design;

incentives;

implementation;

effects;

transparency;

objective justification.

The use of an algorithm itself does not establish an infringement.

21. Dynamic Ranking and Competition

Ranking algorithms can change constantly.

A platform may argue that rankings are determined by:

relevance;

quality;

consumer preferences;

safety;

fraud prevention.

These can constitute legitimate reasons.

Competition authorities must therefore distinguish:

legitimate ranking optimization

from

strategic ranking manipulation designed to disadvantage rivals.

This distinction is central to digital competition cases.

22. Attention Allocation and Advertising Markets

Digital advertising provides another major competition concern.

A platform may control:

consumer attention;

advertising inventory;

advertiser access;

advertising exchange;

measurement;

targeting.

This creates vertical integration.

For example:

Publisher → advertising exchange → advertiser

If one undertaking controls several levels, it may potentially favour its own advertising services.

Competition authorities may therefore examine:

discriminatory auction rules;

preferential access;

self-preferencing;

data advantages;

exclusionary contracts;

conflicts of interest.

23. Attention Allocation and App Stores

App stores are particularly important because they control application discovery.

An app developer may depend upon:

search rankings;

featured placement;

recommendation;

user reviews;

payment systems;

default installation.

A platform operator may therefore have significant influence over whether consumers discover competing applications.

Potential competition issues include:

discriminatory ranking;

self-preferencing;

tying;

excessive commissions;

anti-steering restrictions;

exclusionary access conditions.

24. Attention Allocation and AI

AI introduces a new dimension.

AI assistants may increasingly function as gatekeepers of consumer attention.

Instead of:

Consumer → search engine → ten results

the model could become:

Consumer → AI assistant → selected recommendation.

This potentially gives the AI intermediary considerable influence over:

product discovery;

service selection;

advertising;

information access;

purchasing decisions.

If an AI system controls consumer recommendations while also operating competing services, self-preferencing and foreclosure questions could arise.

25. Attention Allocation and News

News platforms can determine which publishers receive visibility.

Potential competition issues include:

ranking;

recommendation;

indexing;

access to audiences;

advertising allocation.

A dominant platform could potentially become an unavoidable intermediary between publishers and consumers.

However, competition law must distinguish competition concerns from legitimate editorial or content-moderation decisions.

26. Attention Allocation and Consumer Harm

Potential consumer effects include:

Reduced choice

Consumers may encounter fewer competing products.

Higher prices

Reduced competition can potentially permit higher prices or commissions.

Lower quality

Competitive pressure may decline.

Reduced innovation

Startups may have difficulty acquiring customers.

Reduced diversity

Dominant platforms may determine which commercial alternatives become visible.

The precise effects must be established through evidence rather than assumed from platform size alone.

27. Entry Barriers Created by Attention Control

One of the most important effects is customer-acquisition foreclosure.

A new company may be technologically capable of entering a market but unable to obtain sufficient visibility.

The competitive chain becomes:

Incumbent controls attention → entrant receives limited visibility → entrant cannot acquire sufficient users → entrant cannot reach efficient scale → entry becomes commercially unattractive.

This can reinforce incumbent dominance.

28. Switching Costs

Attention platforms can also increase switching costs.

Users may accumulate:

preferences;

profiles;

histories;

subscriptions;

recommendations;

social connections.

A competing platform may therefore need to overcome substantial behavioural and technological switching barriers.

Data portability and interoperability can reduce such barriers where legally and technically feasible.

29. Refusal of Access to Attention Infrastructure

The refusal-to-deal doctrine may become relevant where access to a platform is genuinely indispensable.

But the legal threshold is high.

Under the principles illustrated by Bronner, the relevant questions include:

Is the infrastructure indispensable?

Can it reasonably be replicated?

Is access necessary for effective competition?

Would refusal eliminate effective competition?

Is there an objective justification?

Thus, a successful platform does not automatically have a legal obligation to provide competitors with access to its recommendation or ranking system.

30. Objective Justifications

Platforms may legitimately use ranking systems to achieve:

relevance;

consumer safety;

cybersecurity;

fraud prevention;

quality control;

privacy;

technical efficiency.

For example, excluding fraudulent sellers from prominent placement may protect competition rather than restrict it.

The crucial question is whether the stated justification is genuine and whether the measure is proportionate.

31. Competition Law and Platform Design

Platform design itself can influence competition.

Important design features include:

default settings;

ranking architecture;

recommendation algorithms;

interface placement;

search suggestions;

notification systems;

advertising positions.

Competition authorities may therefore need to examine not only contracts but also technical architecture.

This represents an important evolution from traditional antitrust analysis.

32. Relevant Legal Theories

Attention-allocation dominance may potentially involve:

Abuse of dominance

Where a dominant undertaking exploits control over attention infrastructure.

Self-preferencing

Where the platform systematically favours its own downstream services.

Tying

Where access to attention depends upon purchasing another service.

Exclusive dealing

Where businesses receive attention only if they avoid competing platforms.

Refusal to deal

Where indispensable attention infrastructure is withheld from competitors.

Discrimination

Where similarly situated businesses receive materially different access without objective justification.

Predatory conduct

Where a platform deliberately sacrifices profits to eliminate competitors, subject to the applicable legal test.

33. Competition-Law Analytical Framework

A regulator investigating attention-allocation infrastructure could examine:

34. Potential Remedies

Where unlawful conduct is established, remedies could include:

Behavioural remedies

non-discrimination obligations;

ranking transparency;

restrictions on self-preferencing;

interoperability;

data portability;

prohibition of exclusionary contracts.

Structural remedies

In exceptional cases:

separation of platform and downstream operations;

functional separation;

divestiture.

Regulatory remedies

Modern digital-market regulation may additionally impose:

gatekeeper obligations;

interoperability requirements;

transparency requirements;

restrictions on preferential treatment.

35. Key Case-Law Summary

36. Conclusion

Attention allocation infrastructure is becoming an increasingly important competition-law concept because digital platforms frequently control the mechanisms through which consumers discover and choose products and services.

The principal concern is not simply that a platform has a large audience. Rather, the concern arises when a platform with substantial market power controls a critical route to consumer attention and potentially uses that control to:

favour its own products;

disadvantage rivals;

restrict access;

impose exclusivity;

manipulate ranking;

exploit data advantages;

increase switching costs; or

reinforce barriers to entry.

The Google Shopping case provides the clearest illustration of the competitive significance of digital visibility and ranking, while Microsoft, Google Android, Bronner, Commercial Solvents, United Brands, Hoffmann-La Roche and Intel provide complementary principles concerning technological leverage, access, exclusion and foreclosure.

Ultimately, competition law must distinguish between legitimate algorithmic allocation of scarce consumer attention and strategic use of dominant attention infrastructure to suppress effective competition.