Digital Dependency Cartography And Market Power Visualization

1. Introduction

Digital dependency cartography is the systematic identification and visualization of the technological, commercial, informational, infrastructural, and contractual dependencies that connect firms, consumers, developers, public authorities, and digital platforms.

It asks a question broader than traditional market-share analysis:

Who depends on whom, for what digital input, through which bottleneck, and what happens if access is restricted?

A digital ecosystem may contain cloud infrastructure, operating systems, app stores, payment systems, APIs, identity services, data repositories, advertising exchanges, AI models, distribution channels, and interoperability standards. A firm may have modest market share in a downstream market while exercising substantial power because competitors depend upon it for an indispensable upstream input.

Accordingly, market power can be visualized as a network of dependencies rather than merely as a percentage of sales.

2. Meaning of Digital Dependency Cartography

A dependency map normally identifies:

1. Actors – platforms, suppliers, competitors, consumers, developers and public bodies.
2. Digital assets – data, APIs, algorithms, cloud capacity, operating systems, models and identity infrastructure.
3. Dependency relationships – technical, commercial, contractual or informational.
4. Bottlenecks – inputs that cannot easily be replicated or bypassed.
5. Switching barriers – technical migration costs, data portability problems, contractual restrictions and loss of network effects.
6. Control points – locations where one undertaking can determine access, ranking, interoperability or pricing.
7. Alternative routes – substitute suppliers, interoperable systems and potential multi-homing.
8. Consequences of foreclosure – exclusion, increased costs, reduced innovation or weakened entry.

The result is effectively a market-power topology.

3. From Market Share To Dependency Structure

Traditional competition analysis often begins:

Relevant market → market shares → barriers to entry → dominance → conduct.

Digital dependency cartography adds another analytical layer:

Digital ecosystem → dependency nodes → critical edges → switching costs → alternative paths → bottleneck control → foreclosure capability → competitive effects.

For example:

Consumers    │    ▼ Digital Platform    │ ┌──┼───────────┐ ▼  ▼           ▼ Data  App Store  Advertising │      │          │ ▼      ▼          ▼ AI     Developers  Merchants Model │ ▼ Downstream Services

The most important undertaking may not necessarily be the undertaking with the largest downstream market share. It may be the undertaking occupying the most strategically important node.

4. Major Types Of Digital Dependency

A. Infrastructure Dependency

Businesses may depend upon:

• cloud computing;
• data centres;
• content-delivery networks;
• GPU/AI-compute infrastructure;
• telecommunications networks;
• operating systems.

If migration is technically difficult, infrastructure ownership can generate substantial bargaining power.

B. Data Dependency

A platform may control:

• consumer behaviour data;
• transaction histories;
• location information;
• search data;
• advertising data;
• training datasets.

The competitive importance of data increases when rivals cannot obtain equivalent datasets.

C. Interface Dependency

APIs, SDKs, app stores and interoperability protocols can become gateways through which competitors reach consumers.

D. Distribution Dependency

A digital producer may depend upon:

• search ranking;
• app-store placement;
• social-media distribution;
• marketplace ranking;
• recommendation systems;
• digital advertising exchanges.

Control over distribution can therefore create market power even where the underlying product itself is substitutable.

E. Identity Dependency

Digital identity, authentication and credential systems can become particularly powerful bottlenecks because access to numerous downstream services may depend on a single authentication layer.

F. Payment Dependency

A platform controlling payment infrastructure may influence:

• transaction costs;
• access conditions;
• customer relationships;
• data collection;
• downstream competition.

G. AI Dependency

Emerging AI ecosystems create dependencies involving:

compute → foundation model → API → application → distribution → user data.

A firm controlling several successive layers may possess vertical ecosystem power rather than merely conventional horizontal market power.

5. Cartographic Indicators Of Market Power

A useful dependency map should examine at least seven indicators.

1. Centrality

How many commercially important relationships pass through a particular undertaking?

High centrality can indicate a strategically important intermediary.

2. Exclusivity

Does the undertaking prevent customers or suppliers from using alternatives?

3. Switching Cost

Can the dependent party realistically migrate?

Relevant costs include:

• data migration;
• retraining;
• API redevelopment;
• contractual termination;
• loss of accumulated reputation;
• loss of network effects.

4. Replicability

Can another undertaking reproduce the relevant digital input?

An API may theoretically be reproducible while a large accumulated dataset may not be.

5. Multi-Homing

Can users simultaneously use competing platforms?

High multi-homing generally weakens dependency.

6. Network Effects

Does the value of the service increase as participation increases?

Strong network effects can reinforce concentration.

7. Chokepoint Control

Can the undertaking technically or commercially deny, degrade or condition access?

This may be the most important factor in identifying practical digital power.

6. Visualization Of Digital Market Power

A useful visualization can classify nodes according to their function:

A more sophisticated map can assign each relationship a dependency score based on:

Dependency = Importance × Switching Cost × Lack of Alternatives × Control

This should not be treated as a legal test or mechanically converted into a dominance finding. It is an evidentiary and investigative tool.

7. Relationship With Competition Law

Digital dependency cartography can inform several areas of competition law.

Article 102 / Abuse Of Dominance

A dependency map may help demonstrate:

• dominance;
• refusal to supply;
• discriminatory access;
• tying;
• self-preferencing;
• exclusionary rebates;
• interoperability restrictions;
• leveraging.

Article 101 / Restrictive Agreements

Maps can reveal networks of:

• exclusivity;
• information exchange;
• vertical restraints;
• platform restrictions;
• coordinated conduct.

Merger Control

Cartography is particularly useful for identifying ecosystem acquisitions.

A transaction may appear small in conventional revenue terms while removing an important future competitor or reinforcing a digital bottleneck.

Essential-Facility Analysis

Dependency mapping can help determine whether an input is genuinely indispensable or whether credible alternative routes exist.

Digital Regulation

The same mapping can assist enforcement under ex ante digital-platform regimes because it identifies gatekeepers and structurally important interfaces before conventional competitive harm becomes obvious.

8. Detailed Case Laws

Case 1 — United Brands v Commission

United Brands Company and United Brands Continentaal BV v Commission (Case 27/76)

The European Court of Justice examined dominance through the undertaking's economic strength and its ability to behave independently of competitors, customers and consumers.

Relevance to digital dependency cartography

The principle is highly transferable to digital ecosystems.

A digital platform may possess power because customers and suppliers cannot effectively discipline it, even where its market share alone does not tell the entire story.

Dependency mapping therefore helps identify:

• captive commercial relationships;
• absence of alternatives;
• bargaining asymmetry;
• network effects;
• economic independence.

Digital lesson: dominance can be understood through the structure of dependency surrounding an undertaking, not simply through nominal market share.

Case 2 — Commercial Solvents v Commission

Istituto Chemioterapico Italiano S.p.A. and Commercial Solvents Corporation v Commission (Joined Cases 6/73 and 7/73)

The Court dealt with a dominant undertaking controlling an important upstream input and using that position in a way capable of excluding downstream competitors.

Digital significance

The case illustrates the importance of vertical dependency.

Consider:

Critical Input     ↓ Dominant Supplier     ↓ Downstream Competitors

If downstream businesses cannot obtain a critical input elsewhere, control over the upstream layer can become a mechanism of foreclosure.

Modern equivalents may involve:

• cloud infrastructure;
• operating systems;
• app distribution;
• specialised datasets;
• AI-compute infrastructure.

Case 3 — Bronner v Mediaprint

Oscar Bronner GmbH & Co. KG v Mediaprint Zeitungs und Zeitschriftenverlag GmbH & Co. KG (Case C-7/97)

The Court considered when refusal of access to an infrastructure could constitute an abuse.

It imposed demanding conditions concerning indispensability, elimination of competition and the absence of viable alternatives.

Digital cartographic significance

This case provides an important warning:

Not every dependency is legally sufficient to establish an access obligation.

A digital dependency map must therefore distinguish:

important input ≠ indispensable input.

Investigators should map alternative routes before concluding that a digital infrastructure is indispensable.

For example:

Platform A   │   ├── API X   │   └── API Y → Alternative supplier

If API Y provides a realistic substitute, dependency may be commercially significant but legally insufficient for an essential-facility theory.

Case 4 — Microsoft Corp. v Commission

Microsoft Corp. v Commission (Case T-201/04)

The General Court upheld important aspects of the Commission's approach concerning Microsoft's control over interoperability information and its relationship with work-group server products.

Digital cartography significance

This is one of the clearest precedents for mapping technological dependency.

Microsoft's position involved an important architecture:

Windows   ↓ Interoperability Information   ↓ Server Compatibility   ↓ Downstream Competition

The case demonstrates that technical interfaces can have competition significance.

Modern application

A dependency map should therefore identify:

• APIs;
• protocols;
• technical documentation;
• interoperability information;
• authentication systems;
• SDK restrictions.

Control over these interfaces can create competitive leverage over adjacent markets.

Case 5 — Google Shopping

Google and Alphabet — Google Search (Shopping), Case AT.39740

The European Commission found that Google had favoured its comparison-shopping service in general search results while demoting competing comparison-shopping services.

Cartographic importance

The case illustrates visibility dependency.

A simplified map is:

Consumers    ↑ Search Results    ↑ Google Search    ↑ Ranking Algorithm    ↑ Competitor Visibility

Competitors may technically remain in the market but become commercially dependent upon access to a dominant discovery mechanism.

Key insight

Digital dependency can therefore involve attention and visibility, not merely physical infrastructure.

A cartographic analysis should ask:

• Who controls discovery?
• Who determines ranking?
• Can rivals reach consumers without the gateway?
• Are alternative discovery channels commercially realistic?

Case 6 — Google Android

Google Android, Case AT.40099

The Commission examined Google's contractual arrangements concerning Android devices, including restrictions associated with Google Search and the Play Store.

Digital dependency significance

Android demonstrates ecosystem-layer dependency.

A simplified structure is:

Android OS    ↓ Play Store    ↓ Apps    ↓ Consumers    ↓ Search / Advertising

Control over one layer may reinforce power in another.

This is particularly important for digital cartography because market power may be cumulative.

A firm may control:

• operating-system access;
• app distribution;
• search;
• default placement;
• advertising infrastructure.

The resulting power cannot always be understood by examining each service independently.

Case 7 — Google AdSense

Google AdSense, Case AT.40411

The European Commission examined contractual restrictions associated with online-search advertising intermediation.

Dependency-cartography significance

The case demonstrates the importance of intermediation bottlenecks.

Advertisers and publishers may occupy different sides of the ecosystem:

Advertisers     ↓ Advertising intermediary     ↓ Publishers     ↓ Consumers

When the same undertaking occupies multiple positions, dependency relationships can reinforce one another.

Digital lesson

Competition analysis should therefore map cross-market leverage, not just individual market shares.

Case 8 — Intel

Intel Corp. v Commission (Case C-413/14 P)

The Court of Justice clarified the importance of examining the circumstances of alleged exclusionary rebates, including whether the conduct is capable of producing anticompetitive foreclosure.

Digital relevance

The case is useful when a platform uses commercial incentives to deepen dependency.

For example:

Platform   ↓ Discount / incentive   ↓ Customer exclusivity   ↓ Reduced multi-homing   ↓ Greater dependency

The cartographic question becomes whether contractual incentives progressively close alternative routes to competitors.

9. A Digital Dependency Matrix

A practical competition investigation could use the following matrix:

10. Dependency Chains

The most significant digital power may emerge through chains rather than individual dependencies.

For example:

Cloud  ↓ AI Compute  ↓ Foundation Model  ↓ API  ↓ Application  ↓ Distribution Platform  ↓ Consumer Data  ↓ Advertising

Suppose one undertaking controls several of these layers.

Even if each individual layer has substitutes, the combined switching burden may become substantial.

This produces what can be called compound dependency.

11. Dependency Cascades

A particularly important concept is the dependency cascade.

Example:

1. A developer depends upon an app store.
2. The app store requires a particular payment mechanism.
3. The payment mechanism generates transaction data.
4. The platform uses that data to improve its competing service.
5. The competing service receives preferential distribution.
6. Developers become increasingly dependent upon the same ecosystem.

Thus:

technical dependency → contractual dependency → data dependency → competitive dependency.

The resulting market power may be considerably greater than the power associated with any individual relationship.

12. Visualization Techniques

A. Network Graph

Nodes represent firms or infrastructure; edges represent dependencies.

Best for: ecosystem analysis.

B. Heat Map

Rank dependencies according to:

• switching cost;
• indispensability;
• concentration;
• exclusivity.

Best for: regulatory investigations.

C. Sankey-Style Dependency Flow

Shows movement of:

• users;
• data;
• payments;
• advertising;
• computing resources.

Best for: demonstrating ecosystem leverage.

D. Bottleneck Map

Highlights infrastructure through which multiple competitors must pass.

Best for: essential-facility and gatekeeper analysis.

E. Time-Series Map

Shows how dependency evolves following:

• acquisitions;
• API restrictions;
• technical changes;
• contractual changes;
• defaults;
• interoperability decisions.

Best for: proving increasing dependency over time.

13. Market Power Visualization And Evidence

Visualization itself does not establish an infringement.

It should be supported by evidence such as:

• internal platform documents;
• contracts;
• API-access records;
• switching-cost studies;
• customer testimony;
• technical architecture;
• usage statistics;
• data-access records;
• ranking changes;
• interoperability restrictions;
• pricing information;
• multi-homing evidence.

The map is therefore an analytical representation of evidence, not a substitute for legal proof.

14. Competition Risks Revealed By Dependency Mapping

1. Self-Preferencing

The platform controls both the gateway and competing downstream service.

2. Foreclosure

Competitors are denied access to a critical input.

3. Tying

Access to one essential service is conditioned on adoption of another.

4. Leveraging

Power in one market is transferred into an adjacent market.

5. Data Advantage

The intermediary obtains data from dependent businesses and uses it against them.

6. Interoperability Restriction

Technical barriers make alternative ecosystems harder to use.

7. Switching-Cost Exploitation

The platform increases migration costs after dependency has become established.

8. Ecosystem Enclosure

Several complementary services become integrated into one closed ecosystem.

15. Remedies Suggested By Dependency Cartography

The visualization can also help regulators design remedies.

Structural remedies

• divestiture;
• separation of business units;
• ownership restrictions.

Conduct remedies

• non-discrimination;
• access obligations;
• interoperability;
• API access;
• prohibition of exclusivity;
• restrictions on self-preferencing.

Data remedies

• data portability;
• data-access obligations;
• data silos;
• restrictions on combining datasets.

Technical remedies

• interoperable interfaces;
• switching tools;
• open standards;
• API documentation;
• effective data export.

Monitoring remedies

A regulator may require continuous reporting of:

• access denials;
• API changes;
• ranking changes;
• switching rates;
• interoperability failures.

16. Critical Limitation

Dependency cartography should not automatically equate dependency with dominance.

A small firm can be dependent upon a large firm without the latter possessing legally established dominance.

Similarly:

high market share ≠ automatic dependency

and

dependency ≠ automatic abuse.

The proper analytical sequence is:

Map → Measure → Test Alternatives → Establish Market Power → Examine Conduct → Establish Effects → Select Remedy.

17. Overall Legal Significance

Digital Dependency Cartography is particularly valuable because conventional competition-law indicators can become incomplete in digital ecosystems.

A platform may derive power from:

• control of interfaces;
• data accumulation;
• network effects;
• default positions;
• interoperability;
• cloud infrastructure;
• identity;
• payment systems;
• algorithmic visibility;
• ecosystem integration.

The central question therefore becomes:

Which undertaking occupies the critical nodes through which competitors, suppliers and consumers must pass?

The cases of United Brands, Commercial Solvents, Bronner, Microsoft, Google Shopping, Google Android, Google AdSense and Intel collectively demonstrate different dimensions of this inquiry: economic independence, upstream control, indispensability, interoperability, visibility, ecosystem leverage, intermediation and foreclosure.

Conclusion

Digital Dependency Cartography And Market Power Visualization provides a network-based method for understanding modern digital competition.

Its greatest contribution is to move analysis from a static question—

“What is the firm's market share?”

—to a structural question—

“What dependencies does the firm control, how difficult are they to escape, and how can those dependencies be converted into competitive advantage or foreclosure?”