Competition Law and Market Simulation Platform Dominance

1. Introduction

Market simulation platforms are digital or computational platforms that allow businesses, researchers, regulators, investors, developers, or users to simulate markets and competitive conditions. They may model:

• prices and demand;
• consumer switching;
• market entry and exit;
• mergers and acquisitions;
• supply-chain behaviour;
• algorithmic pricing;
• auctions;
• energy or financial markets;
• AI-agent competition;
• advertising markets; and
• hypothetical regulatory or policy scenarios.

A market simulation platform becomes a competition-law concern when the operator acquires sufficient control over the platform, data, algorithms, interfaces, standards, or access conditions that competitors become dependent upon it.

There is not yet a single established doctrine called “market simulation platform dominance.” Instead, ordinary doctrines concerning dominance, essential facilities, refusal to deal, self-preferencing, tying, discrimination, exclusionary conduct, data advantages, interoperability and network effects can apply.

This is particularly important because a simulation platform can become more than a software product: it can become the infrastructure through which firms understand, predict and potentially participate in markets.

2. Meaning of Market Simulation Platform Dominance

A platform may be considered dominant where it possesses substantial market power in a relevant market and competitors or customers lack sufficient alternatives.

For a simulation platform, dominance can arise from:

1. Large proprietary datasets
2. Superior simulation models
3. Network effects
4. High switching costs
5. Interoperability restrictions
6. Exclusive access to simulation APIs
7. Control over benchmark datasets
8. Integration with trading or procurement systems
9. Self-preferencing
10. Vertical integration
11. Algorithmic advantages
12. Reputation and accumulated model-training data

The important competition-law question is therefore not simply:

“Is the simulation software technologically superior?”

It is:

Does control over the simulation platform give the operator the ability and incentive to restrict competition in the platform market or in neighbouring markets?

3. Relevant Market Definition

A competition authority would normally begin by identifying the relevant product and geographic markets.

Possible product markets include:

A. Market-simulation software

Software used for economic, financial, industrial or commercial simulations.

B. Simulation-as-a-Service

Cloud-based simulation services supplied through APIs or subscriptions.

C. AI-powered market simulation

Platforms using machine learning or AI agents to forecast market behaviour.

D. Simulation-data markets

Markets for datasets required to operate sophisticated simulations.

E. Simulation infrastructure

Cloud computing, computational resources, model hosting and API infrastructure.

F. Vertical simulation applications

Specialised platforms for:

• electricity markets;
• transportation;
• financial markets;
• logistics;
• telecommunications;
• insurance;
• manufacturing; or
• supply-chain management.

Market definition can become difficult because the platform may provide several interconnected services.

4. Two-Sided and Multi-Sided Market Characteristics

Market simulation platforms may operate as multi-sided platforms.

For example:

Data providers → Simulation platform → Business users → Developers/consultants

The value of the platform can increase as more participants use it.

This creates indirect network effects.

More users can generate:

• more data;
• more simulations;
• more feedback;
• better models;
• more integrations;
• more developers; and
• greater credibility.

The resulting feedback loop can be represented as:

More users → More data → Better simulations → More users → Greater market power

This resembles the network-effect analysis developed in modern platform competition cases.

5. Sources of Market Power

A. Data advantage

A dominant simulation platform may possess historical:

• pricing data;
• consumer behaviour data;
• transaction data;
• supply-chain data;
• market-share data;
• bidding data; and
• behavioural datasets.

Competitors may therefore face significant entry barriers.

B. Model advantage

The platform may possess proprietary:

• algorithms;
• forecasting models;
• AI models;
• simulation engines;
• calibration techniques; and
• optimisation tools.

If these cannot readily be replicated, they may contribute to durable market power.

C. Switching costs

Users may become dependent upon:

• proprietary file formats;
• APIs;
• model libraries;
• historical simulation records;
• training datasets;
• customised workflows; and
• integrations.

The greater the switching cost, the greater the possibility of customer lock-in.

6. Major Competition Concerns

6.1 Self-Preferencing

A platform operating simulations for third parties may simultaneously offer its own competing products.

It could manipulate:

• model ranking;
• default parameters;
• simulation visibility;
• recommendations;
• access to computational resources; or
• benchmark results.

For example, suppose Platform X operates a dominant market simulation service and also sells investment-management software.

If simulations involving X's own investment product are systematically displayed more favourably, competition concerns may arise.

The Google Shopping litigation is an important analogy because the EU courts examined Google's favouring of its own specialised shopping service in search results. The General Court treated the conduct as potentially constituting abusive leveraging by a dominant undertaking.

7. Refusal of Access

A simulation platform could deny competitors access to:

• APIs;
• simulation environments;
• datasets;
• model-validation tools;
• computational infrastructure; or
• interoperability interfaces.

The legal question would be whether the refusal substantially restricts competition and whether the requested resource is sufficiently indispensable.

This connects market simulation platforms with the broader essential-facility and refusal-to-deal doctrines.

8. Data Discrimination

One particularly important problem is discriminatory access to simulation data.

Imagine that a dominant platform provides its own affiliated business with:

real-time market data + historical datasets + detailed simulation outputs

while competitors receive:

delayed or incomplete datasets.

This could give the integrated business a competitive advantage.

The Amazon Marketplace proceedings provide a close modern analogy. Authorities examined Amazon's use of third-party seller data and concerns about whether Amazon could use marketplace information to compete against those sellers. Amazon ultimately accepted commitments concerning use of third-party seller data and Buy Box treatment.

9. Algorithmic Discrimination

Simulation platforms increasingly rely upon algorithms.

A dominant platform could theoretically manipulate:

• input weighting;
• model parameters;
• prediction rankings;
• scenario selection;
• confidence intervals;
• recommended strategies; or
• access to computing resources.

Competition law may therefore need to examine not only the output, but also the architecture of the algorithmic system.

Relevant questions include:

1. Are rival users treated equally?
2. Are algorithmic parameters transparent?
3. Can users audit the model?
4. Are affiliated businesses receiving privileged inputs?
5. Does the platform deliberately degrade competing simulations?

10. Tying and Bundling

A dominant simulation platform might require customers to purchase additional products.

For example:

Simulation platform + proprietary cloud service + proprietary dataset

or:

Simulation software + mandatory analytics package.

Competition concerns become stronger where:

• the platform is dominant in one market;
• the tied product is separately identifiable;
• customers are effectively compelled to take both;
• competitors are excluded from the tied market; and
• there is insufficient objective justification.

The Google Android decisions illustrate how control over one ecosystem layer can be leveraged into adjacent markets. The CCI found Google dominant in relevant Android OS and app-store markets and examined conduct involving tying, market access and leveraging.

11. Interoperability Restrictions

Interoperability is particularly important for simulation platforms.

A dominant operator could restrict compatibility with:

• competing simulation engines;
• alternative datasets;
• third-party AI models;
• cloud infrastructure;
• external APIs; or
• industry-standard formats.

Such restrictions may raise competition concerns when they prevent users from multi-homing or switching.

The classic Microsoft European competition litigation is relevant by analogy because interoperability and access to information necessary for competing products formed a central part of the abuse analysis.

12. Network Effects and Entrenchment

A market simulation platform may benefit from a self-reinforcing cycle:

Users

↓

More simulations

↓

More behavioural data

↓

Improved model accuracy

↓

Higher customer demand

↓

More users

This can create an economic moat even without traditional physical infrastructure.

Consequently, market share alone may not adequately describe competitive conditions.

Authorities may also examine:

• user dependence;
• multi-homing;
• switching costs;
• data accumulation;
• interoperability;
• entry barriers;
• access to computing resources; and
• the availability of credible substitutes.

13. Six Important Case Laws

1. Google LLC v Commission — Google Shopping

Case T-612/17, General Court of the European Union (2021)

Google operated a dominant general search service while also operating its own comparison-shopping service.

The European Commission found that Google favoured its own comparison-shopping results over competing services. The General Court upheld the Commission's decision in substantial respects.

Principle

A dominant platform can potentially abuse its position by leveraging control over an important platform or gateway to favour its own downstream service.

Relevance to simulation platforms

A dominant simulation platform could potentially face similar scrutiny if it:

• privileges its own simulation models;
• gives its own analytical products superior placement;
• suppresses competing models; or
• manipulates access conditions for rival services.

2. Epic Games, Inc. v Google LLC

This litigation concerned Google's Android application-distribution ecosystem.

The Ninth Circuit's 2025 decision describes the jury findings concerning Google's alleged monopoly power in Android app distribution and in-app billing, as well as Google's use of contractual arrangements and other mechanisms affecting alternative distribution channels.

Principle

Digital-platform dominance can be reinforced through:

• network effects;
• contractual restrictions;
• default settings;
• restrictions on alternative channels; and
• ecosystem integration.

Relevance

A simulation platform could similarly reinforce dominance if users cannot practically access competing simulation engines or easily migrate their models and data.

3. Competition Commission of India — Google Play Store

The CCI found Google dominant in relevant Android-related markets and examined mandatory use of Google Play's billing system, anti-steering provisions and related conduct.

The CCI specifically recognised strong indirect network effects involving users and app developers.

Principle

A platform's dominance can arise from:

• network effects;
• ecosystem integration;
• lack of substitutability;
• high entry barriers; and
• dependence of one user group upon access to another.

Relevance

A simulation platform connecting data providers, developers and business users may exhibit the same economic structure.

4. Amazon Marketplace — European Commission

In Amazon Marketplace (AT.40462), the Commission examined concerns surrounding Amazon's use of non-public data supplied by third-party sellers.

Amazon offered commitments concerning the use of seller data and competition for the Buy Box.

Principle

A platform that simultaneously:

1. hosts third-party businesses, and
2. competes against those businesses

may create a conflict between platform operator and competitor.

Relevance

This is particularly significant for simulation platforms.

For example:

Platform: supplies simulation infrastructure.

Third parties: use it to develop forecasts.

Platform affiliate: uses the same data to compete with those third parties.

The resulting data advantage may become a competition concern.

5. Booking.com — Case C-264/23

The Court of Justice examined price-parity clauses used by Booking.com.

The case concerned contractual restrictions affecting hotels' ability to offer different prices through alternative sales channels. The Court held that such clauses could not automatically be treated as ancillary restraints and required analysis under the applicable competition-law framework.

Principle

Platform contractual restrictions can affect competition between:

• the platform;
• suppliers;
• competing platforms; and
• alternative distribution channels.

Relevance

A simulation platform could impose similar contractual restrictions, for example:

“Users may not use another simulation provider for the same market models.”

or:

“Data generated through our platform cannot be transferred to competing platforms.”

Such clauses require examination of their competitive effects and justification.

6. Ohio v. American Express Co.

The U.S. Supreme Court addressed competition analysis involving a two-sided transaction platform.

The case is particularly important because it recognised that two-sided transaction platforms can have interdependent sides and that competitive analysis may need to consider effects across the platform rather than examining one side in isolation.

Principle

Competition analysis of a platform may need to account for:

• both sides of the platform;
• indirect network effects;
• platform-wide competitive effects; and
• the relationship between users on different sides.

Relevance

A simulation platform might have:

Side 1: data suppliers
Side 2: simulation users
Side 3: developers/AI-model providers

Conduct affecting one side could therefore alter competition on the other sides.

14. Comparative Case-Law Table

15. Essential-Facility Dimension

The strongest competition concern may arise where the simulation platform becomes indispensable infrastructure.

For example, imagine a platform controlling:

• the industry's largest simulation dataset;
• the accepted market model;
• the principal validation system;
• the dominant API;
• the industry's benchmark scenarios.

Competitors may then be unable to compete effectively without access.

However, mere usefulness is not automatically equivalent to legal indispensability. Competition authorities generally need to establish the relevant legal conditions before imposing access obligations.

16. Data Portability and Switching

Competition can be weakened if users cannot transfer:

• models;
• datasets;
• simulation histories;
• APIs;
• parameter configurations;
• user-generated scenarios; or
• trained AI agents.

Therefore, portability can become a competition issue.

A competitive environment is more open where users can move:

Platform A → Platform B

without losing years of accumulated modelling infrastructure.

17. Simulation Accuracy as a Competitive Parameter

A distinctive issue is that the dominant platform might influence competition through claims of superior predictive accuracy.

Authorities may need to distinguish between:

Legitimate technological superiority

A better model resulting from genuine innovation.

and

Artificial competitive advantage

A platform deliberately:

• withholds data from competitors;
• manipulates benchmarks;
• gives its own models privileged inputs;
• disables interoperability; or
• selectively presents simulation results.

Competition law generally protects competition rather than requiring competitors to have identical technology.

18. Algorithmic Collusion Risk

A market simulation platform can also become a potential coordination infrastructure.

Suppose competing firms use the same dominant simulation platform and the platform:

• collects their commercially sensitive information;
• processes competitors' pricing strategies;
• recommends responses to competitors;
• predicts competitor behaviour; and
• automatically updates recommended prices.

This may create risks concerning:

• information exchange;
• concerted practices;
• algorithmic coordination;
• hub-and-spoke arrangements; and
• facilitated collusion.

The competition issue would depend heavily on the actual information flows and the conduct of the firms and platform.

19. Merger-Control Implications

Suppose a dominant simulation platform acquires a major:

• market-data provider;
• AI modelling company;
• cloud provider;
• economic forecasting firm; or
• competing simulation engine.

Even where traditional turnover thresholds are not particularly high, authorities may examine whether the transaction eliminates a potential competitor or combines complementary datasets.

Relevant theories of harm could include:

Horizontal effects

Elimination of a competing simulation platform.

Vertical effects

Control over both simulation infrastructure and downstream applications.

Conglomerate effects

Bundling simulation with cloud, AI or data services.

Data effects

Combining datasets that rivals cannot reproduce.

20. Remedies

Competition authorities may consider several remedies where unlawful conduct is established.

Structural remedies

• divestiture;
• separation of business units;
• removal of vertical integration.

Behavioural remedies

• non-discriminatory API access;
• data-access obligations;
• interoperability;
• prohibition on self-preferencing;
• transparent ranking;
• fair access conditions.

Data remedies

• data portability;
• restrictions on use of third-party data;
• independent data governance;
• access to essential datasets.

Contractual remedies

• removal of exclusivity;
• removal of anti-steering provisions;
• restrictions on parity clauses.

21. Competition-Law Analytical Framework

A regulator investigating market simulation platform dominance could follow this sequence:

Step 1 — Define relevant market

↓

Step 2 — Identify platform sides

↓

Step 3 — Measure market power

↓

Step 4 — Examine network effects

↓

Step 5 — Identify switching costs

↓

Step 6 — Examine data advantages

↓

Step 7 — Investigate access and interoperability

↓

Step 8 — Test self-preferencing

↓

Step 9 — Examine tying/bundling

↓

Step 10 — Examine discriminatory treatment

↓

Step 11 — Assess foreclosure effects

↓

Step 12 — Examine objective justification and efficiencies

↓

Step 13 — Determine appropriate remedy

22. Key Legal Issues for Future AI-Based Simulation Platforms

The problem becomes even more significant when simulation platforms use autonomous AI agents.

Future competition investigations may involve:

1. AI-agent access discrimination
2. Training-data exclusivity
3. Simulation-model interoperability
4. API foreclosure
5. AI model self-preferencing
6. Algorithmic price coordination
7. Synthetic-data advantages
8. Benchmark manipulation
9. Cloud-compute foreclosure
10. AI ecosystem tying
11. Predictive-model portability
12. Control over industry-standard simulation environments

The EU's current digital-platform regulatory environment demonstrates the increasing importance of gateway power and ecosystem effects; the Commission's DMA framework currently designates major firms including Alphabet, Amazon, Apple, Booking, ByteDance, Meta and Microsoft as gatekeepers for specified core platform services.

23. Conclusion

Market simulation platform dominance represents a modern form of digital infrastructure power. The principal competition-law concern is not the mere existence of a sophisticated simulation platform, but the possibility that control over simulation infrastructure enables the operator to foreclose competitors, exploit dependent users, discriminate in access, leverage data advantages, self-preference affiliated products, restrict interoperability or facilitate coordination.

The most relevant legal principles can be drawn from the case law concerning Google Shopping, Google Play/Android, Epic Games v Google, Amazon Marketplace, Booking.com and American Express.

The central competition-law distinction is therefore:

Innovation-based superiority is generally different from exclusionary conduct that uses platform control to prevent effective competition.

For examination purposes, the topic can be reduced to the formula:

Platform Power + Network Effects + Data Advantage + Lock-in + Access Control + Exclusionary Conduct = Potential Competition-Law Concern

This framework is particularly useful for analysing next-generation AI simulation platforms, digital twins, autonomous-agent markets, financial-market simulators, energy-market simulators and algorithmic market-design platforms.