Civil Law and UAE: Simulation-Based Lawmaking and Policy Testing

1. Simple Meaning

Simulation-based lawmaking means using computer models, data, artificial intelligence, digital twins, economic models, or controlled experiments to test a proposed law or regulatory policy before applying it widely.

In simple words:

First simulate the rule → observe possible effects → identify risks → modify the rule → then implement it.

For example, suppose the UAE wants to introduce a new rule regulating AI-generated contracts. Before making the rule applicable to every business, policymakers could simulate:

how many contracts would be affected;

what disputes could arise;

whether consumers would be protected;

whether businesses would face excessive costs;

how courts might interpret the rule;

whether automated systems could produce discriminatory outcomes.

This approach can make modern lawmaking more evidence-based, adaptable and technology-aware.

2. Meaning in UAE Civil Law

The UAE civil-law system is primarily based on written legislation, rather than judge-made common law.

A major current development is the new Federal Decree-Law No. 25 of 2025 promulgating the Civil Transactions Law, which entered into force on 1 June 2026 and repealed the 1985 Civil Transactions Law. (UAE Legislation)

Simulation therefore does not itself become law.

Instead, simulation can assist:

lawmakers;

regulators;

government departments;

courts and judicial administrators;

economic authorities;

technology regulators.

The final legal rule must still come through the legally authorised legislative or regulatory process.

3. Basic Model

A simple model is:

Problem → Data → Simulation → Risk Assessment → Pilot/Sandbox → Consultation → Legal Rule → Monitoring → Revision

For example:

AI contracts

↓

simulate 100,000 hypothetical transactions

↓

identify disputes

↓

test consumer-protection rules

↓

conduct controlled regulatory experiment

↓

measure results

↓

draft regulation

↓

implement

↓

monitor actual disputes

This creates a feedback loop between law and real-world evidence.

4. Why Simulation-Based Lawmaking Is Important in the UAE

The UAE has a highly technology-oriented economy involving:

artificial intelligence;

blockchain;

fintech;

digital assets;

smart contracts;

autonomous systems;

e-commerce;

digital identity;

cloud computing;

data processing;

smart cities.

These technologies can develop faster than traditional legislation.

Simulation can therefore help answer questions such as:

A. Will the proposed law work?

A proposed rule can be tested against different factual situations.

B. What unintended consequences could arise?

A rule intended to protect consumers might unintentionally increase business costs.

C. How many disputes could result?

Legal models can estimate possible litigation categories.

D. Can courts practically enforce the rule?

A law may appear simple on paper but create substantial evidentiary or procedural problems.

E. What happens in extreme situations?

Simulation can test:

cyberattacks;

financial crises;

mass contractual defaults;

AI errors;

supply-chain disruption;

digital-asset failures.

5. Connection With UAE Civil Liability

Simulation is particularly relevant to civil law because civil disputes often depend upon:

Duty + Conduct + Harm + Causation + Damage

Suppose an AI-controlled system causes financial loss.

A simulation can test:

If the AI makes a predictable error, who should bear the loss?

Possible answers could involve:

manufacturer;

software developer;

operator;

owner;

service provider;

consumer;

insurer.

The resulting legal policy could then determine appropriate liability rules.

Under the current Civil Transactions Law, harmful-act liability and compensation remain important parts of civil responsibility, including rules concerning harm, causation, attribution and compensation.

6. Simulation and Contract Law

Simulation can also be used to test proposed contract rules.

For example, assume a proposed regulation provides:

“A consumer may cancel an AI-generated standard-form contract within seven days.”

Before implementation, policymakers could model:

cancellation rates;

business losses;

consumer behaviour;

fraudulent cancellation;

platform costs;

dispute frequency.

The simulation does not decide the law.

It provides evidence that can assist the competent authority in deciding whether the rule requires modification.

7. Simulation and Regulatory Sandboxes

One of the closest practical relatives of simulation-based lawmaking is the regulatory sandbox.

A sandbox allows a new technology or business model to operate within controlled conditions.

For example:

New fintech technology

→ limited number of users

→ limited transaction value

→ enhanced monitoring

→ temporary regulatory conditions

→ collection of data

→ evaluation

→ permanent regulatory decision

This is different from pure computer simulation because a sandbox involves real-world testing, whereas simulation may involve hypothetical or computer-generated environments.

8. Simulation-Based Lawmaking vs Regulatory Sandbox

Both approaches can be combined.

9. Role of Artificial Intelligence

AI can assist policymakers in analysing:

large volumes of court decisions;

contracts;

regulatory complaints;

consumer behaviour;

economic data;

accident statistics;

financial transactions;

compliance failures.

AI can then identify possible patterns.

However:

AI analysis should support legal decision-making, not replace legally authorised decision-makers.

This is particularly important because AI models can contain:

biased data;

incomplete information;

incorrect assumptions;

false correlations;

opaque reasoning.

10. Digital Economy Court as an Important UAE Example

The DIFC provides a particularly useful illustration of technology-responsive legal institutions.

The DIFC established its Digital Economy Court to deal with sophisticated disputes involving technologies including big data, blockchain, AI, fintech, cloud services, robotics and related technologies. (DIFC Courts)

Its rules also expressly contemplate the use of technology in proceedings. Part 58 permits Digital Economy Court claims involving areas such as AI, blockchain, digital assets and complex databases. The rules also provide for smart forms and AI-driven decision-tree software to collect information needed for proceedings. (DIFC Courts)

This is not the same as simulation-based legislation, but it demonstrates the broader UAE approach of testing and adapting legal institutions to technological change.

11. Six Important Case Laws

A major qualification is necessary:

There is not yet a large body of UAE reported case law specifically deciding “simulation-based lawmaking” as a standalone legal doctrine.

Therefore, the following cases are relevant illustrative authorities concerning technology, digital assets, AI-generated legal material, evidence, and technology-adaptive adjudication. They should not be described as cases that directly establish a general legal doctrine of simulation-based legislation.

Case 1: Gate Mena DMCC v Tabarak Investment Capital Ltd

Gate Mena DMCC (formerly Huobi OTC DMCC) v Tabarak Investment Capital Ltd [2024] DIFC DEC 002

This is particularly relevant to technology-based legal development.

The dispute concerned cryptocurrency and whether Bitcoin could be treated as money or currency for the legal issues before the court. The Digital Economy Court dealt with expert evidence concerning cryptocurrency. The matter ultimately involved a retrial following an earlier Court of Appeal decision. (DIFC Courts)

Relevance

The case demonstrates how courts and legal institutions must examine new technological realities using existing legal concepts.

For policy testing, this supports the idea that proposed rules concerning digital assets should be tested against actual technological characteristics before being applied generally.

Case 2: Techteryx Ltd v Aria Commodities DMCC & Others

Techteryx Ltd v Aria Commodities DMCC & Others [2025] DIFC DEC 001

This dispute concerned a stablecoin and reserves associated with TrueUSD, including claims concerning approximately USD 456 million in reserves. The Digital Economy Court dealt with sophisticated questions involving digital assets, ownership, tracing and financial structures. (DIFC Courts)

The court also granted proprietary and worldwide freezing relief in the proceedings. (DIFC Courts)

Relevance

The case illustrates why policymakers should test proposed digital-asset rules against:

asset tracing;

beneficial ownership;

reserve arrangements;

cross-border transactions;

emergency remedies;

enforcement.

A law designed without testing these situations may leave important gaps.

Case 3: Marwan Mahmoud Khadour v Yousef Salah Hawash & Alphaseed Technology Ltd

Marwan Mahmoud Khadour v Yousef Salah Hawash & Alphaseed Technology Ltd [2022] DIFC CFI 026

The dispute involved technology-related corporate issues and evidence concerning the development of a technological system. The judgment considered evidence concerning prototypes and the relationship between technological development and the parties' claims. (DIFC Courts)

Relevance

This illustrates an important point for simulation-based policy:

A technological prototype may demonstrate possibilities, but it does not automatically prove that the final operational system behaves in the same way.

The same principle applies to legal simulations.

A simulated result is evidence for policy analysis, not automatically proof of what will happen in society.

Case 4: Thamer Abdulaziz Albulaihid v Nasser Shehata & Health Insights

Thamer Abdulaziz Albulaihid & Another v Nasser Shehata & Health Insights FZ-LLC [2023] DIFC CFI 079

The dispute concerned technology development and evidence surrounding a technological system and prototype. The court examined the evidentiary connection between demonstrations, files, metadata and the eventual system. (DIFC Courts)

Relevance

This is highly useful for understanding evidence validation.

For lawmaking simulations, policymakers should ask:

Who created the model?

What data was used?

Was the data complete?

Can the model be independently tested?

Does the simulation reflect actual conditions?

Can the results be reproduced?

A model without reliable inputs can produce unreliable policy conclusions.

Case 5: Klesta Eshja v Salah Masri & Others

Klesta Eshja v Salah Masri & Others [2025] DIFC CFI 066

The case involved pleadings that had been prepared substantially with assistance from artificial intelligence and contained false references and misleading material. The DIFC Court struck out the affected defences and required the defendants to replead subject to conditions. (DIFC Courts)

Relevance

This case provides an important warning for AI-supported lawmaking:

AI-generated output must be independently verified.

If AI can generate incorrect legal authorities in litigation, similar risks can arise when AI is used to generate:

legislative proposals;

regulatory impact assessments;

simulated outcomes;

risk predictions;

legal analysis.

Therefore, human verification is essential.

Case 6: VTB Bank PJSC v Timur Orazbekovich Kuanyshev & Others

VTB Bank PJSC v Timur Orazbekovich Kuanyshev & Others [2025] DIFC CFI 121

This case is particularly useful for the reliability dimension of AI-assisted legal work. The judgment discussed alleged AI-generated material and identified incorrect case references, including authorities that did not exist or had been incorrectly described. It also referred to the DIFC's guidance concerning the use of large language models in proceedings. (DIFC Courts)

Relevance

For simulation-based lawmaking, this demonstrates the need for:

source verification;

audit trails;

human review;

reproducibility;

transparent methodology.

An AI-generated policy simulation should never be accepted merely because the output appears sophisticated.

12. Additional Authority: Digital Economy Court Framework

Although not a case, the DIFC Digital Economy Court framework is especially important.

The rules allow Digital Economy Court claims involving:

fintech;

digital assets;

blockchain;

substantial databases;

artificial intelligence;

cloud data;

e-commerce;

digital payment platforms.

They also permit technology-assisted procedural systems, including smart forms and AI-driven decision-tree systems. (DIFC Courts)

This demonstrates a practical form of legal experimentation and institutional adaptation.

13. Important Legal Principle: Simulation Does Not Replace Legislation

This is the most important point.

A simulation cannot itself:

create a civil obligation;

impose a tax;

create criminal liability;

change property rights;

invalidate a contract;

impose compensation;

create a new statutory duty.

Only an authorised legal instrument can create those legal consequences.

Therefore:

Simulation = policy evidence

not

Simulation = law

14. Human Oversight

A sound UAE model should follow:

Human → Machine → Human

Step 1: Human policymakers identify the legal problem.

Step 2: Machine/model tests possible solutions.

Step 3: Human experts examine the results.

Step 4: Legal and economic impact is assessed.

Step 5: Competent authority makes the policy decision.

Step 6: Legislature/regulator adopts the legally authorised rule.

Step 7: Actual results are monitored.

15. Data Quality Problem

Simulation is only as reliable as its data.

For example:

Suppose a government model predicts that a new consumer rule will reduce disputes by 30%.

Before relying on that number, policymakers should ask:

What data produced the 30%?

Is the data current?

Does it cover different emirates?

Does it include small businesses?

Does it include digital transactions?

Were unusual cases excluded?

Was the model independently tested?

Therefore:

Bad data + sophisticated model = potentially bad policy.

16. Bias and Equality

Simulation can also reproduce historical discrimination.

Suppose historical lending data contains unequal treatment.

If that data is used to train a policy model, the model may reproduce the same pattern.

Therefore, policy testing should include:

bias testing;

demographic impact analysis;

error-rate analysis;

explainability;

independent auditing;

human review.

This becomes particularly important when the proposed regulation concerns:

employment;

housing;

finance;

insurance;

consumer credit;

public services.

17. Simulation and Civil-Law Principles

Simulation should remain consistent with fundamental civil-law concepts.

1. Good Faith

A simulation should not be deliberately manipulated to produce a predetermined result.

2. Protection of Legitimate Rights

Testing a new policy should not unnecessarily disregard existing legal rights.

3. Proportionality

The regulatory burden should correspond reasonably to the problem being addressed.

4. Legal Certainty

People should be able to understand the final legal rule.

5. Equality

Similar situations should generally receive consistent treatment unless a lawful distinction exists.

6. Due Process

Affected persons should have appropriate opportunities to challenge decisions.

18. Simulation and Evidence

Simulation results may be useful evidence for policymakers, but they are not automatically proof in civil litigation.

For example:

Government simulation:
“AI systems are predicted to produce 5% contractual errors.”

This does not automatically establish:

“This particular AI system caused this particular contractual loss.”

The court would still need to consider:

actual evidence;

causation;

responsibility;

damage;

contractual terms;

expert evidence;

applicable law.

19. Simulation and Causation

This distinction is particularly important.

A model may establish:

“Policy X is statistically associated with outcome Y.”

But civil liability normally requires a legally sufficient connection between the defendant's conduct and the claimant's harm.

Thus:

Statistical correlation ≠ automatic legal causation.

Simulation can help investigate causation but cannot automatically replace judicial determination.

20. Advantages

A. Better anticipation of legal consequences

Policymakers can identify problems before implementation.

B. Lower regulatory risk

Potential failures can be detected in advance.

C. Faster adaptation

Digital rules can be modified using evidence.

D. Better economic analysis

Models can estimate costs and benefits.

E. Better consumer protection

Possible harmful outcomes can be tested before large-scale implementation.

F. Technology neutrality

Rules can be tested against different technological scenarios rather than one particular product.

21. Risks

1. False precision

A model may produce a precise-looking number that is actually uncertain.

2. Data bias

Historical data may contain structural problems.

3. Black-box decision-making

Decision-makers may not understand why the model produced an outcome.

4. Overdependence on AI

Human legal judgment may be weakened.

5. Privacy risks

Simulation may require sensitive personal or commercial data.

6. Regulatory capture

A model may be designed around assumptions favoured by particular stakeholders.

7. Simulation-to-reality gap

Real people do not always behave as mathematical models predict.

22. Practical UAE Framework

A possible UAE simulation-based lawmaking framework could be:

23. Example: AI Liability Law

Imagine the UAE considers a new civil-liability framework for autonomous AI systems.

Scenario A

AI makes a harmless error.

Scenario B

AI causes AED 10,000 financial loss.

Scenario C

AI causes AED 10 million commercial loss.

Scenario D

AI acts incorrectly because of defective training data.

Scenario E

AI acts incorrectly because the user ignored a warning.

Scenario F

AI is hacked by a third party.

The simulation could compare different liability models:

Developer liability

vs.

Operator liability

vs.

Shared liability

vs.

Strict liability

vs.

Insurance-based compensation

The final legal choice would still have to be made through the appropriate legal process.

24. Relationship With the New Civil Transactions Law

The transition to the new 2025 Civil Transactions Law, effective from 1 June 2026, is particularly relevant to legal-tech policy because lawmakers can evaluate whether existing civil concepts adequately deal with emerging technologies. (UAE Legislation)

For example, simulation can test how general civil-law principles operate in hypothetical disputes involving:

AI contracts;

automated performance;

digital assets;

algorithmic mistakes;

autonomous machines;

digital evidence;

online consumer transactions;

platform liability.

Older case law based on the repealed 1985 Civil Transactions Law should be treated as historical authority, not automatically as a statement of the current statutory wording.

25. Difference Between Traditional and Simulation-Based Lawmaking

Simulation does not necessarily replace traditional lawmaking. It adds an additional testing layer.

26. Key Case-Law Lessons

These cases do not establish a standalone doctrine called “simulation-based lawmaking.” Their value is illustrative: they show the legal difficulties that policy simulations should attempt to anticipate.

27. Exam-Friendly Definition

Simulation-based lawmaking in UAE civil law is an evidence-driven approach in which proposed legal rules are tested through computer models, AI analysis, digital scenarios, pilots or controlled regulatory environments before broader implementation, while the final legal rule remains subject to the constitutionally and legally authorised legislative or regulatory process.

28. Short Formula

Simulation-Based Lawmaking =

Legal Problem + Data + Model + Scenario Testing + Risk Assessment + Human Review + Pilot + Legal Enactment + Monitoring

29. Conclusion

Simulation-based lawmaking represents a modern method of developing and testing legal policy in a technology-driven UAE economy. It is particularly relevant to AI, fintech, blockchain, digital assets, smart contracts, autonomous systems and digital evidence.

The central principle is:

Test the consequences before making the rule permanent.

The UAE's technology-oriented legal institutions, especially the DIFC Digital Economy Court, demonstrate increasing institutional adaptation to emerging technologies. The Court's framework expressly accommodates disputes involving AI, blockchain, digital assets and other digital technologies. (DIFC Courts)

However, simulation should remain an advisory and testing mechanism, not an independent source of law. The final rule must remain grounded in legislation, delegated regulatory authority, legal certainty, evidence, human oversight and appropriate procedural safeguards.

Quick Revision

Meaning: Testing proposed laws before broad implementation.

Purpose: Predict consequences and identify risks.

Main tools: AI, modelling, digital twins, data analysis, pilots and regulatory sandboxes.

Civil-law relevance: Contracts, tort liability, causation, damages, consumer protection and digital transactions.

Main danger: A technically sophisticated model can still produce a legally or factually wrong result.

Six illustrative cases:

Gate Mena v Tabarak

Techteryx v Aria Commodities

Khadour v Hawash & Alphaseed

Albulaihid v Shehata & Health Insights

Klesta Eshja v Masri

VTB Bank v Kuanyshev

Golden rule: Simulation informs lawmaking; it does not itself make law.