Simulation Lab

Cat_Mackay

This is an innovation prototype that lives solely in Q Labs. Q Labs products are experimental and pre-roadmap, as such they are for awareness and co-innovation interest only.

Q Labs Status: Experimentation

Background

In an increasingly complex business environment, simulations are a key component of any organization’s toolkit for making optimal decisions. Combining simulations with domain models (ontologies) rooted in an organization’s data fabric, allows businesses to explore and evaluate many “what if” scenarios–like changes in market conditions, competitive moves, or operational disruptions–without real-world consequences.

Simulations serve many use cases across industries:

Industry	Use Cases
Banking	Credit & Market Risk: Stress-test portfolios under adverse economic scenarios to anticipate defaults and downturns. Operational Risk: Model internal processes to identify vulnerabilities and improve internal controls. Threat Intelligence: Simulate emerging risks (e.g., cyber fraud) to enhance proactive risk management.
Insurance	Catastrophic Events: Simulate geopolitical and natural disasters to forecast potential claims distributions. Underwriting Optimization: Use simulation insights to adjust premium pricing and design effective reinsurance strategies.
Government	Cybersecurity: Test how cyber-attacks might impact critical infrastructure and public safety. Policy Impact: Simulate long-term economic and social effects of policy interventions. Regulatory Oversight: Inform regulation design to bolster national resilience and public welfare.
Corporations	Supply Chain Resilience: Model disruptions from geopolitical events and external shocks to identify bottlenecks and optimize logistics. Geopolitical Event Modelling: Anticipate impacts from global events to support strategic planning. Threat Intelligence: Integrate competitive and operational risk data to enhance agility and decision-making.

Given the significant role of simulations in Decision Intelligence, we set out to explore potential innovations in this area.

Motivation

Despite the wide use of simulations in the industry, most simulation approaches–whether based on rules or machine learning models–remain primarily quantitative and rely on human experts to encode their domain knowledge into models and model parameters.

The advent of Large Language Models (LLMs) has opened new possibilities in the world of simulations. Given the breadth of their training data –often the entire textual contents of the internet– and their reasoning abilities, LLMs are inherently good at understanding the relationship between events and entities and can understand cause-and-effect relationships for instance, to generate many potential “effects” for a cause event and vice versa.

By leveraging this quality of LLMs we can augment human experts’ ability and knowledge to consider a wider range of scenarios. Moreover, by using LLMs as a bridge between qualitative scenario generation and quantitative analysis, we can automate the large-scale evaluation of many “what if” scenarios. Algorithms such as Monte Carlo Tree Search (MCTS) allow us to identify the most promising scenarios to flag to human experts, bringing significant efficiencies to the process of building robust and large-scale simulation systems.

How it Works

https://player.vimeo.com/video/1060039483?h=3216c2573f&badge=0&autopause=0&player_id=0&app_id=58479

Simulation Lab is our LLM-powered platform that transforms raw data into actionable future scenarios. Here’s a quick rundown of its process:

• Entity-Event Graph Construction: The system builds a dynamic graph that connects real-world entities (such as companies or public agencies) with a series of time-ordered events. Each event is assigned a likelihood and a severity score, while “leads to” relationships capture the causal flow between events.
• Scenario Generation: Using our large language model, Simulation Lab expands an initial set of user-defined events. Users can guide the LLM with focused prompts (e.g., “focus on geopolitical consequences”) and adjust parameters like likelihood, severity, and randomness (temperature) to explore a wide range of potential outcomes.
• Interactive Exploration: A user-friendly, point-and-click interface lets experts build and refine their entity graphs. This visual tool allows you to map out relationships between key actors and drill down into how specific events might cascade through your network.
• Actionable Insights: The result is a curated set of future scenarios that highlight emerging risks and opportunities. These scenarios can then be used to inform decision-making processes, from risk management to strategic planning, as well as to create alerts for early detection of any leading indicators.

By merging expert knowledge with the generative power of LLMs, Simulation Lab bridges the gap between qualitative foresight and quantitative analysis, enabling more robust decision intelligence.

Conclusion

In conclusion, Simulation Lab represents a forward-thinking approach to decision intelligence by merging the generative power of LLMs with expert insights. This simulation environment transforms raw data into dynamic, actionable scenarios, empowering organizations to proactively anticipate risks and seize opportunities in an ever-changing landscape. As we continue to refine and expand this experimental prototype within Q Labs, we invite collaboration and feedback to help shape its evolution and maximize its impact on strategic planning and risk management.

Get in Touch

If you like to hear more about Simulation Lab, please get in touch with us, as we are actively exploring different options for how to take it forward and are open to collaboration.

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