What is Agent-Based Modelling and Why It’s Not AI
In the rapidly advancing world of technology, buzzwords like Artificial Intelligence (AI) and Agent-Based Modelling (ABM) are often thrown around. While both play a critical role in industries like mining, logistics, and environmental management, it’s important to understand that Agent-Based Modelling (ABM) is not AI. Despite some overlap, these two approaches are fundamentally different in how they work and what they offer.
Let’s dive deeper to explore what ABM really is and why it stands apart from AI.
What is Agent-Based Modelling (ABM)?
Agent-Based Modelling is a simulation technique where the focus is on the individual entities within a system, referred to as agents. Each agent operates according to a defined set of rules and behaviours, interacting with other agents and their environment. These interactions result in emergent behaviour—where the collective outcome is greater than the sum of its parts. This makes ABM particularly useful for modelling complex systems with multiple independent actors, such as a network of machines in a mine or the logistics of supply chain operations.
For example, in an underground mining operation, each piece of equipment (like a drilling machine or truck) can be modelled as an agent. These machines interact with one another, face various obstacles, and adapt to the evolving situation around them. ABM allows us to simulate how these individual actions ripple through the system, revealing the overall impact on productivity, resource use, or safety.
Key Features of ABM:
Individualized Agents: Each entity behaves according to a unique set of rules.
Interaction-Driven: System-wide outcomes result from the interactions between agents.
Emergence: ABM excels at highlighting how local interactions lead to complex, unpredictable behaviours at the macro level.
Identify Why: The mechanistic pathway is identified and can then assist with improving performance or preventing catastrophic outcomes.
Human Behaviour: Human behaviour can be incorporated into the simulation in a visible fashion, rather than hidden in source data distributions.
What is Artificial Intelligence (AI)?
Artificial Intelligence, on the other hand, refers to the broad set of technologies designed to replicate human intelligence in machines. AI encompasses techniques like machine learning, neural networks, and natural language processing, allowing systems to analyse data, learn from patterns, make decisions, and optimize tasks autonomously.
In mining, AI might be used to analyse vast amounts of data to predict equipment failures, forecast production, or optimize resource allocation. AI thrives in environments rich with historical data, where it can detect patterns that are too subtle or complex for human analysis. Its strength lies in its ability to improve performance over time as it learns from new data.
Key Features of AI:
Data-Driven: AI relies on large datasets to make informed decisions or predictions.
Learning Capabilities: Machine learning models continuously improve based on new data inputs.
Autonomy: AI can make decisions or solve problems without human intervention, often surpassing human capabilities in areas like pattern recognition.
Why ABM is Not AI
While both ABM and AI are used for modelling and decision-making, they diverge in their methods, focus, and purpose.
Here’s why ABM isn’t AI:
1. Focus on Agents vs. Data
ABM focuses on simulating individual agents and their interactions to reveal system-wide behaviour. It doesn't require vast amounts of historical data to function.
AI depends on large datasets and algorithms to detect patterns, learn from them, and make decisions or predictions.
2. Approach: Rule-Based vs. Learning-Based
ABM is rule-based. Agents follow pre-defined rules or instructions and interact with their environment and other agents. There’s no learning involved - agents don’t change their behaviour unless explicitly reprogrammed.
AI is learning-based. AI systems adapt over time by learning from new data, using this to make better decisions or predictions. It focuses on data-driven optimization and self-improvement.
3. Emergent Behaviour vs. Optimized Solutions
ABM simulates how micro-level behaviours lead to macro-level phenomena. It’s about understanding the dynamics of complex systems by focusing on individual actions.
AI focuses on finding optimized solutions, patterns, and predictions from data. AI is often used to automate decisions or solve specific problems that require predictive modelling.
The biggest misconception is that Agent-Based Modelling is just a subset of AI. While both ABM and AI can be used in similar contexts—like mining optimization—they tackle problems in very different ways. ABM is fundamentally about simulation—it models how agents behave and interact over time. AI, on the other hand, is about learning from data to automate decisions or predict outcomes.
In many cases, ABM and AI can complement each other. For example, an AI system might predict equipment failures based on historical data, while an ABM simulates how different pieces of equipment behave together in a mining operation. However, the approaches are distinct, with AI being a data-driven technology focused on pattern recognition, and ABM being a simulation technique focused on agent interactions.
One complementary is use case is in areas where there is minimal data – an ABM can simulate the problem space and generate synthetic data to show the outcomes. AI can then be focused on this synthetic data to gain understanding and insights. This has the potential to lead to novel solutions to poorly understood or defined problem spaces.
When to Use ABM vs AI
Understanding when to use ABM versus AI depends on the problem you're trying to solve:
Use ABM when you need to understand how individual entities interact within a system and how these interactions lead to larger system-wide outcomes. ABM is particularly valuable when trying to simulate scenarios or test “what if” situations, such as in supply chain management, logistics, or operational planning.
Use AI when you have large datasets and need to predict future outcomes or optimize complex processes. AI is best suited for tasks like predictive maintenance, resource allocation, or demand forecasting, where historical data can inform future decisions.
Where ABM Shines: A Real-World Example
Imagine you are managing a mining operation, and you want to improve your production schedule. Using ABM, you could simulate the interactions between drilling machines, trucks, and workers in a virtual environment. This simulation could reveal potential bottlenecks or inefficiencies that you wouldn’t have identified through data analysis alone.
For instance, by simulating how machines interact in confined underground spaces, you could learn that trucks waiting for ore at certain times cause significant delays in production. This insight might lead you to adjust the schedule, reroute machines, or add more trucks to optimize the process. The power of ABM is in its ability to simulate what happens when individual parts interact, providing a deeper understanding of system behaviour under different conditions.
Conclusion: ABM and AI – Complementary but Distinct
While both Agent-Based Modelling and Artificial Intelligence are transformative tools for modern industries, including mining, they are not the same. ABM is a rule-based simulation method focused on understanding the behaviour and interactions of individual agents. AI, by contrast, is a data-driven approach that leverages machine learning and algorithms to make predictions and automate decisions within current operational envelopes.
Understanding the differences between these two technologies allows businesses to apply the right tool for the right problem, leading to better decisions, more efficient processes, and more innovative solutions.
In essence, ABM and AI can complement each other but serve fundamentally different purposes in the toolbox of modern data-driven decision-making.