The field of systems engineering has always grappled with complexity. It aims to manage, predict, and control the multifarious components, behaviours, and outcomes of complex systems. The understanding of such systems has been substantially expanded with the introduction of a theory from neuroscience known as "active inference".
Active inference is a theory of embodied cognition that proposes that all creatures, from the simplest single-celled organisms to the most complex human beings, interact with their environments to minimise the difference between their expectations and sensory inputs. It hinges on a powerful idea that organisms do not merely passively receive information from their environments, which they then translate into actions. Instead, they are constantly active, trying to predict the sensory inputs before they occur and acting on their environment to bring about these predicted inputs.
Active Inference in Complex Systems
The active inference theory initially developed as a neurobiological theory, has found traction in areas far beyond its origins, with applications in complex systems and systems engineering. But what does active inference bring to the table for these domains?
By its very nature, active inference is a theory about managing uncertainty. It proposes that systems (or organisms) act to minimise their "free energy," a quantity related to uncertainty. The idea is that by minimising free energy, systems reduce the difference between their expectations and the actual sensory input. This active management of uncertainty can be applied to complex systems where uncertainty is inherent and often problematic.
Another facet of active inference is its use of predictive models. These models are not simply static, but continually updated based on the system's interactions with its environment. This constant cycle of prediction, interaction, and updating provides a dynamic, responsive way to manage complex systems. Instead of a top-down, command-and-control approach, it allows for an emergent, bottom-up strategy that can adapt to changing circumstances.
Active inference posits that systems continually adapt to their environment. In a biological context, this adaptation is driven by survival. However, in a complex system or engineering context, this could be driven by goals such as efficiency, stability, or resilience. This aspect of active inference provides a framework for understanding how systems can adapt to change, and it can offer insights into designing systems that are resilient and adaptive by nature.
Active Inference Applied to Systems Engineering
Let's delve into the practical application of active inference in systems engineering using an example. Consider a self-driving vehicle navigating an unpredictable urban environment. This is a complex system where the vehicle must interact with numerous other agents (pedestrians, cyclists, other vehicles), and where conditions can change rapidly.
Navigating Uncertainty: The self-driving car needs to operate amidst myriad uncertainties. Will the pedestrian cross the road? Is the traffic light about to change? Active inference proposes that the vehicle should act to minimise these uncertainties. For instance, it might slow down near a crosswalk, reducing the uncertainty about whether it will hit a pedestrian.
Predictive Modelling: To navigate its environment, the car uses a predictive model. This model is updated based on sensory input (like images and radar data) and the car's interactions with its environment. For instance, the car might learn over time that pedestrians wearing a certain type of uniform often cross the road without warning, and update its model accordingly.
Dynamic Adaptation: The car continually adapts to its environment. If a usual route is blocked, it finds an alternative. If the weather changes, it adjusts its driving style. This adaptability could be built into the vehicle using the principles of active inference.
Active inference, thus, provides a comprehensive framework for understanding and managing complex systems, far beyond its roots in neuroscience. It offers not just a theory, but a method of navigation, predictive modelling, and dynamic adaptation that can be applied to a variety of contexts, from autonomous vehicles to complex supply chains, and even social systems.
In conclusion, while it may have been birthed in the realm of neuroscience, active inference is demonstrating its value across diverse domains, becoming a vital tool for understanding and designing robust, adaptable complex systems.