Complexity is the Norm and not the exception: exploring the dynamics of interconnectedness
By Sibongakonke Dlamini
From intricate networks of community interactions to interdependent ecosystems on our planet, complexity is the engine underpinning social order and function. Carrying this undefined but present fascination in my subconscious, I was drawn to a liberal arts college to study human ecology – an interdisciplinary study of the relationships between humans and their natural or built environments. Drawing from my interest in the natural sciences, I explored and studied a myriad of ways in which humans interact with their surroundings and how these interactions inform much of why things are the way that they are. Having always harboured this curiosity, my interest in complex systems became a matter of course. piece will explore complex systems and practical applications in the field of epidemiology.
What is a Complex System?
There have been numerous definitions of complex systems however; put simply, complex systems are characterized by various interconnected components that interact with each other in non-linear ways. Ernesto Estrada, a professor of physics and mathematics, draws on Edgar Morin’s definition of complex systems: a system where there is a “bidirectional non-separability between the identities of the parts and the identity of the whole”. That is to say, a part cannot be described by itself but derives its identify from its interactions with other parts and how they interact to form a whole system. The behaviour emerging from these complex interactions cannot be predicted if interacting components are studied in isolation.
The Dynamics of Interconnectedness and Emergent Behaviour: Climate Change and Ant Colonies
Morin’s definition further enforces the notion that parts are defined not by themselves but by how they interact and are connected with other parts to make up a whole. There are various ways of describing these interconnections and well-studied approaches include defining feedback loops and emergent behaviour as characteristics of an interconnected system. A feedback loop is when the outputs of a process or pathway feed back into its inputs. An example of a feedback loop nestled within a series of complex ecological interactions is climate change and the release of gases triggers various processes that amplify the phenomenon. For example, increasing temperatures can lead to melting permafrost – a storage repository of carbon. Melting permafrost can therefore set the stage for exposing trapped carbon, contributing to increased greenhouse gases which exacerbate climate change. The interconnections between permafrost and greenhouse gases lead to emergent behaviour that is characteristic of a system that arises from the interactions of its parts rather than being inherent to any individual part.
Another well-defined example of interconnectedness and emergent behaviour in complex systems, is ant colonies. Each ant in a colony has a specific role to play however; on its own, an ant’s behaviour is nonsensical. If we imagine, on the other hand, the complex ways in which ants interact with each other, an emergent property is an ant colony.
Epidemiology is a Study of Complex System
Therefore, much like climate change and ant colonies, epidemiology, the study of epidemics, is rooted in studying the intricate dynamics and interconnections that cede the stage for epidemics to emerge. Infectious and non-communicable disease transmission dynamics are a series of interconnected factors that, through various likelihoods and probabilities, can result in epidemics. The likelihood of epidemics is underpinned by probabilistic theory where several variables interact to result in an outcome. These variables can include population demographics, environmental conditions, pathogen transmission characteristics, and human behaviour all of which form interactions from individual to ecological levels. Mathematical models have, consequently, become an indispensable tool for investigating and quantifying the spread of infectious diseases in epidemiology – in a similar way for studying complex systems.
Keeping in mind limitations of modelling epidemics, one of the biggest factors in simulating an epidemic is incorporating the randomness inherent in infectious disease transmission dynamics – particularly in heterogenous populations – in a model. Epidemiology has moved towards accounting for randomness by using not only deterministic models, but by also using stochastic models. Stochastic models, which account for randomness and uncertainty in a system, present outcomes as probabilistic whereas using deterministic models to investigate epidemics requires well defined variables and predictable behaviour. Stochasticity in infectious disease modelling and transmission dynamics draws parallels to the seemingly random traits inherent in complex systems. However, further inspection of the randomness in complex system can reveal ordered patterns.
How should we be thinking about complex systems and interconnectedness in our day-to-day functioning?
Life is complex and we have strived to characterize it by a series of cause-and-effect behaviour. For example, if I study for 60 minutes for my math test, I will get an A-. Complex systems theory behoves us to move beyond linear thinking and not isolate causal pathways and individual parts, simple as they may be, but to contextualise actions and decision making in the wider sphere of probabilistic outcomes rather than certainty. This way of thinking allows us to gain familiarity with the grey areas littered throughout our journeys and make peace with the likelihood of randomness and unpredictability manifesting in outcomes beyond our control.
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