While a majority of biomedical and public health research still maintains a linear reductive approach to arrive at empiric insight, reality is in most cases neither of these things. A complex adaptive systems approach, like reality, is non-linear and high dimensional. There are many benefits from taking a linear cause-effect reductivist approach in that the complex problem and it’s solution becomes simplified into terms that can be understood and predicted. Where this falls short is that predictions don’t often hold up in real world examples in which the outcomes tend to seem unpredictable.
Genomics, proteomics, transcriptomics and other “omics” techniques have generated an unprecedented amount of molecular genetics data. This data can be combined with larger scale data, from cell processes to environmental influences in disease states, to produce highly sophisticated multi-level models of contributing factors in human health and disease.
An area that is currently seeing an evolution into a more personalised nuanced approach, albeit still linear, is clinical trials. By introducing a biomarker component to clinical trials, for example to evaluate drug efficacy, the number of dimensions to the problem is slightly increased in order to arrive at more targeted and accurate solutions. More specifically, the number of patient sub-categories in the clinical trials increases to accommodate various biomarker groups which may respond more of less well to a different pharmacological approach to the same disease. Increasing the dimensions of the problem beyond this would, for now not be feasible or even helpful. On the other hand, understanding the interplay between biomolecular processes and environmental interactions in order to gain insight into disease processes themselves and thereby, which biochemical pathways for oncology drugs to target, is something that clearly benefits from a non-linear approach.
Another example of a system that benefits from a non-linear approach is public health service provision and the desire to garner insights into changes that increase prevention, early intervention and treatment effectiveness as well as reduce service cost for the government and patient. Both of the above examples require attention to both macro and micro processes.
Whether modelling clinical health services networks or biological processes, complex adaptive systems consist of several common characteristics.
Components of complex adaptive systems
Massive interdependencies and elaborate connectivity
The complex adaptive systems approach shifts emphasis away from studying individual parts (such as seen in conventional medical science which produces notably fragmented results) to characterising the organisation of these parts in terms of their inherently dynamic interactions. CAS are open rather than closed systems because it is exogenous elements impacting on the system that cause the disruption required for growth.
Complex adaptive systems can be understood by relations and networks. System processes occur in networks that link component entities or agents. This approach emphasises that structures are dynamic and it is the process of becoming rather than the being itself that is of empirical interest.
Necessarily transdisciplinary or multi-disciplinary
A complex adaptive systems approach requires a transdisciplinary approach. The collaboration of numerous disparate experts is required in the combining of myriad biological, physical and societal based sciences into a holistic model. This model should aim to represent pertinent simultaneous top-down and bottom up processes that reveal contexts and relationships within the observed system dynamics.
Self-organising, emergent behaviour
Complex adaptive systems are selt-organising in the sense that observed patterns are open ended, potentially unfinished and cannot be predicted by the conventional definition. Rules of cause and effect are context dependent and can’t be applied in a rigid sense.
A self organising dynamic structure, which can be identified as a pattern, emerges as a result of individual spontaneous interactions between individual agents or elements. This pattern then impacts the interactions of individuals in a continual top down, bottom up symbiosis.
While linear models represent a reductionist, closed conceptualisation of the phenomena under analysis, a complex systems approach embraces high dimensionality true to the myriad real world phenomena composing a system. This requires that the system be treated as open and of uncertain ontology and thus lacking predictive capacity with regards to the outcomes of system dynamics.
As an emergent phenomena, the complex adaptive system can be understood by interacting with it rather than through analysis or static modelling. This approach is concerned with “state change” or to evaluate “how things are becoming”, rather than “how thing are”. How did today’s state emerge from yesterday’s trajectories and process dynamics?
Fractal engagement. Fractal engagement entails that the system as a whole orientates through multiple actions. The same data can produce frameworks at the level of responsibility of every individual agent. Using public health intervention as an example, Individual agents make decisions, based on the data, as to what changes they can make tomorrow within their own sphere of competence, rather than overarching changes being dictated and determined in a top down way, or by others.
Feedback loops link individual parts into an overaching dynamic structure. Feedback loops are self-reinforcing and can be positive or negative.
Negative feedback loops are stabalising in that they have a dampening effect on oscillations that causes the system or component to move closer to equilibrium. Positive feedback loops are morphogenic and increase the frequency and amplitude of oscillations driving the system away from homeostasis and leading to changes in the underlying structure or the system.
Positive feedback loops, while facilitating growth and adaptation, tend towards chaos and decay and are thus crucially counterbalanced by simultaneously operating negative feedback loops. Evolution is supposed to occur as a series of phase transitions, back and forth, from ordered to disordered states.
Both top-down and bottom-up “causality”
While CAS models describe elements in terms of possibilities and probabilities, rather than cause and effect in the linear sense, there is a clear interplay between top down and bottom up causality and influence on the dynamic flows and trajectories of any system. This is very much a mirror of real world systems. One example of this is the human body where both conscious thought (top down) and biomolecular processes such as hormonal and neurochemical fluctuations (bottom up) effect mood, which in turn has a lot of flow on effects down stream that cause changes that shirt the system back and forth from health to disease. One such manifestation of this is stress induced illness of various kinds. As a social example, we can of course find many examples of top down and bottom up causation in a public heath or epidemiological setting.
This has been a non-exhaustive description of just some key components of complex adaptive systems. The main purpose is to differentiate the CAS paradigm from the more mainstream biomedical research paradigm and approach. For a deeper dive into the concepts mentioned see the references below.
Carmichael T., Hadžikadić M. (2019) The Fundamentals of Complex Adaptive Systems. In: Carmichael T., Collins A., Hadžikadić M. (eds) Complex Adaptive Systems. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-20309-2_1
Milanesi, L., Romano, P., Castellani, G., Remondini, D., & Liò, P. (2009). Trends in modeling Biomedical Complex Systems. BMC bioinformatics, 10 Suppl 12(Suppl 12), I1. https://doi.org/10.1186/1471-2105-10-S12-I1
Sturmberg J. P. (2021). Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research. Frontiers in psychiatry, 12, 595124. https://doi.org/10.3389/fpsyt.2021.595124