Dynamical systems modelling is a mathematical approach to studying the behaviour of systems that change over time. These systems can be physical, biological, economic, or social in nature, and they are typically characterized by a set of variables that evolve according to certain rules or equations.
CAS (Complex Adaptive Systems) models are a specific type of dynamical systems model that are used to study systems that are complex, adaptive, and composed of many interconnected parts. These systems are often found in natural and social systems, and they are characterized by a high degree of uncertainty, nonlinearity, and emergence.
To build a dynamical systems model, one typically starts by identifying the variables that are relevant to the system being studied and the relationships between them. These relationships are usually represented by a set of equations or rules that describe how the variables change over time. The model is then simulated or analysed to understand the system’s behaviour under different conditions and to make predictions about its future evolution.
CAS models are often used to study systems that exhibit emergent behaviour, where the behaviour of the system as a whole is more than the sum of its parts. These models can help us understand how complex systems self-organize, adapt, and evolve over time, and they have applications in fields such as biology, economics, social science, and computer science.
Whatever the approach, a model is intended to represent the real system, but there are limits to the application of models. The reliability of any model often falls short when attempting to operate within and apply the parameter boundaries of the model to any real life context.
The previous article outlined some basic characteristics of complex adaptive systems (CAS). The CAS approach to modelling real world phenomena requires a different approach to the more conventional predictive modelling paradigm. Complex adaptive systems such as ecosystems, biological systems, or social systems require looking at interacting elements and observing the patterns that arise, creating boundary conditions from these patterns, running experiments or simulations, and responding to the outcomes in an adaptive way.
To further delineate the complex systems domain in practical terms we can use the Cynefin framework developed by David Snowden et al. to contrast the Simple, Complicated, Complex and Chaotic domains. For the purpose of this article the Chaotic domain will be ignored.
Enabling constraints of CAS models
In contrast to complex domain is the “known” or “simple” domain represented by ordered systems such as a surgical operating theatre or clinical trials framework. These ordered systems are rigidly constrained and can be planned and designed in advance based upon prior knowledge. In this context best practice can be applied because an optimal way of doing things is pre-determined.
The intermediary between the simple and complex domains is the “knowable” or ” complicated” domain. An example of such is the biostatistical analysis of basic clinical data. Within a complicated system there is a right answer that we can discover and design for. In this domain we can apply good practice based on expert advice (not best practice) as a right and wrong way of doing things can be determined with analysis.
Complex domain represents a system that is in flux and not predictable in the linear sense. A complex adaptive system can be operating in a state that is anywhere from equilibrium to the edge of chaos. In order to understand the system state one should perform experiments that probe relationships between entities. Due to the lack of linearity, multiple simultaneous experimental probes should occur in parallel, not in sequence, with the goal of better understanding processes. Emergent practice is determined in line with observed, evolving patterns. Ideally, decentralised Interpretation of data should be distributed to system users themselves rather than determined by a single expert in a centralised fashion.
As opposed to operating from a pre-supposed framework, the CAS structure should be allowed to emerge from the data under investigation. This avoids the confirmation bias that occurs when data are fitted to a predefined framework regardless of whether this framework best represents the data being modelled. Following on from this, model boundaries should also be allowed to emerge from the data itself.
Determining unarticulated needs from clusters of agent anecdotes or data points is a method of determining where improvement needs to occur in service provision systems. Yet this method forms an analogy that is mimicked in biological systems as well if an ABM was to be applied in a biomolecular context.
In understanding CAS, dispositionality of system states rather than linear causality should be the focus . Rather than presuming an inherent certainty as to “if I do A, B will result”, instead dispositional states arise as a result of A, which may result in B, but the evolution of which cannot be truly predicted.
“The longer you hold things in a state of transition the more options you’ve got” linear iterations based on a defined requirement with a degree of ambiguity which should be explored rather than eliminated. The opposite of standard statistical approach.
CAS modelling should include real-time feedback loops over multiple agents to avoid cognitive bias. In CAS modelling, every behaviour or interaction will produce unintended consequences, for this reason, David Snowden suggests, Small, fast experiments should be run in parallel, so that any bad, unintended consequences can be mitigated and the good ones amplified.
Modes of analysis and modelling:
System dynamics models (SDM)
- A SDM simulates the movements of entities within the system and can be used to investigate macro behaviour of the system.
- Changes to system state variables over time are modelled using differential equations.
- SDMs are multi-dimentional, non-linear and include feedback mechanisms.
- Visual representations of the model can be produced using stock and flow diagrams to summarise interdependencies between key system variables.
- Dynamic hypotheses of the system model can be represented in a causal loop diagram
- SDM is appropriate for modelling aggregate flows, trends, sub-system behaviour.
Agent based models (ABM)
- ABMs can be used to investigate micro behaviour of the system from more of a bottom-up perspective through Intricate flows of individual based activity.
- State changes of individual agents are simulated by ABMs rather than the broader entites captured by SDM
- Multiple types of agent are operating within the same complex adaptive system modelled
- Data within the ABM can be aggregated to infer more macro or top-down system behaviour.
Agents within the ABM can make decisions, engage in behaviour defined by simple rules and attributes, learn from experience and from feedback from interactions with other agents or the modelled environment. This is as true in models of human systems as it is with molecular scale systems. In both examples agents can par take in communication on a one to one, one to many and one to location basis. Previously popular models such as discrete event simulation (DES) was implemented to model passive agents at a finite time rather than active “decision makers” over dynamic periods that are a feature of ABMs.
- Both ABM and SDM are complimentary techniques for simulating micro and macro level behaviour of complex adaptive systems and therefore engaging in exploratory style analysis of such systems.
- Hybrid models emulate individual agent variability as well as variability in the the behaviour of aggregate entities they compose.
- Simulate macro and micro level system behaviour in many areas of investigation such as health service provision, biomedical science.
Hybrid models have the ability to combine two or more types of simulation within the same model. These models can combine SDMs and ABMs, or other techniques, to address both top-down and bottom-up micro and macro dynamics in a single model that more closely captures whole system behaviour. This has the potential to elevate many of the necessary trade-offs of using one of the simulation types alone.
As software capability develops we are seeing an increased application of hybrid modelling techquiques. Previously wide-spread techniques such as DES and Markov models, which are one-dimentional, uni-directional, linear, are now proving inadequate in the task of modelling the complex adaptive and dynamic world we inhabit.
Model Validation Techniques
SDMs and ABMs are not fitted to observed data but instead use both qualitative and quantitative real world data to inform and develop the model and it’s parameters as a simulation of real world phenomena. For this reason model validation of SDMs and ABMs should be even more rigorous than for more traditional models such as maximum likelihood or least squares methods. Sensitivity analysis and validation tests such as behavioural validity tests can be used to compare model output against real-world data from organisations or experiments, relevant to the scale of the model being validated.
Structure of the model such as
- Checking how the model behaves when subject to extreme parameter values.
- Things like dimensional consistency, boundary adequacy, mass balance
- Sensitivity analysis – how sensitive is the model to changes in key parameters.
Data accrual from diverse data sources challenges and limitations
While complex systems theory has origins in the mathematics chaos theory, there are many examples contemporaneously where complex systems theory has been divorced form the mathematics and statistical modelling and applied in diverse fields such as business and healthcare or social services provision. Mathematical modelling adds validity to complex systems analysis. The problem with completing solely qualitative analysis without the empiricism of mathematical modelling, simulation and checking against a variety of real world data sets, the results