Abstract
The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an ‘accessible to all’ approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.
Highlights
Through the ever-continuing advancements in hardware and software technologies, the scale and sophistication of modelling tools continues to increase
We discovered that 175 simulation replicates were required for our NF-κB agent-based modelling (ABM), and that 1,000 simulation replicates were required for our EAE ABM, in order to mitigate stochastic effects 655 from the use of different pseudo-random number generator (PRNG) seed values, and develop confidence that simulation results are representative of the condition(s) on which the simulation was run
One of the main strengths of the computational systems approach, is that it focuses on three key properties of complex systems: 1) system structures, 2) system dynamics, and 3) system control [79]
Summary
Through the ever-continuing advancements in hardware and software technologies, the scale and sophistication of modelling tools continues to increase. The agent-based approach has been used in a diverse range of disciplines including the modelling 25 of: diseases in biology [8]; financial markets [9] and the British banking sector [10] in economics; the movement emergent segregation of communities within a reimplementation of Shelling’s Bounded Neighbourhood Model [11] and violent crime [12] in social science; the effects of communication technologies on virtual project team performance [13] in management science; and seasonal impacts in 30 agriculture [14] We believe that this has been driven primarily by the ability of agent-based modelling and simulation to investigate questions regarding the causal relationships and mechanistic underpinnings to system dynamics, which traditional modelling techniques, such as differential equation-based, system dynamics (system-based models), and discrete event simulation (process-based 35 models) cannot address [3, 16]. This is supported by Epstein [4] who states that “agent-based models provide computational demonstrations that a given microspecification is sufficient to generate a macrostructure of interest.” In addition, the popularity of an agent-based approach has further risen through the increase in the number of software development frameworks for agent-based 40 modelling and simulation [17]
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