As social media technologies alter the variation, transmission and sorting of online information, short-term cultural evolution is transformed. In these media contexts, cultural evolution is an intra-generational process with much 'horizontal' transmission. As a pertinent case study, here we test variations of culture-evolutionary neutral models on recently-available Twitter data documenting the spread of true and false information. Using Approximate Bayesian Computation to resolve the full joint probability distribution of models with different social learning biases, emphasizing context versus content, we explore the dynamics of online information cascades: Are they driven by the intrinsic content of the message, or the extrinsic value (e.g., as a social badge) whose intrinsic value is arbitrary? Despite the obvious relevance of specific learning biases at the individual level, our tests at the online population scale indicate that unbiased learning model performs better at modelling information cascades whether true or false.

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Capturing the coupled dynamics between individual behavioural decisions that affect disease transmission and the epidemiology of outbreaks is critical to pandemic mitigation strategy. We develop a multiplex network approach to model how adherence to health-protective behaviours that impact COVID-19 spread are shaped by perceived risks and resulting community norms. We focus on three synergistic dynamics governing individual behavioural choices: (i) social construction of concern, (ii) awareness of disease incidence, and (iii) reassurance by lack of disease. We show why policies enacted early or broadly can cause communities to become reassured and therefore unwilling to maintain or adopt actions. Public health policies for which success relies on collective action should therefore exploit the behaviourally receptive phase ; the period between the generation of sufficient concern to foster adoption of novel actions and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

Complexity science refers to the theoretical research perspectives and the formal modelling tools designed to study complex systems. A complex system consists of separate entities interacting following a set of (often simple) rules that collectively give rise to unexpected patterns featuring vastly different properties than the entities that produced them. In recent years a number of case studies have shown that such approaches have great potential for furthering our understanding of the past phenomena explored in Roman Studies. We argue complexity science and formal modelling have great potential for Roman Studies by offering four key advantages: (1) the ability to deal with emergent properties in complex Roman systems; (2) the means to formally specify theories about past Roman phenomena; (3) the power to test aspects of these theories as hypotheses using formal modelling approaches; and (4) the capacity to do all of this in a transparent, reproducible, and cumulative scientific framework. We present a ten-point manifesto that articulates arguments for the more common use in Roman Studies of perspectives, concepts and tools from the broader field of complexity science, which are complementary to empirical inductive approaches. There will be a need for constant constructive collaboration between Romanists with diverse fields of expertise in order to usefully embed complexity science and formal modelling in Roman Studies.