Fig. 2: Overview of our volunteer behaviour model.
From: Optimising self-organised volunteer efforts in response to the COVID-19 pandemic
a Estimate the parameters in the volunteer behaviour model from the Pioneers data. The parameters, including users’ participation rate \(P_{\mathrm{t}}\left( u \right)\), organiser preference \(O_{\mathrm{t}}\left( {o,u} \right)\) and task preference \(T_{\mathrm{t}}\left( {c,o} \right)\), are visualised as probability mass functions for each timestep. b Capture the uncertainties in volunteer behaviour by computing the normalised conditional entropy (NCE) from model parameters. c Detect the time intervals when the self-organisation effect exists by fitting a double-exponential model; quantify the self-organisation effect in terms of organisational speed measured by \(T_{{\mathrm{Half}}}/T_{{\mathrm{Fall}}}\). d Analyse the dynamic factors that have caused self-organisation change in volunteer behaviours using time-series based causal network discovery.
