text: ing the temporal dynamics of belief changes in experimental participants. Dynamic belief 
  trajectories can then be related to other (for example, physiological) measures, as is usual 
  in model-based neuroscience [65]. This method can also, in principle, be used for fitting 
  models to other types of experimentally observable systems, like animals, organoids [66], 
  and simulated or emergent systems [67]. The package can also be used for agent-based 
  modelling in general, for repeating earlier analyses with sampling based model-fitting 
  and for comparing POMDP-based AIF models directly to other types of models. 
  
  Since they implement full approximate Bayesian inferences, AIF models are compu- 
  tationally more demanding than many approaches traditionally used in cognitive and 
  agent-based modelling, in particular when the dimensionality of the generative model is 
  large. This means that models with highly multidimensional or complex behaviour and 
  large numbers of agents can be computationally infeasible to implement, especially given 
  the additional computational demands introduced by fitting these models to empirical 
  data. Avenues for addressing this implicit scaling problem were proposed in the context of 
  machine learning applications [68,69], and with the use of simplifying assumptions—the 
  use of which are ubiquitous in computational modelling—AIF has been used to model 
  multi-agent phenomena, such as opinion dynamics [15,70], coordinated foraging [71] and 
  fish school movements [12]. It remains to be explored how AIF models can be applied to 
  highly complex natural phenomena, such as a concrete election, which underscores the 
  need for efficient but flexible and accessible software tools in the field. 