We first calibrate and then analyze our ABM using suites of Monte Carlo simulations, applied to a representative set of training cases of government formation in European parliamentary democracies. For each to the twenty training cases, we execute 1,000 model runs, randomizing model parameters for each run as follows. For each observable parameter, for each model run for each training case, we take the empirically observed value and perturb this with parameterized random noise. For unobservable model parameters, we randomly sample from the full range of possible values. The 1,000 runs for each case thus yield a distribution of model-predicted outcome for that case. We calibrate unobservable model parameters by selecting ranges of these associated with empirically accurate model predictions. We analyze the (calibrated and uncalibrated) model by summarizing the mapping of model inputs into model outputs in the artificial data generated by the set of Monte Carlos, using theoretically informed logistic regressions. This is the computational analogue of analyses based on deductive “comparative statics” generated by traditional formal theorists.

In the European Union, regulatory analysis of artificial intelligence in general and personalisation more specifically often starts with data protection law, more specifically the General Data Protection Regulation (GDPR). This is unsurprising due to the fact that training data often contains personal data and that the output of these systems can also take the form of personal data. There are, however, limits to data protection’s ability to function as a general AI law. This chapter highlights the importance of being realistic about the GDPR’s opportunities and limitations in this respect. It examines the application of certain elements of the GDPR to data-driven personalisation and highlights that whereas the Regulation indeed applies to the processing of personal data, it would be erroneous to frame it as a general ‘AI law’ capable of addressing all normative concerns around personalisation.

Empirical-statistical downscaling combines observations with global climate model outputs to generate high-resolution spatial and temporal projections. This chapter describes some of the common methods and models used in spatial and temporal disaggregation and bias correction, focusing on aspects of their design and performance that are relevant to their application for quantifying local to regional-scale impacts.