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Programma di Ricerca su "Big Data and cognitive insights for effective administrative law"

Componenti
Prof.ssa Nicoletta Rangone – Coordinatore (Professore  ordinario LUMSA)
Prof.ssa Maria De Benedetto (Professore  ordinario, Roma Tre)
Prof.ssa Fabiana Di Porto (Professore associato, Università del Salendo)
Dott.ssa Rossana Amoroso (Dottoranda LUMSA)  

Partenariati nazionali e internazionali
University College of London (Prof. Helen Xanthaki, President of the International Association for Legislation)
LUISS (Prof. Nicola Lupo, Jean Monnet Chair on Understanding European Representative Democracy)

Obiettivi della ricerca
The research will explore how cognitive insight and Big Data can help decision-makers in drafting effective rules, administrative decisions and enforcement strategies. These enrichments of decision-making approach can be considered the most advanced border of better regulation.
a) Empirical evidence in decision-making is traditionally collected through good regulation tools, such as consultations and impact assessment. This well established approach might be further enriched by empirical evidence on regulates reaction to a given regulatory treatment. The insights from cognitive sciences (Kahneman 1973; Kahneman & Tversky 1979; Simon 1955 and 1971; Tverky and Kahneman 1981; Schultz, Nolan, Cialdini, Goldstein, Griskevicius 2007; Vandenbergh, Carrico, Bressman 2011; Johnson and Goldstein 2003; Thaler and Bernatzi 2004) are increasingly recognized as crucial in improving rule-making (EC 2013, 2015), as they provide regulators with more fine-tuned information about how real people and firms actually make choices and react to regulation, being heavily context-dependent and determined by limited cognitive capacity (due to heuristics, biases, emotional reactions and social norms). Cognitive insights might also enrich the regulatory toolkit through new regulatory strategies: nudging and empowerment (Alemanno and Sibony 2015). Law scholars had written intensively on behavioural regulation (Jolls 1998; Amir and Lobel 2008; Alemanno and Spina 2014; Sunstein 1996, 2013; Van Aaken 2015) and on the side effects of the paternalistic approach (Glaeser 2006; Bovens 2012). Morevoer, the EC include biased behaviours among the problems that can justify a regulatory intervention (EC “Behavioural Insights Applied Policy” 2016; EC “Better Regulation Toolbox” Tool #14). However, a comprehensive approach to impact assessment and consultation which considers all drivers that could play a role in their effectiveness (and thus of the rules adopted using these tools) has not been yet developed. How impact assessment and consultation with stakeholders – in order to be effective - should be designed in order to neutralise or bring out emotions, social norms and people’s cognitive limitations will be the first aim of the research project.
b) Big Data analytics is currently used to prevent terrorist attacks, identify suspicious behaviour, assess risks of street crime (so-called predictive policing), target inspections, predict tax underreporting, detect social security frauds, assess the risk of corruption (Cate 2008; Keats Citron 2008; Joh 2014; Crawford and Schultz 2014; Maciejewski 2016; Falcone 2017). Data analysis has also begun to be employed as a basis for regulations and administrative decisions (Steinbock 2005; Citron 2008). Applications of Big Data analytics in administrative decision-making might rapidly grow in a near future (Cuellar 2016; Coglianese and Lehr, 2017; Yeung 2017; Pijnenburg, Kowalczyk, van der Hel-van Dijk 2017; F. Di Porto 2017; Kleinberg, Ludwig, Mullainthan, Sunstein 2019). The investigation on the potential impact of Big Data on transparency, accountability of decision-making and due process is still at an early stage. Moreover, how to improve enforcement strategies (namely control planning and inspections) by using Big Data in compliance risk assessment is also an topic which is still overshadowed. The second aim of this research is to fill these gaps.

Corsi di Laurea Master e Post Laurea

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