PRIN 2022

Models for dynamic reasoning under partial knowledge to make interpretable decisions

Finanziato dall’Unione europea - Next Generation EU, Missione 4 Componente 2

Project number: 2022AP3B3B – CUP: J53D23004340006

Aim of the Project

The general goal of the project is to design interpretable decision models that essentially rest upon the concepts of “ambiguous beliefs”, “vagueness” and “conditioning”. By ambiguous beliefs we mean the simultaneous treatment of classes of probability measures (or their envelopes), by vagueness we intend the encoding of human language expressions, and by conditioning we refer to the capability of considering different information contexts and of updating. The project aims at giving a multidisciplinary approach for integrating decision models, learning and numerical methods. This will provide new insights into economic applications and identify new targets and strategies to justify anomalies in the markets and inform decision making for the benefit of social community.

Tasks of the Project

T1: Study of conditioning for non-additive uncertainty measures and dynamic hybrid models
T2: Development of normative decision models under ambiguous beliefs
T3: Design of data fusion techniques in the framework of imprecise probabilities
T4: Extension of stochastic processes theory under ambiguous beliefs
T5: Study of portfolio selection and pricing under partial knowledge
T6: Design of methods for micro-expression identification for fraud detection

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