SYNERGY
Financiado
Developing new behavioural models at the intersection of psychology econometric...
Developing new behavioural models at the intersection of psychology econometrics and machine learning
The SYNERGY project will unify three key paradigms for the mathematical modelling of human behaviour, namely: i) process models in psychology and cognate disciplines that seek to explain how decisions are made; ii) econometric and...
The SYNERGY project will unify three key paradigms for the mathematical modelling of human behaviour, namely: i) process models in psychology and cognate disciplines that seek to explain how decisions are made; ii) econometric and behavioural models that explain which factors influence the decision process and to what extent; and iii) data-driven (machine learning) methods that focus on the outcome of the decision process. The different aims and assumptions of these paradigms have resulted in very distinct strengths and weaknesses for each discipline. Only the synergy of the three will fulfil the promise of producing models that are behaviourally consistent, applicable to real-world problems, computationally tractable, and balance a priori assumptions with data-driven insights.
Integrating the three approaches into new Data-Driven Behavioural Models (DDBMs) is a novel, ambitious and highly complex undertaking, but one that is timely given the rapidly changing world, increasing use of models and big data for prediction, and growing interaction between humans and intelligent machines that require the latter to accurately predict human behaviour to enable safe and efficient use of AI. The proposed work will result in a paradigm shift for behavioural modelling, with impact in many application domains. SYNERGY will provide analysts with a powerful new toolkit that will allow efficient large-scale behavioural modelling on increasingly rich data while providing interpretable outputs and retaining important foundations in behavioural science.
Alongside major methodological contributions, the proposed research includes large-scale empirical work, applying the new DDBMs to real-world problems with implications for national policy. This includes case studies to understand and predict travel and other behaviour in a COVID-19 environment, and to establish the benefits that more behaviourally consistent AI routines for autonomous vehicles can have for road traffic safety.
ver más
Duración del proyecto: 74 meses
Fecha Inicio: 2021-11-19
Fecha Fin: 2028-01-31
Fecha Fin: 2028-01-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2021-11-19
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-ADG:
ERC ADVANCED GRANT
Cerrada
hace 4 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
UNIVERSITY OF LEEDS
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
320.07K€ |
Participante