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SYNERGY

Financiado

Cerrado

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... ver más

31/01/2028

UNIVLEEDS

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITY OF LEEDS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-11-19

ERC-2020-ADG: ERC ADVANCED GRANT

I+D

Cerrada hace 4 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

UNIVLEEDS

2.50M€ | Lider

RDMES TECHNOLOGIES

320.07K€ | Participante

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Duración del proyecto: 74 meses Fecha Inicio: 2021-11-19
Fecha Fin: 2028-01-31

Líder del proyecto

UNIVERSITY OF LEEDS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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