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Genetically Evolving Models of Science

The development of scientific models suffers from two related problems: ever-growing number of experimental results and scientists’ cognitive limitations (including cognitive biases). This multidisciplinary project (psychology, co... see more

31/10/2024

LONDON SCHOOL OF E...

2M€

Project Budget: 2M€

Project leader

LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2024-10-31

ERC-2018-ADG: ERC Advanced Grant Scope:Objectives

I+D

Cerrada does 6 years

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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

3 Participantes

LONDON SCHOOL OF ECONOMICS A...

2.09M€ | Leader

MOEHS IBERICA

847.66K€ | participant

UNIVERSITY OF HERTFORDSHIRE

95.40K€ | participant

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Project duration: 60 months Date Start: 2019-10-10
End date: 2024-10-31

Project leader

LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 2M€

participation deadline Sin fecha límite de participación.

Project description The development of scientific models suffers from two related problems: ever-growing number of experimental results and scientists’ cognitive limitations (including cognitive biases). This multidisciplinary project (psychology, computer modelling, computer science and cognitive neuroscience) addresses these problems by developing a novel methodology for generating scientific models automatically. The methodology is general and can be applied to any science where experimental data are available. The method treats models as computer programs and evolves a population of models using genetic programming. The extent to which the models fit the empirical data is used as a fitness function. The best models–potentially modified by cross-over and mutation–are selected for the next generation. Pilot simulations have established the validity of the methodology with simple experiments. To demonstrate that the methodology is sound, can be used with complex datasets and can be generalised across sciences, four related strands of research are planned. First, ‘Building New Tools’ develops the methodology and creates techniques to understand and compare the evolved models. Second, ‘Explaining Human Data’ uses the methodology to explain a wide range of data on human cognition. This will be done in two steps: (a) data without learning (working memory and attention); and (b) data with learning (categorisation, implicit learning and explicit learning). Third, ‘Explaining Animal Data’ develops models to account for various aspects of animal behaviour, focusing on conditioning and categorisation. Finally, ‘Explaining Neuroscience Data’ extends the methodology to account for data combining information about cognitive and brain processes. This project explores virgin territory and thus opens up a new field of research. It combines insights from experimental psychology, cognitive modelling, cognitive neuroscience and computer science, disciplines in which the PI has strong track record.

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