Library Header Image
LSE Research Online LSE Library Services

GEMS: genetically evolving models in science

Pirrone, Angelo and Gobet, Fernand ORCID: 0000-0002-9317-6886 (2022) GEMS: genetically evolving models in science. Sistemi Intelligenti, 34 (1). 107 - 115. ISSN 1120-9550

[img] Text (PirroneGobet2021_ita) - Accepted Version
Download (192kB)

Identification Number: 10.1422/101896


Computational scientific discovery is an important area of research in cognitive science. Not only can a scientific theory be represented by a computer program, but it can also be discovered by computers, using techniques and methodologies from artificial intelligence. Here, we present a new methodology currently under development, called GEMS, that has been successfully applied in cognitive science in order to semiautomatically generate scientific theories. GEMS is an application of genetic programming that, given the protocol of an experiment, a set of experimental data and elementary operations (in our case, elementary psychological processes), evolves programs that ‘behave’ like an experimental subject. From one generation to the next, the programs are improved thanks to evolutionary plausible mechanisms that aim to minimise the discrepancy between model predictions and data. Interestingly, GEMS makes it possible to perform an efficient search in the combinatorial model space; the output of GEMS is a formal scientific theory of a specific dataset, expressed as a computer program. In this paper, we present the main features of GEMS with an example of how it could be applied in a hypothetical scenario; we discuss the theoretical implications of this approach for scientific discovery; and we present ideas for future research in cognitive science using GEMS.

Item Type: Article
Official URL:
Additional Information: © 2021 Società Editrice Il Mulino S.p.A.
Divisions: CPNSS
Subjects: Q Science > QA Mathematics > QA76 Computer software
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 11 Oct 2021 11:00
Last Modified: 12 Jul 2024 18:33

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics