Dr Umberto Gostoli

  • Research Associate (MRC/CSO Social & Public Health Sciences Unit)

email: Umberto.Gostoli@glasgow.ac.uk
pronouns: He/him/his

MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR

Import to contacts

ORCID iDhttps://orcid.org/0000-0002-2394-3613

Biography

Dr. Umberto Gostoli joined the Complexity Programme at the MRC/CSO Social and Public Health Sciences Unit in October 2017. He obtained an MSc in European Studies at the London School of Economics and a PhD in Economics at the University of Bologna. After his PhD studies, he started to work as a post-doctoral researcher at the Polytechnic University of Marche, in Italy. Before joining the Unit he worked as a researcher in various international universities, among which the University of Southampton, the University of Manchester and the National Chengchi University, in Taiwan.

As part of the Complexity in Health Improvement programme and the Complex Systems Science workstream, he develops agent-based models (ABM) of socio-economic systems to improve our understanding of how demographic, social and economic factors interact to determine trends in public health.

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2013 | 2012 | 2009 | 2008
Number of items: 19.

2024

Gostoli, U. , Hinsch, M. and Silverman, E. (2024) Growing Populations From the ‘Bottom-up’: an ABM Approach to the Generation of Synthetic Populations. In: 18th Social Simulation Conference (SSC23), Glasgow, UK, 04-08 Sep 2023, pp. 49-61. ISBN 9783031577840 (doi: 10.1007/978-3-031-57785-7_5)

2023

Gostoli, U. and Silverman, E. (2023) Self-isolation and testing behaviour during the COVID-19 pandemic: an agent-based model. Artificial Life, 29(1), pp. 94-117. (doi: 10.1162/artl_a_00392) (PMID:36269874)

2022

Gostoli, U. and Silverman, E. (2022) An agent-based model of social care provision during the early stages of Covid-19. Scientific Reports, 12, 16534. (doi: 10.1038/s41598-022-20846-9) (PMID:36192471) (PMCID:PMC9528879)

2021

Silverman, E. , Gostoli, U. , Picascia, S. , Almagor, J. , McCann, M. , Shaw, R. and Angione, C. (2021) Situating agent-based modelling in population health research. Emerging Themes in Epidemiology, 18, 10. (doi: 10.1186/s12982-021-00102-7) (PMID:34330302) (PMCID:PMC8325181)

Gostoli, U. and Silverman, E. (2021) Towards a Behavioural Model of Covid-19 Spread. 3rd International Workshop on Agent-Based Modelling of Human Behaviour (ABMHuB'21), 19 Jul 2021.

Silverman, E. and Gostoli, U. (2021) PHASE: Facilitating Agent-Based Modelling in Population Health. In: Winter Simulation Conference 2020, Orlando, FL, USA, 13-16 Dec 2020, pp. 135-146. ISBN 9781728194998

2020

Gostoli, U. and Silverman, E. (2020) Social and child care provision in kinship networks: an agent-based model. PLoS ONE, 15(12), e0242779. (doi: 10.1371/journal.pone.0242779) (PMID:33264347) (PMCID:PMC7710125)

Silverman, E. and Gostoli, U. (2020) Modelling Informal Caring Behaviours in the Scottish Population. ALife Conference 2020: ABMHuB Workshop, 13-18 July 2020.

Gostoli, U. and Silverman, E. (2020) Sound behavioural theories, not data, is what makes computational models useful. Review of Artificial Societies and Social Simulation, 22 Apr.

2019

Gostoli, U. and Silverman, E. (2019) Social and Child Care Provision and Kinship Networks: An Agent-Based Model. In: 15th Annual Social Simulation Conference (SSC 2019), Mainz, Germany, 23-27 Sept 2019,

Gostoli, U. and Silverman, E. (2019) Modelling social care provision in an agent-based framework with kinship networks. Royal Society Open Science, 6(7), 190029. (doi: 10.1098/rsos.190029) (PMID:31417710) (PMCID:PMC6689582)

2018

Warren, P. A., Gostoli, U. , Farmer, G. D., El-Deredy, W. and Hahn, U. (2018) A re-examination of “bias” in human randomness perception. Journal of Experimental Psychology: Human Perception and Performance, 44(5), pp. 663-680. (doi: 10.1037/xhp0000462) (PMID:29058943) (PMCID:PMC5933241)

2017

Chen, S.-H. and Gostoli, U. (2017) Coordination in the El Farol Bar problem: the role of social preferences and social networks. Journal of Economic Interaction and Coordination, 12(1), pp. 59-93. (doi: 10.1007/s11403-015-0150-z)

2016

Chen, S.-H. and Gostoli, U. (2016) On the Complexity of the El Farol Bar Game: A Sensitivity Analysis. In: Agent-Directed Simulation Symposium, Pasadena, CA, USA, 3-6 Apr 2016, p. 4. ISBN 9781510823150

2015

Gaffeo, E., Gallegati, M. and Gostoli, U. (2015) An agent-based “proof of principle” for Walrasian macroeconomic theory. Computational and Mathematical Organization Theory, 21(2), pp. 150-183. (doi: 10.1007/s10588-014-9180-7)

2013

Chen, S.-H. and Gostoli, U. (2013) Agent-based modeling of the El Farol Bar modeling. In: Simulation in Computational Finance and Economics: Tools and Emerging Applications. IGI Global: Hershey, PA, pp. 359-377. ISBN 9781466620117

2012

Chen, S.-H., Gostoli, U. , Tai, C.-C. and Shih, K.-C. (2012) To whom and where the hill becomes diffult to climb: effects of personality and cognitive capacity in experimental DA markets. Journal of Behavioral Finance and Economics, 2(2), pp. 41-75.

2009

Gostoli, U. (2009) A cognitively founded model of the emergence of social conventions. European Journal of Economic and Social Systems, 22(1), pp. 63-82.

2008

Gostoli, U. (2008) A cognitively founded model of the social emergence of lexicon. Journal of Artificial Societies and Social Simulation, 11(1), 2.

This list was generated on Fri Nov 22 22:53:46 2024 GMT.
Jump to: Articles | Book Sections | Conference or Workshop Item | Conference Proceedings
Number of items: 19.

Articles

Gostoli, U. and Silverman, E. (2023) Self-isolation and testing behaviour during the COVID-19 pandemic: an agent-based model. Artificial Life, 29(1), pp. 94-117. (doi: 10.1162/artl_a_00392) (PMID:36269874)

Gostoli, U. and Silverman, E. (2022) An agent-based model of social care provision during the early stages of Covid-19. Scientific Reports, 12, 16534. (doi: 10.1038/s41598-022-20846-9) (PMID:36192471) (PMCID:PMC9528879)

Silverman, E. , Gostoli, U. , Picascia, S. , Almagor, J. , McCann, M. , Shaw, R. and Angione, C. (2021) Situating agent-based modelling in population health research. Emerging Themes in Epidemiology, 18, 10. (doi: 10.1186/s12982-021-00102-7) (PMID:34330302) (PMCID:PMC8325181)

Gostoli, U. and Silverman, E. (2020) Social and child care provision in kinship networks: an agent-based model. PLoS ONE, 15(12), e0242779. (doi: 10.1371/journal.pone.0242779) (PMID:33264347) (PMCID:PMC7710125)

Gostoli, U. and Silverman, E. (2020) Sound behavioural theories, not data, is what makes computational models useful. Review of Artificial Societies and Social Simulation, 22 Apr.

Gostoli, U. and Silverman, E. (2019) Modelling social care provision in an agent-based framework with kinship networks. Royal Society Open Science, 6(7), 190029. (doi: 10.1098/rsos.190029) (PMID:31417710) (PMCID:PMC6689582)

Warren, P. A., Gostoli, U. , Farmer, G. D., El-Deredy, W. and Hahn, U. (2018) A re-examination of “bias” in human randomness perception. Journal of Experimental Psychology: Human Perception and Performance, 44(5), pp. 663-680. (doi: 10.1037/xhp0000462) (PMID:29058943) (PMCID:PMC5933241)

Chen, S.-H. and Gostoli, U. (2017) Coordination in the El Farol Bar problem: the role of social preferences and social networks. Journal of Economic Interaction and Coordination, 12(1), pp. 59-93. (doi: 10.1007/s11403-015-0150-z)

Gaffeo, E., Gallegati, M. and Gostoli, U. (2015) An agent-based “proof of principle” for Walrasian macroeconomic theory. Computational and Mathematical Organization Theory, 21(2), pp. 150-183. (doi: 10.1007/s10588-014-9180-7)

Chen, S.-H., Gostoli, U. , Tai, C.-C. and Shih, K.-C. (2012) To whom and where the hill becomes diffult to climb: effects of personality and cognitive capacity in experimental DA markets. Journal of Behavioral Finance and Economics, 2(2), pp. 41-75.

Gostoli, U. (2009) A cognitively founded model of the emergence of social conventions. European Journal of Economic and Social Systems, 22(1), pp. 63-82.

Gostoli, U. (2008) A cognitively founded model of the social emergence of lexicon. Journal of Artificial Societies and Social Simulation, 11(1), 2.

Book Sections

Chen, S.-H. and Gostoli, U. (2013) Agent-based modeling of the El Farol Bar modeling. In: Simulation in Computational Finance and Economics: Tools and Emerging Applications. IGI Global: Hershey, PA, pp. 359-377. ISBN 9781466620117

Conference or Workshop Item

Gostoli, U. and Silverman, E. (2021) Towards a Behavioural Model of Covid-19 Spread. 3rd International Workshop on Agent-Based Modelling of Human Behaviour (ABMHuB'21), 19 Jul 2021.

Silverman, E. and Gostoli, U. (2020) Modelling Informal Caring Behaviours in the Scottish Population. ALife Conference 2020: ABMHuB Workshop, 13-18 July 2020.

Conference Proceedings

Gostoli, U. , Hinsch, M. and Silverman, E. (2024) Growing Populations From the ‘Bottom-up’: an ABM Approach to the Generation of Synthetic Populations. In: 18th Social Simulation Conference (SSC23), Glasgow, UK, 04-08 Sep 2023, pp. 49-61. ISBN 9783031577840 (doi: 10.1007/978-3-031-57785-7_5)

Silverman, E. and Gostoli, U. (2021) PHASE: Facilitating Agent-Based Modelling in Population Health. In: Winter Simulation Conference 2020, Orlando, FL, USA, 13-16 Dec 2020, pp. 135-146. ISBN 9781728194998

Gostoli, U. and Silverman, E. (2019) Social and Child Care Provision and Kinship Networks: An Agent-Based Model. In: 15th Annual Social Simulation Conference (SSC 2019), Mainz, Germany, 23-27 Sept 2019,

Chen, S.-H. and Gostoli, U. (2016) On the Complexity of the El Farol Bar Game: A Sensitivity Analysis. In: Agent-Directed Simulation Symposium, Pasadena, CA, USA, 3-6 Apr 2016, p. 4. ISBN 9781510823150

This list was generated on Fri Nov 22 22:53:46 2024 GMT.

Research datasets

Jump to: 2019
Number of items: 1.

2019

Gostoli, U. and Silverman, E. (2019) Modelling social care provision in an agent-based framework with kinship networks. [Data Collection]

This list was generated on Sat Nov 23 07:29:20 2024 GMT.