This chapter introduces the proceedings of the Social Simulation Conference 2022 by providing a brief overview of the impact of social simulation in various research areas. By focusing on the key role of agent-based modeling, we argue that social simulation has a unique position in the wider data science area. This is because it can enrich the predominantly inductive, data-driven, pattern oriented approach of computational social science with deductive, hypothesis-driven, explanatory, mechanism-detection models. Furthermore, social simulation can also work in areas and for contexts where data is not available, experiments cannot be performed or in which scenario exploration is paramount. We would also like to focus on areas and aspects where methodological improvement and cross-methodological integration are required to enhance the potential of social simulation in various communities. In the final section, we introduce the structure and sections of the proceedings.

The New Frontiers of Social Simulation in the Data Science Era: An Introduction to the Proceedings / F. Renzini, C. Debernardi, F. Bianchi, M. Cremonini, F. Squazzoni (SPRINGER PROCEEDINGS IN COMPLEXITY). - In: Advances in Social Simulation / [a cura di] F. Squazzoni. - [s.l] : Springer, 2023 Nov. - ISBN 978-3-031-34919-5. - pp. 1-10 (( Intervento presentato al 17. convegno Social Simulation Conference tenutosi a Milano nel 2022 [10.1007/978-3-031-34920-1_1].

The New Frontiers of Social Simulation in the Data Science Era: An Introduction to the Proceedings

F. Renzini;C. Debernardi;F. Bianchi;M. Cremonini;F. Squazzoni
Ultimo
2023

Abstract

This chapter introduces the proceedings of the Social Simulation Conference 2022 by providing a brief overview of the impact of social simulation in various research areas. By focusing on the key role of agent-based modeling, we argue that social simulation has a unique position in the wider data science area. This is because it can enrich the predominantly inductive, data-driven, pattern oriented approach of computational social science with deductive, hypothesis-driven, explanatory, mechanism-detection models. Furthermore, social simulation can also work in areas and for contexts where data is not available, experiments cannot be performed or in which scenario exploration is paramount. We would also like to focus on areas and aspects where methodological improvement and cross-methodological integration are required to enhance the potential of social simulation in various communities. In the final section, we introduce the structure and sections of the proceedings.
Social simulation; Agent-based modeling; Computational modeling; Computational social science Data science
Settore SPS/07 - Sociologia Generale
nov-2023
European Social Simulation Association
Book Part (author)
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1005814
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact