Human-computer interaction is now a central element in the lives of individuals and organizations. This interaction has been revolutionized in recent years by advances in artificial intelligence (AI). AI is a computing technology that enables machines to operate with highly advanced computational quality, capable of performing complex actions and “reasoning,” learning from mistakes, and performing functions previously reserved for human intelligence. Artificial intelligence is mainly used in business to perform tasks that would take a long time for humans. The research world has adapted to this progress. Psychological, social, and organizational phenomena are increasingly complex to study, and ethical constraints often exacerbate the difficulty. New research methods have emerged to enable these studies to be carried out and advanced, in line with the new resources made available by AI in the last decade. These computational social sciences allow researchers to use technology to simulate the behavior of individuals or large groups.
The current line of research uses the tool of computational simulation, a technique used to study how processes at the micro level affect outcomes at the macro level (Hughes et al., 2012). Applied to the social sciences, the value of this tool lies in its ability to study social processes or emergent phenomena that would be too complex to reproduce in reality. Simulation is achieved by reconstructing a virtual context that can generate individual and social behaviors through unique programming languages and software (e.g., NetLogo, SeSam, Repast). The APRESO Research Center adheres to this strand by promoting the development of computational models that study social processes within organizations and allow the formulation of new theories (and their applications) through computational simulations of human behavior.
References
Buchanan, M. (2009). Economics: Meltdown modelling. Nature, 460(7256), 680–682.
Cioffi-Revilla, C. (2014). Introduction to Computational Social Science. Principles and Applications. London, UK: Springer-Verlag.
Epstein, J. M. (2007). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton: Princeton University Press
Epstein, J. M. (2008). Why Model?. Journal of Artificial Societies and Social Simulation, 11(4).
Gilbert, N. (2008). Agent-Based Models. SAGE Publications, 153(153), 98.
Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Maidenhead, United Kingdom: Open University Press.
Research areas involved in the project | |
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Formazione e organizzazioni
work and organizational psychology |
Title | Authors | Year |
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Theories for Computing Prosocial Behavior | Costantini, Arianna; Scalco, Andrea; Sartori, Riccardo; Mas Tur, Elena; Ceschi, Andrea | 2019 |
Advanced Modeling Methods for Studying Individual Differences and Dynamics in Organizations: Introduction to the Special Issue | Ceschi, Andrea; Sartori, Riccardo; Guastello, Sj | 2018 |
Application of Psychological Theories in Agent-Based Modeling: The Case of the Theory of Planned Behavior | Scalco, Andrea; Ceschi, Andrea; Sartori, Riccardo | 2018 |
The pursuit of happiness: A model of group formation | Scalco, Andrea; Ceschi, Andrea; Sartori, Riccardo | 2017 |
Compassion and Prosocial Behavior. Is it Possible to Simulate them Virtually? | Ceschi, Andrea; Scalco, Andrea; Dickert, Stephan; Sartori, Riccardo | 2015 |
Cooperate or Defect? How an Agent Based Model Simulation on Helping Behavior Can Be an Educational Tool | Ceschi, Andrea; Dorina, Hysenbelli; Sartori, Riccardo; Tacconi, Giuseppe | 2014 |
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