David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while diﬀerent cognitive settings may aﬀect the aggregate number of scientiﬁc articles produced, they do not generally lead to diﬀerent distributions of number of articles per author. The paper concludes with a discussion of the correspondence between our model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Ron Sun & Isaac Naveh (2007). Social Institution, Cognition, and Survival: A Cognitive–Social Simulation. Mind and Society 6 (2):115-142.
Nigel Gilbert & Pietro Terna (2000). How to Build and Use Agent-Based Models in Social Science. Mind and Society 1 (1):57-72.
Denis Phan & Franck Varenne (2010). Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting. Journal of Artificial Societies and Social Simulation 13 (1).
Shaun Nichols, Stephen P. Stich, Alan M. Leslie & David B. Klein (1996). Varieties of Off-Line Simulation. In Peter Carruthers & Peter K. Smith (eds.), [Book Chapter]. Cambridge University Press 39-74.
David Henderson (2011). Lets Be Flexible: Our Interpretive/Explanatory Toolbox, or In Praise of Using a Range of Tools. Journal of the Philosophy of History 5 (2):261-299.
Franck Varenne (2009). Models and Simulations in the Historical Emergence of the Science of Complexity. In Ma Aziz-Alaoui & C. Bertelle (eds.), From System Complexity to Emergent Properties. Springer 3--21.
Johannes Lenhard (2006). Surprised by a Nanowire: Simulation, Control, and Understanding. Philosophy of Science 73 (5):605-616.
Alvin I. Goldman & Chandra S. Sripada (2005). Simulationist Models of Face-Based Emotion Recognition. Cognition 94 (3):193-213.
Added to index2010-12-22
Total downloads7 ( #304,000 of 1,726,249 )
Recent downloads (6 months)6 ( #118,705 of 1,726,249 )
How can I increase my downloads?