R. Sun (ed.), Grounding Social Sciences in Cognitive Sciences. MIT Press, Cambridge, MA. 2012.
R. Sun (ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.
Special Issue of Cognitive Systems Research on multi-disciplinary studies of multi-agent learning . Special issue editor: Ron Sun. Vol.2, No.1, pp.1-96, 2001.
R. Sun, Cognitive social simulation. In: R. Sun (ed.), The Cambridge Handbook of Computational Psychology, pp.530-548 . Cambridge University Press, New York. 2008.
R. Sun, Prolegomena to integrating cognitive modeling and social simulation . In: R. Sun (ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp.3-26. Cambridge University Press, New York. 2006.
R. Sun, J. Allen, and E. Werbin, Modeling emotion contagion within a computational cognitive architecture. Journal of Cognition and Culture, in press.
R. Sun, Exploring culture from the standpoint of a cognitive architecture. Philosophical Psychology, Vol.33, No.2, pp.155-180. 2020. https://doi.org/10.1080/09515089.2020.1719054
R. Sun, Full human-machine symbiosis and truly intelligent cognitive systems. AI and Society: Journal of Knowledge, Culture and Communication, Vol.35, No.1, pp 17–28. 2020. https://doi.org/10.1007/s00146-017-0775-7
R. Sun, Cognitive social simulation for policy making. Policy Insights from the Behavioral and Brain Sciences, Vol.5, No.2, pp.240-246. 2018. https://doi.org/10.1177/2372732218785925
R. Sun and P. Fleischer, A cognitive social simulation of tribal survival strategies: The importance of cognitive and motivational factors. Journal of Cognition and Culture, Vol.12, No.3-4, pp.287-321, 2012.
R. Sun, Cognitive social simulation incorporating cognitive architectures . IEEE Intelligent Systems, Vol.22, No.5, pp.33-39. September/October, 2007.
R. Sun and I. Naveh, Social institution, cognition, and survival: A cognitive-social simulation . Mind and Society, Vol.6, No.2, pp.115-142. 2007.
I. Naveh and R. Sun, A cognitively based simulation of academic science . Computational and Mathematical Organization Theory, Vol.12, pp.313-337. 2006.
R. Sun and D. Qi, MARLBS: Team cooperation through bidding . International Journal of Computational Intelligence Research, Vol.1, No.1, pp.42-58. 2005. [formatted PDF]
D. Qi and R. Sun, Learning to cooperate in solving the traveling salesman problem. International Journal of Neural Systems, Vol.15, No.1&2, pp.151-162. 2005.
R. Sun and I. Naveh, Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation, Vol.7, No.3. 2004. [ http://jasss.soc.surrey.ac.uk/7/3/5.html ]
D. Qi and R. Sun, A multi-agent system integrating reinforcement learning, bidding and genetic algorithms. Web Intelligence and Agent Systems, Vol.1, No.3-4, pp.187-202. 2003.
R. Sun Cognitive science meets multi-agent systems: a prolegomenon. Philosophical Psychology, Vol.14, No.1, pp.5-28. 2001. [ PDF] [formatted PDF]
R. Sun Individual action and collective function (an editorial). Cognitive Systems Research, Vol.2, No.1, 2001. [ PDF] [Elsevier-formatted PDF]
R. Sun and T. Peterson, Multi-agent reinforcement learning: weighting and partitioning. Neural Networks, 1999.
N. Wilson and R. Sun, A mechanistic account of stress-induced performance degradation. Cognitive Computation, in press. https://rdcu.be/b34GG https://dx.doi.org/10.1007/s12559-020-09725-5
R. Sun, Why is a computational framework for motivational and metacognitive control needed? Journal of Experimental and Theoretical Artificial Intelligence, Vol.30, No.1, pp.13-37. 2018. http://www.tandfonline.com/eprint/4GPCrukk7qzn46kWjGZF/full
S. Bretz and R. Sun, Two models of moral judgment. Cognitive Science, Vol.42, No.S1, pp.4-37. 2018. https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.12517
R. Sun, N. Wilson, and M. Lynch, Emotion: A unified mechanistic interpretation from a cognitive architecture. Cognitive Computation, Vol.8, No.1, pp.1-14. 2016. [formatted PDF]
R. Sun and N. Wilson. Roles of implicit processes: instinct, intuition, and personality. Mind and Society, Vol.13, pp.109–134. 2014. [formatted PDF]
S. Helie and R. Sun, Autonomous learning in psychologically-oriented cognitive architectures: A survey. New Ideas in Psychology. Vol.34, pp.37–55. 2014. [formatted PDF]
R. Sun and N. Wilson, A model of personality should be a cognitive architecture itself. Cognitive Systems Research, Vol.29–30, pp.1–30. 2014. [formatted PDF]
R. Sun, Moral judgment, human motivation, and neural networks. Cognitive Computation, Vol.5, No.4, pp.566-579. 2013.
R. Sun, Autonomous generation of symbolic representations through subsymbolic activities. Philosophical Psychology, Vol.26, No.6, pp.888-912. 2013. [formatted PDF]
R. Sun and S. Helie, Psychologically realistic cognitive agents: Taking human cognition seriously. Journal of Experimental and Theoretical Artificial Intelligence, Vol.25, pp.65-92. 2013. [PDF]
R. Sun, Motivational representations within a computational cognitive architecture. Cognitive Computation, Vol.1, No.1, pp.91-103. 2009. [PDF]
R. Sun, L. A. Coward, and M. J. Zenzen, On levels of cognitive modeling . Philosophical Psychology, Vol.18, No.5, pp.613-637. 2005. [Formatted PDF]
R. Sun and C. Sessions, Self-segmentation of sequences: Automatic formation of hierarchies of sequential behaviors. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, Vol.30, No.3, pp.403-418. 2000. [PDF]
R. Sun and I. Naveh, Cognitive simulation of academic science. Proceedings of the International Joint Conference on Neural Networks, Atlanta, Georgia, USA. pp.3011-3017. IEEE Press, Piscataway, NJ. 2009.
R. Sun, Cognitive Architectures and multi-agent social simulation. In: D. Lukose and Z. Shi (eds.), Multi-Agent Systems for Society (Lecture Notes in Artificial Intelligence, Volume 4078), pp.7-21. Springer-Verlag, Berlin. 2009.
R. Sun and I. Naveh, A cognitively based simulation of simple organizations. Proceedings of the 27th Annual Conference of the Cognitive Science Society, Stresa, Italy. Lawrence Erlbaum Associates, Mahwah, NJ. 2005.
R. Sun and D. Qi, Learning cooperation through bidding. Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2004), ACM Press, New York. 2004.
D. Qi and R. Sun, A comparison of team evolution operators. Proceedings of the International Conference on Intelligent Agent Technology (IAT-2004), IEEE Computer Science Press, Los Alamitos, CA. 2004.
D. Qi and R. Sun, Integrating reinforcement learning, bidding and genetic algorithms. Proceedings of the International Conference on Intelligent Agent Technology (IAT-2003), Halifax, Canada. IEEE Compute Science Press. 2003.
R. Sun, Meta-learning processes in multi-agent systems. In: Intelligent Agent Technology (IAT-2001). pp.210-219. World Scientific, Singapore. [PDF] [PDF]
R. Sun and D. Qi, Rationality assumptions and optimality of co-learning. Proceedings of PRIMA'2000, Lecture Notes in Artificial Intelligence, Springer-Verlag, Heidelberg, Germany. 2000. [PDF] [PDF]
R. Sun and C. Sessions, Multi-agent reinforcement learning with bidding for segmenting action sequences. From Animals to Animats: Proceedings of the International Conference of Simulation of Adaptive Behavior (SAB'2000). Paris, France. MIT Press, Cambridge, MA. 2000.
R. Sun and T. Peterson, Automatic partitioning for multi-agent reinforcement learning. From Animals to Animats: Proceedings of the International Conference of Simulation of Adaptive Behavior (SAB'2000). Paris, France. MIT Press, Cambridge, MA. 2000.
R. Sun and C. Sessions, Bidding in reinforcement learning, a paradigm for multi-agent systems. Proceedings of The Third International Conference on Autonomous Agents (AGENTS'99), Seattle, WA. May, 1-5, 1999.
R. Sun and C. Sessions, Self segmentation of sequences. Proceedings of International Joint Conference on Neural Networks, Washington, DC. July 10-15, 1999. IEEE Press, Piscataway, NJ.
R. Sun and T. Peterson, Automatic partitioning for multi-agent reinforcement learning. Proceedings of International Conference on Neural Information Processing (ICONIP' 98). KitaKyushu, Japan. October, 1998.
T. Peterson and R. Sun, An RBF network alternative to a hybrid architecture. Proceedings of IEEE International Conference on Neural Networks, Anchorage, Alaska. May 4-9, 1998. IEEE Press, Piscataway, NJ.
Resources for cognitive social sciences