[Submissions aiming at IEEE CPS publication must follow the new deadline (30 August) strictly. Others may follow the timelines for the domestic track in TAAI 2013 (http://taai2013.nccu.edu.tw).]

This workshop is motivated by the constant efforts made to search for a unified framework for social sciences. Among several different threads which have been developed over the last decade, a major one is the use of the agent-based model as a unified framework to study the complexity of social dynamics. This development is built upon a view that Stephen Wolfram (Wolfram, 2002) coined it as `computational equivalence’ (CE), i.e., social processes characterized as interactions of heterogeneous agents can be regarded as an equivalent of computation, or even more, universal computation.

While the historical root of CE can be traced all the way back to Alan Turing and other pioneers before or around the 1950s, it is the extensive use of cellular automata, such as Game of Life in the 1970s or Elementary Cellular Automata in the 1980s, that enhances the general awareness of the computational nature of social sciences, and facilitate the emergence of computational social science. A milestone of the development of computational social science is the recent publication of the 4-volume collections of 66 influential articles written over the last four decades (Gilbert, 2010). This collection not only clearly indicates what computational social science was and is, but also feature its possible becoming. This CCB workshop can be considered as a part of this on-going development.

In addition to the computational thread, we also notice another unified efforts emanating from cognitive science and psychology, i.e., the cognitive and behavioral social science. The attempt to have a cognitive social science even has a longer history than computational social science. Herbert Simon has a great influence in initiating this development. In his academic lifetime he constantly called for the conversation between cognitive science and social sciences. The endeavor was followed by Daniel Kahneman, Amos Tversky, Paul Slovic, Richard Thaler, Reinhard Selten, Gerd Gingerenzer, Vela Velupillai, to name a few. This long series of efforts eventually prompt the advent of cognitive social science (Turner, 2001; Sun, 2012). This line of development has extended into a blend of various methodologies, some using laboratory approach (in particular, accompanied by the recent progresses in neuroscience), some relying on computation modeling and simulation approach. They together provide social sciences a foundation from brain to mind and further to decision-making.

Computational social science has the interactions of heterogeneous agents following different rules as the main theme, but it may not pay much attention on the fine details of these agents and the associated rules. In empirical-oriented, agent-based models, data from human-subject experiments are, however, employed to design reasonable artificial agents. Hence, a cross between computational social science and cognitive and behavioral social science is clearly seen; nonetheless, their relation is not limited to empirical calibration.

In fact, both the upward causation and downward causation of agent-based modeling may involve genes, neurons, personality, culture, and all these fine details, as part of the mechanisms. Alternatively put, they are the subroutines or modules everywhere seen in the interaction processes. While it is not necessary for all agent-based models to have genes or neurons as their elementary units, knowledge of cognitive and behavioral social science can help us decide, for example (as a metaphor), among the 256 rules used in Wolfram’s cellular automata, which ones are more human-like? On the other hand, it is also desirable to know how these fine details can amplify them on the social emergence and hence, for example, how amygdala can help herding behavior and enhance instability of financial markets. Can we design a treatment that people can easily develop trust relation, which in turn beef up the team production, GDP and happiness index?

Being capable of addressing the question with that depth (foundation, individual) and breath (aggregation, society) is the purpose of bringing together these two different but closely related treads. We believe that the cross-fertilization of these two unified social sciences is the next step of each of the two, and we believe that computational, cognitive and behavioral social science is the future of an integrated social science. The uniqueness of this workshop is to bring the scholars from both strands together and begin the constructive and fascinating conversation.

We welcome submissions addressing various social dynamics, such as voting, identity, segregation, social exclusion, discrimination, financial crisis, urban dynamics, social networks, leadership, congestion, disease transmission, gossip and mass media, culture and social norms, interpersonal relations, pro-social behavior, using (agent-based) computational, cognitive and behavioral modeling, laboratory and field experiments. We do hope that participants will come from different sister disciplines, such as economics, political science, sociology, social anthropology, ethnology, geography, psychology, communication, law, management science, linguistics, religion, cultural studies, physics, computer sciences, mathematics,…etc.

Organizers
Shu-Heng Chen, National Chengchi University, Taiwan
Ming Hsu, University of California at Berkeley, USA
Akira Namatmae, National Defense Academy, Japan
Leonid Perlovsky, Harvard University, USA
John Staddon, Duke University, USA
Paul Wang, Duke University, USA
Yinqxu Wang, University of Calgary, Canada