【徵稿啟事】International Workshop on Computational, Cognitive and Behavioral Social Science (CCB)
International Workshop on Computational, Cognitive and Behavioral Social Science (CCB)
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; Zenil, 2013) coined 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. A similar view is widely shared among social scientists (Axelrod, 1997; Albin and Foley, 1998; Velupillai, 2000, 2010; Miller and Page, 2007; Mirowski, 2007).
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 and studies of cellular automata, such as John Conway’s Game of Life initiated in the late 1960s or Stephen Wolfram’s Elementary Cellular Automata in the early 1980s, that enhances the general awareness of the computational nature of social sciences and facilitates the emergence of this new discipline, known as "computational social science" (CSS). Thomas Schelling’s work on the segregation model (Schelling, 1971) is considered as one of earliest and classic work in this CSS. This work is an enlightenment for many physicists who later become interested in social sciences (Buchanan, 2007).
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, edited by Nigel Gilbert (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 long endeavor.
In addition to the computational social science, we also notice another unified efforts emanating from cognitive science and psychology, which is known as the cognitive and behavioral social science. The attempt to have a cognitive social science has an even longer history than computational social science. Herbert Simon had a great influence on 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 prompts the advent of the name "cognitive social science" (Turner, 2001; Sun, 2012).
This line of development has extended into a blend of various methodologies, some using laboratory approach (partially accompanied by the recent progresses in neuroscience), some relying on computation modeling and simulation approach. For the former, the success of experimental economics has gradually spread to other social disciplines, such as political sciences, sociology, and management; as to the later, the early mathematical psychological model of reinforcement learning (Bush and Mosteller, 1955) has been further extended by behavioral economists into generalized reinforcement learning model (Camerer and Ho, 1999), not to mention the fusion of various heuristics borrowed from computer scientists and psychologists, such as case-based decisions, decision trees (fast-and-frugal heuristics), and many other computational intelligence tools. They together provide social sciences a foundation from brain to mind and further to decision-making, which enhance our understandings of preference and expectation formation, choice and judgement making, learning with experiences, and risk management.
Computational social science has the interactions of heterogeneous agents following different rules as the main theme, but it may not pay much attention to the fine details on who these agents are, their cognitive constraints, and their behavioral rules. Nonetheless, in the empirical-oriented, agent-based models, data from human-subject experiments are, however, employed to design reasonable artificial agents. This development is convincing enough that there will be more fruitful collaborations between computational social science and cognitive and behavioral social science in the future.
In fact, both the upward causation and downward causation of agent-based modeling may involve genes, neurons, personality, and culture as part of the mechanisms. to put it alternatively, these fine details are the subroutines or modules to be seen everywhere 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 (just as a convenient metaphor), among the 256 rules in Wolfram’s one-dimensional 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 in the social emergence; 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?
The exemplar questions may not be rigorously shaped, but they give the idea and the flavor. Being capable of addressing the question with that depth of individuals (foundation) and breath of the society (aggregation) 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 together the scholars from both strands and begin the constructive and fascinating conversation.
We welcome submissions addressing various social dynamics, such as, but definitely not limited to, voting, identity, segregation, social exclusion, discrimination, financial instability, 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, learning, decision-making under risky environment, gambling, crime, national security, etc.
We sincerely welcome participants from different sister disciplines, such as economics, ecology, political science, public health, sociology, social anthropology, ethnology, geography, psychology, communication, law, management science, linguistics, religion, cultural studies, biology, physics, computer sciences, mathematics, neuroscience and genetics,…etc.
|Call for papers||15 March 2013|
|Manuscripts due||23 August 2013|
|Acceptance notification||4 October 2013|
|Camera-ready versions due||14 October 2013|
|Paper author registrations due||14 October 2013|
|Early-bird registration due||28 October 2013|
|Conference dates||6-8 December 2013|
All papers are to be submitted electronically through the TAAI 2013 submission website: https://www.easychair.org/conferences/?conf=taai2013. Each submission should be regarded as an undertaking that, if the paper is accepted, at least one of the authors must attend the conference to present the work.
· 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
· Yingxu Wang, University of Calgary, Canada
· New Mathematics and Natural Computation
International Program Committee
· Yuji Aruka, Chuo University, Japan
· Shih-Fen Cheng, Singapore Management University, Singapore
· Siew Ann Cheong, Nanyang Technological University, Singapore
· Ya-Chi Huang, Lunghwa University of Science and Technology, Taiwan
· Kai Pui Lam, Chinese University of Hong Kong, Hong Kong
· Honggang Li, Beijing Normal University, China
· Sai-Ping Li, Academia Sinica, Taiwan
· Chia-Yang Lin, National Chengchi University, Taiwan
· Wen-Jong Ma, National Chengchi University, Taiwan
· Akira Namatame, National Defense Academy, Japan
· Heping Pan, Chongqing Institute of Finance, China
· Da Ren, Tianjin University, China
· Kwok Yip Szeto, Hong Kong University of Science and Technoloygy, Hong Kong
· Chung-Ching Tai, Tunghai University, Taiwan
· Bing-Hong Wang, University of Science and Technology of China, China
· Guocheng Wang, Chinese Academy of Social Sciences, China
· Zhijian Wang, Zhejiang University, China
· Zhongyu Wang, Harbin Institute of Technology, China
· Haijun Yang, Beijing University of Aeronautics and Astronautics, China
· Chia-Hsuan Yeh, Yuan Ze University, Taiwan
· Nai-Shing Yen, National Chengchi University, Taiwan
· Tongkui Yu, Southwest University, China
· Wei Zhang, Tianjin University, China
· Yu Zhang, St. John’s University, USA
· Wei-Xing Zhou, East China University of Science and Technology, China