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Social simulation focuses on processes to provide some forms of historical perspectives in explaining social phenomena. This chapter presents three representative examples of cognitive social simulation. It looks into a few representative examples of the kind of social simulation that takes cognition of individual agents into consideration seriously. Game-theoretical interaction is an excellent domain for researching multiagent interactions. The chapter discusses types, issues, and directions of cognitive social simulation and looks into some possible dimensions for categorizing cognitive social simulation. A variety of modeling works has been done on group and/or organizational dynamics on the basis of cognitive models. By combining cognitive models and social simulation models, cognitive social simulation is poised to address issues of the interaction of cognition and sociality, in addition to advancing the state of the art in understanding cognitive and social processes.
Sara Solla, Physics and Astronomy, Northwestern University, Evanston, IL 60208,; Physiology, Northwestern University Medical School, Chicago, IL 60611, USA,
Ole Winther, CONNECT, The Niels Bohr Institute, 2100 Copenhagen Ø, Denmark; Theoretical Physics II, Lund University, S-223 62 Lund, Sweden
The recently proposed Bayesian approach to online learning is applied to learning a rule defined as a noisy single layer perceptron with either continuous or binary weights. In the Bayesian online approach the exact posterior distribution is approximated by a simpler parametric posterior that is updated online as new examples are incorporated to the dataset. In the case of continuous weights, the approximate posterior is chosen to be Gaussian. The computational complexity of the resulting online algorithm is found to be at least as high as that of the Bayesian offline approach, making the online approach less attractive. A Hebbian approximation based on casting the full covariance matrix into an isotropic diagonal form significantly reduces the computational complexity and yields a previously identified optimal Hebbian algorithm. In the case of binary weights, the approximate posterior is chosen to be a biased binary distribution. The resulting online algorithm is derived and shown to outperform several other online approaches to this problem.
Neural networks are adaptive systems characterized by a set of parameters w, the weights and biases that specify the connectivity among the neuronal computational elements. Of particular interest is the ability of these systems to learn from examples. Traditional formulations of the learning problem are based on a dynamical prescription for the adaptation of the parameters w. The learning process thus generates a trajectory in w space that starts from a random initial assignment w0 and leads to a specific w* that is in some sense optimal.
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