Abstract
Research in the field of collective intelligence is currently focused mainly on determining ways to provide a more and more accurate prediction. However, the development of collective intelligence requires a more formal approach. Thus the natural next step is to introduce the formal model of collective. Many scientists seem to see this need, but available solutions usually focus on narrow specialization. The problems within the scope of collective intelligence field typically require complex models. Sometimes more than one model has to be used. This paper addresses both issues. Authors introduce graph-based meta-model of collective that intend to describe all collective’s properties based on psychological knowledge, especially on Surowiecki’s work. Moreover, we introduced the taxonomy of metrics that allow assessing the qualitative aspects of crowd’s structure and dynamics.
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Notes
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Without any consequent loss of knowledge to agent i.
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Jodłowiec, M., Krótkiewicz, M., Palak, R., Wojtkiewicz, K. (2019). Graph-Based Crowd Definition for Assessing Wise Crowd Measures. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_6
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