Since the information available is fundamental for our perceptions and opinions, we are interested in understanding the conditions allowing for a good information to be disseminated. This paper explores opinion dynamics by means of multi-agent based simulations when agents get informed by different sources of information. The scenario implemented includes three main streams of information acquisition, differing in both the contents and the perceived reliability of the messages spread. Agents' internal opinion is updated either by accessing one of the information sources, namely media and experts, or by exchanging information with one another. They are also endowed with cognitive mechanisms to accept, reject or partially consider the acquired information. We expect that peer-to--peer communication and reliable information sources are able both to reduce biased perceptions and to inhibit information cheating, possibly performed by the media as stated by the agenda-setting theory. In the paper, after having shortly presented both the hypotheses and the model, the simulation design will be specified and results will be discussed with respect to the hypotheses. Some considerations and ideas for future studies will conclude the paper.
Much like previous studies, in this paper agents exchanging information are modeled as likely to adjust their opinions only if the preceding and the received information are close enough to each other. Such aspect is modeled by introducing a real number @math , which stands for tolerance or uncertainty ( @cite_16 ) such that an agent with opinion @math interacts only with agents whose opinions is in the interval @math .
@cite_16
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Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.