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WCSS 2010 - Talk WCSS 2010 - Talk Presentation Transcript

  • Turning Information Into Knowledge: The Role of P2P Communication Walter Quattrociocchi°* Rosaria Conte° Elena Lodi* °LABSS/ISTC-CNR *Science Dept. University of Siena Kassel WCSS’10
  • Premise 1/2 • Current simulation models of opinion dynamics (Duffuant et el.,2001; 2002; Hegselmann and Krause, 2002) are based on Social Impact Theory (Latané 1981; Nowak et al., 1990), where influence is said to depend on distance, number, and strength (i.e., persuasiveness) of sources. • Simulation-based studies of opinion dynamics observe how opinions spread and aggregate as a function of the distance among values assigned to them. • But we know that social structures influence opinions, more or less steadily
  • Premise 2/2 • Informational influence (since Sherif, 1936) under ambiguous stimuli  Agenda setting theory (McCombs and Shaw, 1972): correlation between frequency of information delivered by media and social perceptions.  Let us see a recent confirmation of this theory.
  • A case study. Effect of media in the last Italian political campaign (reproduced from Diamanti, 2008)
  • Hence  When speaking about opinion As in bounded confidence model dynamics we must take agents’ (Deffuant et al), • opinions are numerically defined confidence into account (certainty) • agents exchange opinions based on the distance between their values: they adjust opinions only if preceding and received + information are close enough, modelled by introducing a parameter t for tolerance above which opinions are resistant to change.  social scientists’ intuition and Agents are exposed to different evidence gathered, that entities and force with variable landscape of social influence influence Receive inputs from distinct is far from flat! sources of information
  • Research questions • In this paper we intend to explore the impact of different interacting communication systems and information sources on information quality • Correlation between frequency of information delivered by media and social perceptions? • Does peer-to-peer communication amplify effects of old media, or exercise independent influence
  • Preliminary definitions • Scale Free Network • Agents are connected in a scale free network, in which nodes are progressively added by introducing links to the existing nodes on a “preferential attachment” schema. • The construction strategy of the algorithm aims at maintaining the link probability between any couple of nodes proportional to the number of existing links already connected to the selected node. • Bounded Confidence Model (BDM): • Agents mix their opinions when differences is smaller than threshold. More precisely…
  • The media Consider the set • ml , mr represent values of events related to welfare and security issues reported on by the media • V1 is the subset of agents that receive information from the central media.
  • Interacting peers Agents’ preferences are set by a uniform random distribution. Interacting peers, v V, are nodes of the Social Network The more the agent’s opinions - with respect to welfare and security - vl and vr approximate 1, the more each issue is important for the agent.
  • P2P interaction • After broadcasting, each agent communicates with neighbors within a distance set to 1. • Following BCM convention if the difference between two agents’ opinions - respectively represented by x and xi - is lower than threshold (x−xi < t) these opinions will be mixed by applying:
  • Information quality • To reproduce Italian central media in the last political campaign, media are assumed to deliver false information. • The closer a reported information is to the opposite information spread by central networks, the higher its quality.
  • Interaction between media and peers • Peers acquire information from media according to a passive protocol, by acquiring the values they send and comparing them with their previous preferences. • Information is accepted or not, based on bounded confidence mechanism. • The agent’s preferences vl , vr and the information from the media mld , mrd are transformed in two new agent’s preferences. The function generates two new values for vl , vr. • t stands for peer agents’ tolerance, i.e., subjective disposition to accept others’ information. The higher the value of t, the higher the agent’s disposition to accept others’ inputs.
  • • Baseline experiment with nine scenarios, with number of agents set to 100, no media broadcasting and increasing levels of tolerance Baseline • (from 0.1 to 0.9 at step 0.1) for 100 turns. Simulation is performed 10 times per scenario, results and results are averaged. At beginning opinions (welfare and security) are set up randomly within interval ]0, 1[: both opinions fluctuate around average value of initial distribution, meaning that, over a scale free network, P2P communication leads to a flat distribution of opinions.
  • How about mixed communicatiion? • How do they interact? In Broadcast particular, does P2P communication amplify or inhibit and P2P the effect of central media?
  • Conclusions • MB drives opinions by steering information among agents. It represents a fundamental medium for knowledge diffusion. • The wider the audience reached by the broadcasting system, the stronger its influence especially when people are poorly self-confident and more likely to accept incoming information. False information spreads fast and easy. • Is there any way to contrast such an influence? • Peer-to-peer communication over a scale-free network can inhibit and slower invasion of informational lemons. • With reasonable level of tolerance, P2P communication inhibits effects of MB until this has reached the 40% of the audience. • P2P communication, thanks to reciprocation, allows self-confidence to act as an efficient filter of information.
  • Next  Level of confidence is not enough: effect of other types of mentall states on opinion dynamics (beliefs, doctrines, ideologies, ideals, etc.). What is an opinion? Whatʼs the difference from beliefs?  More sophisticated mechanisms of belief/opinion formation, revision and transmission, focusing on oneʼs representations of other s beliefs;  Different types of P2P communication (for example, reporting oneʼs Vs othersʼ beliefs);  Wise agents, i.e. a subset of agents having direct access to knowledge  Social structuresʼ properties, possibly implementing one real- world network, and checking effects of speed in P2P communication.
  • Thank you!!!