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Fo undations for C ollective A wareness P l atforms (FOCAL)

Fo undations for C ollective A wareness P l atforms (FOCAL). Ioannis Stavrakakis (University of Athens). Partners. University of Athens (coordinator) University of Florence – Centre for the Study of Complex Dynamics Cardiff University. Objectives of the project.

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Fo undations for C ollective A wareness P l atforms (FOCAL)

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  1. Foundations for Collective Awareness Platforms (FOCAL) Ioannis Stavrakakis (University of Athens)

  2. Partners • University of Athens (coordinator) • University of Florence – Centre for the Study of Complex Dynamics • Cardiff University FOCAL

  3. Objectives of the project • Investigate collective awareness platforms wrt • Market/game-theoretic dimensions • The role of incentives for contribution in CAPS • The study of CAPS as multiplayer games with non-linear payoff • Psychological and sociological dimensions • The cognitive task of a user that deals with a CAP, the processes that underlie the opinion dynamics of individuals • Privacy concerns about the data and location of the end-users that contribute to CAPS FOCAL

  4. Relevance to EINS JRA activities • FOCAL mainly contributes to: • JRA1: Towards a Theory of Internet Science • Task R1.4: Collective Network Intelligence • JRA5: Internet Privacy and Identity, Trust and Reputation Mechanisms • Task R5.2: Analysis of privacy, reputation and trust in social networks • JRA6: Virtual Communities • Task R6.2: Mutual impact between virtual Internet communities and human social communities • Task R6.5: Dissemination and collection of user cases catalogue • JRA7: Internet as a critical infrastructure; Security, Resilience and Dependability aspects • Task R7.2.2: Social aspects in understanding Internet as critical infrastructure and implications for future networks Project Acronym

  5. University of Florence – Center for the Study of Complex Dynamics • Franco Bagnoli • Ph.D in Theoretical Physics from the University Paris VI (France) • Researcher in Physics in the department of Physics of University of Florence • Co-head of the Laboratory of Physics of Complex Systems (FiSiCo) • Member of the Center for the Study of Complex Systems (CSDC – University of Florence) FOCAL

  6. University of Florence – Center for the Study of Complex Dynamics • Andrea Guazzini • Ph.D in Complex system and non-linear dynamics • Researcher at the department of Education and Psychology and the lab for the study of the human virtual dynamics of University of Florence • Research interests: experimental and cognitive psychology, neuropsychology, social cognition and virtual social dynamics FOCAL

  7. Cardiff University • George Theodorakopoulos • Background (PhD @ Maryland) • Trust in ad hoc networks • Malicious users, no trusted 3rd-party • Game theory, Distributed algorithms • Past 4 years (started at EPFL) • Privacy  Location privacy • Quantify privacy + Protect privacy • Privacy as estimation under noise • Optimal protection against localization attacks FOCAL

  8. Private information and privacy FOCAL

  9. Trust, Privacy, Security • Quality – Privacy tradeoff in CAPs • More information Better quality • Info is sensitive:users won’t share • How much and what kind of information CAPs ask for? • How does CAP quality degrade with less information? *Contribution to EINS JRA5 Task R5.2, Deliv D5.2 (M36) FOCAL

  10. Future Contributions? • Other potential contributions (future?) • Trust + Reputation (JRA 5) • Vulnerability to malicious users (JRA 7) • Trust algorithm behavior in the presence of malicious users • “Optimal” trust mechanism? FOCAL

  11. Market/game-theoretic dimensions FOCAL

  12. Market dimensions • What types of incentives engage humans into mechanisms of active contribution and sharing of knowledge? • Private incentives: e.g., monetary, the possibility of winning an ipad • Public: e.g., reputation FOCAL

  13. Game-theoretic dimensions • The CAPS is a paradigm of service provision whose utility depends on the number of users in a non-linear way • e.g., tragedy-of-commons phenomena in environments with a limited resource: a group of agents can form a “lobby” to exploit the resource but if many agents join the group, then the resource vanishes • With respect to this, in this project we seek to • formalize instances of CAPS as games with non-linear payoff • provide insights for the general dependence of strategies on the payoff in the broader class of multiplayer games with non-linear payoff FOCAL

  14. Psychological and sociological dimensions FOCAL

  15. Socio-psychological aspects in CAPS • CAPS largely rely on the collaboration and contributions of human beings • with very different mixtures of personalities, attitudes, socio-psychological and cognitive biases • whose decisions are subject to time, computational and knowledge limitations • whose decisions depend on many psychological aspects (social group dynamics) FOCAL

  16. High-level questions • What is effectively the cognitive task of a user that deals with a CAP? • What are the processes that underlie the opinion dynamics of individuals? • What is the role of the end-user community on users behavior/decisions? FOCAL

  17. Methodology (1) • Gamification techniques: • set up game experiments with real subjects in virtual groups that interact through collective awareness platforms (e.g., customized chat sessions) • perform measurements on the impact of information on users’ decisions and the group dynamics (e.g., network of connections, expression of emotions) • correlate the measurements to surveys on opinion and attitude changes FOCAL

  18. Methodology (2) • We will start developing a model of collective intelligence, drawing inputs from • Neural network theory • synchronization of cognitive activities by means of communication  collective intelligence • Social learning theory • The social behavior is learned primarily by observing and imitating the actions of others and influenced by rewards and punishments • A. Bandura: • the social learning can occur with live demonstration, verbal instruction, symbolically • A person’s behavior, environment and personal qualities reciprocally influence each other FOCAL

  19. CAPS classification • Initial work by UNIFL: preliminary list of information necessary for CAPS classification • Open or closed? (some projects are reserved to specific participants) • Audience (estimated number of participants. Who are they? Target?) • Interaction infrastructure (web site/social networks/app/email...) • Cost of participation (money and/or time) • Expected benefit and how this scales with the number of participants (eventually grouped in factions) - Impact on non-users • Social impact (i.e., promoting “good” habits) • Reputation mechanisms (i.e., 4Square, facebook) • Data required to access (and kind of access) [No Data, False Identity, Verifiable Identity] • Privacy information (data required for registration and during the usual working, e.g., 4square collects data about actual location) • … FOCAL

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