1 / 19

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.

quinto
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related