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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

Foundations for Collective Awareness Platforms (FOCAL)

Ioannis Stavrakakis (University of Athens)

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

FOCAL

objectives of the project
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

relevance to eins jra activities
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

university of florence center for the study of complex dynamics
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

university of florence center for the study of complex dynamics1
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

cardiff university
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

trust privacy security
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

future contributions
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

market dimensions
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

game theoretic dimensions
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

socio psychological aspects in caps
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

high level questions
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

methodology 1
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

methodology 2
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

caps classification
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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