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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)


  • University of Athens (coordinator)

  • University of Florence – Centre for the Study of Complex Dynamics

  • Cardiff University


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


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)


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


Cardiff university
Cardiff University Dynamics

  • 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


Trust privacy security
Trust, Privacy, Security Dynamics

  • 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)


Future contributions
Future Contributions? Dynamics

  • 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?


Market dimensions
Market dimensions Dynamics

  • 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


Game theoretic dimensions
Game-theoretic dimensions Dynamics

  • 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


Socio psychological aspects in caps
Socio-psychological aspects in CAPS Dynamics

  • 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)


High level questions
High-level questions Dynamics

  • 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?


Methodology 1
Methodology (1) Dynamics

  • 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


Methodology 2
Methodology (2) Dynamics

  • 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


Caps classification
CAPS classification Dynamics

  • 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)