Network of Excellence in Internet Science Network of Excellence in Internet Science (EINS) 3 rd Plenary Meeting Munich, November 2013 FP7-ICT EINS Foundations for Collective Awareness Platforms (FOCAL) Ioannis Stavrakakis (University of Athens)

3rd EINS Plenary, Munich, November 2013 Partners  University of Athens (coordinator)  University of Florence – Centre for the Study of Complex Dynamics  Cardiff University FOCAL

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 Cardiff University  George Theodorakopoulos  Background Maryland) Trust in ad hoc networks Malicious users, no trusted 3 rd -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

3rd EINS Plenary, Munich, November 2013 Private information and privacy FOCAL

3rd EINS Plenary, Munich, November 2013 Trust, Privacy, Security FOCAL  Info is sensitive: users won’t share  More information  Better quality  Quality – Privacy tradeoff in CAPs  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)

3rd EINS Plenary, Munich, November 2013 Future Contributions? FOCAL  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?

3rd EINS Plenary, Munich, November 2013 Market/game-theoretic dimensions FOCAL

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 Psychological and sociological dimensions FOCAL

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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

3rd EINS Plenary, Munich, November 2013 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/ ...)  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