Foundations for Collective Awareness
Platforms (FOCAL)
Network of Excellence in Internet Science (EINS)
3rd Plenary Meeting
Munich, 25-26 November 2013
Ioannis Stavrakakis (University of Athens)
FP7-ICT-2011.1.6-288021 EINS
Network of
Excellence in
Internet Science
Partners
 University of Athens (coordinator)
 University of Florence – Centre for the Study
of Complex Dynamics
 Cardiff University
FOCAL
3rd EINS Plenary, Munich, 25-26 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, 25-26 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, 25-26 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, 25-26 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, 25-26 November 2013
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
3rd EINS Plenary, Munich, 25-26 November 2013
Private information and privacy
FOCAL
3rd EINS Plenary, Munich, 25-26 November 2013
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
3rd EINS Plenary, Munich, 25-26 November 2013
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
3rd EINS Plenary, Munich, 25-26 November 2013
Market/game-theoretic dimensions
FOCAL
3rd EINS Plenary, Munich, 25-26 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, 25-26 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, 25-26 November 2013
Psychological and sociological dimensions
FOCAL
3rd EINS Plenary, Munich, 25-26 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, 25-26 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, 25-26 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, 25-26 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, 25-26 November 2013
CAPS classification
 Initial work by UNIFL: preliminary list of
information necessary for CAPS classification








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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
3rd EINS Plenary, Munich, 25-26 November 2013