D3.1 - Roadmap for FET Initiatives in Social Collective Intelligence

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Roadmap for FET Initiatives in Social Collective Intelligence

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Social-IST Social Collective Intelligence Grant Agreement Nº 317681

D3.1 Roadmap for FET Initiatives in Social Collective Intelligence WP3 – High Impact Application Areas and Roadmapping Version: Due Date:

Social-IST/CN/wp3/1.0 31/10/2013

Delivery Date:

19/11/2013

Nature: Dissemination Level:

R PU

Lead partner:

CN

Authors:

Iacopo Carreras (CN), Stuart Anderson (UEDIN), David Robertson (UEDIN), Daniele Miorandi (CN) Daniele Miorandi (CN)

Internal reviewer:

www.social-ist.eu The coordination and support action has received funding from the European Union's Seventh Framework Programme [FP7/2007-2013] under grant agreement n° 317681

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Executive Summary This document includes the outcomes of the activities carried out by the Social-IST Consortium on the identification and analysis of the application areas for which R&D&I initiatives on Social Collective Intelligence (SCI) can have a major impact. This serves as the basis for defining a set of recommendations and a possible roadmap to be taken into consideration when drafting future FET initiatives in the field of Social Collective Intelligence.

In a preliminary phase the Consortium identified, through desktop search, six relevant application areas for SCI, namely the Future of Work, the Future of Learning, Mobility and Transport in Cities of the Future, Healthcare and Well Being, Smart Energy and the Future of Science and Innovation. These areas have been analysed and discussed in details, in particular by means of (i) the two workshops held with the Social-IST Scientific Panel experts (ii) a Web survey open to the research community at large (iii) the final project event held in Oct. 2013. For each area, a number of scenarios were elaborated, leading to the identification of impacts on science, technology and society and of emerging research challenges.

The results of this analysis have been used for defining a roadmap for future EU initiatives in the field of SCI. This included (i) a proposal in terms of research methodology for running SCI projects and initiatives, (ii) a taxonomy of the most relevant research communities (iii) a mapping to the Horizon2020 Work programme and related calls.

This document is expected to provide some key insights on how to potentially exploit a Social Collective Intelligence approach in future calls and EU initiatives.

Document History Version History Version

Status

Date

Author(s)

0.1

Draft

30/10/2013

Iacopo Carreras

0.2

Draft

4/11/2013

Daniele Miorandi

0.9

Draft

11/11/2013

Iacopo Carreras

1.0

Final

19/11/2013

Daniele Miorandi

Summary of Changes Version

Section(s)

Synopsis of Change

0.1

All

First draft material added

0.2

All

Comments and various revisions

0.9

All

Revised according to comments

1.0

All

Revised & proofread

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Contents 1 Introduction ........................................................................................................................ 4 2 High Impact Application Areas ......................................................................................... 6 2.1 The Future of Work .................................................................................................... 6 2.2 The Future of Learning ............................................................................................... 8 2.3 Mobility and Transport in Cities of the Future ......................................................... 10 2.4 Healthcare and Wellbeing ......................................................................................... 11 2.5 Smart Energy ............................................................................................................ 13 2.6 The Future of Science and Innovation ...................................................................... 14 3 Towards a Roadmap for Future EU Initiatives on Social Collective Intelligence ........... 16 3.1 A Proposal for a SCI Research Methodology ........................................................... 17 3.2 Relevant Research Communities .............................................................................. 18 3.3 Mapping to H2020 Research Priorities ..................................................................... 21 4 Conclusions and Final Recommendations ....................................................................... 23 5 References ........................................................................................................................ 25

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Introduction

Social Collective Intelligence [1] (SCI) is emerging as an extremely promising paradigm to deal with complex societal problems. We are talking about problems that our existing arrangements for collective action (e.g. regional and national government, current democratic forms) struggle to tackle, unless we significantly progress in our ability to harness the synergies between human skills and computers power in an effective way. A classical example is climate change, which requires great mobilizations to be tackled, but at the moment is being dealt with as a manageable, economically rationalized activities by governments who consider it part of normal business.

At the moment Big Data is seen a major transformer of business and public service [2]. We would support the view that the wide availability of rich data has the potential to transform our economies and societies. However, we can already see that conventional approaches to the processing of big data are running into two major areas of difficulty: (a) behavioural data exhibits the classical problems of highly contextualised data: this causes misinterpretation when the data is analysed (in particular if such analysis is automated) without a clear understanding of its context, except in the simplest of situations; and (b) there are serious questions around the governance and ethics of the processing of Big Data. The first aspect limits the depth of application of the Big Data approach; the second one limits the breadth of application because the prosumers creating Big Data respond negatively to monopoly ownership of data either in the private or public arena. Our argument is that the “Big Computer” for Big Data will be in the form of Social Collective Intelligence, since this offers both the capacity to contextualise and manage the mobility and interpretation of data and the capacity to build transparent and responsive management structures to empower prosumers in the management of their products.

In the last few years, we have witnessed the emergence of novel forms of Internet-powered socio-technical systems using extremely distributed forms of decentralized collaboration among individuals. The underpinning concept is that complex problems can be effectively tackled by leveraging on a deeply interwoven web of individuals and collectives geographically dispersed, but connected and empowered by means of modern Internet-based technologies. The Internet connects peers and empowers individuals, to co-create, to engage in projects no matter where they are, to collaborate, co-design and form communities. This may have a truly transformational impact on many, diverse application areas.

In this deliverable we report on the work done by the Social-IST Consortium in identifying those application areas that we do believe will be mostly impacted by a Social Collective Intelligence approach in the coming years. The applied methodology consisted in two consecutive phases. In the first phase, we have analysed relevant existing projects and initiatives in terms of application areas, and clustered them around six distinct themes: •

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The organization of work, where the ability to combine the strengths of humans and ICT can lead to new, disruptive approaches to the way companies are organized and structured; The future of learning, where the barriers between lecturers and students blur and the classroom becomes just one of the many dimensions of the educational process; Mobility and transport, where the prosumption of mobility data and transportation services will transform citizens into an active component and a resource for policymakers willing to improve sustainable mobility;

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Healthcare and well being, where the creation of technology-mediated communities of patients, care-givers and doctors can generate innovative ways to address the challenges faced by existing healthcare systems; Energy, where community of sharing, together with massively distributed power plants is expected to enable more sustainable ways for producing and sharing energy; The future of science and innovation, where citizens, by providing their cognitive experience, could become part of a distributed collective experimental facility, available to the scientific community at large.

In a second phase, we have run consultations to revise and analyse the identified application areas. This was carried out, in particular, by means of consultation workshops held in Venice and Edinburgh with the Social-IST Scientific Panel. We asked the Scientific Panel members to rank the Social -IST High Impact Application Areas along two key dimensions: • Intelligence: this reflects the level of intelligence that would emerge from a social collective intelligence approach applied to a given application domain.

• Relevance of collectiveness: this dimension reflects the role played by collectives, versus single individuals, in determining the advances in a specific application domain.

The outcomes of the consultation workshops are depicted in Figure 1. As it is possible to observe, it seems that there was a general agreement on some areas. In particular, Science and Innovation was considered as the application area mostly affected by Social Collective Intelligence approaches. This regarded both the intelligence, which is presumably emerging by a SCI approach, as well as the relevance of the collective aspect.

In Healthcare, SCI is expected to provide a significant level of intelligence, which is not available with existing/legacy technologies. At the same time, the role of collectives was not perceived as determinant.

Fut. Education!

Healthcare!

Mob.&Transp.!

Sci&Inn!

Smart Energy!

Intelligence!

Fut. of Work!

Relevance of Collectiveness!

Figure 1: Results of the consultation workshops held with the Social-IST Scientific Panel.

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The impacts on the Future of Work have been considered less relevant on both dimensions.

Finally, different opinions were expressed for Mobility and Transportation and Smart Energy.

In both cases, besides being in disagreement, the impact of SCI has been judged as marginal both on the Intelligence as well as on collective dimensions.

The outcomes of the consultation workshops were further refined by the results of the Web survey run by the project and on the basis of the discussion that took place at the Social-IST Final Event.

The remainder of this deliverable is organized as follows. In Sec. 2 we describe in detail the six application areas identified, highlighting the expected impact of a social collective intelligence approach, and outlining the major challenges to be tackled. In Sec. 3 we (i) present a proposal for the research methodology of future FET initiatives involving a social collective intelligence approach (ii) describe the most relevant research communities on which to build a critical mass for R&D&I initiatives in the field (iii) provide a mapping to H2020 priorities. Finally, Sec. 4 will conclude the deliverable with some general remarks and recommendations for future initiatives in the field.

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High Impact Application Areas

In the following, we will elaborate on each one of the considered application domains in more detail, providing a brief overview of the challenges and on how social collective intelligence could achieve transformational impacts.

2.1 The Future of Work Particularly in Europe, but potentially globally, we are facing huge challenges in the sphere of work: youth unemployment is destroying the talent of generations across Europe and has the potential to violently disrupt the social fabric of our communities. Europe’s declining manufacturing influence in the global economy and the overall fall in the need for labour in mass produced goods has challenged Europe’s leaders to develop a strategy for manufacturing renewal that has yet to see any great success. Furthermore, we still lack tools to drive the Digital Economy in the creation of new forms of value in order to meet the demands of 21st century society.

In the medium to long term, Social Collective Intelligence has the potential to transform our conception of value production and the concept of work by: • Supporting radically new innovation cultures where small businesses or individuals can build and develop niche markets for highly specialised products and services by dynamically building collectives around specific interests and organising production to meet the needs of that interest group [19]. This will allow a much deeper mining of demand in the long tail for highly specialised products and services that can be sustained only by targeting a global market with only a very small proportion of the population participating in the market. At larger scale, companies will organise open innovation mechanisms that draw on very wide participation to leverage on a deep pool of creativity in order to suggest both radical transformative innovations and the myriad of smaller scale innovations that extend the lifetime and market reach of products and services.

• Exploring new forms of organisation for the delivery of public services and the

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creation of public goods. The goal here is to start with forms of organisation like coproduction and develop more radical approaches to developing communities to innovate and deliver public services [21]. Such networks of communities will depend on Social Collective Intelligence to synergise across communities and unlock the creativity and innovation potential of communities across society. This will also see the development of new forms of public procurement that build complex Social Collective Intelligence communities that span public service, private organisations and communities to leverage the contribution of all players in service delivery.

Today’s organization of work is rapidly evolving in order to keep the pace with the new interconnected and interdependent environment in which modern organizations are required to operate: tougher competition, faster innovation cycles, greater customers expectations, more dispersed talent recruitment and knowledge discovery [4]. All these factors are affecting the way people work together, create and share knowledge, design and run innovation cycles. In particular, innovation is currently evolving into a process that modern organizations cannot fully manage and control internally. Outside Innovation is emerging as an approach able to harness the creativity and knowledge of outsiders to both develop and commercialize products and services [7].

Social collective intelligence can play a major role in this process and in how large corporations plan and organize their activities, disaggregating and disassembling the work in innovative ways, discovering knowledge and ideas on a global scale, easily diversifying their expertise and skills.

This calls for a radical shift in the way large organizations are structured and organized.

Relying on collective intelligence means introducing a structure where management and control are radically distributed (in logical as well as, potentially, geographical terms), creativity is participative and motivation is creative rather than enforced. New roles and responsibilities will be integrated in the organization’s culture and strategy. Bottom-up innovation is better harnessed through influence rather than power, a challenge to the mindset of traditional organizations that “view leadership through the lens of control”. This will affect not only the structure and organization of companies, but also their business models, which will evolve in order to embrace the full power of Social Collective Intelligence.

Expected Impacts: The expected impacts of SCI research on the Future of Work can be summarized as follows: • Shortening innovation cycles: by leveraging on the distributed knowledge and creativity of a distributed pool of diverse and skilled workers, whose interactions are supported and coordinated by novel ICT. • Spontaneous innovation: people will collaborate to collectively advance arts, science, and technology in creative yet profitable ways. By leveraging such spontaneous collectives of innovators, companies will be able to extend their knowledge network far beyond the borders of the organization.

• Novel opportunities and structures for the labour market: radically new forms of virtual organizations will emerge, in which physical presence will be occasional, and competences will be dynamically recruited on a global scale. Preliminary examples of modern Internet enabled labour markets are Elance [26] or Freelance [27], which are developing automated profiling, matching, training and trading of work and labour.

• Beyond strictly hierarchical organizations: traditional roles in organizations will be highly impacted by collective intelligence, with a more distributed control of work and responsibilities.

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S&T Challenges: Some of the technical challenges to be addressed can be summarized as follows: • How to devise new forms of organizations able to fully exploit the power of social collective intelligence?

• How to support through innovative business models the way the distributed knowledge is offered and consumed?

2.2 The Future of Learning We live in a globalised world, with increasingly interconnected markets, dispersed knowledge and multi-disciplinary domains intertwined in many ways. In this new setting, knowledge represents one of the most important resources and lays the basis for a knowledge economy, where innovative ideas and highly technical expertise hold the key to the new global competitive challenge of modern countries. Differently from the past, knowledge is now dispersed across many sources of information, which go beyond those delivered through formal education such as in Universities. As an example, Social Networks, which are enabling individuals to express opinions, share expertise with a greater audience and on a global scale, are becoming at all effects modern instruments to discover, acquire and share knowledge.

Learning, which represents the way to acquire, modify or reinforce knowledge, is expected to adapt to this new context where complexity, large-scale and multi-disciplinarity are some of the key challenges to be addressed. It is necessary to reconsider how learning is conceived, organized and managed today, in a more holistic and systemic perspective [19], taking into account: • Multiple application domains, since information originates from heterogeneous sources, which can only be characterized through a multi-disciplinary approach; • Multiple cultural domains, since in the trend towards globalization, cultural heritage affects in many ways how information is being generated and consumed.

In particular education, which is the institutional way to deliver learning, is expected to play a fundamental role in the preparation of students to become effective contributors to progress and sustainable growth. This calls for a radical change in the way education systems are designed and operated today. This is in part already happening. The digital age is rapidly changing the model of the higher education, as we used to know it for the last decades.

Online, multimedia versions of university courses are attracting hundreds of thousands of students across the globe; millions of dollars of venture investments and a growing interest from university administrators. Universities are redesigning the way courses are delivered to students: Massive Open Online Courses (MOOCs) are increasingly becoming available to students, with the possibility of creating new, multidisciplinary curricula. Through MOOCs outstanding teaching is brought to multitudes of students who otherwise would not have access to or could not afford it, including those in remote places and those in the middle of their professional careers.

The result of this process is a shift from a traditional one-to-many educational approach, to a many-to-many model, where formal education no longer comprises the majority of our learning: learning now occurs in a variety of forms, through communities of practice, through completion of work-related tasks, through participation to online contests etc. Education is becoming distributed (virtual) classroom lectures are intertwined with teamwork and home assignments. Students attend virtual lectures and review other explanatory material alone, and

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then gather in classrooms and in virtual fora to explore the subject matter more in depth and contribute to what becomes a truly collective knowledge creation process. Flipped classroom [14], which inverts traditional teaching methods and promotes a collective model of learning, whereby knowledge acquired by students gets shared in a peer-to-peer fashion, will become the rule rather than the exception. The role of education will go beyond sole teaching, and will be of diagnosis and problem solving, with the Internet being used for access to and sharing of knowledge.

This trend is changing education on a global scale: geographical barriers to education are vanishing, and anyone becomes able to apply for the best courses and universities worldwide.

Excellence from different universities can be combined in order to create ad-hoc, multidisciplinary curricula. This requires developing new skills in education and problem solving. As an example, how to know what balance of skills you need to address a problem, and whether your collective social intelligence has the capacity to tackle it? How to get external perspectives on the quality of a given solution? How to engage adequately with interest groups so you can recognise what is a solution?

A new generation of students will arise, multi-disciplinary by nature, and capable of working in a truly distributed environment in which collaboration occurs based on excellence, common interests and sharing of knowledge. Through the pervasive usage of social media, the boundary between “students” and “teachers” will blur, leading to the emergence of collectives and communities engaged in a process of acquiring, sharing and developing knowledge.

Social Collective Intelligence will play a fundamental role in two ways: on the one hand it will empower anyone to create its own personal learning path, whether through institutional frameworks such as the training delivered in Universities, or through informal knowledge sources, such as web, blogs, social networks, communities of practitioners.

On the other, this will enable knowledge to emerge as the result of a spontaneous collective process, which enriches traditional education, with modern forms of information prosumption.

Expected Impacts: The expected impacts of SCI on the Future of Learning can be summarized as follows: • Blurring the boundaries of education across space and time: learning becomes a ‘liquid’ phenomenon, taking place anywhere and at any time, breaking the conventional classroom paradigm.

• Efficiency: by scaling education and knowledge transmission to the global scale, a more efficient and qualified educational system can be delivered.

S&T Challenges: Some of the technical challenges to be addressed can be summarized as follows: • How to rethink the way knowledge is learned in this highly dynamic and multidisciplinary context?

• How to re-design existing training courses and educational curricula in such a new participative and interactive environment?

• How to design ICT platforms able to deliver massive yet personalized multidisciplinary courses, able to efficiently combine formal training with collective knowledge sharing/creation processes?

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How to systematically capture the informal knowledge generated by a collective learning process?

How to validate students produced by this new learning paradigm?

2.3 Mobility and Transport in Cities of the Future In the last few years we have been witnessing a major shift in social structure, with more than 50% of the world population now living in large cities. Cities are consequently playing an increasingly relevant role in terms of the overall development of economy and society.

Ensuring an effective and sustainable transport of goods and mobility of citizens in cities represents a major challenge to be tackled to achieve sustainable progresses in economy, quality of life and society. Currently, almost all metropolitan areas in the world suffer from traffic congestions, with harmful environmental and economic consequences. This comes, to a large extent, from an inadequate match between demand and offer of mobility services, which creates huge waste of money for inefficiency in economic systems. Despite the great efforts in infrastructuring modern cities with “intelligent” sensors and monitoring systems, the problem still remains. This is due to the semantic gap existing between the “signals” gathered from the urban and transportation infrastructure and their interpretation for taking informed and effective decisions.

Social Collective Intelligence can play a major role by first providing a collective social interpretation of such signals, and then letting collectives autonomously take informed actions. This will be the result of how users’ consumption and provisioning of mobility services varies in order to compensate what is offered by the mobility and transportation infrastructure. Indeed, thanks to modern ICT technologies, users are nowadays fully involved in their own personal mobility: they are becoming prosumers of both data and services, contributing to the emergence of a new transportation infrastructure, which is personalised, contextualised and makes use of different mobility means at the same time. A new culture of Urban Mobility [25] is emerging, where a social collective approach is embraced to tackle many of the problems affecting today’s mobility and transportation services. The underlying idea is to leverage communities of users, who can themselves become an active part of the mobility infrastructure, in order to deliver or improve existing mobility services. Example of such services include: • Collaborative navigation services, where drivers, with their daily commuting become a very accurate source of such information and knowledge on the transportation infrastructure and on traffic patterns [8].

• Peer-to-peer car sharing, in which anyone can become part of a global car-sharing marketplace, making his or her vehicle available for others to rent for short periods of time. Hence, the typical fleet of traditional car rental services gets replaced by a virtual fleet of participating users [10].

• Car pooling, where one or more people travelling to the same destination or nearby destination by car share all or part of the journey. The motivation for users is to reduce the cost of commuting.

• Collaborative parking, where drivers locate and share free parking spots in their vicinity by marking them when driving. Services matching a car leaving a parking spot, with the one searching for it are now emerging as community based approach to alleviate the problem of parking in large cities.

• Collaborative mapping, where open source software and open data intersect in order to create accurate maps of the world. OpenStreetMap is the most representative

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example of this [11].

Impacts: Social Collective Intelligence can have a major impact on mobility and transportation in cities of the future, in particular on: • Economy: a more efficient mobility and transportation infrastructure will have an impact on the economy of urban areas by ensuring a better movement of vehicles, people, and goods.

• Environment: a more efficient and sustainable mobility infrastructure will lead to reduced energy consumption and greenhouse gas emissions related to urban transport, improving the environmental sustainability of smart cities.

S&T Challenges: Some of the technical challenges to be addressed can be summarized as follows: • Behavioural change: promoting sustainable mobility will require implementing measures that reduce private traffic and increase collective transit, and provide a more environmentally-friendly transport routing, by offering a spectrum of innovative and efficient solutions that meet the diverse individual and collective needs of mobility, and so reduce ingrained habits and the propension to use private cars.

• Coordination: a truly collective mobility approach requires a coordinated effort in the provisioning, consumption and management of mobility resources. Conflicting expectations will need to be reconciled in order to provide sustainable mobility solutions to different collectives of users.

2.4 Healthcare and Wellbeing Globally, Health and Wellbeing provision faces three massive challenges: the demographic challenge of the radical shift in population towards the elderly across the world; the medical knowledge challenge of the rapid acceleration of new treatments and formal sources of medical personal data combined with an explosion in personal behavioural data that has health and wellbeing uses; and the fiscal challenge of facing a problem of massively growing scale and complexity within fixed or diminishing budgets.

The response of Health and Care systems around the world is to integrate Health and Care provision and target radical improvements in self-care to enable more independent living and well being; to take measures to reduce demand on primary care services through a radical redesign of those services; and to reduce demand on acute services by re-envisioning the hospital and other acute services to build “virtual hospitals” where people may be receiving acute care over a long period but will only spend very short episodes in hospital facilities [12].

The approach to all three of these areas of radical redesign in the delivery of Health and Care is to “virtualize” care provision and incorporate a transformation in the nature of Health and Care work by moving to systems based on co-design, co-realisation and co-production of Health and Care services. In these co-modes of work we see a blending of the work of formal health and care practitioners with carers, family, third sector and private sector provision.

This transformation will require significant progress in our capacity to provide and govern social collective intelligence systems. In particular: • The central role of the transformation from data to knowledge across these systems

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will depend critically on the deep blending of human and machine processing. This will go far beyond simple analytics because the complexity of the data is greater and there is a need for deep personalisation of service delivery based on understanding and contextualisation of time series data from remote and diagnostic monitors, combined with noisier, less organised behavioural data gathered from a myriad of sources.

The organisation of work in this context where there are much more fluid lines of responsibility and the potential for much more volatility in levels of availability and competence will need new means of monitoring the delivery network and ensuring that appropriate capacity is available at all times. This will require the development of monitors and human/machine analytical capacity to ensure safety and predictability of service. This will also have huge impact on governance issues and the means by which we assure delivery.

The identification, development and deployment of expertise throughout the Health and Care delivery network will demand the repurposing of operational data for training. Re-contextualising this is a very demanding task that again will require social collective intelligence to achieve the high quality outcomes both in terms of growing capacity to deal with Health and Care situations, but also in responsiveness and rate of adaptation to new learning demands. For example, in the case of epidemics, an evolving pandemic threat will require very rapid acquisition of knowledge across the whole delivery network.

These three facets of Social Collective Intelligence (high quality big data analysis; new modes of work organisation and governance; and expertise and learning in the network) will play a key role in tackling the challenges in Health and Care delivery but, as our capacity to build and understand Social Collective Intelligence develops, many more applications in Health and Care will become evident.

Impacts: The expected impact of SCI in Healthcare and Well-being can be summarized in the following dimensions: • Society: the co-production of health and care services will have a major impact on society and in particular it will enable more independent living and well-being.

Further, new social roles will emerge in the health and care systems, with family members directly involved in the delivery of services.

• Economy: the reduction of the demand on primary care services will lead to major savings, releasing the resources, which will be needed in order to run the healthcare systems.

S&T Challenges: Some of the scientific and technical questions to be addressed in order to apply SCI to Healthcare and well-being are: • How to handle the governance of data, ensuring an adequate level of anonymity, despite its great level of heterogeneity?

• How to properly interpret users data and transform it into actionable knowledge?

• How to redesign Healthcare services in order to enable co-production?

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2.5 Smart Energy Traditionally, energy systems were designed around a few-to-many paradigm. Few, large and fully controlled power plants were used to deliver energy to a number of distributed, purely passive end customers. New technologies and changes in regulations have dramatically changed this picture: • Renewable energy sources are getting integrated at scale. Generation from renewables is inherently distributed and intermittent by nature. The system operator is no longer in full control of the energy generation side.

• Smart appliances offer the opportunity to control the energy demand, by scheduling (deferring) the execution of energy-consuming tasks according to contextual information (e.g., real-time price of electricity or user’s needs).

• Electric vehicles could become a form of distributed energy storage, adding one extra degree of freedom for balancing energy demand and supply.

• Energy consumers are becoming energy prosumers, who can produce energy thanks to small-scale renewable power plants (e.g., PV panels) or micro co-generation (combined heat and power technologies).

• Energy markets are opening up, providing the opportunity to trade energy.

The concept of Social Collective Intelligence will find application in smart energy systems by enabling the arising of new forms of prosumers’ collectives. Such collectives will pool together resources, in the form of: • Power generation, creating an effective form of “Virtual Power Plant”; • Storage capabilities (e.g., by electric vehicles), creating an effective form of “LargeScale Virtual Energy Storage”; • Controllable demand, by aggregating energy requests from smart appliances; • Knowledge, in the form of environmentally friendly behaviours and tips for a more sustainable life-style.

Such collectives, supported by advanced ICT tools, could access energy markets, placing bids and obtaining better deals than what single individuals could achieve. Collectives will express a multi-dimensional value system, whereby the goal will not be limited to the pure economicistic dimension (“reduce energy bill”), but, rather, reflect other aspects (e.g., the opportunity to donate excess energy generated by renewable energy sources to NGOs or other given users). The arising of such collective awareness would also change the perception of ‘energy’ at the societal level, from a (costly) commodity into a common good, whose production and sharing contributes to the social welfare and progress.

Impacts: The expected impact of SCI in the area of smart energy can be summarized as follows: • Energy costs: such a distributed approach to energy generation and distribution will lead to a more efficient utilization of energy resources, which will be ultimately reflected in the energy costs.

• Society: energy will be perceived as a social good, to be preserved valorised and possibly shared on the basis of a multidimensional value system.

S&T Challenges: Enabling the vision of a spontaneously managed energy generation and consumption requires the following questions to be addressed: • How to achieve the necessary flexibility in the energy distribution network on a sufficiently large scale?

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How to design and deploy the necessary control infrastructure, and associated ICT tools necessary for monitoring and regulating the network operations?

Which role will be played by distributed energy storage?

2.6 The Future of Science and Innovation Multi-disciplinarity, complexity and large-scale represent some of the key challenges that science and innovation are confronting with today. Many of the scientific problems, at all scales, are beyond the capacity of any single individual to solve or act upon. This is due to the scale of the required cognitive capabilities, and to the necessary computing power required for decomposing a given problem into atomic units. Latest scientific approaches to science, while being able to fully exploit the increasing availability of computing and storage resources, have relatively low human or social involvement.

In this respect, Social Collective Intelligence, thanks to its capacity at different scales to make sense of problems, promises to harness the distributed knowledge of people in order to progress science and innovation.

We are moving into an era where science does not happen any longer in closed laboratories of top-notch academic institutions or high-tech companies. Or at least, not in the way it used to be during the last decades. Thanks to the extraordinary developments in ICT, the work of scientists can nowadays be complemented by that of non-professionals.

The foremost example of such a trend is probably FoldIt [15], a multiplayer online game through which users contributed to unveil and predict protein structures. The resulting Nature paper [16] builds on the contribution of more than 57,000 individuals who played the online game. This represents a disruptive change to the traditional concept of authorship in academic publication.

Similarly, individuals can nowadays simulate the flow of water through nanotubes on a home PC to help in the design of new water filters, or create networks of earthquake detectors using just the motion sensors in laptop computers. This new trend, called citizen cyberscience [27], represents only one example of how SCI is evolving the way R&D is performed. Citizens can collectively provide the necessary intelligence to interpret experimental data, as elaborated and provided by scientists. By providing their cognitive surplus, citizens could become part of a distributed collective experimental facility, available to scientists. Taking this one step further, citizens can, in collaboration with scientists, contribute to the definition of a given problem, and then become active components of the related research activities, collecting data and participating in its interpretation.

Innocentive [17], Ninesigma [18] and Kaggle [26] represent other examples of how the distributed technical knowledge of people can be leveraged and engaged to expand the frontiers of innovation. By issuing contests for solving complex technical problems, corporations are able to expand their expertise beyond the borders of their company.

Applicants from any part of the world have the chance to take part to engineering and science problems subscribed by corporations such as Boeing, DuPont, Philips and Procter & Gamble, and to be rewarded for their work.

Impacts: The expected impacts of SCI on science and innovation can be summarized as follows: • Cost and efficiency of the scientific progress and of innovation processes: the distributed knowledge of individuals and collectives can be leveraged in order to improve the efficiency of science. Citizen can act as scientists, teaming up with experts to tackle major scientific challenges, or provide the distributed knowledge and experience needed by scientists for achieving novel results. This is expected to Public

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significantly accelerate certain science processes that would normally require high investments, and provide the means for reaching quicker breakthroughs.

Enhanced public engagement: science will become participative, with citizens actively involved in the understanding of scientific challenges and contributing by various means to their solving. People will provide a broad base for new era of empirical science.

S&T Challenges: Despite the great promises, there are still various open questions to be addressed in order to take full advantage of a SCI-based approach to science and innovation: • How to provide the necessary ICT facilities in order to enlarge the number of potential participants, and foster a deliberative and inclusive scientific process?

• How to create a truly social collective intelligence methodology for science and innovation, facilitating the training of citizens as scientists, letting them collectively interact within a community of practitioners?

• How to engage citizens in science with the most appropriate tools (collaborative platforms, serious games, etc.) for the most appropriate objective (e.g., data collection, data interpretation)?

• Who owns Intellectual Property Rights of a SCI innovation?

• How to evaluate intellectual artefacts when those artefacts are complex, and domain specific knowledge is scarce or their evaluation is difficult?

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Towards a Roadmap for Future EU Initiatives on Social Collective Intelligence

Building a research roadmap for the development of Social Collective Intelligence (SCI) research is new, challenging and points to the expanded role of FET in H2020. In this expanded role, FET goes beyond the traditional boundaries of basic ICT research to cover more disciplines and interdisciplinary work.

The most distinctive aspect of research in SCI is the need for a coordinated approach across three axes: • Foundational understanding of the interaction between ICT and computational ‘social’ elements; • Empirical study of rapidly evolving socio-technical systems “in the wild”; and • Governance, policy and regulation aspects that shape the environment within which SCI develops.

Today SCI is an emerging field where we are seeing rapid development of pre-SCI structures in use by large populations around the world. Examples of such pre-SCI structures include the use of social networking tools to collectively tackle certain challenges, or relying on the knowledge of people for exploiting outside innovation (e.g., Innocentive [17] and Kaggle [26]). Alongside, there is slower development of our understanding of these structures and huge opportunities to develop innovations based on the developing science of SCI that will see products and services being designed on the full capability of SCI. By understanding the initially unstructured and highly multidisciplinary nature of SCI research and innovation, a FET SCI Roadmap will provide an integrating focus that will amplify Europe’s ability to play a leading role in the development of this high-impact, innovative field. SCI has the potential to influence global organisations and to lead the transformation of our economy to a fully digital economy where new modes of work and production will play a dominant role.

The overarching goal of a FET SCI roadmap is to provide an environment that encourages integration of the communities needed in order to achieve rapid research progress on SCI. Integration in SCI R&D&I initiatives should cover two complementary aspects: • Vertical integration, where diverse relevant research communities are provided with an integrating focus on SCI that enables the exploitation of mutual synergies. This is particularly relevant for SCI because the field requires tight integration of perspectives and knowledge from ICT and Social Sciences, disciplines that have developed for a too long time in isolation.

• Horizontal integration, where different National/Regional initiatives (often with an application-driven approach) are coordinated to achieve large-scale impact. Regional diversity-awareness will also allow us to consider how to capture variability and how SCI systems evolve differently depending on the environment. This drive for crossregion comparison will help drive sharing of experience and best practice and will provide a platform for innovation that is transferrable into diverse markets and contexts.

We believe that integration can only be achieved through coordinated EU-level initiatives in the field. By bringing together all key stakeholders (not just the relevant research communities but also industry and innovative SMEs/startups in particular, policy-makers, collectives and communities) in a purposeful and coherent way FET can create the ecosystem necessary to make rapid development in our understanding of SCI. Understanding the foundations of SCI coupled to the capacity to create tools, components, services, governance and regulation will allow us to innovate effectively in SCI within a sound governance framework.

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In particular, the Social-IST Consortium identified [24] the need for the development of a coherent set of scientific foundations for the field of Social Collective Intelligence. Such foundations require integration of work from researchers in ICT and the Social Sciences. In addition we can see rapid development in global systems that display SCI-like characteristics.

For example, modern Internet enabled labour markets such as Elance [26] or Freelance [27], are developing automated profiling, matching, training and trading of work and labour that could provide the human “engine” behind SCI in many contexts. But to be able to achieve this we need understanding and tools. This “embedded in society” nature of emerging SCI systems means our scientific foundations should be based upon proper “considerations of use” [28] and be subject to empirical test in order to result useful and meaningful work that is directly linked to the phenomenon developing “in the wild”. This calls for approaches that break the traditional barrier separating ‘blue-sky/academic research’ and ‘applied research’, a conceptual distinction that makes no sense in SCI because, in the language of Social Science, SCI is a performative technology where the wide scale deployment of the technology actually transforms society and the practices around the technology. Understanding these sorts of phenomena is at the heart of the foundational analysis and practical deployment of SCI. 3.1 A Proposal for a SCI Research Methodology Taking inspiration from the startup movement, and in particular from the concept of Lean Startup [23], the Social-IST Consortium believes that a programme on SCI should adopt an innovative “lean research” methodology in order to achieve truly transformational impacts.

Lean research should be based upon the following guidelines: • Experiments from day zero. SCI systems are naturally people-centric. This requires adopting and extending user-centric/co-design approaches well beyond what currently done in ICT projects. Empirical activities with individuals and collectives should represent the starting point around which new technological enablers will be introduced iteratively/incrementally. This experimental set-up should both be “labbased” using living labs or experience labs or experimental facilities combined with real-world settings where SCI is already emerging, for example in e-labour markets, digital science, e-health and care, or in e-mediated creative and cultural industries.

Using lab and live settings in parallel will allow the development of new syntheses of scientific and social scientific methodologies that will be based on empirical evidence through the use of detailed observations and big data.

• Fast incremental cycles. SCI projects should be based upon an agile, incremental approach, whereby technologies/solutions/systems developed in the lab are concurrently exposed to test and evaluation ‘in the wild’ (i.e., with real users and realworld applications in selected, rapidly evolving real-world contexts), and where carefully monitored exposure in the real world is calibrated to the experimental setting in order to adjust/adapt/evolve the solution iteratively in response to experience. This process will also consider the extent to which the technologies transform the social context in an attempt to evaluate potential risk and to scope the potential reach and effects of the deployment of SCI technologies in different settings.

• Bring stakeholders into the pictures. To achieve a real impact SCI projects must directly involve stakeholders in empirical activities. This requires substantial efforts and the development of skills and competences, which are out of the background of most scientists. This is particularly relevant for young researchers, who should be educated to work with stakeholders as part of their working methodology. This may involve significant transformations in the way researchers work. For example, it may be necessary to embed a researcher with SCI knowledge and development skills into a live context to “co-realize” solutions for stakeholders. This will require careful Public

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monitoring and control to understand the effects and transform them into transferrable knowledge.

Engaging the public sector is a key element. The public sector is the locus for many systems that could benefit from SCI approaches and the public sector is also a key regulator for such systems and is often responsible for the development and deployment of governance regimes. These components are critical to SCI and without the active engagement of the public sector it will be impossible fully to understand the development, operation, evolution and oversight of SCI. Sustainability is a must, not a plus. SCI has sustainability “built in” - any true SCI system is “self-propelling” using the effort and resources of participants to power the enterprise via individual and collective motivation and incentives. If this is not the case, the SCI will never “power up” when it is switched on (of course achieving critical mass or operating levels of activity is an interesting issue in itself for some SCI systems since they may need special measures or incentives to achieve eventual sustainability). Thus SCI projects cannot afford the luxury of not considering the development of structures and operating models that lack sustainable business models (understood in the widest sense). The design of SCI systems and applications should always include the development of appropriate measures for becoming selfsustainable. Business models are not restricted to the monetary aspects, but will provide a clear description of the different value classes utilized by the SCI together with the value created by the system/application and of the measures needed for its deployment (including the design of appropriate incentives for individuals and collectives).

Sharing and open access. Open access policies should be adopted to ensure re-use of the knowledge generated within the projects. This will bring along a twofold advantage. On the one hand, it will reduce the risk of duplication of efforts among different projects, which may well encounter similar problems in their execution. On the other one, it will foster the take-up by third parties of the knowledge developed inside the projects, maximizing impacts beyond the boundary of consortia. Openness is a key feature of the development of SCI because part of the work will result in the creation of an infrastructure built out of existing and evolving structures combined with new tools, components and services developed by the SCI programme.

The adoption of a ‘lean research’ approach builds also upon epistemological considerations.

SCI is about innovating in complex socio-technical systems, which are based on a set of interwoven feedback loops involving individuals, collectives and technologies. And any type of innovation or change in such context can effectively be understood as a ‘perturbation’ (to use a physics terminology) of the current state, whose effect cannot be fully predicted a priori. As such, any additional innovation to be introduced would not be predictable and need to be tested in vivo.

3.2 Relevant Research Communities Social Collective Intelligence, as a field of investigation, presents a strong multi-disciplinary character. A key success factor for obtaining significant progresses is represented by the ability of building upon a composite and diverse set of competences. It is therefore of primary importance to involve and engage the most relevant research communities.

In order to identify the most relevant communities, we will refer to the map of the FET Proactive Initiatives [19], depicted in the following figure (we have considered only FP6 and FP7 related ones):

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Figure 2: Complete list of FET Proactive Initiatives.

Starting from these research communities, we have confined the focus on the ones, which could play a significant role in the development of initiatives on Social Collective Intelligence. In particular, we have identified 8 communities (Figure 3), each one covering a different facet of SCI. In the following we report, for each of them, the objectives set forward by the corresponding FET proactive initiative and discuss their relevance and potential role in terms of SCI research initiatives.

Figure 3: FET Research communities mostly related to Social Collective Intelligence.

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FOCAS, Fundamental of Collective Adaptive Systems 1 Objectives: to design new functionalities for adaptive ICT systems enabled through novel principles, methods and technologies for designing and operating collective adaptive systems.

Relevance to SCI: Social collective intelligence will emerge from the disperse interactions of collectives of people. FOCAS can contribute to defining the fundamental operating principles and foundational frameworks for the design and operation of such systems.

SYMBIOSYS, Symbiosis between humans and computers 2 Objectives: to understand human behaviour during interaction with ICT, going beyond conventional approaches. This includes systems that can operate autonomously in the real world through e.g. scene and context understanding, anticipation and reaction / adaptation to changes, manipulation and navigation, as well as symbiotic human-machine relations.

Relevance to SCI: SYMBIOSYS will provide key insights on how to maximally exploit the combination of humans and machines in the creation of hybrid systems. In particular, new theories and models of human cognition and emotion will provide the basis for understanding the emergence of intelligence from a collective of people.

DyM-CS, Dynamics of Multi-Level Complex Systems 3 Objectives: to make steps towards a general theory of complex systems through contributions in the area of dynamics of multi-level systems.

Relevance to SCI: collectives of people are inherently multi-level complex systems.

Understanding and modelling their interactions will be of fundamental importance for progressing the field of social collective intelligence.

AWARENESS, Self-Awareness in Autonomic Systems 4 Objectives: to create computing and communication systems that are able to optimise overall performance and resource usage in response to changing conditions, adapting to both context (such as user behaviour) and internal changes (such as topology).

Relevance to SCI: the intelligence of a collective is an unstructured process, which simply emerges by the loose interactions supported through the use of advanced ICT tools. Such an open and uncontrolled environment, both in terms of participants as well as in term of supporting infrastructure, requires autonomic features to be present for design of future-proof systems.

PERADA, Pervasive Adaptation 5 Objectives: Technologies and design paradigms for massive-scale pervasive information and communication systems, capable of autonomously adapting to highly dynamic and open technological and user contexts.

Relevance to SCI: PERADA will provide the necessary infrastructural support for the design of massive-scale pervasive information and communication systems. This can be considered 1

FOCAS, http://cordis.europa.eu/fp7/ict/fet-proactive/focas_en.html SYMBIOSIS, http://cordis.europa.eu/fp7/ict/fet-proactive/symbiosis_en.html 3 DyM-CS, http://cordis.europa.eu/fp7/ict/fet-proactive/dymcs_en.html 4 FET Awareness, http://cordis.europa.eu/fp7/ict/fet-proactive/aware_en.html 5 PERADA, http://cordis.europa.eu/fp7/ict/fet-proactive/perada_en.html 2

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as the nervous system of social collective intelligence, in which the role of (big-) data will become more and more important.

HC-CO, Human Computer Confluence 6 Objectives: to investigate and demonstrate new possibilities emerging at the confluence between the human and technological realms. HC-CO will examine new modalities for individual and group perception, actions and experience in augmented, virtual spaces.

Relevance to SCI: the intelligence of a collective is rooted in a distributed knowledge creation process, where human and technological realms will be intertwined in novel yet creative ways. HC-CO will provide the necessary tools and expertise for the social sensemaking of large volumes of data.

COSI-ICT, Science of Complex Systems for socially intelligence ICT 7 Objectives: to develop key concepts and tools for a data-intensive science of large scale techno-social systems, i.e., systems in which ICT is tightly entangled with human, social and business structures which, as a result, mutually transform each other, for instance through evolution of acceptance, trust, innovative uses and technology changes.

Relevance to SCI: COSI-ICT embraces the research community involved in the study of data-driven and data-intensive socio technical systems, in which ICT is tightly entangled with human and business structures. COSI-ICT will provide the theoretical and experimental foundations for designing and simulating SCI systems at scale.

CREATE: Technologies and scientific foundations in the field of creativity 8 Objectives: to address creativity and the tools and environments in which it takes place.

Research activities will contribute to equipping different industries with more effective creative tools, expand the potential of technology in the human creative processes and advance the scientific understanding of creativity, thus providing the basis for future innovative technologies. This will be complemented by support activities that promote ways of closer interaction and networking within and between different segments of creative industries.

Relevance to SCI: Social Collective Intelligence is based on the notion that intelligence is a process that can emerge by harnessing the expertise and knowledge of a distributed network of people. In particular, the creativity of collectives is what will ultimately lead to intelligence, and needs therefore to be deeply understood, especially in the context of sociotechnical systems where computers are augmented by humans, and vice-versa. In this respect, CREATE can provide the necessary knowhow to better understand how to harness creativity in Social Collective Intelligence.

3.3 Mapping to H2020 Research Priorities The European Commission is currently launching Horizon2020 and the related Work Programme for 2014-2015. Horizon2020 is structured around the following three pillars: (i) Excellent Science, which targets strengthening Europe's excellence in science and ensuring a steady stream of world-class research to secure Europe's long-term competitiveness, (ii) 6 7 8

HC-CO, http://cordis.europa.eu/fp7/ict/fet-proactive/hcco_en.html COSI-ICT, http://cordis.europa.eu/fp7/ict/fet-proactive/cosiict_en.html Creative ICT, http://cordis.europa.eu/fp7/ict/fet-proactive/creativity_en.html

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Industrial Leadership, which is aiming at making Europe a more attractive location to invest in research and innovation, and (iii) Societal Challenges, which is addressing major concerns shared by citizens in Europe and elsewhere.

Within the Excellent Science, Future and Emerging Technologies (FET) is expected to explore novel and high-risk research directions both from a science and cutting-edge engineering perspectives.

We strongly believe that Social Collective Intelligence could play a fundamental role in bridging the Excellent Science and the Societal Challenges pillars. In particular, we have observed a good correspondence between the application areas for which SCI can have an impact and the list of Societal Challenges identified in Horizon2020. Namely: Societal Challenge 1 - Health 3 - Energy 4 - Transport 6 - Reflective societies

SCI Application Area Healthcare and Wellbeing Smart Energy Mobility and Transport in cities of the Future The Future of Science and Innovation

Promoting research in the field of Social Collective Intelligence can have an impact on a multitude of application areas, in which SCI represents not only a research methodology, but also an opportunity to further engage citizens in identifying solutions to problems affecting their daily lives. In this respect, FET can play an important role in acting as a catalyst towards new forms of research and innovation, in which Societal Problems are tackled together with the society itself, bringing the research close to the citizens, and leveraging their creativity and knowledge to achieve a major progress.

This requires coordinated EU-level initiatives in the field, which include by design different stakeholders (research communities, industry, policy makers, SMEs) in a purposeful and coherent way, with clear objectives determined by the Societal Challenges present in Horizon2020 and a SCI-based methodology.

Concerning the current Work Programme for FET in H2020, a number of priorities have been identified and will serve as the basis for growing emerging research excellence and communities in Europe. Among these Themes that appeared in preliminary drafts of the Work Programme for 2014-2015, we have identified two on which Social Collective Intelligence can have a major impact: Global System Science (Topic 9) and Knowing, doing and being (Topic 5). In the following we will elaborate these topics in more detail.

Topic 9: Global System Science Global System Science (GSS) is a FET topic bridging the link between scientific knowledge and the evaluation of their impact on major societal challenges. In particular, according to the draft programme, GSS will be targeting the following challenge: The challenge is to improve the way scientific knowledge can stimulate, guide, and help evaluate policy and societal responses to global challenges like climate change, financial crisis, pandemics, and global growth of cities. Policy challenges shall be addressed by radically novel tools for producing and delivering scientific knowledge to the policy processes.

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Social Collective Intelligence has the potential to provide GSS with the necessary tools and “social facilities” to harness the distributed knowledge of people in order to target real-world societal problems. When it comes to social informatics, where challenges are deeply intertwined with the complex relations of citizens’ daily routines, applying an experimentally driven approach involving people is a fundamental requirement for achieving significant and rapid progresses. This will provide a twofold advantage: on the one hand, any devised policy can be rapidly evaluated/judged by collectives of users, thus shortening the research and innovation cycles. On the other one, citizens will become an active component of the research process itself, by collectively gathering data, analysing it from their perspective and providing interpretation both as single citizens, as well as members of a given collective. By means of advanced ICT tools (e.g., interactive data visualizations) and engaging methodologies (e.g., gamification), citizens can provide not only the necessary data, but also the necessary intelligence to socially make sense of complex problems.

Topic 5: Knowing, doing and being.

This research initiative addresses the challenges of future artificial cognitive systems, with application to various fields, including robotics. The main ambition is the introduction of new approaches to long-term development of individual and social knowledge and identities, especially in highly heterogeneous and dynamic settings, addressing the following challenges: This initiative addresses the interdisciplinary fundamentals of knowing, thinking, doing and being, in close synergy with foundational research on future artificial cognitive systems and robots. It aims at renewing ties between the different disciplines studying knowledge (especially beyond the 'declarative' and action oriented kind of knowledge), cognition (e.g., perception, understanding, learning, action) and related issues (e.g., embodiment, thinking, development, insight, knowledge as a social construct, identity, responsibility, culture…) from various perspectives (e.g., neural, behavioural, social, epistemological), enriching the basis for research that takes artificial cognitive systems beyond the level of dull task execution or problem solving.

Social sense-making and problem solving is what characterize the intelligence emerging from the cooperation of a distributed collective of people, with knowledge creation and formation as a primary by-product. This represents a great playground for experimenting novel studies on thinking and doing, where a distributed network of people can be harnessed to explore the emergence of knowledge, taking into account different social and cultural aspects. And in particular, how knowledge can be created as a social construct.

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Conclusions and Final Recommendations

This deliverable presents the final conclusions of the work done within Social-IST Workpackage 3 (High Impact Application Areas and Roadmapping). This Workpackage has been mostly concerned with the identification of application areas for which a Social Collective Intelligence approach can have a major impact. The focus on application areas in a FET project has been motivated by two factors. First, Social Collective Intelligence is a domain that starts and ends on applications. Indeed, a given application area is what motivates collectives of people to team up in order to tackle certain challenges. The motivation for people to participate can be very different (e.g., recognition, money, personal benefit, fun, etc.), and is rooted in a given application area. At the same time, such collectives Public

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of users will generate intelligence that will ultimately contribute and positively affect the given application domain.

This motivated the study of application areas for SCI with a focus on expected impacts and emerging S&T challenges. From this analysis we have identified six areas that we explored further together with the support of the Social-IST Scientific Panel. We have run two exploratory workshops in which each participant was asked to interactively provide an input on the relevance of a given application scenario, the existing challenges (technical, regulatory, etc.) and the key stakeholders involved. The final results of these workshops have been summarized in this deliverable, and used in the identification of the most relevant research communities and a list of recommendations for future SCI research initiatives in Horizon2020.

The final conclusions and recommendations from this work can be summarized as follows: • We strongly believe that Social Collective Intelligence represents an extremely promising paradigm to deal with complex societal challenges by harnessing the synergies between human skills and computers power in an effective way. Social Collective Intelligence should represent a new research methodology for many initiatives, where humans capacity to collaboratively make sense of problems is harnessed on a global scale.

• A SCI approach to future research initiatives is based on an experimentally-driven and user-centred methodology. It requires fast iteration cycles, in which preliminary results feed iterative loops of ideas and innovation. This will allow intelligence to emerge as a bottom-up process, in which people’s knowledge and creativity is harnessed at scale. This requires research initiatives to be based from the very beginning on a SCI approach, and projects to be designed accordingly. Impact assessment methodologies should be an integrated part of any SCI initiative and should steer real-world experimentations towards early success, upon which to build the following steps.

• FET has the opportunity to promote European excellence in the field of SCI by supporting the usage of a SCI-based approach in future research initiatives. This will serve a two-fold role. On the one hand, it will allow to bridge fundamental research to Societal Challenges. On the other, it will put Europe at the fore-front of innovation in this field, with a major impact on many different industrial sectors thanks to the ability of SCI to exploit people creativity and co-production.

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References

[1] Thomas W. Malone, Robert Laubacher, Chrysanthos Dellarocas. Harnessing Crowds: Mapping the Genome of Collective Intelligence. CCI Working Paper 2009-001 [2] Manyika, James, et al. "Big data: The next frontier for innovation, competition, and productivity."

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McKinsey Technology and Innovation Reports, http://www.mckinsey.com/insights/mgi/research/technology_and_innovation [3] MIT handbook of collective intelligence.

Available from: http://scripts.mit.edu/~cci/HCI/index.php?title=Main_Page [4] Eric Lesser, David Ransom, Rawn Shah and Bob Pulver. Collective Intelligence, Capitalizing the crowd. IBM Global Business Services [5] Henry Jenkins. Confronting the Challenges of Participatory Culture: Media Education for the 21st Century.

[6] Nicholas Carr. The crisis of higher education. MIT Technology review, http://www.technologyreview.com/featuredstory/429376/the-crisis-in-higher-education [7] Gardener, Robert C. and Wallace E. Lambert. Attitudes and Motivation in Second Language Learning. Newbury House Publishers, Inc. 1972.; Boudreau, K. and K. Lakhani. “How to Manage Outside Innovation.” MIT Sloan Management Review. 2009. http://sloanreview.mit.edu/the-magazine/2009-summer/50413/how-to-manageoutsideinnovation [8] Waze, outsmarting traffic together, http://www.waze.com [9] Zipcar, http://www.zipcar.com [10] Wheelz, Rent cars from people in your neighborhood, http://www.wheelz.com [11] http://www.openstreetmap.org/ [12] http://ec.europa.eu/research/innovation-union/index_en.cfm?section=active-healthyageing [13] http://www.kurbkarma.com [14] The Flipped Classroom, http://www.knewton.com/flipped-classroom [15] FoldIt, http://fold.it [16] Cooper, Seth, Firas Khatib, Adrien Treuille, Janos Barbero, Jeehyung Lee, Michael Beenen, Andrew Leaver-Fay, David Baker, and Zoran Popović. "Predicting protein structures with a multiplayer online game." Nature 466, no. 7307 (2010): 756-760.

[17] Innocentive, www.innocentive.com [18] Ninesigma, www.ninesigma.com [19] N. Glock-Grunerich, University 2.0: Informing our collective intelligence, In M. Tovey, Collective Intelligence, creating a prosperous world at peace, p. 131-144 [20] H. Masum and M. Tovey, “Scaling up open problem solving. In M. Tovey, Collective Intelligence, creating a prosperous world at peace, p. 485-494 [21] E. Von Hippel and G. V. Lrogh, Open Source Software and the “Private-Collective” [22] FET Proactive Initiatives, http://cordis.europa.eu/fp7/ict/fet-proactive/areas_en.html [23] Eric Reis, The Lean Startup, How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. The New York Times.

[24] SOCIAL-IST Deliverable D2.1, White paper on Research Challenges in Social Collective Intelligence.

[25] European Commission, GREEN PAPER: Towards a new culture for urban mobility.

Sept. 2007 [26] Kaggle, http://www.kaggle.com [27] Citizen Cyberscience Centre, http://www.citizencyberscience.net [28] Donald E. Stokes, Pasteur's Quadrant – Basic Science and Technological Innovation, Brookings Institution Press, 1997.

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[29] Elance, https://www.elance.com [30] Freelancer, http://www.freelancer.com

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