International Research Roadmap on ICT Tools for Governance and Policy Modelling (Interim Version)The present deliverable, developed under WP2 Content Production, is one of the core output of the project: the Research Roadmap on Policy-‐Making 2.0. It aims at providing a common basis and a common set of concepts for researchers in a highly multidisciplinary context. But most of all, it aims to provide a clear outline of what technologies are available now for policy-‐makers to improve their work , and what can become available tomorrow.Crossover builds on the results of the Crossroad project1, which delivered a research roadmap on the same topic in 2011. With respect to the previous roadmap, this document is firstly a revised and updated version. Beside this, is contains some fundamental novelties: -‐ a demand-‐driven approach: rather than focussing on the technology, the present roadmap starts from the needs and the activities of policy-‐making and then links the research challenges to them -‐ an additional emphasis on cases and applications: for each research challenge, we indicate relevant cases and practical solutions -‐ a clearer thematic focus on ICT for Governance and Policy-‐Modeling, by dropping more peripheral grand challenges of Government Service Utility and Scientific Base for ICT-‐ enabled Governance -‐ a global coverage: while Crossroad focussed on Europe, Crossover includes cases and experiences from all over the world -‐ a living roadmap: the present deliverable is accompanied by an online repositories of tools, people and applicationsCROSSOVER RrojectCP_9dc50786-0341-11e2-851f-09c7522abe92The CROSSOVER project aims to consolidate and expand the existing community on ICT for Governance and Policy Modeling (built largely within FP7) by: -‐ bringing together and reinforcing the links between the different global communities of researchers and experts: it will create directories of experts and solutions, and animate knowledge exchange across communities of practice both offline and online; -‐ reaching out and raising the awareness of non-‐experts and potential users, with special regard to high-‐level policy-‐makers and policy advisors: it will produce multimedia content, a practical handbook and high-‐level policy conferences with competition for prizes; -‐ establishing the scientific and political basis for long-‐lasting interest and commitment to next generation policy-‐making, beyond the mere availability of FP7 funding: it will focus on use cases and a demand-‐driven approach, involving policy-‐makers and advisors in high-‐level conference, defining a collaborative stakeholders’ declaration and developing a sustainability plan. The CROSSOVER project will pursue this goal through a combination of content production, ad hoc and well-‐designed online and offline animation; as well as strong links with existing communities outside the CROSSOVER project and outside the realm of e-‐Government._bdaf3412-3b23-11e2-b375-4576cf2fd515To provide a clear outline of what technologies are available for policy-makers to improve their work and what can become available tomorrow._bdaf37e6-3b23-11e2-b375-4576cf2fd515Demanda demand-‐driven approach: rather than focussing on the technology, the present roadmap starts from the needs and the activities of policy-‐making and then links the research challenges to themCasesan additional emphasis on cases and applications: for each research challenge, we indicate relevant cases and practical solutionsFocusa clearer thematic focus on ICT for Governance and Policy-‐Modeling, by dropping more peripheral grand challenges of Government Service Utility and Scientific Base for ICT-‐ enabled GovernanceThemesGlobal Coveragea global coverage: while Crossroad focussed on Europe, Crossover includes cases and experiences from all over the worldLiving Roadmapsa living roadmap: the present deliverable is accompanied by an online repositories of tools, people and applicationsOnline Repositoriesonline repositories of tools, people and applicationsToolsPeopleApplicationsPolicy Making Challenges[Identify] the key challenges of policy-makers_bdaf3958-3b23-11e2-b375-4576cf2fd5151Policy-MakersNeedless to say, the current policy-‐making process is far from perfect. Dramatic crisis seem to happen too often, and government struggle to anticipate and deal with them, as the financial crisis has shown. Citizens feel a sense of mistrust towards government, as shown by the decrease in voters turnout in the elections. In this section, we analyze and identify the specific challenges of policy-‐making. The goal is to clearly spell out "what is the problem" that policy-‐making 2.0 tools can help to solve. The challenges have been identified on desk-‐based research of "government failure" in a variety of context, and are illustrated by real-‐life examples. One first overarching challenge is the emergence of a distributed governance model. The traditional division of “market” and “state” no longer fits a reality where public decision and action is effectively carried out by a plurality of actors. Traditionally, the policy cycle is designed as a set of activities belonging to government, from the agenda setting to the delivery and evaluation. However in recent years it has been increasingly recognized that public governance involves a wide range of stakeholders, who are increasingly involved not only in agenda-‐setting but in designing the policies, adopting them (through the increasing role of self-‐regulation), implementing them (through collaboration, voluntary action, corporate social responsibility), and evaluating them (such as in the case of civil society as watchdog of government). As Elinor Ostrom put it in her lecture delivered when receiving the Nobel Prize in Economics: “A core goal of public policy should be to facilitate the development of institutions that bring out the best in humans. We need to ask how diverse polycentric institutions help or hinder the innovativeness, learning, adapting, trustworthiness, levels of cooperation of participants, and the achievement of more effective, equitable, and sustainable outcomes at multiple scales”. This constatation leads to important implications for the Crossover roadmap: policy-‐making 2.0 tools are not just tools for government, but for all stakeholders to participate in the policy-‐making process.Problem Detection & UnderstandingDetect and understand problems before they become unsolvable_bdaf3a48-3b23-11e2-b375-4576cf2fd5151.1The continuous struggle for evidence-‐based policy-‐making can have some important and potentially negative implications in terms of the capacity of quick prompt identification of problems. Policy-‐ makers have to balance the need for prompt reaction with the need for justified action, by distinguishing signal from noise. Delayed actions are often ineffective; at the same time, short-‐term evidence can lead to opposite effects. In any case, government have scarce resources and need to prioritize interventions on the most important problems. For instance, the linear models adopted in econometric forecasts significantly underestimated the risks of the housing bubble in the late 2000s, and the systemic reaction that it would lead to, thereby leading to delayed reactions. The detection of the hozone hole was delayed because satellite detection instruments were calibrated to consider as "errors" measurement outside a certain margin; it turned out that correct low measurement of ozone were assessed as false negative. Systemic changes do not happen gradually, but become visible only when it's too late to intervene or the cost of the intervention are too high. For example, ICT is today recognized as a key driver of productivity growth, but evidence to prove this became available at a distance of years from the initial investment. The problem is in this case therefore twofold: to collect data more rapidly; and to analyze it with a wider variety of models that account for systemic, long term effects.Citizen InvolvementGenerate high involvement of citizens in policy-making_bdaf3b42-3b23-11e2-b375-4576cf2fd5151.2CitizensThe involvement of citizens in policy-‐making remains too often associated with short-‐termism and populism. It is difficult to engage citizens in policy discussions in the first place: public policy issues are not generally appealing and interesting as citizens fail to understand the relevance of the issues and to see "what's in it for me". The decline in voters turnout and the lack of trust in politicians reflects this. More importantly, there are innumerable cases where the "right" policies are not adopted because citizens "would not understand" (EXAMPLE). While the Internet has long promised an opportunity for widespread involvement, e-‐participation initiatives often struggle to generate participation. (REF) Participation is often limited to those that are already interested in politics, rather than involving those that are not. When participation occurs, online debates tend to focus on eye-‐catching issues and polarized positions, in part because of the limits of the technology available (REF). It is extremely difficult and time consuming to generate open, large scale and meaningful discussion.Ideas & AlternativesIdentify “good ideas” and innovative solutions to long-standing problems_bdaf3c3c-3b23-11e2-b375-4576cf2fd5151.3Innovation in policy-‐making is a slow process. Because of the technical nature of issues at hand, the policy discussion is often restricted to a restricted circles. Innovative policies tend to be "imported" through "institutional isomorphism". Innovative ideas, from both civil servants and citizens, fail to surface to the top hierarchy and are often blocked for institutional resistance. Existing instruments for large scale brainstorming remain limited in usage, and fail to surface the most innovative ideas (REF IBM paper). Crowdsourcing typically focus on the most “attractive” ideas, rather than the most insightful.Risk ReductionReduce uncertainty on the possible impacts of policies_bdaf3d36-3b23-11e2-b375-4576cf2fd5151.4When policy options have been developed, simulations are carried out to anticipate the likely impact of policies. The option with the most positive impact is normally the one that is proposed for adoption. Existing methodologies and tools for the simulation of policy impacts work decently well with well known, linear phenomena on which a massive quantity of historical data are available. However, they are not effective in times of crisis and fast change, which unfortunately turn out to be exactly the situations where government intervention is most needed. On the top of that, often the analytical frameworks and tools adopted in governments and international institutions are well below the academic standards. Furthermore many international institutions, such as the IMF, tend to recruit personnel from a very exclusive group of economic departments in which the very concept of general equilibrium was developed. As an example nowadays the European Central Bank bases its analysis of the EURO Area economy and monetary policy on a derived version of the DSGE model developed by Frank Smet and Raf Wouters in 20032. Smet and Wouters’ model is deeply microfounded, allowing for a rigorous theoretical structure of the model. Moreover in this setting the reduced form parameters are related to deep structural parameter in order to mitigate Lucas’ critique, while the utility of agents can be taken as a measure of welfare in the economics. However, the DSGE models suffer from several shortcuts jeopardizing their ability to predict, let alone to prevent, global crisis: • Agents are assumed to be perfectly rational, having perfect access to information and adapting instantly to new situations in order to maximize their long-‐run personal advantage • So far agents have entered the models as homogeneous representative entities, while it would be a step forward being able to take into account agents heterogeneity • Canonical models consider atomistic agents with little or no interactions and thereby are not able to cope with network externalities But most of all it is the very notion of stable steady state equilibrium which prevents standard models from dealing with crisis. A stable steady state equilibrium is a condition according to which the behaviour of a dynamical system does not change over time or in which a change in one direction is a mere temporary deviation. This condition is proper of general equilibrium theory, in which a stable steady state is believed to be the norm rather than the exception. When in the canonical model we are out of equilibrium, the situation is seen just as a short lapse before the return to the steady state. This is in sharp contrast with the very notion of crisis, which represents a steady deviation from the equilibrium. Loosely speaking, the crisis phenomenon is not even conceived within the framework of standard models. All these flaws are not only related to DSGE models, Computational General Equilibrium (CGE) or macro-‐econometric forecasting models, but generally affect the traditional policy making tools. In this view it would be very important to find new frameworks capable of avoiding those shortcuts. We need to move away from the equilibrium paradigm in order to be able to assess other issues: evolutionary dynamics; heterogeneity of technologies and firm; political and legal determinants of social stability; incentive structures; better modelling technological change, innovation diffusion and economic systems (taking into account finance, debt and insurance); interactions between heterogeneous economic agents (firms and households) and central governments; heterogeneous responses to government incentives; economic dependence from the ecosystem. Furthermore in the future it will become more and more important to anticipate non-‐linear potentially catastrophic impacts from climate change (draught and global warming); threshold climate effects such as poles’ sea-‐ice withdraw, out-‐gassing from melting permafrost, Indian monsoon, oceans acidification, and finally effects on economic well being due to social instability (social conflict, anarchy and mass people movements). Trichet, the former head of ECB, clearly put it: “This doesn't mean we have to abandon DSGE...(but)...atomistic rational agents don't capture behaviour during a crisis...rational expectations theory has brought macroeconomics a long way ... but there is a clear case to re-‐ examine the assumptions” Lack of understanding of systemic impact has driven to short term policies which failed in grasping long term or systemic consequences: -‐ -‐The clearest example of this approach works through the sovereign debt. In some periods some European countries (e.g. Italy) increased expenditure and public debt to cope with short term necessities, without taking into account the long term effect determined by higher interest rates on private investments and consumption through crowding out and fiscal pressure -‐ Another clear example of short-‐termism are the financial policies pursued in south East Asia at the beginning of the 90s. Many countries, such as Thailand, liberalized their financial markets fostering the inflow of investments aimed at sustaining growth. Unfortunately those capitals triggered a real estate bubble which has been at the roots of the 1997-‐1998 crisis -‐ In 2008 the Central Bank of Iceland yielded liquidity loans for saving banks on the verge of default on the basis of newly-‐issued, uncovered bonds, i.e. effectively printing fiat money on demand, causing a significant rise in inflation. To cope with this rise in prices, the Iceland Central Bank had to keep very high interest rates thereby leading to an economic bubble -‐ According to a great number of economists the financial crisis was triggered by US government policies spanning across two administration which were intended to ensure citizens’ right but instead determined an unprecedented high number of risky mortgages, as well as the decline in mortgage underwriting standards that ensued. According to the “Financial Crisis Inquiry Commission Report3” those policies, together with the deregulation of the financial system, might have been catalyzed the crisis. -‐ Other examples can be the bail out of financial institutions: in the short run those actions maintain employment and economic standards, while in the long they induce moral hazard, keep operating inefficient companies and decrease the trust of economic agents in regulation, which is the funding pillar of our economic systemLong-Term ThinkingEnsure long-term thinking_bdaf3e44-3b23-11e2-b375-4576cf2fd5151.5In traditional economics, decisions are utility-‐maximising. Agents rationally evaluate the consequences of their actions, and take the decision that maximize their utility. However, it is well known that this rationalistic view does not fully capture human nature. We tend to overestimate short-‐term impact and underestimate the long term (Saffo). In policy-‐making, short-‐termism is a frequent issue. People are reluctant to accept short-‐term sacrifices for long-‐term benefits. Politicians have elections typically every 5 years, and often their decisions are taken to maximize the impact “before the elections”. There is also the perception that layperson is less sensitive to long term consequences, which are instead better understood by experts. Overall, long-‐term impact is less visible and easier to hide. As a result, decision are taken looking at short-‐term benefits, even though they will bring long term problems. Climate change is a typical policy area where sub-‐optimal decisions were taken because the short-‐ term costs were considered to outweigh the long term consequences. The long term impact was not visible, while the short term sacrifices were.Behavioural ChangeEncourage behavioural change and uptake_bdaf3f66-3b23-11e2-b375-4576cf2fd5151.6Once policies are adopted, a key challenge is to make sure all stakeholders comply to regulations or follow the recommendations. It is well known how the greatest resistance to a policy is not active opposition, but lack of application. For instance, several programmes to reduce alcohol dependency problems in the UK failed as they did excessively rely on positive and negative incentives such as prohibition and taxes, but did not take into account peer-‐pressure and social relationships. They failed to leverage “the power of networks” (Ormerod 2010). For instance, any policy related to reduction of alcohol consumption through prohibitions and taxes is designed to fail as long as it does not take into account social networks, as binge drinkers typically have friends who also have similar problems. In another classical example (Christakis and Fowler 1997), a large scale longitudinal study showed that the chances of a person becoming obese rose by 57 per cent if he or she had a friend who became obese. The identification of social networks and the role of peer pressure in changing behaviour is not considered in traditional policy-‐making tools.Crisis ManagementManage crisis and the “unknown unknown”_bdaf4092-3b23-11e2-b375-4576cf2fd5151.7The job of policy-‐makers is increasingly one of crisis management. There is robust evidence that the world is increasingly interconnected, and unstable (also because of climate change). Crisis are by definition sudden and unpredictable. Dealing with unpredictability is therefore a key requirement of policy-‐making, but the present capacity to deal with crisis is designed for a world where crisis are exceptional, rather than the rule. Donald Rumsfeld, former secretary of state, famously said during the Iraq war that while the US government was capable of dealing with the “known unknown”, the difficulty was the increasing recurrence of “unknown unknown”: those things that we don’t known that we don’t know. There is evidence that the instability and chaotic natures of our world is increasing, because of its increasing connectedness and of long term changes such as climate change. Every year, intense climate phenomena throw our cities in disarray, because of snow, flooding, fires. Each crisis seems to find our decision-‐makers unprepared and unable to deal with it promptly. . As Taleb (2007) puts it, we live in the age of "Extremistan": a world of "tipping points" (Schelling 1969) “cascades” and "power laws" (Barabasi 2003) where extreme events are "the new normal". There are many indications of this extreme instability, not only in negative episodes such as the financial crisis but also in positive development, such as the continuous emergence of new players on the market epitomised by Google. The random vulnerability of today’s world is well illustrated by this chart from the EC DG RESEARCH.ActionMoving from conversations to action_bdaf4222-3b23-11e2-b375-4576cf2fd5151.8The collaborative action of people is able to achieve seemingly unachievable goals: experiences such as ZooGalaxy and Wikipedia show that mass collaboration can help achieve disruptive innovation. Yet too often web-‐based collaboration is confined to complaints and discussions, rather than action. As one blogger put it, paraphrasing Marx: “Philosophers have only interpreted the world: the point is to complain about it”.4 For example, the recent Italian elections saw an explosion of activity in social media discussing about the different candidates. This energy then which then failed to translate into concrete action in the aftermath of the elections. (REF)TransparencyDetect non-compliance and mis-spending through better transparency_bdaf436c-3b23-11e2-b375-4576cf2fd5151.9In times of crisis, it is ever more important for government to ensure that money is well spent and policies are duly implemented. But monitoring is a cost in itself, and a certain margin of mis-‐ spending is somehow “natural”. Yet the cost of this mismanagement is staggering: for instance, in 2010, 7.7% of all Structural Funds money is spent in error or against EU rules.5 OECD estimates place the cost of corruption equals 5% of global GDP.ImpactUnderstand the impact of policies_bdaf44ac-3b23-11e2-b375-4576cf2fd5151.10Measuring the impact of policies remains a challenge. Ideally, policy-‐makers would like to have real-‐ time clear evidence on the direct impact of their choice. Instead, the effects of a policy are oftend delayed in time; the ultimate impact is affected by a multitude of factors in addition to the policy. Timely and robust evaluation remains an unsolvable puzzle. This is particularly true for research and innovation policy, where the results from investment are naturally expected at years of distance. As Kuhlmann (1994) puts it, “the results of evaluations necessarily arrive too late to be incorporated into the policy-‐making process”.Research ChallengesIdentify policy-making research challenges_bdaf4628-3b23-11e2-b375-4576cf2fd5152In this section, we illustrate in detail each research challenge, by describing: -‐ the definition, -‐ the potential opportunities for governance, -‐ the state of the art of market and research, -‐ the existing challenges and -‐ the recommended research themes. The research challenges are organised in 2 groups: the first regroups 6 challenges on Policy Modeling, and the second on Collaborative Governance.Policy Modelling_bdaf477c-3b23-11e2-b375-4576cf2fd5152.1Systems of Atomized ModelsModel systems by using already existing models or composing more comprehensive models by using smaller building blocks_bdaf48d0-3b23-11e2-b375-4576cf2fd5152.1.1This research challenge seeks to find the way to model a system by using already existing models or composing more comprehensive models by using smaller building blocks, sometimes also called “atoms”, either by reusing existing objects/models or by generating/building them from the very beginning. Therefore, the most important issue is the definition/identification of proper (or most apt) modelling standards, procedures and methodologies by using existing ones or by defining new ones. Further to that, the present sub-‐challenge calls for establishing the formal mechanisms by which models might be integrated in order to build bigger models or to simply exchange data and valuable information between the models. Finally, the issue of model interoperability as well as the availability of interoperable modelling environments should be tackled.Collaborative Modelling_bdaf4b46-3b23-11e2-b375-4576cf2fd5152.1.2Collaborative modeling refers to a process where a number of people actively contribute to the creation of a model. The weakest form of involvement is feedback to the session facilitator, similar to the conventional way of modeling. Stronger forms are proposals for changes or (partial) model proposals. In this particular approach the modeling process should be supported by a combination of narrative scenarios, modeling rules, and e-‐Participation tools (all integrated via an ICT e-‐Governance platform): so the policy model for a given domain can be created iteratively using cooperation of several stakeholder groups (decision makers, analysts, companies, civic society, and the general public).AccessEasy Access to Information and Knowledge Creation_bdaf4cb8-3b23-11e2-b375-4576cf2fd5152.1.3Model ValidationSubstantiate that computerised models within their domains of applicability possess satisfactory ranges of accuracy consistent with the intended application of the models_bdaf4e34-3b23-11e2-b375-4576cf2fd5152.1.4Policy makers need and use information stemming from simulations in order to develop more effective policies. As citizens, public administration and other stakeholders are affected by decisions based on these models, the reliability of applied models is crucial. Model validation can be defined as ”substantiation that a computerised model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model” (Schlesinger, 1979). Therefore, a policy model should be developed for a specific purpose (or context) and its validity determined with respect to that purpose (or context). If the purpose of such a model is to answer a variety of questions, the validity of the model needs to be determined with respect to each question. A model is considered valid for a set of experimental conditions if the model’s accuracy is within its acceptable range, which is the amount of accuracy required for the model’s intended purpose. The substantiation that a model is valid is generally considered to be a process and is usually part of the (total) policy model development process (Sargent, 2008). For this purpose, specific and integrated techniques and ICT tools are required to be developed for policy modelling.Immersive SimulationIntegrate visualisation techniques within an integrated simulation environment._bdaf4ff6-3b23-11e2-b375-4576cf2fd5152.1.5As policy models grow in size and complexity, the process of analysing and visualising the resulting large amounts of data becomes an increasingly difficult task. Traditionally, data analysis and visualisation were performed as post-‐processing steps after a simulation had been completed. As simulations increased in size, this task became increasingly difficult, often requiring significant computation, high-‐performance machines, high capacity storage, and high bandwidth networks. Computational steering is an emerging technology that addresses this problem by “closing the loop” and providing a mechanism for integrating modelling, simulation, data analysis and visualisation. This integration allows a researcher to interactively control simulations and perform data analysis while avoiding many of the pitfalls associated with the traditional batch / post processing cycle. This research challenge refers to the issue of the integration of visualisation techniques within an integrated simulation environment. This integration plays a crucial role in making the policy modelling process more extensive and, at the same time, comprehensible. In fact, the real aim of interactive simulation is, on the one hand, to allow model developers to easily manage complex models and their integration with data (e.g. real-‐time data or qualitative data integration) and, on the other hand, to allow the other stakeholders not only to better understand the simulation results, but also to understand the model and, eventually, to be involved in the modelling process. Interactive simulation can dramatically increase the efficiency and effectiveness of the modelling and simulation process, allowing the inclusion and automation of some phases (e.g. output and feedback analysis) that were not managed in a structured way up to this point.Output Analysis and Knowledge SynthesisDeal with the issue of output analysis of a policy model and, at the same time, of feedback analysis in order to incrementally increase and synthesise the knowledge of the system._bdaf517c-3b23-11e2-b375-4576cf2fd5152.1.6Inputs driving a simulation are often random variables. For example, in a simulation of a manufacturing system, the processing times required at a station may have random variations or the arrival times of new tasks may not be known in advance. In a bank, customers arrive at random times and the amount of time spent at the counter is not known beforehand. In financial simulations, future returns are unknown. Because of the randomness in the components driving simulations, the output from a simulation is also random, so statistical techniques must be used to analyse the results. However, output is obviously related with the input, according to the assumption that it is basically the structure of a system to drive its behavior. In particular, the output processes are often non-‐stationary and auto-‐correlated and classical statistical techniques based on independent identically distributed observations are not directly applicable. In addition, by observing a simulation output, it is possible to infer the general structure of a system, so ultimately gaining insights on that system and being able to synthesise knowledge on it. There is also the possibility to review the initial assumptions by observing the outcome and by comparing it to the expected response of a system, i.e. performing a modelling feedback on the initial model. Finally, one of the most important uses of simulation output analysis is the comparison of competing systems or alternative system configurations. Visualisation tools are essentials for the correct execution of this iterative step. The present research challenge deals with the issue of output analysis of a policy model and, at the same time, of feedback analysis in order to incrementally increase and synthesise the knowledge of the system.Data-‐powered Collaborative Governance_bdaf52f8-3b23-11e2-b375-4576cf2fd5152.2Big DataCollect and analyze data at an unprecedented depth and scale_bdaf54ba-3b23-11e2-b375-4576cf2fd5152.2.1Big Data refers to dataset that cannot be stored, captured, managed and analysed by the mean of conventional database software. Thereby Big Data is a subjective rather than a technical definition, because it does not involve a quantitative threshold (e.g. in terms of terabytes), but instead a moving technological one. Keeping that in mind, the definition of Big Data in many sectors ranges from a few terabytes7 to multiple petabytes8. The definition of Big Data does not merely involve the use of very large data sets, but concerns also a computational turn in thought and research9. On the one hand, big data As stated by Latour10 when the tool is changed, also the entire social theory going with it is different. In this view Big Data has emerged a system of knowledge that is already changing the objects of knowledge itself, as it has the capability to inform how we conceive human networks and community. Big Data creates a radical shift in how we think research itself. As argued by Lazer et al.11, not only we are offered the possibility to collect and analyze data at an unprecedented depth and scale, but also there is a change in the processes of research, the constitution of knowledge, the engagement with information and the nature and the categorization of reality.Opinion Mining & Sentiment AnalysisGive policy-makers and citizens effective way to make sense of this mass conversation and interact meaningfully with thousands of others._bdaf564a-3b23-11e2-b375-4576cf2fd5152.2.2The explosion of social media has created unprecedented opportunities for citizens to publicly voice their opinions, but has created serious bottlenecks when it comes to making sense of these opinions. At the same time, the urgency to gain a real-‐time understanding of citizens concerns has grown: because of the viral nature of social media (where attention is very unevenly and fastly distributed) some issues rapidly and unpredictably become important through word-‐of-‐mouth. Policy-‐makers and citizens don’t yet have an effective way to make sense of this mass conversation and interact meaningfully with thousands of others. As a result of this paradox, the public debate in social media is characterized by short-‐termism and auto-‐referentiality. Many experts consider social media as a missed opportunity for better policy debate. At the sameVisual Analytics_bdaf57e4-3b23-11e2-b375-4576cf2fd5152.2.3The
explosion
in
computing
techniques
led
to
the
generation
of
a
tremendous
amount
of
data
which
are
stored
in
the
internet
and
processed
in
the
IT
infrastructures
all
over
the
world.
Some
examples
of
new
technologies
for
data
collections80
are:
web
logs;
RFID;
sensor
networks;
social
networks;
social
data
(due
to
the
Social
data
revolution),
Internet
text
and
documents;
Internet
search
indexing;
call
detail
records;
astronomy,
atmospheric
science,
genomics,
biogeochemical,
biological;
military
surveillance;
medical
records;
photography
archives;
video
archives;
large-‐scale
eCommerce.
In
managing
this
huge
amount
of
data,
when
it
comes
to
human-‐computer
interaction
there
is
a
need
to
distil
the
most
important
information
to
be
presented
it
in
a
humanly
understandable
and
comprehensive
way.
Here
it
comes
visualisation,
which
is
a
way
to
interpret
and
translate
data
from
computer
understandable
formats
to
human
ones
by
employing
graphical
models,
charts,
graphs
and
other
images
that
are
conventional
for
humans81.
In
a
sense
we
can
define
visualisation
as
any
technique
for
creating
images,
diagrams,
or
animations
to
communicate
a
message
or
an
idea.
In
contrast
with
visualisation
traditionally
seen
as
the
output
of
the
analytical
process,
visual
analytics
considers
visualisation
as
a
dynamic
tool
that
aims
at
integrating
the
outstanding
capabilities
of
humans
in
terms
of
visual
information
exploration
and
the
enormous
processing
power
of
computers
to
form
a
powerful
knowledge
discovery
environment.
In
this
view
visual
analytics
is
useful
for
tackling
the
increasing
amount
of
data
available,
and
for
using
in
the
best
way
the
information
contained
in
the
data
itself.
Moreover
visual
analytics
aims
at
present
the
data
in
way
suitable
for
informing
the
policy
making
process.
More
in
particular
the
interdisciplinary
field
of
visual
analytics
aims
at
combining
human
perception
and
computing
power
in
order
to
solve
the
information
overload
problem.
In
Thomas
and
Cook’
definition82,
visual
analytics
is
“the
science
of
analytical
reasoning
supported
by
interactive
visual
interfaces”.
Precisely
visual
analytics
is
an
iterative
process
that
involves
information
gathering,
data
preprocessing,
knowledge
representation,
interaction
and
decision
making.
The
characteristic
of
this
filed
is
that
it
entails
the
association
of
data-‐mining
and
text-‐mining
technologies,
used
for
preprocessing
massive
amounts
of
data,
and
information
visualisation 83 ,
which
is
useful
for
disentangling
important
from
trivial
and
useless
information.
In
a
certain
way
information
visualisation
becomes
a
tool
in
a
semi-‐automated
analytical
process
characterized
by
the
cooperation
between
humans
and
computers,
in
which
is
the
user
who
decides
the
direction
of
the
analysis
relating
to
a
particular
task,
while
the
system
works
as
an
interaction
tool.
It
is
somehow
difficult
to
distinguish
among
information
visualisation,
scientific
visualization84
and
visual
analytics.
In
poor
terms
we
can
say
that
scientific
visualisation
deals
with
data
having
a
natural
geometric
structure,
while
information
visualization
handles
abstract
data
structures
such
as
trees
or
graphs,
and
finally
visual
analytics
deals
properly
with
sense-‐making
and
reasoning.
More
in
particular
information
visualization
is
mostly
applied
to
data
not
belonging
to
scientific
inquiry,
e.g.
graphical
representations
of
data
for
business,
government,
news
and
social
media.
Visualization
work
does
not
necessarily
deal
with
an
analysis
task
nor
does
it
always
use
advanced
data
analysis
algorithms.
On
the
other
hand
visual
analytics
can
be
seen
as
an
integral
approach
to
decision-‐making,
combining
visualization,
human
factors
and
data
analysis.
It
entails
identifying
the
best
algorithm
for
a
given
analysis
task,
to
be
integrated
with
the
best
automated
analysis
algorithms
with
appropriate
visualization
and
interaction
techniques.
Visualization
and
visual
analytics
should
be
considered
in
strict
integration
with
other
research
areas,
such
as
modelling
and
simulation85,
social
network
analysis,
participatory
sensing,
open
linked
data,
visual
computing.
The
disciplines
in
the
domain
of
visualization
and
visual
analytics
are:
Human-‐Computer
Interaction
(HCI),
Usability
Engineering,
Cognitive
and
Perceptual
Science,
Decision
Science,
Information
Visualisation,
Scientific
Visualisation,
Databases,
Data
Mining,
Statistics,
Knowledge
Discovery,
Data
Management
&
Knowledge
Representation,
Presentation,
Production
and
Dissemination,
Statistics,
Interaction,
Geospatial
Analytics,
Graphics
and
Rendering,
Cognition,
Perception,
and
Interaction.
As
far
the
visual
analytics
methodologies
are
concerned,
in
the
CROSSOVER
taxonomy
we
can
identify
the
following:
visualisation
of
a
single,
static,
embedded
data
set;
visualisation
of
multiple
static
data
sets;
visualisation
of
a
single
live
data
feed
or
updating
data
set;
and
finally
visualisation
of
multiple
data
points,
including
live
feeds
or
updates.Serious Gaming for Behavioural Change_bdaf59c4-3b23-11e2-b375-4576cf2fd5152.2.4So
far,
collaborative
ICTs
have
dramatically
augmented
the
capacity
of
people
to
connect
and
collaborate.
Yet,
less
impact
has
been
achieved
in
terms
of
actual
change
and
action,
as
most
collaboration
remain
confined
to
an
elite
of
highly-‐motivated
individuals
and
faces
the
traditional
limits
of
human
attention
and
motivation.
As
illustrated
in
other
challenges,
ICT
can
improve
data
collection
and
analysis,
but
if
attention
and
motivation
are
not
present,
little
impact
can
be
achieved.
This
challenge
deals
with
the
closing
loop
of
collaboration
and
depicts
ICT
solutions
that
enable
behavioural
change
and
action.
Even
when
citizens
and
government
are
fully
aware
of
necessary
policy
choices,
they
might
irrationally
choose
short-‐term
benefits.
Simulation
and
serious
gaming
offer
opportunities
to
impact
on
personal
incentives
to
action
and
showing
long-‐term
and
systemic
effects
of
individual
choices,
thereby
lowering
the
engagement
barrier
to
collaborative
governance
and
augmenting
its
impact.
In
particular,
serious
gaming
have
been
developed
for
educational
purposes
and
raising
awareness
on
particular
issues
while
not
requiring
high
levels
of
engagement.
Simulation
tools
enable
users
to
see
the
systemic
and
long-‐term
impact
of
their
action
in
a
very
concrete
and
tangible
form,
thereby
encouraging
more
responsible
behaviour
and
long-‐term
thinking.
Gaming
engages
users
through
the
“fun”
and
“social”
dimension,
thereby
providing
incentives
towards
action.
Feedback
and
simulation
systems
include
both
individual
and
government
behaviour,
thereby
allowing
policy-‐makers
and
citizens
to
detect
the
impact
of
both
individual
and
policy
choices.
Engagement
of
domain
experts
is
a
crucial
issue
for
building
reliable
games
and
simulation
tools.
Toolkits
and
modules
enable
a
wider
audience
of
stakeholders
to
take
a
direct,
active
role
in
games
development,
thereby
enabling
all
relevant
knowledge
to
be
elicited
and
captured
by
the
simulation
and
gaming
scenarios
and
models.
Pre-‐built
toolkit
enables
the
creation
directly
by
thematic
experts
and
not
by
technology
experts.Open Government Data_bdaf5b5e-3b23-11e2-b375-4576cf2fd5152.2.5The
current
emergence
of
open
data
portals
in
the
government
context
is
opening
great
opportunities
for
collaborative
government,
but
it
is
happening
in
a
scattered
way
leading
to
sub-‐
optimal
data
reuse
and
impact.
Open
data
publication
needs
to
meet
requirements
for
timely
publication
but
at
the
same
time
to
ensure
sufficient
data
quality.
At
a
more
advanced
level,
publication
of
linked
data
requires
significant
effort
and
has
encountered
unequal
success,
but
the
benefits
are
high
in
terms
of
data
interoperability
and
deriving
reuse
and
data
quality.
Linked
data
refers
to
a
set
of
best
practices
for
exposing,
sharing,
and
connecting
structured
pieces
of
data,
information,
and
knowledge
on
the
Web.
Simplifying
and
lowering
costs
of
open
data
publication
is
indeed
a
key
area
of
research.
Curating
tools
(selecting,
aggregating
and
presenting)
and
on-‐the-‐fly
data
quality
agreements
will
reduce
the
cost
and
time
of
data
quality
assurance.
Finer
grained
data
privacy
solutions
will
also
contribute
to
increase
the
amount
of
public
data
being
published,
as
well
as
enable
real-‐time
publication
of
data.Collaborative Governance_bdaf5d02-3b23-11e2-b375-4576cf2fd5152.2.6While
all
challenges
provide
opportunities
for
a
more
effective
large-‐scale
collaboration
in
public
action,
the
relevant
institutional
design
is
far
from
being
introduced.
The
formal
inclusion
of
citizens
input
in
the
policy-‐making
process,
the
deriving
institutional
rules,
the
legitimacy
and
accountability
framework
are
all
issues
that
have
so
far
been
little
explored.
Instant,
open
governance
implies
a
substantial
increase
in
feedback
loops
that
are
of
a
different
scale
with
respect
to
the
present
context.
Any
system
stability
is
affected
by
the
number,
speed
and
intensity
of
feedback
loops,
and
the
institutional
context
has
been
designed
for
less
and
slower
loops.
The
definition
and
design
of
public
sector
role
is
being
directly
affected
by
the
radical
increase
in
bottom-‐up
collaboration,
deriving
from
the
lower
cost
of
self-‐organisation.
There
are
also
important
questions
to
be
answered
–
where
does
the
legitimacy
come
from,
how
to
gain
and
maintain
the
trust
of
users,
how
to
identify
the
users
online.
There
is
also
a
very
important
issue
of
how
to
take
into
the
account
the
diversity
of
the
standpoints,
i.e.
how
to
achieve
a
consensual
answer
to
controversial
social
issues,
especially
when
we
do
not
offer
alternatives
(ready-‐made
options)
but
start
from
an
open
question
and
work
throughout
different
options
proposed
by
participants.
Furthermore,
the
trade-‐off
between
direct
or
representative
model
of
democracy
will
have
to
be
analysed
in
this
context.
It
is
far
from
being
proved
that
the
open
and
collaborative
governance
is
really
inclusive
and
representative
of
all
the
social
groups,
including
the
disadvantaged
and
of
all
standpoints.
There
is
a
visible
risk
that
online
collaboration
increases
the
divide,
rather
than
reduces
it.
The
management
of
institutional
bodies
is
changing:
innovative
ideas
and
insight
coming
from
employees
and
citizens
are
key
resources
to
be
exploited,
and
meritocracy
and
transparency
are entering
an
once
stable
and
conservative
workforce.
Enhanced
collaboration
with
citizens
and
private
third
parties
should
be
accompanied
by
adequate
legal
and
accountability
frameworks,
mapping
incentives
to
participation
and
enabling
business
models
for
different
stakeholders.
The
privacy
paradigm
is
changing
and
appropriate,
more
dynamic
frameworks
have
to
be
designed,
taking
into
account
the
willingness
of
citizens
to
share
information
and
at
the
same
time
ensuring
their
full
awareness
of
the
implications
and
their
control
over
the
data
usage.Participatory Sensing_bdaf5eec-3b23-11e2-b375-4576cf2fd5152.2.7Participatory
sensing
refers
to
the
usage
of
sensors,
usually
embedded
in
personal
devices
such
as
smartphones
to
allow
citizens
to
feed
data
of
public
interest.
This
could
include
anything
from
photos
to
passive
monitoring
of
movement
in
the
traffic.
Participatory
sensing
involves
higher
commitment
from
citizens
in
contrary
to
opportunistic
sensing,
where
user
may
not
be
aware
of
active
applications.
The
diffusion
of
mobile
phones
significantly
lowers
the
barriers
of
participation
and
data
input
by
citizens,
with
automated
geo-‐tagging
and
time-‐stamping:
given
the
right
architecture,
they
could
act
as
sensor
nodes
and
location-‐aware
data
collection
instruments.
While
traditional
sensor
nodes
are
centralised,
these
sensors
are
under
the
owners’
control.
This
would
give
way
to
data
availability
at
an
unprecedented
scale.Identity Management_bdaf6090-3b23-11e2-b375-4576cf2fd5152.2.8Digital
identity
management
has
long
been
a
policy
priority
in
the
EU
Member
States,
and
large-‐scale
investments
have
been
deployed.
In
the
context
of
collaborative
governance,
digital
identity
constitutes
a
fundamental
pillar
of
trustworthy
cooperation.
Identity
management
systems
include
control
and
management
of
credentials
used
to
authenticate
one
entity
to
another,
and
authorise
an
entity
to
adopt
a
specific
role
or
perform
a
specific
task.
Global
in
nature,
they
should
support
non-‐
repudiation
mechanisms
and
policies;
dynamic
management
of
identities,
roles,
and
permissions;
privacy
protection
mechanisms
and
revocation
of
permissions,
roles,
and
identity
credentials.
Furthermore,
all
the
identities
and
associated
assertions
and
credentials
must
be
machine
processable
and
human
understandable.
At
the
EU
level,
the
goal
is
to
provide
an
interoperable
privacy
protecting
infrastructure
for
eID
that
is
federated
across
countries,
with
multiple
levels
of
security
for
different
services,
relying
on
authentic
sources,
and
usable
in
a
private
sector
context.
Alongside
this,
a
flexible,
context-‐dependent
and
interoperable
identity
management
system
is
required
for
large-‐scale
deployment.
In
particular,
federated
identity
management
systems
that
ensure
flexible
deployment
and
seamless
integration
of
users’
preferred
identities,
including
commercial
(such
as
Facebook
connect)
and
open
source
solutions
(such
as
OpenID)
are
needed.
Particular
focus
should
be
put
on
usable
delegation
of
privileges,
which
is
very
important
for
workflows
and
integrating
services.
Electronic
identity
management
should
identify
non-‐humans
(devices,
sensors)
as
well
as
humans,
in
order
to
ensure
validated
identity
in
the
context
of
participatory
sensing
and
the
Internet
of
Things.
At
the
same
time,
eIdentity
management
should
take
into
account
the
risks
of
information
centralization
in
terms
of
data
privacy
and
security.
Cost-‐benefit
considerations
of
centralised
versus
federated
systems
remains
a
key
issue.
Identity
federation
can
be
accomplished
in
any
number
of
ways,
some
of
which
involve
the
use
of
Internet
standards,
such
as
the
OASIS
Security
Assertion
Markup
Language
(SAML)
specifications,
with
the
use
of
open
source
technologies
and/or
other
openly
published
specifications.2012-06-292012-11-30https://dl.dropbox.com/u/5690004/D2.2.1 International Research Roadmap on ICT Tools for Governance and Policy Modelling-1.pdfOwenAmburOwen.Ambur@verizon.net