<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.0 20120330//EN" "JATS-archivearticle1.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Orchestrating Global Systems Science and Information Technologies for Policy Modelling: The SYMPHONY Approach</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Efthimios Bothos</string-name>
          <xref ref-type="aff" rid="aff6">6</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Niki Nikolakakou</string-name>
          <email>nikolaka@mail.ntua.gr</email>
          <xref ref-type="aff" rid="aff6">6</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Gregoris Metnzas</string-name>
          <xref ref-type="aff" rid="aff6">6</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Marko Raberto</string-name>
          <email>marco.raberto@unige.it</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>An- drea Teglio</string-name>
          <email>teglio@uji.es</email>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Silvano Cincotti</string-name>
          <email>silvano.cincotti@unige.it</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Franziska Schütze</string-name>
          <email>franziska.schuetze@globalclimateforum.org</email>
          <xref ref-type="aff" rid="aff4">4</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Hendrik Zimmermann</string-name>
          <email>zimmermann@germanwatch.org</email>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Marko Grobelnik</string-name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Anna Triantafillou</string-name>
          <email>a.triantafillou@atc.gr</email>
          <xref ref-type="aff" rid="aff5">5</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>DIME-CINEF, Università di Genova</institution>
          ,
          <country country="IT">Italy</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Department of Economics, Universitat Jaume I</institution>
          ,
          <addr-line>Castellón</addr-line>
          ,
          <country country="ES">Spain</country>
        </aff>
        <aff id="aff2">
          <label>2</label>
          <institution>Department of Knowledge Technologies Jozef Stefan Institute</institution>
          ,
          <addr-line>Ljubljana</addr-line>
          ,
          <country country="SI">Slovenia</country>
        </aff>
        <aff id="aff3">
          <label>3</label>
          <institution>Germanwatch e.V.</institution>
          ,
          <addr-line>Berlin</addr-line>
          ,
          <country country="DE">Germany</country>
        </aff>
        <aff id="aff4">
          <label>4</label>
          <institution>Global Climate Forum e.V.</institution>
          ,
          <addr-line>Berlin</addr-line>
          ,
          <country country="DE">Germany</country>
        </aff>
        <aff id="aff5">
          <label>5</label>
          <institution>Innovation Lab, Athens Technology Center</institution>
          ,
          <addr-line>Athens</addr-line>
          ,
          <country country="GR">Greece</country>
        </aff>
        <aff id="aff6">
          <label>6</label>
          <institution>National Technical University of Athens</institution>
          ,
          <country country="GR">Greece</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>In our globalized world public policy making and society at large face challenges like climate change and financial crises that are global, shared worldwide and tightly connected with policies across different sectors. Solutions for addressing such highly interconnected challenges in a 'system of systems' world, tend to address only subsystems and so fail to achieve systemic change and anticipate impact and unintended consequences of public action. Pursuing the necessity of informing the policy decision process and proactively sensing possible problems concerning global matters we are proposing a novel computational platform called SYMPHONY that offers a solution for designing and testing policies and regulatory measures. Our aim is to offer policy modellers and policy makers tools that will support them to make decisions which will prevent and mitigate economic and financial crises as well as foster an economically and ecologically sustainable growth path.</p>
      </abstract>
      <kwd-group>
        <kwd />
        <kwd>Policy Modelling</kwd>
        <kwd>Agent Based Simulation</kwd>
        <kwd>Social Media Mining</kwd>
        <kwd>Information Markets</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1 Introduction</title>
      <p>Over the last years, a great concern of the global political agenda has been to find
ways to overcome the crisis and therefore look for effective policy instruments and
even for new theoretical economic frameworks. Responses to this concern still miss
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
adequate tools for exploring and governing the complex global dynamics of our
economic and social system. In this paper we describe a computational framework called
SYMPHONY that aims to offer solution for designing and testing policies and
regulatory measures with respect to preventing and mitigating economic and financial crises
and fostering an economically and ecologically sustainable growth path.</p>
      <p>The main component of SYMPHONY is an agent-based macroeconomic artificial
economy, accessible through an on-line interface that allows policy makers and policy
modellers to set up economic scenarios and run computational experiments. In
addition, citizens can use the agent-based artificial economy by playing the role of a
particular economic agent through a gamified environment. Citizens’ participation into
the SYMPHONY platform aims at raising awareness about the economic process and
improving the efficacy and the transparency of the decision making process.</p>
      <p>SYMPHONY includes social media mining tools able to collect and analyse
relevant information and human sentiments from the web, including social networks,
blogs and news streams. These tools allow policy makers and experts to explore the
evolution of people’s beliefs regarding the real world economy. Moreover it
incorporates information markets able to collect feedback on specific economic issues
through the design of information contracts that aggregate participants’ preferences
and expectations. The proposed platform increases the transparency of the policy
making process, enhances citizens engagement, improves the credibility and
effectiveness of policy institutions and provides insights on the interplay between policy
making and expectation formation by citizens.</p>
      <p>The paper is structured as follows. Section 2 presents the policy making problems
which we aim to support with the SYMHPONY platform. Section 3 describes the
overall architecture of the platform and provides the details of each of the
SYMPHONY components. Section 4 shows how the SYMPHONY platform can be
used through a set of use case scenarios. We conclude in Section 5 with our final
remarks and our plans for future work.</p>
    </sec>
    <sec id="sec-2">
      <title>2 The Problem Space</title>
      <p>In our globalized world, public policy making and society at large face challenges
like the climate change and financial crises that are global, shared worldwide and
tightly connected with policies across different sectors. An issue of major concern is
that solutions for addressing such highly interconnected challenges in a ‘system of
systems’ world, tend to address only subsystems and so fail to achieve systemic
change and anticipate impact and unintended consequences of public action.</p>
      <p>
        Global Systems Science (GSS) is about providing solutions to global problems of
interconnected systems in order to support policy making and decision making [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ].
This involves looking at the whole of our planet and its societies as well as providing
a transdisciplinary and transformative perspective that connects all kinds of scientific
knowledge, and engaging as many people as possible in collective actions. Global
Systems Science studies systems like the financial system, the climate system, the
global city system, and more, develops evidence, concepts and doubts concerning
such systems, helps practitioners dealing with them to reflect on their experiences and
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
to assess possible consequences of their actions, and combines advanced computing
technologies with conversations bridging the gap between science and society.
Examples of global systems include the global financial markets, the worldwide factors
affecting climate change and the energy industry. A major problem in global systems
refers to how they can self-stabilize when shocks occur despite their distributed
control. SYMPHONY deals with two challenges faced by societies in the global system,
namely financial stability and sustainability transition.
      </p>
      <sec id="sec-2-1">
        <title>2.2 The Challenge of Financial Stability</title>
        <p>In the last thirty years, most of the advanced and developing economies have
undertaken a profound transformation of their financial sector; major changes can be
identified in the deregulation of financial markets, the liberalization of capital
transfers, and in the privatization of the banking system. An important consequence of this
process has been the financialization of the economy, namely, the increasing
relevance that the financial sector has assumed with respect to the real one. Although the
process of deregulation and privatization has not been limited only to the financial
sections of the economy, in the case of the financial sector we see the most important
and far-reaching changes. This is because of the importance of finance in modern
services-oriented economies and the digital communication revolution, benefitted
from activities characterized by immaterial assets and centralized exchange.</p>
        <p>
          The financial market liberalization, the privatization of banking systems and of
state-owned manufacturing and services companies, as well as of some forms of
social security provisions, such as pensions, have determined the so-called
financialization of the economy [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ]. The increasing relevance of financial markets, actors and
institutions in the operation and in the governance of the economy during the last
thirty years can be observed in many ways. Financial trading activities have increased
exponentially and have been characterized by the emergence of new actors like hedge
funds and private equity firms. The privatization of public companies has augmented
the supply of stock shares on one side, while the privatization of retirement financing
has created new demand for financial assets on the other side.
        </p>
        <p>
          Financing in capital markets has been made easier by financial innovation and
securitization, which created new financial instruments such as collateralized debt
obligations and mortgage backed securities, where households’ and companies’ loans
have been transformed into tradable assets. Financial innovation has allowed an
increase of the debt to GDP ratio of the private sector on one side, in particular for
households and the financial sector, and has fuelled bubbles in the financial and real
estate market on the other side. Both have been self-reinforcing during the boom
period, given that inflated financial and real estate assets have been used as collateral
for debt, thus fuelling again household’s consumption and debt. The contribution to
GDP of the finance, insurance and real estate (FIRE) sector has increased from nearly
15% to more than 20% in the last thirty years in US [
          <xref ref-type="bibr" rid="ref3">3</xref>
          ].
        </p>
        <p>
          The rising of household debt-income ratios and corporate debt-equity ratios has
made the economy increasingly financially fragile and potentially unstable. In fact,
these debt growth dynamics are unsustainable in the long run and the economy may
become vulnerable to debt-deflation and prolonged recessions [
          <xref ref-type="bibr" rid="ref4">4</xref>
          ]. Internationally,
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
financial fragility has become already evident in the 1990s with the Mexico (1994),
the Asian (1997/1998) and the Russian (1998) crisis, which demonstrated the degree
to which a too rapid market liberalization can lead to a currency crisis, in which a
sudden reversal of capital flows is followed by financial instability and a consequent
sharp decline in economic activity. The crisis of 2007-2009 is a historical and
economic event of major relevance which is causing a critical discussion about the
cultural and theoretical underpinnings of the deregulation and liberalization process both
in the political and in the academic domain.
        </p>
        <p>In this complex environment policy makers require support to monitor citizens and
experts expectations on macroeconomic variables which vary depending on the
policies they implement as well as tools to simulate and test different economic policy
scenarios.
2.3</p>
      </sec>
      <sec id="sec-2-2">
        <title>The Challenge of Sustainability Transition</title>
        <p>In order to reach the emission’s reduction level necessary for staying below the
politically agreed limit of 2°C temperature increase, great efforts at international level
are needed. International climate negotiations show that it is becoming more difficult
to reach agreements because industrialized countries are reluctant to increase their
commitment due to the fact that they fear negative impacts on economic growth.
Additionally, developing countries want to catch up in terms of economic
development and standards of living and see strong emission targets as a threat to their
development. Only if industrialized nations can demonstrate that a different growth model
based on a minimal amount of emissions, waste and use of resources is possible, will
developing and emerging economies be willing and able to leapfrog the industrial
growth model. Instead, industrialized nations have been struggling with the financial
crisis and the economic difficulties it triggered. Finding a way out of economic
recession has become a top priority. However, it is questionable that there will be an
economic recovery under business as usual if the financial crisis is tackled in isolation.</p>
        <p>The current discussion around climate policy is centred around finding
international agreements on emission reductions. For Europe this means setting a target at EU
level which is in line with international agreements. However, even in the absence of
an international agreement, the EU targets will not lose their relevance and will be an
important guidepost for investors, producers and consumers alike. It is important to
note that this discussion is very much focused on the overall economic costs (mainly
in terms of GDP) of such a policy and how these costs can be shared among the
member states. The economic opportunities are often left aside/understudied.</p>
        <p>The most important targets currently in effect are the 2020 targets. These targets
were set in 2007 and enacted in 2009 through the "climate and energy package''.
Today they are widely known as the "20-20-20'' targets, which stands for: 20% reduction
in EU Green House Gas (GHG) emissions, a 20% share of renewable energy in gross
final energy consumption and 20% reduction in total primary energy consumption of
the EU (all 2020 levels compared to 1990). Following this, in 2011, the European
Commission defined the long term GHG emission reduction target for 2050, which
would be in line with the EU's contribution to the global political goal of staying
below a 2 °C temperature increase. The target is 80%-95% below 1990 levels. In
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
2013 the European Commission started a discussion process around the intermediate
goals for 2030 by publishing a proposal called the ‘Green paper: A 2030 framework
for climate and energy policy 2030’1. The results reduction targets for 2030: 40%
GHG emission reductions, 27% share of renewable energy, no energy efficiency
target.</p>
        <p>In order to define policies that will allow Europe to reach the defined emissions’
reduction targets, policy makers need support to analyse and assess climate change
mitigation policies and verify their potential to trigger a sustainability transition.</p>
      </sec>
      <sec id="sec-2-3">
        <title>3 The SYMPHONY Solution</title>
        <p>SYMPHONY is an integrated set of innovative tools for supporting policy making
as shown in Figure 1. The main engine is an agent-based macroeconomic artificial
economy (or agent based model) that is accessible through a web interface. It is
possible to connect to the agent-based economy in order to run simulations or to
participate by taking the role of one specific economic agent. Expert users, including policy
makers, are able to set up different economic scenarios and to run simulations using
the web interface. A complete set of visual and quantitative tools for the analysis of
the real time outcomes of the simulations are available to the user. Citizens are also
able to participate in the agent-based artificial economy through a game-like interface
by playing the role of a particular economic agent. They take decisions, according to
their role, and observe the consequences of their decisions. A player in the role of a
commercial bank can, for instance, may modify the rules for granting credit to firms
or households and observe the effects of her/his decision on the internal variables of
the bank (bank’s balance sheet for example) and on the overall economy. Citizens’
participation in the artificial economy aims at raising awareness on the economic
processes and at improving the efficacy and the transparency of the policy decision
making process.</p>
        <p>Besides the agent-based model, the SYMPHONY solution includes social media
mining tools and techniques able to collect and analyse relevant information and
human sentiments from the web, including social networks, blogs, news stream, etc.
Policy makers and experts use these tools in order to explore the evolution of people’s
beliefs about the real world economy. In particular, the tools are used to set up real
time measures of citizen’s expectations on economic indicators. The platform
incorporates a second tool for information gathering. Users can create information markets
in order to collect feedback on specific economic issues. It is possible to design
information contracts that are traded in the markets, disclosing valuable information
about participants’ preferences and expectations. By combining these information
extracting tools, policy makers and experts acquire a deeper vision of sentiments, trust
and expectations which are prevailing among the economic agents.</p>
        <p>The SYMPHONY solution, increases the transparency of the policy making
process, enhances citizens’ engagement, and improves the credibility and effectiveness of
policy institutions.
1 http://ec.europa.eu/clima/policies/2030/index_en.htm
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.</p>
      </sec>
      <sec id="sec-2-4">
        <title>3.1 Agent Based Modelling</title>
        <p>
          Agent based models (ABMs) are computerized simulations of a number of
decision makers (agents) and institutions, which interact through prescribed rules [
          <xref ref-type="bibr" rid="ref5">5</xref>
          ]. The
agents may represent various entities and institutions including consumers,
policymakers, banks and governments and act according to rules specifying their behaviour
while considering their current situation and the state of the artificial world where
they live. ABMs do not rely on the assumption that the economy will move towards a
predetermined equilibrium state and can handle a wider range of nonlinear behaviour
than conventional equilibrium models. With the use of ABMs policy-makers can
simulate different policy scenarios and quantitatively explore their consequences.
        </p>
        <p>
          The SYMPHONY ABM is based on the EURACE macro agent-based model [
          <xref ref-type="bibr" rid="ref6">6</xref>
          ].
The ABM represents a fully integrated macro-economy consisting of the real sector,
the credit sector, the financial sector, the public sector, the foreign sector, the real
estate sector and the environment in order to introduce sustainability aspects. The real
sector represents the production of consumption and capital goods with labour, the
capital goods and energy as factors of production and relative markets, and the
technological innovation whereas the credit sector represents the financing production
plans of firms. The financial sector consists of the exchange of claims on the equity
capital of producers as well as of governments’ liabilities, whereas the public sector
models the policy making, i.e., the fiscal policy made by Governments and the
monetary policy set by the Central Bank. Finally the foreign sector introduces the
possibility of exchanges between different countries and thus the application of different
economic policies.
        </p>
        <p>The interface to the ABM is gamified in the sense that even non-experts (including
citizens) can use it through an intuitive (see Figure 2 for an indicative view). Users
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
assume a role (e.g. the central bank) and modify related parameters (e.g. the interest
rates set by the central bank) that affect the evolution of the simulated economy.</p>
        <p>Fig. 2: The SYMPHONY game interface for the role “Central Bank”. Users can
set variables such as the interest rates and the amount of fiat money, affecting the
evolution of the simulated economy.
3.2</p>
      </sec>
      <sec id="sec-2-5">
        <title>Nowcasting expectations with Social Media Mining.</title>
        <p>
          Nowcasting in economics is a process of measuring the state of the economy in a real
time [
          <xref ref-type="bibr" rid="ref7">7</xref>
          ]. For instance, Giannone et al., [
          <xref ref-type="bibr" rid="ref8">8</xref>
          ] show how the process of nowcasting can
be formalized with a model describing the direction of change in GDP before official
figures for GDP are published. Recently, new approaches merging social media
mining with nowcasting have been developed. Social media is a source of information,
opinions, ideas or any kind of other expression of individuals or organizations which
arose with the development of the web and enable instant and open global
communication. There is a long list of social media applications that includes Twitter,
YouTube, Instagram, Facebook, Snapchat. These applications are intensively
worldwide, for instance more than 350,000 tweets are send every minute [
          <xref ref-type="bibr" rid="ref9">9</xref>
          ]. Approaches
for nowcasting with social media utilize vast data streams to assess standard
economic measures in shorter periods of time. Several approaches are based on sentiment
mining of social media streams and assume that sentiment values obtained from social
media streams can be correlated with financial data streams ([
          <xref ref-type="bibr" rid="ref10">10</xref>
          ], [
          <xref ref-type="bibr" rid="ref11">11</xref>
          ]).
        </p>
        <p>In SYMPHONY we perform social media mining in interconnection with mining
electronic news media. We perform analysis across-languages and consider different
aspects of the data including text, sentiment, geographical spread and diffusion
dynamics. We do this to obtain a rich set of features which are used for correlations and
in the final phase for predicting current actual values of macroeconomic indicators,
including the sensibility of citizens to environmental problems and their willingness
to change job.</p>
        <p>Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.</p>
      </sec>
      <sec id="sec-2-6">
        <title>3.3 Information Markets</title>
        <p>Pursuing the necessity of informing the policy decision process and proactively
sensing possible problems concerning global matters, in SYPMHONY we use the
`market´ as an institution which efficiently aggregates diverse information using the
price mechanism and the Web as the medium where ‘Information Markets’ (IMs) can
be created and run.</p>
        <p>
          IMs are considered an example of collective intelligence because of their capability
to aggregate and nowcast information that arrives with a lag by making use of
specifically designed contracts that yield payments based on the outcome of uncertain future
events [
          <xref ref-type="bibr" rid="ref12">12</xref>
          ]. Contract prices provide a reasonable estimate of what the traders in
aggregate believe to be the probability of the aforementioned events, and as such,
markets are able to generate forecasts. ΙMs are characterized by their accuracy, easy
deployment, and ability to dynamically incorporate new information available to traders
by continuously adjusting an event’s price and hence its probability conditioned to the
new market information.
        </p>
        <p>Fig. 3: View of the PolicyOracle trading interface. Users can buy or sell shares of
contracts that represent different answers to questions related to trends of policy
indices. Moreover they can provide comments and opinions in a discussion forum.</p>
        <p>In SYMPHONY we propose a new web-application called PolicyOracle for
making predictions with the benefit of the “wisdom of crowd” effect. PolicyOracle is an
ΙΜ for collecting, aggregating and interpreting stakeholders’ and citizens’ opinions,
expectations and preferences in order to improve public decision-making.
PolicyOracle’s primary focus is on sustainability transition issues by exploring economic and
financial policies. Our platform supports decisions on policy matters contingent on
the status of key policy variables e.g., “Price of carbon needed to reach 30% GHG
emissions reduction by 2030”. Relevant policy decisions could be private or public
whereas markets provide information related to a variety of public policy matters such
as costs, benefits, net benefits of policy options or the likelihood of certain events
depending on the choices of policy makers. The current version of the platform,
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
among others, includes functionalities that allow trading of virtual contracts using an
implementation of an automated market maker and discussions on the policy indices
that are presented as contracts to participants (see Figure 3).</p>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>4 Indicative Use Case Scenarios</title>
      <p>In this section we provide two indicative scenarios which show how the SYPHONY
solution can be used in order to support policy decisions.</p>
      <sec id="sec-3-1">
        <title>4.1 The Voice of the Crowd: Sensing the Views of the TechnoSociety</title>
        <p>Policy makers and policy modellers can use the SYMPHONY social media mining
and information markets services to monitor expectations on key policy variables in
near real time. These expectations reflect the beliefs of citizens and stakeholders
about the real world economy.</p>
        <p>Consider that Mr Smith is an executive at the European Central Bank. In order to
make financial policy related decisions he has to observe expectations on issues of the
real world economy. In our example he wants to have access to near real time
expectations on unemployment rates. Mr. Smith opens his web-browser and enters the
SYMPHONY platform url. SYMPHONY is offered as a software-as-a-service and
can be accessed by all stakeholders who wish to use the toolset. Administrators for the
SYMPHONY platform have been appointed by the Central Bank and have already
setup the proper access rights. Consider also Kristian, a sales assistant in Hamburg
that he finds it hard to find a job. He tweets “I will never find a job … #unemployed”.
The SYMPHONY social media mining platform processes information shared in
social media by Kristian and other people from all around Europe in order to infer
citizens’ expectations on job finding. The platform analyses the text and sentiment of
the social media information and derives related beliefs and expectations. By
selecting different keywords, Mr. Smith can view the trends related to other key variables
of the economy with respect to time (how the beliefs of social media users vary in
time), including employment rates, inflation rates, interest rates and expected growth
rates. By selecting different social media mining features (sentiment, frequency etc.)
the policy maker can visualize social media data in different perspectives.</p>
        <p>Mr Smith wants to get the expectations of expert stakeholders on the key variables
of the economy he is interested in. He creates a new information market and places
some questions on which he would like to have the opinion of other stakeholders
including the European GDP in the next quarter, the unemployment rates and the debt
level of European countries. Once the information market is ready, he invites
participants inlcuding co-workers and executives from other organizations.
Participants login and buy or sell information contracts according to their
expectations. The contract prices gradually represent the current expectations of
participants on the questions set in the market. Mr Smith regularly visits the
information market and acquires an overview of the expectations of the participants.
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.</p>
        <p>With SYMPHONY Mr Smith can observe the expectations of citizens and experts
and make informed decisions without having to wait for statistical reports that arrive
with a lag.</p>
      </sec>
      <sec id="sec-3-2">
        <title>4.2 Let’s Play Together: Citizens and Policymakers Collaboratively Explore</title>
      </sec>
      <sec id="sec-3-3">
        <title>Policies with the SYMPHONY Gamification environment</title>
        <p>The SYPHONY gamification environment provides dynamic data-driven games
that lets policy makers and citizens, interested in economics and monetary policies,
come together to interact with SYMPHONY’s agent-based artificial economic world.
Players’ decisions impact the in-game virtual economy, and each player is made to
feel the repercussions of financial decisions made by everyone taking part.</p>
        <p>Within the game there are five roles players can take charge of: household, firm
(CGP - Consumer goods producers), commercial bank, central bank, government,
Retail sellers (malls) and Investment goods producers (IGP). Each role requires the
player to work to a set of objectives: for household players it consists of being able to
survive on a budget, firms to produce a successful business, the central bank to keep
interest rates down and the government to ensure the correct policies are enacted.
Decisions made by government and central bank players can have dramatic effects on
households and firms, vice-versa if households and firms aren’t making enough
output they can affect central bank and government players. Each role has certain effects
on each other, allowing players to feel connected to each other within the game
environment. A chat room function is available to allow players to communicate with
each other and provide a space where players can share game strategies, help each
other out and share economic tips about playing the game. The SYMPHONY
gamification provides a different experience every time the game is played based on the
underlying ABM simulation. A general game scenario runs over a course of a week.
Policy makers/stakeholders are able to set the scenario conditions and invite users to
join. Potential players can sign up to a game scenario by visiting the game website
and are informed when the scenario begins through email. Consider the following set
of players.</p>
        <p>Mr Linn is an executive at the European Central Bank, responsible for monetary
policy issues. He has been recently informed about the SYMPHONY game and
decides to participate. He creates an account and selects the role of central bank from
the available options. He can now control a set of variables and make actions
associated with the role of central bank, including settings of the monetary policy (e.g.
interest rates in %), capital requirements for banks (the maximum ratio between total
weighted assets of a bank and its equity capital) and make decisions to perform
unconventional policies (as quantitative easing by buying government bonds), thus
affecting the course of the game.</p>
        <p>Marta, a dentist in Ljubljana. She saw the SYMPHONY game being mentioned in
a post in Facebook and has decided to participate. She creates an account and selects
the role of household from the available options. The options she can set and are
associated with the household role include: how much to consume, how much to save,
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
how much to invest in the financial market, which assets (firms’ stocks and
government bonds) to buy and which to sell. Another player</p>
        <p>Mark is a member of the parliament in Germany. He has been informed of the
SYMPHONY game from an email newsletter and decides to join. From the available
roles he selects the one of a Prime Minister which allows him to take fiscal policy
decisions. Indicative examples include: setting tax rates on corporate profits,
household labour and capital income, setting public expenditure, e.g.: unemployment
benefits, household’s transfers, and public wages and issuing government bonds to finance
public deficit.</p>
        <p>Giancarlo owns a small business in Italy. A local newspaper had an article on the
SYMPHONY game which sounded interesting. He accessed the provided url and
decided to join. He selects the role of firm manager. Among the available actions of
his role in the game are: how much to produce, how many people to hire and how
much to invest.</p>
        <p>The games starts and users are notified with an email that they should make their
actions. Mr Linn sets the interest rates at 2%, Marta decides to buy more goods and
invest in stocks part of the income she receives within the game, Mark allocates more
money to public expenditure for unemployment, Giancarlo decides to hire more
employees in his firm and request a loan that is evaluated positively from Pedro’s bank.
The economy in the game evolves and players understand how their decisions affect
the system.</p>
      </sec>
    </sec>
    <sec id="sec-4">
      <title>5 Conclusions</title>
      <p>The SYMPHONY solution aims to be a holistic platform for modelling policies.
Policy modellers and policy makers using the SYMPHONY macro-economic engine
will have the ability to test concepts, directions, thoughts and drafts as soon as
possible and at the minimum cost, with the use of artificial economies whereas citizens and
stakeholders will participate through the a gamification layer and understand the inner
workings of the economic system. Policy makers will be able to run “in vitro” trial
runs of a policy concept and obtain not only reactions and overall sentiment, but
actual input and feedback both in terms of anticipated impact, scrutiny and even new ideas
and expressed suggested alternatives. Using the SYMHPONY social media mining
and information market services, policy modellers and policy makers will be able to
monitor expectations on key economic variables in near real time for more informed
decisions.</p>
      <p>The SYMPHONY platform will be evaluated by supporting real life global
problems in cooperation with organizations such as the Global Climate Forum (GCF),
Germanwatch (GW) and the Bank of England.</p>
      <p>Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes.
Work reported in this paper is partially funded by the European Commission project
SYMPHONY (FP7 grant agreement no.: 611875) under the objective “ICT for
Governance and Policy Modelling”.</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          1.
          <string-name>
            <surname>Jaeger</surname>
            <given-names>C.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Jansson</surname>
            <given-names>P</given-names>
          </string-name>
          ., van der Leeuw S.,
          <string-name>
            <surname>Resch</surname>
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Tàbara</surname>
            <given-names>J. D. GSS</given-names>
          </string-name>
          :
          <article-title>Towards a Research Program for Global Systems Science</article-title>
          , Second Open Global Systems Science Conference, Brussels (
          <year>2013</year>
          )
          <fpage>10</fpage>
          -
          <lpage>12</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          2.
          <string-name>
            <surname>Epstein</surname>
            ,
            <given-names>G.</given-names>
          </string-name>
          <article-title>Financialization and the World Economy</article-title>
          , Edward
          <string-name>
            <surname>Elgar</surname>
          </string-name>
          (
          <year>2005</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          3.
          <string-name>
            <surname>Palley</surname>
            ,
            <given-names>T.I..</given-names>
          </string-name>
          <article-title>Financialization: What It Is</article-title>
          and Why It Matters, Working Paper n.
          <volume>525</volume>
          , The Levy Economics Institute of Bard College (
          <year>2007</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          4.
          <string-name>
            <surname>Minsky</surname>
            ,
            <given-names>H.</given-names>
          </string-name>
          <article-title>Stabilizing an Unstable Economy</article-title>
          . Yale University Press (
          <year>1986</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          5.
          <string-name>
            <surname>Farmer</surname>
            ,
            <given-names>J. D.</given-names>
          </string-name>
          , &amp;
          <string-name>
            <surname>Foley</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          <article-title>The economy needs agent-based modelling</article-title>
          .
          <source>Nature</source>
          ,
          <volume>460</volume>
          (
          <issue>7256</issue>
          ) (
          <year>2009</year>
          )
          <fpage>685</fpage>
          -
          <lpage>686</lpage>
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          6.
          <string-name>
            <surname>Cincotti</surname>
            ,
            <given-names>S.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Raberto</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Teglio</surname>
            ,
            <given-names>A.</given-names>
          </string-name>
          <article-title>The EURACE macroeconomic model and simulator</article-title>
          . In: M.
          <string-name>
            <surname>Aoki</surname>
            ,
            <given-names>K.</given-names>
          </string-name>
          <string-name>
            <surname>Binmore</surname>
            ,
            <given-names>S.</given-names>
          </string-name>
          <string-name>
            <surname>Deakin</surname>
            ,
            <given-names>H.</given-names>
          </string-name>
          <string-name>
            <surname>Gintis</surname>
          </string-name>
          . Complexity and Institutions: Markets, Norms and Corporations, Palgrave
          <string-name>
            <surname>Macmillan</surname>
          </string-name>
          (
          <year>2012</year>
          )
          <fpage>81</fpage>
          -
          <lpage>106</lpage>
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          7.
          <string-name>
            <surname>Lansdall-Welfare</surname>
            ,
            <given-names>T.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Lampos</surname>
            ,
            <given-names>V.</given-names>
          </string-name>
          , &amp;
          <string-name>
            <surname>Cristianini</surname>
            ,
            <given-names>N.</given-names>
          </string-name>
          <article-title>Nowcasting the mood of the nation</article-title>
          .
          <source>Significance</source>
          , Vol.
          <volume>9</volume>
          (
          <issue>4</issue>
          ) (
          <year>2012</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          8.
          <string-name>
            <surname>Giannone</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Reichlin</surname>
            ,
            <given-names>L.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Small</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          <article-title>Nowcasting: The real-time informational content of macroeconomic data</article-title>
          .
          <source>Journal of Monetary Economics, Elsevier</source>
          , Vol.
          <volume>55</volume>
          (
          <issue>4</issue>
          ), (
          <year>2008</year>
          )
          <fpage>665</fpage>
          -
          <lpage>676</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          9.
          <string-name>
            <surname>Weil</surname>
            ,
            <given-names>K.</given-names>
          </string-name>
          "
          <string-name>
            <surname>Measuring</surname>
            <given-names>Tweets</given-names>
          </string-name>
          ,
          <article-title>"</article-title>
          <source>Twitter Official Blog, 22 February</source>
          <year>2010</year>
          . [Online]. Available: https://blog.twitter.com/2010/measuring-tweets.
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          10.
          <string-name>
            <surname>Bollen</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Mao</surname>
            ,
            <given-names>H.</given-names>
          </string-name>
          , &amp;
          <string-name>
            <surname>Zeng</surname>
            ,
            <given-names>X-J</given-names>
          </string-name>
          .
          <article-title>Twitter mood predicts the stock market</article-title>
          .
          <source>J. Comput. Science</source>
          Vol.
          <volume>2</volume>
          (
          <issue>1</issue>
          ), (
          <year>2011</year>
          )
          <fpage>1</fpage>
          -
          <lpage>8</lpage>
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          11.
          <string-name>
            <surname>Feldman</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Rosenfeld</surname>
            ,
            <given-names>B.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Bar-Haim</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          , &amp;
          <string-name>
            <surname>Fresko</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          <article-title>The Stock Sonar - Sentiment Analysis of Stocks Based on a Hybrid Approach</article-title>
          . IAAI (
          <year>2011</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation>
          12.
          <string-name>
            <surname>Arrow</surname>
            ,
            <given-names>K. J.</given-names>
          </string-name>
          , et al.
          <article-title>The promise of prediction markets</article-title>
          .
          <source>Science</source>
          (
          <year>2008</year>
          )
          <fpage>877</fpage>
          -
          <lpage>878</lpage>
          .
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>