This document proposes a framework called Policy Compass that uses various technologies to improve policy analysis and evaluation. It consists of 5 pillars: (1) prosperity indexes to measure welfare using open data, (2) open public data sources, (3) fuzzy cognitive maps to model policy impacts, (4) argumentation technology to support debates, and (5) deliberation platforms and social media to engage citizens. The goal is to make the policy process more evidence-based, transparent, and accountable by helping visualize policy effects, stimulate public discussion, and clarify policy outcomes for both decision-makers and citizens. The approach will be validated through pilot tests of policy case studies in the UK and Russia.

1. FUSING OPEN PUBLIC DATA, PROSPERITY INDEXES,
FUZZY COGNITIVE MAPS AND ARGUMENTATION
TECHNOLOGY FOR MORE FACTUAL, EVIDENCE-BASED
AND ACCOUNTABLE POLICY ANALYSIS AND
EVALUATION
Ourania Markaki, Panagiotis Kokkinakos, Sotirios Koussouris,
John Psarras, National Technical University of Athens
Yuri Glickman, Fraunhofer FOKUS
and Habin Lee, Brunel University London
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland

2. Introduction
• Information and Communication Technologies (ICT) and Web 2.0 are
transforming the way citizens and the civil society interact, debate and
participate in public life, by:
• Making participation in policy making and political processes possible at large
• Fostering communication between politicians and the civil society
• Simplifying decision making processes
• Demystifying legislative texts
• Offering advanced visualization capabilities
e-Participation is the ICT-supported participation in
governance procedures
• e-Participation is about connecting ordinary people with politics and
policy making
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 2

3. The Problem
• Internet has evolved into a rich source
for information but also to an
instrument of spreading misinformation
and propaganda
• Lack of consensus about a suitable
metric for measuring progress
• Difficulty of objectively assessing the
impacts of government policies
The Proposed Approach
 Open Public Data
 Prosperity Indicators
 Fuzzy Cognitive Maps
 Argumentation Technology
 Deliberation Platforms and Social
Media
The Context
Improve the quality and
transparency of the policy
analysis and evaluation phases
of the policy cycle for a variety
of stakeholders, ranging from
citizens to policy makers
Analysis
Adoption
Analysis
Policy
cycle
Implementation/
Implementation
Monitoring
Agenda
Setting
/Monitoring
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 3

4. The Proposed Approach: Overview
• A research prototype of an easy-to-use, highly visual and intuitive tool for:
• Constructing prosperity and other policy metrics with an easy-to-use visual
language for defining variables and functions over open data sources.
• Constructing graphs and charts visualizing metrics for selected
geographical regions and time periods.
• Annotating graphs and charts with political or policy events.
• Constructing causal models with an easy-to-use visual tool for Fuzzy
Cognitive Maps (FCM).
• Sharing and debating prosperity graphs and FCM across popular social
media platforms.
• Summarizing and visualizing the debates in argument maps and
conducting structured surveys about policy issues
• Aggregating opinions on policy issues, to formulate a common position in a
party or interest group.
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 4

5. Pillar I: Prosperity Indexes
• Prosperity metrics capture the level of welfare and quality of life in a
given region or society.
• Prosperity is a vague and subjective concept with essential
psychological, social and economic aspects.
• There is no consensus about how to objectively measure prosperity
• Indicators of economic growth:
• Gross Domestic Product (GDP)
• Genuine Progress Index (GPI)
• Index of Sustainable Economic Welfare (ISEW)
• GINI Index
• Alternatives:
• Human Development Index (HDI)
• Legatum Prosperity Index
• “Healthy life years statistics” by Eurostat
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 5

6. Policy Compass Pillars (1/5)
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland
6
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Define higher
level metrics
from lower
level ones
Construct
metrics by
operationalizin
g open data
sources
Predict the
evolution of
prosperity
indicators by
applying causal
policy models
Define
prosperity
metrics
collectively
Weigh
prosperity
aspects
according to
the opinions
expressed

7. Pillar II: Open Public Data
• Open and unrestricted access to large scale data sets is essential for
political engagement and scientific research
• Available large scale data sets have nowadays their own self-contained
existence rules.
• Micro-data can be used to construct new indicators of multifaceted
nature.
• Sources of micro-data:
• Eurobarometer surveys
• European Union Statistics on Income and Living Conditions (EU-SILC) by Eurostat
• Urban Audit (the European cities Eurostat)
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 7

8. Policy Compass Pillars (2/5)
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland
8
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Define higher
level metrics
from lower
level ones
Construct
metrics by
operationalizin
g open data
sources
Use historical
events to
annotate
metric
visualizations
Access open
data sources,
Publish data
sets & their
metadata
Predict the
evolution of
prosperity
indicators by
applying causal
policy models
Use historical
data to
validate
causal policy
models
Use open
public data to
bolster one’s
opinion
Define
prosperity
metrics
collectively
Weigh
prosperity
aspects
according to
the opinions
expressed

9. Pillar III: Fuzzy Cognitive Maps
• Fuzzy Cognitive Maps (FCMs) provide a well-founded, general-purpose and
intuitive method for modelling and simulating relationships between variables.
• FCMs have been introduced by B. Kosko (1986) as a fuzzified version of
Cognitive Maps, originally introduced by political scientist R. Axelrod (1976).
• An FCM is a fuzzy directed graph of nodes and edges, where nodes
represent fuzzy concepts, describing behavioral characteristics of a system
that occur to some degree, and directed edges represent the causal
relationships among these concepts.
• The graph edges are weighted by a real
value from the interval [-1, 1], which
expresses the strength of the relation
between two concepts.
• FCMs have been widely used to model
and simulate policies and their effects.
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 9

10. Policy Compass Pillars (3/5)
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland
10
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Define higher
level metrics
from lower
level ones
Construct
metrics by
operationalizin
g open data
sources
Use historical
events to
annotate
metric
visualizations
Access open
data sources,
Publish data
sets & their
metadata
Develop ideas on
the correlations
among policies
and prosperity
fluctuations
Simulate
causal policy
models based
on open data
sets
Predict the
evolution of
prosperity
indicators by
applying causal
policy models
Use historical
data to
validate
causal policy
models
Use open
public data to
bolster one’s
opinion
Develop and
apply own
causal policy
models
Define the
strength of
correlations
according to
the opinions
expressed
Define
prosperity
metrics
collectively
Define policy
impact models
collectively
Weigh
prosperity
aspects
according to
the opinions
expressed

11. Pillar IV: Argumentation Technology
• Argumentation support systems are computer software for helping people
participate in various kinds of goal-directed dialogues in which arguments are
exchanged.
• The idea of using argumentation support systems for e-Participation can be
traced back at least to Horst Rittel’s pioneering work in the early 1970s who
used visual maps of arguments, to help people collaborate and find solutions
to what he called “wicked problems”.
• “Wicked problems” have no algorithmic, scientific or objectively optimal
solutions for a variety of reasons, including the lack of consensus among
stakeholders about utilities and values.
• Typically, e-Participation projects make use of generic groupware systems
(e.g. discussion fora, online surveys, etc.) not providing though specific
technical support for argumentation.
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 11

12. Policy Compass Pillars (4/5)
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland
12
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Define higher
level metrics
from lower
level ones
Construct
metrics by
operationalizin
g open data
sources
Use historical
events to
annotate
metric
visualizations
Access open
data sources,
Publish data
sets & their
metadata
Develop ideas on
the correlations
among policies
and prosperity
fluctuations
Simulate
causal policy
models based
on open data
sets
Debate on
prosperity
metrics
Reuse
argumentation
outcomes as
structured
open data
Predict the
evolution of
prosperity
indicators by
applying causal
policy models
Use historical
data to
validate
causal policy
models
Use open
public data to
bolster one’s
opinion
Develop and
apply own
causal policy
models
Define the
strength of
correlations
according to
the opinions
expressed
Debate on
causal models
underlying
policies
Summarize
and visualize
debates in
argument
maps
Define
prosperity
metrics
collectively
Define policy
impact models
collectively
Aggregate poll
outcomes to
formulate a
common
position
Weigh
prosperity
aspects
according to
the opinions
expressed

13. Pillar V: Deliberation Platforms and Social Media
• Deliberation platforms incarnate the efforts taken by government
agencies, to increase citizens’ engagement in their decision and
policy making processes.
• The first wave of deliberation platforms has witnessed extensive
information on government activities, decisions, plans and policies,
the proliferation of e-voting and e-consultation spaces, along with
various types of e-fora.
• Still, the first generation of deliberation platforms did not meet the
original expectations.
• The advent of Web 2.0 tools has created a more vivid environment
and the popularity of the social media has set a new battlefield for the
concept of e-Participation.
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 13

14. Policy Compass Pillars (5/5)
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland
14
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Pillar I:
Prosperity
Indexes
Pillar II: Open
Public Data
Pillar III: Fuzzy
Cognitive
Maps
Pillar IV:
Argumentation
Pillar V:
Deliberation
Platforms &
Social Media
Define higher
level metrics
from lower
level ones
Construct
metrics by
operationalizin
g open data
sources
Use historical
events to
annotate
metric
visualizations
Access open
data sources,
Publish data
sets & their
metadata
Develop ideas on
the correlations
among policies
and prosperity
fluctuations
Simulate
causal policy
models based
on open data
sets
Debate on
prosperity
metrics
Reuse
argumentation
outcomes as
structured
open data
Share own
developed
prosperity
metrics
Predict the
evolution of
prosperity
indicators by
applying causal
policy models
Use historical
data to
validate
causal policy
models
Use open
public data to
bolster one’s
opinion
Develop and
apply own
causal policy
models
Define the
strength of
correlations
according to
the opinions
expressed
Debate on
causal models
underlying
policies
Summarize
and visualize
debates in
argument
maps
Share own
developed
causal policy
models
Poll public
opinion on
policy issues
Define
prosperity
metrics
collectively
Define policy
impact models
collectively
Aggregate poll
outcomes to
formulate a
common
position
Ensure
citizens’ wide
participation
Weigh
prosperity
aspects
according to
the opinions
expressed

15. Use Case Scenarios (1/2)
Online
Deliberation
and
Argument
Mapping
Policy
Compass
Policy
Analysis
Policy
Monitoring
and
Evaluation
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 15

16. Use Case Scenarios (2/2)
Policy
Monitoring
and
Evaluation
Data
Discovery and
Processing
Metrics
Definition
Metrics
Calculation
and
Visualization
Graphs
Annotation
Seeking
further
explanation of
policy impacts
Sharing
Knowledge
and Results
Policy
Analysis
Discovery of
Open Data
related to
Policies
Creating or
Refining
Causal
Networks
Turning
Causal
Networks to
FCMs
FCMs
Simulation
and Impacts
Visualization
Sharing
Knowledge
and Results
Online
Deliberation
and Argument
Mapping
Initiation of
/Participation
in
Deliberations
Transforming
Structured
Discussions
into Argument
Maps
Navigation
through
Argument
Maps
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 16

17. Discussion and Conclusions
• A framework for empowering citizens and policy makers to better
assess government policies.
• Benefits of the approach:
Decision makers:
• Visualize the effects of their politics
• Stimulate public debate
• Communicate policy outcomes to
citizens clearer
• Build confidence in progress towards
societal goals
Citizens:
• Engage in the development of
prosperity indices
• Monitor and critically discuss the
quality of public policies
• Learn about the multiple dimensions
and social and economic
consequences of policies
• Improve the objectivity and evidential
basis of their arguments
• Assessment and validation of the proposed approach is foreseen
through the development of real case pilot scenarios for policy
analysis and evaluation on the basis of two trials, organized in UK
(Cambridgeshire) and Russia (St. Petersburg).
2 September 2014 ePart 2014 - Trinity College Dublin, Ireland 17

18. www.PolicyCompass.eu
www.twitter.com/PolicyCompassEU
www.facebook.com/PolicyCompass
PolicyCompass
Thank you!
Ourania Markaki, omarkaki@epu.ntua.gr