This is a presentation given at IFIP eGov Conference at Guimaraes, Portugal, September 2016

1. MODELLING AND SIMULATION
APPROACHES FOR TECHNO-SOCIETAL
PHENOMENA
Yannis Charalabidis
Aggeliki Androutsopoulou
University of the Aegean

2. “We lack systematic evidence as to whether
social science studies of policy interventions,
and steps to increase their relevance to and
use for policy making, are having the results
their sponsors hope for. . . .”
[Knowledge and Policy:The Uncertain Connection, 1978,
National Research Council, U.S.]

3.  Financial Crisis
 Poverty
 Unemployment
 System risk
 Sustainability
 Climate change
 Energy sufficiency
 Shortage of natural resources
 Crisis Management (Floods,
Earthquakes)
 Pollution
 Urbanization
 Pandemics (virus, physical
diseases)
 Ageing population
 Migration patterns
 Cities
 Crime
 Terrorism
GLOBAL SOCIETAL CHALLENGES
Social
EnvironmentalEconomic

4. GLOBAL SYSTEM SCIENCE
① identify problems and challenges for societies
② measure their magnitude and seriousness
③ review alternative policy interventions
④ systematically assess the consequences of policy
interventions
⑤ evaluate the policy results, using factual information

5. MODELLING OBJECTIVES
• Reduce the uncertainty of complex heterogeneous techno-
societal phenomena
• Prediction of social indicators, demographics, assessment
of socio-economic impacts
• Formalisation of processes/mechanisms/behaviours
• Collect evidence for effective transparent decision making
• Provision of a holistic view of our connected world

6. MODELLING APPROACHES
High
Abstraction
Less Details
Macro Level
Middle
Abstraction
Medium Details
Meso Level
Low Abstraction
More Details
Micro Level Individual objects, exact sizes, distances, velocities, timings, …
Agent
Based
 Active objects
 Individual
behavior rules
 Direct or indirect
interaction
 Environment
models
Discrete
Event
 Entities (passive
objects)
 Flowcharts and/or
transport networks
 Resources
Aggregates, Global Casual Dependencies, Feedback Dynamics, …
System Dynamics
 Levels (aggregates)
 Stock-and-Flow Diagrams
 Feedback loops
Dynamic System
• Physical state variables
• Block diagrams and/or algebraic-
differential equations
ContinuousDiscrete
System-
Level
Process-
Centric Individual-
Centric

7. EXAMPLE: THE ICT DIFFUSSION MODEL
(modelling and prediction of citizens
acquiring broadband access)
broadband
coverage projects
broadband
coveragecoverage
rate
broadband
users
potential
internet
users
broadband
take up rate
TECHNOLOGY
FACTOR
cost of
access
internet
access at
homeaccess rate
internet
users
usage rate
digitally
skilled
peopleliteracy rate
need to use
physical
disabilities
perception of
benefits
fear about
safety
LITERACY
FACTOR
training
e-awareness
non
internet
users
potential
coverage
digitally
illiterate
peoplepotential
internet
access
access points
COVERAGE
CAGR availability of
access points
Private
investment
non
broadband
usersnon bb(pstn)
takeup rate
<Time>

8. EXAMPLE: FORECASTING PARTICIPATION IN
ONLINE POLICY DELIBERATION

9. MODELLING CHALLENGES
• Interventions in one area often have implications
on other domains through subtle, and hidden
connectivity
• Hard to construct models of adequate depth to
cover complex interlinked societal challenges
• Hard to capture behavioral aspects and can have
unprecedented effects

10. A PROPOSITION ON A HOLISTIC
MODELLING APPROACH
• A Hybrid Collaborative Modelling approach supported by a
web platform for:
• Reuse and combination of different interoperable models and model
types
• Effective data handling and integration of multiple sources
• Capturing and processing of societal factors, together with widely
adopted socio-economic indicators
• Supporting advanced modelling techniques state of the art dynamic
simulation approaches
• Combination with social behavior factors, social participation and
crowdsourcing initiatives

11. ARCHITECTURE OF A HYBRID MODELLING
AND SIMULATION PLATFORM

12. PLATFORM FUNCTIONALITIES
• Storage and retrieval of different types of interoperable models
• Easy to use graphical interface for the design of simulation models by
non modelling experts
• Online workspace for asynchronous collaboration or during Group
Model Building sessions
• Import and Export capabilities of models in machine readable formats
(e.g. XMILE)
• Visualisation and export of simulation results e.g. graphs or
standardised result files that can be processed by third party tools.
• API exposure to third party applications

13. PLATFORM PRINCIPLES
• Usability
• Scalability
• Adaptability
• Interoperability (with existing systems and
infrastructures)
• Sustainability
• Platform independence
• Maintenance of the system (modifications and
upgrades)

14. KEY ISSUES – THE PROCESS
• Study phenomena
• Define Models (find the main qualities and indicators)
• Describe Models in machine-understandable format
• Combine and reuse models and algorithms
• Find Enough Data for model instantiation
• Provide computation power to run and maintain
• Simulate
• Produce analytics
• Forecast
• Create narratives

15. CONCLUSIONS
• There is a need for (big) data-based social phenomena modelling, towards understanding and
forecasting behavior
• Several methods and tools of modelling and simulation exist, not interoperable to each other and
rather “passive”
• New platforms for modelling and simulation need to
• support knowledge bases with models for different phenomena,
• combine macro-micro simulation
• allow for interoperable combination of different models
• cater for the dynamic input of citizens (probing)
• If we succeed in modelling, we might start prognosing social phenomena or even find the right
solutions to societal challenges

16. Yannis Charalabidis
Associate Professor
Head, Information Systems Laboratory
Department of Information
and Communication Systems Engineering
University of The Aegean
yannisx@aegean.gr
www.charalabidis.gr
@yannisc