Chapter 5
From Building a Model to Adaptive Robust
Decision Making Using Systems Modeling
Erik Pruyt
Abstract Starting from the state-of-the-art and recent evolutions in the field of
system dynamics modeling and simulation, this chapter sketches a plausible near
term future of the broader field of systems modeling and simulation. In the near
term future, different systems modeling schools are expected to further integrate and
accelerate the adoption of methods and techniques from related fields like policy
analysis, data science, machine learning, and computer science. The resulting future
state of the art of the modeling field is illustrated by three recent pilot projects. Each
of these projects required further integration of different modeling and simulation
approaches and related disciplines as discussed in this chapter. These examples also
illustrate which gaps need to be filled in order to meet the expectations of real decision
makers facing complex uncertain issues.
5.1
Introduction
Many systems, issues, and grand challenges are characterized by dynamic complexity, i.e., intricate time evolutionary behavior, often on multiple dimensions of
interest. Many dynamically complex systems and issues are relatively well known,
but have persisted for a long time due to the fact that their dynamic complexity makes
them hard to understand and properly manage or solve. Other complex systems
and issues—especially rapidly changing systems and future grand challenges—are
largely unknown and unpredictable. Most unaided human beings are notoriously
bad at dealing with dynamically complex issues—whether the issues dealt with are
persistent or unknown. That is, without the help of computational approaches, most
human beings are unable to assess potential dynamics of complex systems and issues,
and are unable to assess the appropriateness of policies to manage or address them.
E. Pruyt ()
Faculty of Technology, Policy, and Management, Delft University of Technology, Delft,
The Netherlands
e-mail: E.Pruyt@tudelft.nl
Netherlands Institute for Advanced Study, Wassenaar, The Netherlands
© Springer International Publishing Switzerland 2015
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_5
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Modeling and simulation is a field that develops and applies computational methods to study complex systems and solve problems related to complex issues. Over
the past half century, multiple modeling methods for simulating such issues and
for advising decision makers facing them have emerged or have been further developed. Examples include system dynamics (SD) modeling, discrete event simulation
(DES), multi-actor systems modeling (MAS), agent-based modeling (ABM), and
complex adaptive systems modeling (CAS). All too often, these developments have
taken place in distinct fields, such as the SD field or the ABM field, developing into
separate “schools,” each ascribing dynamic complexity to the complex underlying
mechanisms they focus on, such as feedback effects and accumulation effects in SD or
heterogenous actor-specific (inter)actions in ABM. The isolated development within
separate traditions has limited the potential to learn across fields and advance faster
and more effectively towards the shared goal of understanding complex systems and
supporting decision makers facing complex issues.
Recent evolutions in modeling and simulation together with the recent explosive
growth in computational power, data, social media, and other evolutions in computer
science have created new opportunities for model-based analysis and decision making. These internal and external evolutions are likely to break through silos of old,
open up new opportunities for social simulation and model-based decision making,
and stir up the broader field of systems modeling and simulation. Today, different
modeling approaches are already used in parallel, in series, and in mixed form, and
several hybrid approaches are emerging. But not only are different modeling traditions being mixed and matched in multiple ways, modeling and simulation fields
have also started to adopt—or have accelerated their adoption of—useful methods
and techniques from other disciplines including operations research, policy analysis,
data analytics, machine learning, and computer science. The field of modeling and
simulation is consequently turning into an interdisciplinary field in which various
modeling schools and related disciplines are gradually being integrated. In practice, the blending process and the adoption of methodological innovations have just
started. Although some ways to integrate systems modeling methods and many innovations have been demonstrated, further integration and massive adoption are still
awaited. Moreover, other multi-methods and potential innovations are still in an
experimental phase or are yet to be demonstrated and adopted.
In this chapter, some of these developments will be discussed, a picture of the near
future state of the art of modeling and simulation is drawn, and a few examples of
integrated systems modeling are briefly discussed. The SD method is used to illustrate
these developments. Starting with a short introduction to the traditional SD method
in Sect. 5.2, some recent and current innovations in SD are discussed in Sect. 5.3,
resulting in a picture of the state of modeling and simulation in Sect. 5.4. A few
examples are then briefly discussed in Sect. 5.5 to illustrate what these developments
could result in and what the future state-of-the-art of systems modeling and simulation
could look like. Finally, conclusions are drawn in Sect. 5.6.
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5.2
77
System Dynamics Modeling and Simulation of Old
System dynamics was first developed in the second half of the 1950s by Jay W.
Forrester and was further developed into a consistent method built on specific methodological choices1 . It is a method for modeling and simulating dynamically complex
systems or issues characterized by feedback effects and accumulation effects. Feedback means that the present and future of issues or systems, depend—through a
chain of causal relations—on their own past. In SD models, system boundaries are
set broadly enough to include all important feedback effects and generative mechanisms. Accumulation relates not only to building up real stocks—of people, items,
(infra)structures, etc.,—but also to building up mental or other states. In SD models, stock variables and the underlying integral equations are used to group largely
homogenous persons/items/. . . and keep track of their aggregated dynamics over
time. Together, feedback and accumulation effects generate dynamically complex
behavior both inside SD models and—so it is assumed in SD—in real systems.
Other important characteristic of SD are (i) the reliance on relatively enduring
conceptual systems representations in people’s minds, aka mental models (Doyle and
Ford 1999, p. 414), as prime source of “rich” information (Forrester 1961; Doyle
and Ford 1998); (ii) the use of causal loop diagrams and stock-flow diagrams to
represent feedback and accumulation effects (Lane 2000); (iii) the use of credibility
and fitness for purpose as main criteria for model validation (Barlas 1996); and (iv)
the interpretation of simulation runs in terms of general behavior patterns, aka modes
of behavior (Meadows and Robinson 1985).
In SD, the behavior of a system is to be explained by a dynamic hypothesis, i.e.,
a causal theory for the behavior (Lane 2000; Sterman 2000). This causal theory is
formalized as a model that can be simulated to generate dynamic behavior. Simulating
the model thus allows one to explore the link between the hypothesized system
structure and the time evolutionary behavior arising out of it (Lane 2000).
Not surprisingly, these characteristics make SD particularly useful for dealing
with complex systems or issues that are characterized by important system feedback
effects and accumulation effects. SD modeling is mostly used to model core system
structures or core structures underlying issues, to simulate their resulting behavior,
and to study the link between the underlying causal structure of issues and models and
the resulting behavior. SD models, which are mostly relatively small and manageable,
thus allow for experimentation in a virtual laboratory. As a consequence, SD models
are also extremely useful for model-based policy analysis, for designing adaptive
policies (i.e., policies that automatically adapt to the circumstances), and for testing
their policy robustness (i.e., whether they perform well enough across a large variety
of circumstances).
1
See Forrester (1991, 2007), Sterman (2007) for accounts of the inception of the SD field. See
Sterman (2000), Pruyt (2013) for introductions to SD. And see Forrester (1961, 1969), Homer
(2012) for well-known examples of traditional SD.
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In terms of application domains, SD is used for studying many complex social–
technical systems and solving policy problems in many application domains, for
example, in health policy, resource policy, energy policy, environmental policy,
housing policy, education policy, innovation policy, social–economic policy, and
other public policy domains. But it is also used for studying all sorts of business
dynamics problems, for strategic planning, for solving supply chain problems, etc.
At the inception of the SD method, SD models were almost entirely continuous,
i.e., systems of differential equations, but over time more and more discrete and other
noncontinuous elements crept in. Other evolutionary adaptations in line with ideas
from the earliest days of the field, like the use of Group Model Building to elicit
mental models of groups of stakeholders (Vennix 1996) or the use of SD models as
engines for serious games, were also readily adopted by almost the entire field. But
slightly more revolutionary innovations were not as easily and massively adopted.
In other words, the identity and appearance of traditional SD was well established
by the mid-1980s and does—at first sight—not seem to have changed fundamentally
since then.
5.3
5.3.1
Recent Innovations and Expected Evolutions
Recent and Current Innovations
Looking in somewhat more detail at innovations within the SD field and its adoption of innovations from other fields shows that many—often seemingly more
revolutionary—innovations have been introduced and demonstrated, but that they
have not been massively adopted yet.
For instance, in terms of quantitative modeling, system dynamicists have invested
in spatially specific SD modeling (Ruth and Pieper 1994; Struben 2005; BenDor and
Kaza 2012), individual agent-based SD modeling as well as mixed and hybrid ABMSD modeling (Castillo and Saysal 2005; Osgood 2009; Feola et al. 2012; Rahmandad
and Sterman 2008), and micro–macro modeling (Fallah-Fini et al. 2014). Examples
of recent developments in simulation setup and execution include model calibration
and bootstrapping (Oliva 2003; Dogan 2007), different types of sampling (Fiddaman
2002; Ford 1990; Clemson et al. 1995; Islam and Pruyt 2014), multi-model and multimethod simulation (Pruyt and Kwakkel 2014; Moorlag 2014), and different types of
optimization approaches used for a variety of purposes (Coyle 1985; Miller 1998;
Coyle 1999; Graham and Ariza 1998; Hamarat et al. 2013, 2014). Recent innovations
in model testing, analysis, and visualization of model outputs in SD include the
development and application of new methods for sensitivity and uncertainty analysis
(Hearne 2010; Eker et al. 2014), formal model analysis methods to study the link
between structure and behavior (Kampmann and Oliva 2008, 2009; Saleh et al.
2010), methods for testing policy robustness across wide ranges of uncertainties
(Lempert et al. 2003), statistical packages and screening techniques (Ford and Flynn
2005; Taylor et al. 2010), pattern testing and time series classification techniques
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(Yücel and Barlas 2011; Yücel 2012; Sucullu andYücel 2014; Islam and Pruyt 2014),
and machine learning techniques (Pruyt et al. 2013; Kwakkel et al. 2014; Pruyt et
al. 2014c). These methods and techniques can be used together with SD models to
identify root causes of problems, to identify adaptive policies that properly address
these root causes, to test and optimize the effectiveness of policies across wide ranges
of assumptions (i.e., policy robustness), etc. From this perspective, these methods
and techniques are actually just evolutionary innovations in line with early SD ideas.
And large-scale adoption of the aforementioned innovations would allow the SD
field, and by extension the larger systems modeling field, to move from “experiential
art” to “computational science.”
Most of the aforementioned innovations are actually integrated in particular SD
approaches like in exploratory system dynamics modelling and analysis (ESDMA),
which is an SD approach for studying dynamic complexity under deep uncertainty.
Deep uncertainty could be defined as a situation in which analysts do not know or
cannot agree on (i) an underlying model, (ii) probability distributions of key variables
and parameters, and/or (iii) the value of alternative outcomes (Lempert et al. 2003). It
is often encountered in situations characterized by either too little information or too
much information (e.g., conflicting information or different worldviews). ESDMA
is the combination of exploratory modeling and analysis (EMA), aka robust decision
making, developed during the past two decades (Bankes 1993; Lempert et al. 2000;
Bankes 2002; Lempert et al. 2006) and SD modeling. EMA is a research methodology
for developing and using models to support decision making under deep uncertainty.
It is not a modeling method, in spite of the fact that it requires computational models.
EMA can be useful when relevant information that can be exploited by building
computational models exists, but this information is insufficient to specify a single
model that accurately describes system behavior (Kwakkel and Pruyt 2013a). In
such situations, it is better to construct and use ensembles of plausible models since
ensembles of models can capture more of the un/available information than any
individual model (Bankes 2002). Ensembles of models can then be used to deal with
model uncertainty, different perspectives, value diversity, inconsistent information,
etc.—in short, with deep uncertainty.2
In EMA (and thus in ESDMA), the influence of a plethora of uncertainties, including method and model uncertainty, are systematically assessed and used to design
policies: sampling and multi-model/multi-method simulation are used to generate
ensembles of simulation runs to which time series classification and machine learning
techniques are applied for generating insights. Multi-objective robust optimization
(Hamarat et al. 2013, 2014) is used to identify policy levers and define policy triggers,
and by doing so, support the design of adaptive robust policies. And regret-based
approaches are used to test policy robustness across large ensembles of plausible
runs (Lempert et al. 2003). EMA and ESDMA can be performed with TU Delft’s
2
For ESDMA, see among else Pruyt and Hamarat (2010), Logtens et al. (2012), Pruyt et al. (2013),
Kwakkel and Pruyt (2013a, b), Kwakkel et al. (2013), Pruyt and Kwakkel (2014).
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EMA workbench software, which is an open source tool3 that integrates multimethod, multi-model, multi-policy simulation with data management, visualization,
and analysis.
The latter is just one of the recent innovations in modeling and simulation software
and platforms: online modeling and simulation platforms, online flight simulator
and gaming platforms, and packages for making hybrid models have been developed
too. And modeling and simulation across platforms will also become reality soon:
the eXtensible Model Interchange LanguagE (XMILE) project (Diker and Allen
2005; Eberlein and Chichakly 2013) aims at facilitating the storage, sharing, and
combination of simulation models and parts thereof across software packages and
across modeling schools and may ease the interconnection with (real-time) databases,
statistical and analytical software packages, and organizational information and communication technology (ICT) infrastructures. Note that this is already possible today
with scripting languages and software packages with scripting capabilities like the
aforementioned EMA workbench.
5.3.2
Current and Expected Evolutions
Three current evolutions are expected to further reinforce this shift from “experiential
art” to “computational science.”
The first evolution relates to the development of “smarter” methods, techniques,
and tools (i.e., methods, techniques, and tools that provide more insights and deeper
understanding at reduced computational cost). Similar to the development of formal
model analysis techniques that smartened the traditional SD approach, new methods, techniques, and tools are currently being developed to smarten modeling and
simulation approaches that rely on “brute force” sampling, for example, adaptive
output-oriented sampling to span the space of possible dynamics (Islam and Pruyt
2014) or smarter machine learning techniques (Pruyt et al. 2013; Kwakkel et al. 2014;
Pruyt et al. 2014c) and time series classification techniques (Yücel and Barlas 2011;
Yücel 2012; Sucullu and Yücel 2014; Islam and Pruyt 2014), and (multi-objective)
robust optimization techniques (Hamarat et al. 2013, 2014).
Partly related to the previous evolution are developments relates to “big data,”
data management, and data science. Although traditional SD modeling is sometimes
called data-poor modeling, it does not mean it is, nor should be. SD software packages
allow one to get data from, and write simulation runs to, databases. Moreover,
data are also used in SD to calibrate parameters or bootstrap parameter ranges. But
more could be done, especially in the era of “big data.” Big data simply refers
here to much more data than was until recently manageable. Big data requires data
science techniques to make it manageable and useful. Data science may be used in
3
The EMA workbench can be downloaded for free from http://simulation.tbm.tudelft.nl/
ema-workbench/contents.html
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modeling and simulation (i) to obtain useful inputs from data (e.g., from real-time big
data sources), (ii) to analyze and interpret model-generated data (i.e., big artificial
data), (iii) to compare simulated and real dynamics (i.e., for monitoring and control),
and (iv) to infer parts of models from data (Pruyt et al. 2014c). Interestingly, data
science techniques that are useful for obtaining useful inputs from data may also be
made useful for analyzing and interpreting model-generated data, and vice versa.
Online social media are interesting sources of real-world big data for modeling and
simulation, both as inputs to models, to compare simulated and real dynamics, and to
inform model development or model selection. There are many application domains
in which the combination of data science and modeling and simulation would be
beneficial. Examples, some of which are elaborated below, include policy making
with regard to crime fighting, infectious diseases, cybersecurity, national safety and
security, financial stress testing, energy transitions, and marketing.
Another urgently needed innovation relates to model-based empowerment of decision makers. Although existing flight simulator and gaming platforms are useful for
developing and distributing educational flight simulators and games, and interfaces
can be built in SD packages, using them to develop interfaces for real-world real-time
decision making and integrating them into existing ICT systems is difficult and time
consuming. In many cases, companies and organizations want these capabilities inhouse, even in their boardroom, instead of being dependent on analyses by external
or internal analysts. The latter requires user-friendly interfaces on top of (sets of)
models possibly connected to real-time data sources. These interfaces should allow
for experimentation, simulation, thoroughly analysis of simulation results, adaptive
robust policy design, and policy robustness testing.
5.4
Future State of Practice of Systems Modeling and
Simulation
These recent evolutions in modeling and simulation together with the recent explosive
growth in computational power, data, social media, and other evolutions in computer
science may herald the beginning of a new wave of innovation and adoption, moving
the modeling and simulation field from building a single model to simultaneously
simulating multiple models and uncertainties; from single method to multi-method
and hybrid modeling and simulation; from modeling and simulation with sparse
data to modeling and simulation with (near real-time) big data; from simulating and
analyzing a few simulation runs to simulating and simultaneously analyzing wellselected ensembles of runs; from using models for intuitive policy testing to using
models as instruments for designing adaptive robust policies; and from developing
educational flight simulators to fully integrated decision support.
For each of the modeling schools, additional adaptations could be foreseen too.
In case of SD, it may for example involve a shift from developing purely endogenous to largely endogenous models; from fully aggregated models to sufficiently
spatially explicit and heterogenous models; from qualitative participatory modeling
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Fig. 5.1 Picture of the state of science/future state of the art of modeling and simulation
to quantitative participatory simulation; and from using SD to combining problem
structuring and policy analysis tools, modeling and simulation, machine learning
techniques, and (multi-objective) robust optimization.
Adoption of these recent, current, and expected innovations could result in the
future state of the art4 of systems modeling as displayed in Fig. 5.1. As indicated
by (I) in Fig. 5.1, it will be possible to simultaneously use multiple hypotheses (i.e.,
simulation models from the same or different traditions or hybrids), for different goals
including the search for deeper understanding and policy insights, experimentation in
a virtual laboratory, future-oriented exploration, robust policy design, and robustness
testing under deep uncertainty. Sets of simulation models may be used to represent
different perspectives or plausible theories, to deal with methodological uncertainty,
or to deal with a plethora of important characteristics (e.g., agent characteristics,
feedback and accumulation effects, spatial and network effects) without necessarily
having to integrate them in a single simulation model. The main advantages of using
multiple models for doing so are that each of the models in the ensemble of models
remains manageable and that the ensemble of simulation runs generated with the
4
Given the fact that it takes a while before innovations are adopted by software developers and
practitioners, this picture of the current state of science is at the same time a plausible picture of the
medium term future of the field of modeling and simulation.
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ensemble of models is likely to be more diverse which allows for testing policy
robustness across a wider range of plausible futures.
Some of these models may be connected to real-time or near real-time data
streams, and some models may even be inferred in part with smart data science
tools from data sources (see (II) in Fig. 5.1). Storing the outputs of these simulation
models in databases and applying data science techniques may enhance our understanding, may generate policy insights, and may allow for testing policy robustness
across large multidimensional uncertainty spaces (see (III) in Fig. 5.1). And userfriendly interfaces on top of these interconnected models may eventually empower
policy makers, enabling them to really do model-based policy making.
Note, however, that the integrated systems modeling approach sketched in Fig. 5.1
may only suit a limited set of goals, decision makers, and issues. Single model
simulation properly serves many goals, decision makers, and issues well enough for
multi-model/multi-method, data-rich, exploratory, policy-oriented approaches not
to be required. However, there are most certainly goals, decision makers, and issues
that do.
5.5
Examples
Although all of the above is possible today, it should be noted that this is the current
state of science, not the state of common practice yet. Applying all these methods
and techniques to real issues is still challenging, and shows where innovations are
most needed. The following examples illustrate what is possible today as well as
what the most important gaps are that remain to be filled.
The first example shows that relatively simple systems models simulated under
deep uncertainty allow for generating useful ensembles of many simulation runs.
Using methods and techniques from related disciplines to analyze the resulting artificial data sets helps to generate important policy insights. And simulation of policies
across the ensembles allows to test for policy robustness. This first case nevertheless
shows that there are opportunities for multi-method and hybrid approaches as well
as for connecting systems models to real-time data streams.
The second example extends the first example towards a system-of-systems approach with many simulation models generating even larger ensembles of simulation
runs. Smart sampling and scenario discovery techniques are then required to reduce
the resulting data sets to manageable proportions.
The third example shows a recent attempt to develop a smart model-based
decision-support system for dealing with another deeply uncertain issue. This example shows that it is almost possible to empower decision makers. Interfaces with
advanced analytical capabilities as well as easier and better integration with existing
ICT systems are required though. This example also illustrates the need for more
advanced hybrid systems models as well as the need to connect systems models to
real-time geo-spatial data.
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5.5.1 Assessing the Risk, and Monitoring, of New Infectious
Diseases
The first case, which is described in more detail in (Pruyt and Hamarat 2010; Pruyt et
al. 2013), relates to assessing outbreaks of new flu variants. Outbreaks of new (variants of) infectious diseases are deeply uncertain. For example, in the first months
after the first reports about the outbreak of a new flu variant in Mexico and the USA,
much remained unknown about the possible dynamics and consequences of this possible epidemic/pandemic of the new flu variant, referred to today as new influenza
A(H1N1)v. Table 5.1 shows that more and better information became available over
time, but also that many uncertainties remained. However, even with these remaining
uncertainties, it is possible to model and simulate this flu variant under deep uncertainty, for example with the simplistic simulation model displayed in Fig. 5.2, since
flu outbreaks can be modeled.
Simulating this model thousands of times over very wide uncertainty ranges for
each of the uncertain variables generates the 3D cloud of potential outbreaks displayed in Fig. 5.3a. In this figure, the worst flu peak (0–50 months) is displayed
on the X-axis, the infected fraction during the worst flu peak (0–50 %) is displayed
on the Y -axis, and the cumulative number of fatal cases in the Western world (0–
50.000.000) is displayed on the Z-axis. This 3D plot shows that the most catastrophic
outbreaks are likely to happen within the first year or during the first winter season
following the outbreak. Using machine learning algorithms to explore this ensemble
of simulation runs helps to generate important policy insights (e.g., which policy
levers to address). Testing different variants of the same policy shows that adaptive
policies outperform their static counterparts (compare Fig. 5.3b and c). Figure 5.3d
finally shows that adaptive policies can be further improved using multi-objective
robust optimization.
However, taking deep uncertainty seriously into account would require simulating
more than a single model from a single modeling method: it would be better to
simultaneously simulate CAS, ABM, SD, and hybrid models under deep uncertainty
and use the resulting ensemble of simulation runs. Moreover, near real-time geospatial data (from twitter, medical records, etc.) may also be used in combination
with simulation models, for example, to gradually reduce the ensemble of modelgenerated data. Both suggested improvements would be possible today.
5.5.2
Integrated Risk-Capability Analysis under Deep Uncertainty
The second example relates to risk assessment and capability planning for National
Safety and Security. Since 2001, many nations have invested in the development
of all-hazard integrated risk-capability assessment (IRCA) approaches. All-hazard
IRCAs integrate scenario-based risk assessment, capability analysis, and capabilitybased planning approaches to reduce all sorts of risks—from natural hazards, over
technical failures to malicious threats—by enhancing capabilities for dealing with
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Unknown
Unknown
Antiviral suscep.
% asymptomatic
Future?
CFR stands for case fatality ratio
Unknown
Age distribution
a
Unknown
CFR UK
Unknown
Unknown
Possible
Unknown
Unknown
Unknown
a
Unknown
Unknown
Indications
Elderly less affected?
Unknown
0.1%?
4%?
Unknown
Long tail?
–
Unknown
Unknown
Unknown
Unknown
Incubation
Unknown
(elderly)
Unknown
Unknown
–
–
Unknown
0.1%?
2%?
–
Unknown
Idem
1.4–1.6
Indications
1.4–1.9
17%?
Unknown
Virulence
Unknown
1–2; prob.
Unknown
20 May
1–2; prob.
Unknown
CFR USA
Unknown
Immunity
Unknown
Unknown
CFR Mexico
Unknown
Ro
08 May
(up to 8 days)
Unknown
Infectivity
30 April
period
24 April
Date
Unknown
Unknown
–
Skewed tow. younger
Unknown
0.2%?
0.4–1.8%?
range 1–7 days
Median 3–4 days
Unknown
Idem
–
Unknown
12 June
Unknown
Indications
–
Idem
0.3%(–1%)?
0.4%?
–
Idem
self-limiting
Mild and
Idem
–
Unknown
20 July
Unknown
33–50%
–
Idem
0.1–0.2%?
–
–
Idem
Idem
Idem
[R ≤ 2]
Unknown
21 August
Table 5.1 Information and unknowns provided by the European Centre for Disease Prevention and Control (ECDC) from 24 April until 21 August
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Fig. 5.2 Region 1 of a two-region system dynamics (SD) flu model
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Fig. 5.3 3D scatter plots of 20,000 Latin-Hypercube samples for region 1 with X-axis: worst flu
peak (0–50 months); Y -axis: infected fraction during the worst flu peak (0–50 %); Z-axis: fatal
cases (0–5 × 107 )
them. Current IRCAs mainly allow dealing with one or a few specific scenarios for
a limited set of relatively simple event-based and relatively certain risks, but not for
dealing with a plethora of risks that are highly uncertain and complex, combinations of measures and capabilities with uncertain and dynamic effects, and divergent
opinions about degrees of (un)desirability of risks and capability investments.
The next generation model-based IRCAs may solve many of the shortcomings of
the IRCAs that are currently being used. Figure 5.4 displays a next generation IRCA
for dealing with all sorts of highly uncertain dynamic risks. This IRCA approach,
described in more detail in Pruyt et al. (2012), combines EMA and modeling and
simulation, both for the risk assessment and the capability analysis phases. First,
risks—like outbreaks of new flu variants—are modeled and simulated many times
across their multidimensional uncertainty spaces to generate an ensemble of plausible
risk scenarios for each of the risks. Time series classification and machine learning
techniques are then used to identify much smaller ensembles of exemplars that are
representative for the larger ensembles. These ensembles of exemplars are then used
as inputs to a generic capability analysis model. The capability analysis model is
subsequently simulated for different capabilities strategies under deep uncertainty
(i.e., simulating the uncertainty pertaining to their effectiveness) over all ensembles
of exemplars to calculate the potential of capabilities strategies to reduce these risks.
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Fig. 5.4 Model-based integrated risk-capability analysis (IRCA)
Finally, multi-objective robust optimization helps to identify capabilities strategies
that are robust.
Not only does this systems-of-systems approach allow to generate thousands of
variants per risk type over many types of risks and to perform capability analyses across all sorts of risk and under uncertainty, it also allows one to find sets
of capabilities that are effective across many uncertain risks. Hence, this integrated
model-based approach allows for dealing with capabilities in an all-hazard way under
deep uncertainty.
This approach is currently being smartened using adaptive output-oriented sampling techniques and new time-series classification methods that together help to
identify the largest variety of dynamics with the minimal amount of simulations.
Covering the largest variety of dynamics with the minimal amount of exemplars is
desirable, for performing automated multi-hazard capability analysis over many risks
is—due to the nature of the multi-objective robust optimization techniques used—
computationally very expensive. This approach is also being changed from a multimodel approach into a multi-method approach. Whereas, until recently, sets of SD
models were used; there are good reasons to extend this approach to other types of
systems modeling approaches that may be better suited for particular risks or—using
multiple approaches—help to deal with methodological uncertainty. Finally, settings
of some of the risks and capabilities, as well as exogenous uncertainties, may also
be fed with (near) real-world data.
5.5.3
Policing Under Deep Uncertainty
The third example relates to another deeply uncertain issue, high-impact crimes
(HIC). An SD model and related tools (see Fig. 5.5) were developed some years ago
in view of increasing the effectiveness of the fight against HIC, more specifically the
fight against robbery and burglary. HICs require a systemic perspective and approach:
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Fig. 5.5 (I) Exploratory system dynamics modelling and analysis (ESDMA) model, (II) interface
for policy makers, (III) analytical module for analyzing the high-impact crimes (HIC)system under
deep uncertainty, (IV) real-world pilots based on analyses, and (V) monitoring of real-world data
from the pilots and the HIC system
These crimes are characterized by important systemic effects in time and space, such
as learning and specialization effects, “waterbed effects” between different HICs
and precincts, accumulations (prison time) and delays (in policing and jurisdiction),
preventive effects, and other causal effects (ex-post preventive measures). HICs are
also characterized by deep uncertainty: Most perpetrators are unknown and even
though their archetypal crime-related habits may be known to some extent at some
point in time, accurate time and geographically specific predictions cannot be made.
At the same time, is part of the HIC system well known and is a lot of real-world
information related to these crimes available.
Important players in the HIC system besides the police and (potential) perpetrators
are potential victims (households and shopkeepers), partners in the judicial system
(the public prosecution service, the prison system, etc.). Hence, the HIC system
is dynamically complex, deeply uncertain, but also data rich, and contingent upon
external conditions.
The main goals of this pilot project were to support strategic policy making under
deep uncertainty and to test and monitor the effectiveness of policies to fight HIC.
The SD model (I) was used as an engine behind the interface for policy makers
(II) to explore plausible effects of policies under deep uncertainty and identify realworld pilots that could possibly increase the understanding about the system and
effectiveness of interventions (III), to implement these pilots (IV), and monitor their
outcomes (V). Real-world data from the pilots and improved understanding about
the functioning of the real system allow for improving the model.
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Today, a lot of real-world geo-spatial information related to HICs is available
online and in (near) real time which allows to automatically update the data and
model, and hence, increase its value for the policy makers. The model used in this
project was an ESDMA model. That is, uncertainties were included by means of
sets of plausible assumptions and uncertainty ranges. Although this could already
be argued to be a multi-model approach, hybrid models or a multi-method approach
would really be needed to deal more properly with systems, agents, and spatial
characteristics. Moreover, better interfaces and connectors to existing ICT systems
and databases would also be needed to turn this pilot into a real decision-support
system that would allow chiefs of police to experiment in a virtual world connected
to the real world, and to develop and test adaptive robust policies on the spot.
5.6
Conclusions
Recent and current evolutions in modeling and simulation together with the recent
explosive growth in computational power, data, social media, and other evolutions
in computer science have created new opportunities for model-based analysis and
decision making.
Multi-method and hybrid modeling and simulation approaches are being developed to make existing modeling and simulation approaches appropriate for dealing
with agent system characteristics, spatial and network aspects, deep uncertainty, and
other important aspects. Data science and machine learning techniques are currently
being developed into techniques that can provide useful inputs for simulation models
as well as for building models. Machine learning algorithms, formal model analysis
methods, analytical approaches, and new visualization techniques are being developed to make sense of models and generate useful policy insights. And methods and
tools are being developed to turn intuitive policy making into model-based policy
design. Some of these evolutions were discussed and illustrated in this chapter.
It was also argued and shown that easier connectors to databases, to social media,
to other computer programs, and to ICT systems, as well as better interfacing software
need to be developed to allow any systems modeler to turn systems models into
real decision-support systems. Doing so would turn the art of modeling into the
computational science of simulation. It would most likely also shift the focus of
attention from building a model to using ensembles of systems models for adaptive
robust decision making.
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Chapter 6
Features and Added Value of Simulation Models
Using Different Modelling Approaches
Supporting Policy-Making: A Comparative
Analysis
Dragana Majstorovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis
and Petra Ahrweiler
Abstract Using computer simulations in examining, explaining and predicting social processes and relationships as well as measuring the possible impact of policies
has become an important part of policy-making. This chapter presents a comparative analysis of simulation models utilised in the field of policy-making. Different
models and modelling theories and approaches are examined and compared to each
other with respect to their role in public decision-making processes. The analysis
has shown that none of the theories alone is able to address all aspects of complex
policy interactions, which indicates the need for the development of hybrid simulation models consisting of a combinatory set of models built on different modelling
theories. Building such hybrid simulation models will also demand the development
of new and more comprehensive simulation modelling platforms.
D. Majstorovic () · M. A. Wimmer
University of Koblenz-Landau, Koblenz, Germany
e-mail: majstorovic@uni-koblenz.de
M. A. Wimmer
e-mail: wimmer@uni-koblenz.de
R. Lay-Yee · P. Davis
Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre),
University of Auckland, Private Bag 92019, 1142 Auckland, New Zealand
e-mail: r.layyee@auckland.ac.nz
P. Davis
e-mail: pb.davis@auckland.ac.nz
P. Ahrweiler
EA European Academy of Technology and Innovation Assessment GmbH,
Bad Neuenahr-Ahrweiler, Germany
e-mail: Petra.Ahrweiler@ea-aw.de
© Springer International Publishing Switzerland 2015
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_6
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6.1
D. Majstorovic et al.
Introduction
Using computer simulation as a tool in examining, explaining and predicting social
processes and relationships started intensively during 1990s (Gilbert and Troitzsch
2005). Since 2000s, a growing recognition of simulation models playing a role in
public decision modelling processes can be noted (van Egmond and Zeiss 2010). One
reason for this increased attention is that simulation models enable the examination of
complex social processes and interactions between different entities and the potential
impact of policies. For example, simulation models can be used to examine the impact
of measures such as school closure and vaccination in stopping the spread of influenza
as the cases described in Sect. 3.1 and 3.2 demonstrate; or to examine the influence of
different policies in the early years of life as the case outlined in Sect. 3.3 evidences.
This chapter presents a comparative analysis of different simulation models with
respect to their role in public decision-making processes. The focus is on investigating
the differences between simulation models and their underlying modelling theories in
order to find variables that impact the effectiveness of the usage of simulation models
in policy-making. The ultimate goal is to provide an understanding of the peculiarities
and the added value of different kinds of simulation models generated on the basis of
particular modelling approaches. The chapter also aims at giving indications of how
existing approaches to policy simulation can and should be combined to effectively
support public policy-making in a comprehensive way.
This comparative analysis was performed as part of the eGovPoliNet1 initiative, which aims at developing an international multidisciplinary policy community
in information and communication (ICT) solutions for governance and policy
modelling. eGovPoliNet brings researchers from different disciplines and communities together for sharing ideas, discussing knowledge assets and developing
joint research findings. The project fosters a multidisciplinary approach to investigate different concepts in policy modelling. In investigating these concepts,
researchers from different disciplines (such as information systems, e-government
and e-participation, computer science, social sciences, sociology, psychology, organisational sciences, administrative sciences, etc.) collaborate to study the—so
far mostly mono-disciplinary—approaches towards policy modelling. With this approach, eGovPoliNet aims at contributing to overcoming the existing fragmentation
of research in policy modelling across different disciplines.
The research carried out in this paper was based on the literature study of policy
modelling approaches whereby the authors collaborated with expertise from their
own academic background. On the other hand, a comparative analysis of five different simulation models was performed using a framework of comparison developed
along the eGovPoliNet initiative. The selection of the cases was based on the authors’
1
eGovPoliNet—Building a global multidisciplinary digital governance and policy modelling research.and practice community. See http://www.policy-community.eu/ (last access: 28th July
2014).
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