A recent overview of the integration of System Dynamics
and Agent-based Modelling and Simulation
Graciela d. C. Nava Guerrero", Philipp Schwarz*?, Jill H Slinger!?
1 Faculty of Technology, Policy and M Delft University of Technology, Netherlands
2 Faculty of Civil Engineering and Geosciences, Delft University of Technology, Netherlands
Correspondance should be addressed to gnavaquerrero@ student.tudelft.nl and philipp.scwh@ gmail.com
March 22, 2016
Abstract: Modelling and simulation aim to reproduce the structure and imitate the behavior of real-life
systems. For complex dynamic systems, System Dynamics (SD) and Agent-based (AB) modelling are two
widely used modelling paradigms that prior to the early 2010’s have traditionally been viewed as mutually
exclusive altematives. This literature review seeks to update the work of Scholl (2001) and Macal, (2010) by
providing an overview of attempts to integrate SD and AB over the last ten years. First, the building blocks of
both paradigms are presented. Second, their capabilities are contrasted, in orderto explore how their integration
can yield insights that cannot be generated with one methodology alone. Then, an overview is provided of
recent work comparing the outcomes of both i and fyi ities for Finally,
acritical reflection is presented. The literature review concludes that while paradigm emulation has contributed
to expanding the applications of SD, it is the dynamic combination of the two approaches that has become the
most promising research line. Integrating SD and AB, and even tools and methods from other disciplines,
makes it possible to avoid their individual pitfalls and, hence, to exploit the full potential of their
complementary characteristics, so as to provide a more complete representation of complex dynamic systems.
Word count: 4974
Keywords: System Dynamic: Agent-Based Modelling - Hybrid Models - Complex Dynamic Systems -
multi-paradigm approach - Literature Review
1 Introduction
Modelling and simulation of complex social systems aim at increasing the understanding of the system
and testing policies with the objective to support decision-making and at times policy implementation
(Meadows and Robinson, 2002). The advantage of computational models are their capability to embrace
complex real-life systems characterized by dynamic nonlinear relationships. A nother substantial benefit
is that what-if scenarios can be tested, but intervention in reality is not required.
Agent-based (AB) modelling and System Dynamics (SD) are two widely used methodologies in
modelling complex dynamic system. While System Dynamics has a long tradition since it was founded
in the late 1950s by Forrester (1958), AB is as yet in its infancy - implying that its complete potential
has not yet been utilized (Bonabeau, 2002). Both approaches have been applied to many socio-economic
problem domains including health care (Demarest, 2011; Figueredo, Aickelin, & Siebers, 2011;
Figueredo, Siebers, Aickelin, Whitbrook, & Garibaldi, 2015; Kirandeep, Eldabi, & Young, 2013;
Mellor, Smith, Learmonth, Netshandama, & Dillingham, 2012), supply chains (Angerhofer &
Angelides, 2000; Georgiadis, Vlachos, & Iakovou, 2005; Gjerdrum, Shah, & Papageorgiou, 2001; Tako
& Robinson, 2012; Xue, Li, Shen, & Wang, 2005) and technology adoption (Chen, 2011; Fisher,
i
Norvell, Sonka, & Nelson, 2000; Moser & Barrett, 2006; Schwarz & Emst, 2009; C Swinerd &
McNaught, 2014; Zhang & Nuttall, 2007).
More than a decade ago, Scholl (2001) made a call forjoint research between SD and ABM by
comparing and contrasting both approaches, and more recent works have enriched those comparisons
(Lattila et al., 2010; Macal, 2010). However, during the last decade, and particularly during the last five
years, an explosive growth in computational capacity has enabled the emergence of more, and more
diverse, joint research in the field of modelling and simulation (Pruyt, 2015).
This article seeks to update the work of Scholl (2001) and Macal (2010) by providing an overview
of attempts to integrate SD and AB over the last ten years, with an emphasis on hybrid SD-AB models
published over the last five years. The research strategy comprised a systematic literature review.
Combinations of the following key words were used: agent-based modeling, combining, differential
equations models, system dynamics, and hybrid models. The objective was to compile literature related
to the ongoing discussions on the complementary potential of integrating SD and ABM, and to provide
an overview of recent case studies. The research question was formulated as:
What are the potential benefits of integrating System Dynamics and Agent-based
and what is the state-of-the-art in its application?
The reviewed literature was retrieved from several research databases, including ACM Digital Library,
Elsevier, Springer-link, EBSCO Host, and IEEEX plore. The works by Scholl (2001), Lattila et al.
(2010), Macal (2010), Schieritz and Grobler (2003) and Behdani (2012) were used as a guide in
structuring the research process.
The remainder of this paper is ordered as follows: Section 2 gives a short overview about the SD
and AB paradigms, including theories behind the paradigms and building blocks and characteristics of
the resulting models. Section 3 contrasts the capabilities of SD and AB, in order to explore how their
integration can yield insights that cannot be generated with only one methodology alone. This section
draws from a review of recent studies that combine both paradigms. Section 4 presents how both
methods have been integrated during the last decade, and explores expected developments in this field.
Lastly, Section 5 concludes by answering the research question and delineating opportunities for future
research.
2 Single Paradigms: System Dynamics and Agent-based
Prior to the 2010s, the SD and AB paradigms developed as separate schools of modeling and simulation
(Pruyt, 2015), in spite of both paradigms being used for the analysis of complex dynamic systems
(Phelan, 1999). This Section presents an overview of the fundamental theories behind each paradigm
and of the building blocks and characteristics of their corresponding models.
2.1 System Dynamics (SD) Models
More than 50 years ago, Forrester (1958) founded SD around two notions from systems theory (Phelan,
1999): first, aggregated-level variables affect each other through feedback loops; second, system’s
structure drives system’s behavior. These notions challenge the predominant rather simplistic cause-
and-effect thinking of traditional science, decoded into independent and dependent variables. Instead,
systems theory explains the behavior of complex dynamic systems endogenously: it identifies feedback
effects that are often hidden because they are delayed at large time scales. Consequently, systems
dynamics modelling targets the underlying causes of problems instead of only treating their symptoms
(Forrester, 1958; Sterman, 2000).
“Dn
In practice, the building blocks in specifying an SD model are stocks, flows and auxiliary
variables (Forrester, 1958; Sterman, 2000). Stocks represent the accumulation of material and
information, caused by the action of inflows and outflows. While stocks are mathematically described
by integral equations, flows are described by differential equations (Macal, 2010; Parunak et al., 1998)
. The solution of these sets of equations describes the aggregated state of the system. This state changes
continuously over time and depends on the previous state of the system. These sets of equations are
solved through numerical integration at discrete time steps (Forrester, 1958; Meadows, 2009; Sterman,
2000).
2.2. Agent-based (AB) Models
The theory of complex adaptive systems (CAS) states that systems do not have central control and do
not have a fixed structure. Based on this theory, the AB paradigm models the structure of a system as
the result of decentralized decisions of individual entities or agents over time (Macal, 2010; Macal and
North, 2006). Therefore, instead of assuming a given system structure, agents’ decisions shape and
change the state and structure of the system. In turn, agents react to the dynamic changes in the system,
which can potentially alter their decision rules.
It follows that the main building blocks of AB are autonomous agents, their decision rules and
actions, and the environment in which they interact (Bonabeau, 2002; Epstein and A xtell, 1996; Phelan,
1999). Although agents’ decision rules usually govern agents’ behavior to achieve individual benefits
(Macal and North, 2006), collective intelligence may also emerge when agents coordinate their
decisions to achieve common goals (Phelan, 1999). Therefore, analyzing solely the internal mechanism
of agents does not explain the macro level observations (Epstein, 2006). Moreover, agents’ decision
making is typically based on limited observed knowledge (their view on the world) rather than on
complete knowledge of the entire state of the system (Jennings et al., 1998).
3 Potential benefits of integrating System Dynamics and Agent-based
The contrasts between SD and AB, including scope, the focus on system behavior or on emergent
behavior, aggregation level and the current capacity to study heterogeneity and spatial variability, make
the application of each paradigm more suited to different situations (Macal, 2010; Scholl, 2001; Teose
et al., 2011; Wakeland et al., 2004). Nevertheless, knowledge about the differences between SD and
AB does not necessarily result in an appropriate choice of paradigm: one paradigm alone cannot always
provide enough insights to analyze the complex system of interest (Lattila et al., 2010; Macal, 2010;
Rahmandad, 2004; Scholl, 2001; Shafiei et al., 2013a).
In this Section, five characteristics in which SD and AB differ fundamentally are explained first.
Second, the potential benefits of combining both paradigms are clarified.
3.1 Contrasting SD and AB - five fundamental differences
The applicability, strengths and weak points of SD and AB paradigms have been compared by designing
independent models of the same system and contrasting their outcomes. Recent contributions include,
but are not limited to Figueredo and Aickelin (2011), Macal, (2010), Milling and Schieritz (2003),
Parunak et al. (1998), Rahmandad and Sterman (2008), Schryver et al. (2015). For this article, a number
of such comparisons were reviewed and five fundamental characteristics in which SD and AB differ
were identified. These aspects include the paradigms’ capacity to model continuous aggregated and
discrete disaggregated system states; physical space, topographies, and network structures; stochastic
& deterministic phenomena; learning and adaption; and ease of model building and interpretation. The
paragraphs below elaborate on each of these aspects.
3.1.1 System states: continuous aggregated vs. discrete disaggregated
SD and AB paradigms differ in the level of aggregation and their handling of time. On the one hand,
SD excels at representing continuous aggregated systems. This paradigm can account for a wide range
of feedback effects, at the cost of reducing real world diversity to aggregated average values by
assuming homogeneity and perfect mixing within stocks and flows (Parunak et al., 1998; Rahmandad
and Sterman, 2008; Sterman, 2000). However, While SD excels at modeling continuous processes, it
has difficulties in coping with discrete events (Parunak et al., 1998). Therefore, AB is more appropriate
to model discontinuous system properties (Bonabeau, 2002).
In contrast to SD, the AB paradigm inherently includes heterogeneity between agents. To account
for the diversity of agents in the real world, agents act according to properties and decision rules that
can be derived from distribution functions (Bonabeau, 2002; Epstein, 2006; Macal, 2010). By
accounting for the diversity within and between agents, AB is suitable to study problems where the
distribution of resources, costs or benefits is the focus of interest (Bonabeau, 2002; Osgood, 2007).
Empirical research has emphasized a tension between the level of analysis and the scope of the
system under study when using SD or AB alone (Alam Napitupulu, 2014; Cherif and Davidsson, 2010;
Figueredo et al., 2015; Silva et al., 2011; Thompson and Reimann, 2010). While SD can study large
systems by handling highly aggregated data, AB typically studies heterogeneous systems with relatively
limited scope.
In practice, choosing a paradigm to describe a system at an appropriate level of analysis is not
straight forward. In reality, this aspect is observer dependent: the same system can be described with
both discrete and continuous representations. Rahmandad & Sterman (2008) demonstrate that the
outcomes of equivalent SD and AB models are alike under many conditions. Other authors have come
to the same conclusion by comparing single SD and AB models in the fields of health sciences (Ahmed
et al., 2013; Figueredo et al., 2015; Figueredo and Aickelin, 2011b), economy (Alam Napitupulu, 2014),
transportation (Silva et al., 2011), software development (Cherif and Davidsson, 2010), land use (Haase
and Schwarz, 2009) and education (Thompson and Reimann, 2010).
3.1.2 Stochastic & deterministic phenomena
SD and AB can both model deterministic systems: systems which do not contain randomness and thus
yield the same result from a given initial state (Brock, 1986). However, in AB models, decision mules,
actions and properties are normally derived from distribution functions, and are therefore probabilistic
(Bonabeau, 2002).
Due to its stochastic character, the AB paradigm can naturally account for outlier values that
would not be shown in an aggregated system representation. Outlier values represent random events,
such as Black Swans, that are unlikely but can alter the system radically. Therefore, when assumptions
of homogeneity and perfect mixing can be made for a particular study, SD and AB can produce
outcomes that are not statistically different (Ahmed et al., 2013; Rahmandad and Sterman, 2008).
However, when heterogeneous clustered agent networks are central for answering the problem, AB is
usually a more appropriate paradigm to study the problem (Rahmandad and Sterman, 2008).
However, there is a trade-off between the stochasticity of an AB model and its computational
requirement (Osgood, 2007; Rahmandad and Sterman, 2008). A conflict in goals arises between the
richness of feedback structure captured endogenously, the number of agents and their complexity of
interaction, and the exhaustiveness of the sensitivity analysis (Rahmandad & Sterman, 2008). As a
result, AB can be discarded as the preferred method in modeling and simulation studies due to its high
computational resource demands (Ahmed, Greensmith, & Aickelin, 2013; Figueredo & Aickelin, 2011;
Figueredo et al., 2011; Figueredo et al., 2015; Silva, Coelho, Novaes, & Lima Jr, 2011).
3.1.3. Physical space, topographies, & network structures
Inherently, SD was not designed to cope with spatial diffusion and propagation processes, but to model
the aggregate properties of such systems and so provide strategic insight into their behaviour. When the
number of entities is small and when the entities are highly dispersed or clustered, this can be
problematic (Rahmandad & Sterman, 2008). Emerging paradigms, such as spatial system dynamics
(SSD), are trying to overcome this limitation (Ahmad and Simonovic, 2004; Neuwirth and Peck, 2013).
SSD is based on coupling SD with geographic information systems (GIS) to provide feedback effects
across physical space (Ahmad and Simonovic, 2004).
In contrast, AB has the capability to distinguish physical space, topographies, and other network
structures (Bonabeau, 2002; Parunak et al., 1998; Rahmandad & Sterman, 2008). The former allows
the explicit study of the dynamics across landscapes or networks (Osgood, 2007). Hence, AB models
have proven attractive for classes of modelling problems where topographies (particularly irregular and
clustered) are crucial with respect to understanding the problem and the assessment of policies.
Furthermore, the characteristics of mobile agents in a network, able to alter system structure, can be
utilized to account generally for evolving systems in which relations disintegrate and are created
dynamically over time (Scholl, 2001). This property and the possibility to construct goal-oriented agents
makes AB models ideally suited to model many social systems and implement concepts from social
and behavioral science such as bounded rationality (Edmonds, 1999; Manson, 2006; March and Simon,
1958).
3.1.4 Learning & adaption processes
Experience based learning effects and adaptation processes such as the “eroding quality standards”
archetype are frequently modelled in SD.. Nevertheless, explicit individual learning and adoption
processes are a focus within AB models (Bonabeau, 2002). For this, machine learning algorithms are
used to design agents that have the ability to modify their own decision mules (Parunak et al., 1998;
Phelan, 1999; Scholl, 2001; Stone and Veloso, 2000).
3.1.5 Ease of model building and interpretation
As the previous examples demonstrate, AB model have numerous virtues in specific contexts. However,
these virtues often come at the cost of more time consuming modeling simulation and interpretation
processes (Osgood, 2007). Indeed, the interpretation of AB model outputs at aggregate level is still in
its infancy. Whereas the formulation of an SD model makes use of system level observables to identify
the feedback loops that govern the system’s behavior (Rahmandad & Sterman, 2008), the construction
of an AB model requires not only knowledge of the system at an aggregated level, but also in-depth
insights on decision processes of agents and their behavior (Macal, 2010; Macal & North, 2006).
Moreover, AB models require knowledge on the disaggregated distributions of agent properties for
parametrization (Macal, 2010; Macal & North, 2006).
Next, as described in the paragraphs devoted to stochastic and deterministic phenomena, AB
models have considerably longer simulation times than their SD counterparts.
Additionally, the interpretation of simulation results is typically easier for SD models than for
AB models, because the underlying dynamics of these models are transparent and the toolbox for
analyzing and understanding simulation results is already well developed. This availability of methods
facilitates the rapid development of small models to explore the driving dynamics of current ‘hot’ issues
(Pruyt, 2013; Pruyt et al., 2009).
Finally, SDs popularity has been facilitated by the availability of several drag-and-drop software
tools for constructing and analyzing models, including Vensim® (www.vensim.com), Stella®
(www.stella.com) and PowerSim® (www.powersim.com) (Borshchev and Filippov, 2004). Until
recently, one of the obstacles for wider adoption of AB had been the limited availability of easy to use
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tools that do not require programming skills (Parunak et al., 1998; Wilensky, 1999). However, the
emergence of software such as AnyLogic® (“Multimethod Simulation Software and Solutions,” n.d.)
and NOVA ® (Salter, 2013) may facilitate faster adoption.
3-2 Potential benefits of combining SD and AB
Despite fundamental differences, both modelling approaches are effective in describing and simulating
complex dynamic systems. AB has received increasing attention because it holds promise of significant
benefits compared with other modeling paradigms, including SD (Bonabeau, 2002; Epstein, 2006;
Jennings et al., 1998; Macal and North, 2006). Nevertheless, there is no simple dividing line indicating
when which modelling approach will provide superior results. Instead, the choice of paradigm depends
on the problem and the purpose of the modelling exercise and should take into account the paradigms’
capabilities, limitations and tradeoffs (Figueredo and Aickelin, 2011b; Parunak et al., 1998).
By combining SD and AB, some components can be modelled discretely and in a disaggregated
fashion when this is needed, while other components can be modelled continuously and in an aggregated
fashion, based on the different system characteristics and the specific model purpose (Osgood, 2007).
In this way, a hybrid SD-AB model facilitates the definition of appropriate levels of aggregation for
each component of the system. Furthermore, for many modelling problems, a combination of SD and
AB can reduce computation times, provide the strategic overview characteristic of SD, while still
capturing relevant elements of the individual heterogeneity and stochasticity of entities and processes.
Another potential advantage of combining SD and AB is that this can be seen as a way to enhance
the capability of SD models to cope with spatially explicit problems. The resulting models permit
arranging agents in a spatial or network structure, while integrating important properties of SD, such as
continuity and non-linear multi-loop feedback. This approach can be refined when the individuals are
mobile and consequently the spatial dimension becomes dynamic. Besides this, it is possible to use
multiple SD sub-models to create different properties across a spatial grid. As a result, individuals
interact with a different SD sub-models depending on their position (Vincenot et al., 2011). Agents can
plausibly even interact with more than one SD sub-model at a time.
4 Recent efforts to integrate SD and AB
While no unified definition exists for hybrid SD-AB models, countless architectures are possible for
coupling or matching SD and AB. This section discusses first, how AB features have been incorporated
through emulation into the field of SD. Then, it presents three classifications of possible architectures
for hybrid SD-AB models. Finally, it summarizes recent efforts and breakthroughs in the design of
hybrid SD-AB models, and sketches the state-of-the-art of SD and AB integration. The focus lies on
work conducted within the last decade and particularly in the last five years.
4.1 Emulation of AB features within the SD field
In the field of SD, some authors have made attempts to emulate the capabilities of AB without changing
the overall SD paradigm. Pasaoglu et al. (2016), Powell and Coyle (2005) and Wu, Kefan, Hua, Shi,
and Olson (2010), for instance, integrated an AB perspective in the construction of an SD model. Teose
et al. (2011) embedded SD notions into AB models using Gillespie’s t-leap algorithm, an equation that
connects the paradigms by interpreting rates of flow into movement of agents.
While paradigm emulation has contributed to expanding the applications of SD during the last
few years, it is the appropriate combination of the two approaches that has become the most promising
research line (C Swinerd & McNaught, 2014). Integrating SD and AB makes it possible to avoid their
individual pitfalls and, hence, to exploit the full potential of their complementary characteristics, so as
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to provide a more complete representation of complex dynamic systems (Scholl, 2001; Stemate et al.,
2007).
4.2, Hybrid SD-AB architectures
Swinerd and McNaught (2012), Kirandeep et al. (2013) and Vincenot et al. (2011) proposed different
architectures for hybrid SD-AB models.
Based on Shanthikumar and Sargent (1983), Swinerd and McNaught (2012) presented three
classes that vary depending on how the model’s modules, either SD or AB single paradigm meta-
models, interact to produce the model’s outcome. In the first class, the sequential class, the outcome of
each module forms the input for the next module; the outcome of the final module represents the
model’s outcome. The second class, the interfaced class, includes non-sequential combinations of
modules that do not influence each other but combine their independent outcomes to produce the model
outcome. Lastly, in the integrated class, modules and even model outcomes provide feedback to one
another.
The second classification, developed by Kirandeep et al. (2013), presents two classes that are
analogous to the aforementioned sequential and integrated ones.
Vincenot et al. (2011), in turn, identified four reference cases or typical SD-AB structures. Case
1 refers to AB agents interacting within their environment, an SD module. Emergent properties from
the AB module can dynamically parameterize the SD module. Case 2 refers to AB agents containing
SD modules that determine their dynamic decision rules and spatial structures. In Case 3, individuals
interact with an environment made of more than one SD module. Unlike Case 1, Case 3 is spatially
explicit and the SD module with which an agent interacts depends on the agent’s position and the SD
module’s area of influence. Finally, Case 4 refers to SD-ABM model swapping. This case reduces
computation time by allowing only modules of the same paradigm to run at any given time. During the
run, threshold values or events cause the change from modules of one paradigm to modules from the
other one.
However, the architectures of Swinerd and McNaught (2012), Kirandeep et al.’s (2013), and
Vincenot et al. (2011) are non-exhaustive in nature. While Chris Swinerd and McNaught (2012)’s
interfaced class implies that modules in a hybrid model do not necessarily have to be connected during
the simulation, all the reference cases by Vincenot, Giannino, Rietkerk, Moriya, and Mazzoleni (2010)
consider interaction between the modules during the simulation. In practice, the architecture of hybrid
SD-AB models is usually based on the specific needs of the problem under study. Examples are
provided in the following sub-section.
4.3 Recent hybrid SD-AB models and modeling environments
Hybrid SD-AB models have proven useful in studying diffusion processes of technological innovation.
In their independent studies, Swinerd and McNaught (2014) and Shafiei et al. (2013) embedded
individual agents in an SD environment. In Swinerd and McNaught’s model, an SD module is
embedded in each agent to dynamically parameterize its properties. Similarly, Swinerd and McNaught
(2015) simulated the international diffusion of consumer technology by modeling nations as agents,
with internal decision processes consisting of SD models, and global diffusion processes with an
equation-based rate model.
Hybrid SD-AB models have also been developed in other fields. Jo et al. (2015) designed a
dynamic alternative to cost benefit analysis for infrastructure projects. This work integrates AB and SD
modules by enabling dynamic feedback from the SD states to the AB environment, and from the AB
environment to the SD rates of change. Tran (2016) developed a multi-paradigm framework to analyze
techno-behavioral dynamics in networks, and to assess the impact of technology on society. This
framework integrates the notions of system dynamics to explore the most aggregated and macro layers
Ten
of the system, and notions from agent-based to study network structures and individual behavior. Lewe
et al. (2014) studied intercity transportation by integrating SD and AB modules to represent macro-
level, and micro-level variables, respectively. Kolominsky-Rabas et al. (2015) developed the
framework ProHTA, a hybrid SD-AB tool the aim of which is to the assessment of innovative health
technologies prior to their launch.
Other examples explicitly include discrete event simulation models, in addition to the SD and
AB components. For instance, a study of the elements of a hybrid simulation model for blood supply
chains (Onggo, 2015); a feasibility assessment of hybrid approaches in the context of complex
healthcare operation management (Viana, 2014); an analysis of real workforce choices (Flynn et al.,
2014); a hybrid approach to integrate safety behaviour into construction planning, by Goh and Askar
Ali (n.d., in press) and the study of reusability in hybrid simulation by Djanatliev et al. (2014), to
mention but a few.
The availability of modelling environments that can handle multiple paradigms, including SD, AB
and discrete events has also increased. For instance, Salter (2013) reports on NOVA®, a modeling and
simulation platform that supports the integration of both paradigms. Moreover, this work envisions the
integration of Geographic Information Systems (GIS) within the platform. Other platforms include
Anylogic® (“Multimethod Simulation Software and Solutions,” n.d.), which supports modeling and
simulation with SD, AB, discrete events and incorporates certain GIS features, as well as NetLogo
(Wilensky, 1999), a free and open source modeling environment with similar capabilities.
4.4 Exploring the next generation of hybrid paradigms
As explained in the previous Sections, integrating the SD and AB paradigms is a promising approach
to overcome the limitations of each single paradigm. However, the integration of SD and AB is only a
piece in a bigger puzzle (Pruyt, 2015). Recent innovations suggest that, in the future, mainstream
research frameworks and methods to model complex dynamic systems will reach beyond the boundaries
of SD, AB, and even beyond the reach of hybrid SD-AB paradigms.
Currently, the adoption and diffusion of methods and techniques from other disciplines, such as
data analytics and machine learning, are tuming modeling and simulation into an interdisciplinary field
(Pruyt, 2015). This process of blending tools and methods across disciplines, which has just started, is
enabling the emergence of a new generation of computational models with radically expanded
capabilities that promise to deliver significant breakthroughs.
For several reasons, the development of this new generation of computational models is likely to
occur using high-level programming language, such as Python, R Project and Java, instead of
commercial and closed source modeling environments (Pérez, Granger, & Hunter, 2011). First, many
scientific disciplines use these languages for scientific computing and quantitative data analysis. The
open source environment fosters transparency and reproducibility of research, while these languages
facilitate the balance between full flexibility of general-purpose programming languages and ease of
use. In addition their object-orientation supports the implementation of multi-model approaches.
Examples of the methodological innovations that will lead to the new generation of models
include Exploratory Model Analysis (EMA) (Kwakkel and Pruyt, 2015, 2013) and data analytics using
tools such as PySD (Houghton, and Siegel, 2015).
5 Conclusion
This literature review seeks to update the work of Scholl (2001) and Macal (2010) by providing an
overview of attempts to integrate SD and AB over the preceding decade, with a particular focus on the
last five years. The review described the building blocks of both paradigms and contrasted their
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capabilities to explore how their integration can yield insights that cannot be generated with one
methodology alone. Five fundamental characteristics in which SD and AB differ were identified. These
characteristics are the paradigms’ capacity to model continuous aggregated and discrete disaggregated
system states; physical space, topographies, and network structures; stochastic & deterministic
phenomena; learning and adaption; and ease of model building and interpretation.
This article also provided an overview of recent work on the integration of SD and AB paradigms,
and the development of multi-paradigm and multidisciplinary modeling and simulation frameworks.
However, he unique contribution of this paper is the conclusion that while paradigm emulation has
contributed to expanding the applications of SD, the dynamic combination of the two approaches is the
most promising research line. Integrating SD and AB, as well as tools and methods from other
disciplines, makes it possible to avoid their individual pitfalls and, hence, to exploit the full potential of
their complementary characteristics, to provide more complete representations of complex dynamic
systems.
Ultimately, the widespread adoption of hybrid SD-AB models will depend on the development
of tools that are able to effectively integrate different modelling paradigms. Therefore, an area of
research that should be encouraged is the development and refinement of free and open source hybrid
modelling tools that they are easy to use and in which models can be documented.
Furthermore, this review concludes that although SD and AB are only a piece in the bigger puzzle
of innovative modeling and simulation environments, their integration into hybrid models plays an
important role in these exciting times. Breakthroughs in the integration of SD and AB can yield insights
in how to build and use smarter modeling tools to support decision-making.
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« I~
\
+ Urban population
Urban food demand (a A and urban space
+
\
\soeee 7
Urban food
© availability \
eo a) \
Organization of the food \ \
ly and distributic “A
EN cies Anz) food policy at urban
and rural level
| %
\
crop and livestock 4
production or processing
and infrastructures
%
Rs
a —
~~ congestion <¢——~
Figure 4 - System archetypes applied to food supply and distribution system
(Armendariz et al. 2015a).
3.3. ANALYSIS
After the identification of the main dynamics and stocks from the food production and
food distribution subsystems from FFFA and their conceptualization into a single general
system (Armendariz et al., 2015b), we have selected the main modules that allow us to
conceptualize in a simple way the environment and complexity of FSDS.
The diagram in Figure 5 is an example of the general framework setting to describe a
complex issue in a simple and simplified way without loosing its validity. A preliminary
version of this framework setting proposed by the authors to capture the structure where
FSDS are embedded and its dynamics. The general framework will be described below
by focusing on the identified loops.
14
URBAN SPACE &
INFRASTRUCTURE
Figure 5 - Framework setting of FSDS suggested by Armendariz et al. (2015b).
Within the system, loop R1 represent the relationship among urban population and urban
space and the dynamics of the urban geographical boundary. It depends on population
dynamics and job dynamics. It also summarizes the urban planning and the infrastructure
building process, which mainly depends on local factors. Natural resources are reduced as
the urban space and infrastructures increase, especially if we think in land use, land
consumption, pollution or variation of available non-renewable natural stocks. In this
sense, B1 indicates the carrying capacity of the system and the limit to growth. The urban
space for markets and food logistics are also part of urban planning, aimed to optimize
the efficiency of the organization of the system, it consists of a balancing loop (B2) as
urban space is limited its availability impacts the distribution and production processes,
within this dynamic, land dedicated to urban food production means less land to roads
15
building. B3 indicates the ecological footprint of the FSDS that has a similar dynamics
than Bl. There is an internal reinforcing loop (R2), which indicates the food and
distribution process are only possible after the urban space use and this, after the
resources use, which includes non-urban land and non-renewal resources. An external
loop with these same modules (B5) indicates that production and distribution processes
make also use of the resources by its part, competing with the infrastructure and urban
dynamics resources use. Both of these dynamics are expressing the risk of
overconsumption resource rate for the maintenance of, not just the food and distribution
activities, but also the system itself.
Urban population, intended as human aggregation (geographically, social status, cohort,
etc.) is one of the main drivers of the current food demand understood as the amount of
food which the people is looking for in a certain market condition. It depends on different
factors: population number, income level of people living in the area and food availability
provided by supply and distribution chains. Population dynamics are undoubtedly one of
the most important drivers of food demand. However, production and distribution
management deficiencies contribute to decrease the food supply of cities, to increase the
nutritional gap of the people and the food demand.
The module Food demand at market level means amount of food demanded useful to
calculate food availability. According to FFFA, food demand at market level calculation
requires necessarily the consideration of people’s income. This should not be confused
with absolute food demand, which will be an indicator of food requirement per capita
multiply by the total population which can be useful to calculate other indicators as food
nutritional gap, food sufficiency. The relation among food demand at market level and
the production and distribution system is described by the (B4) loop, it consists of a
balancing feedback loop aimed to increase food supply and reduce food demand. In this
sense more production and distribution reduces food demand because increases food
availability, which in turn decreases demand. The balancing loop (B4) determines the
food gap.
We found another external loop (R5) between urban space and infrastructure and food
demand. Food demand pushes for the production processes and those in order to function
16
require certain, urban space and infrastructure in order to meet the requirement. This
reinforcing loop implies the burden of the food demand on the urban space, which
requires more than short term thinking to provide a consistent solution; this will be
further explained in the policies section.
Food demand at the market level is also limited or boosted by economy and job dynamics,
which puts a pressure on production and distribution activities from where job and food
market revenues are obtained (R3). An external balancing loop (B6) was found after
capturing the effect of economy on the urban space and infrastructure, economy will
boost the production and distribution process that will compete with other industries and
households for space and infrastructure. The development of food production and
distribution activities will rule economic dynamics as employment and therefore, income,
for people working in those.
Indirect effects of food market in economic growth and technology improvement are
shown with R4. The improvement of the efficiency of the supply chain depends on the
organization and the technology levels applied to production and processing which can
have an impact in the FSDS resource use and urban space due to technological advances
in distribution or production processes (B9). In this case B7 represents the food demand
out of the population change, which determines the need for production and distribution
processes and those the urban space and infrastructure change, which attracts population.
Population allows for the economic dynamics, which increases their income to demand
food in the market (B8) that follows the dynamic previously explained.
17
3.4 POLICIES
The red arrow in the system (Figure 5) is the main question of FFFA: “how to meet the
urban food needs in developing countries and those transition?” Policies aimed to
improve food security and food availability at market level, or to boost the willingness to
buy the available food in the market, are directly stimulated by the variation in food
demand at market level. The reduction of the food demand gaps is the most generic
objective of the food policies, but the effectiveness of policies have to consider important
leverage point in the system components. Following the FFFA perspective, future
policies should target few main areas:
1) Urban space and logistic, aimed to optimize the organizational part of the system; it
should aim to built an effective platform of food distribution able to support both the
increasing population and the increasing supply chain without increase the food
demand gap. Urban land and people density, low urban congestion, adequate food
road maps and number and type of regular markets and informal markets are the key
variables of this area that will reach the attention of policy makers (B10).
2
Technology level used in production and processing activities, in order to improve the
efficiency of the supply chain, control pollution, reduce wastes, etc.; it consists of a
balancing feedback loop aimed to increase food supply and reduce food demand
(B12);
3
Economic and jobs dynamics are needed to be the engine of food access (R6). In
addition, there is the hypothesis that the socio-economic aspects of urban population
are highly related with undernourishment/obesity problems and with the type and
quality of preferred foods by the consumers, in this case, a health assessment should
be considered out of the economic development. All the considered loops are aimed
to balance the forces that are driving the system behavior and to enhance system
equilibrium, which will cause the reduction of public policies.
4
An important “side effect” of the food policy could be represented by the relationship
with natural resources; land use, environmental sustainability and social metabolism
variables are strongly related with supply chains, consumers’ life and waste disposal
and it should be taken into account when food policies are designed in rural and urban
areas. In fact, a dangerous reinforcing loop, that links natural resources and growth of
18
human activities (both for supply chain and consumption) might drive an exponential
decay of the system inputs (R2). For that reason food policy have to directly take into
account the environmental impact of the entire chain and its future sustainability.
New indicators should be developed and FAO’s Food into the city collection late
publications shows urban planning as a central idea for policy. When policies of urban
planning are formulated, blue arrow in the system diagram (figure 5), they have indirect
or side effects in FSDS (B1, B2, R1, R2, BS).
3.5 FINDINGS
The identification of main stocks and dynamics of FFFA and its conceptualization and
characterization in a general FSDS structure allowed the identification of the main
dynamics where the urbanization loop and the pressures it represented in the system
(land use, congestion, rural population attraction) appeared to support this paper’s
hypothesis which stated the main problem of FSDS intended to meet urban food needs is
not just the Urban Population growth but the cities’ structure driven by the urbanization
paradigm and current increasing trend in developing and in-transition countries due to its
great consequences related to the congestion, land change, resource waste and population
attractor.
After this, as a primary analytical step, a framework setting was developed, which
explains in a more simplified way and at an abstract level the dynamics between land,
population, distribution and production process, resources, technology and job dynamics.
This systemic view provided us the insight that increasing the efficiency of the food
production and distribution (production, assembling, handling, processing, packaging,
transport, storage, wholesaling and retailing) could lead to increase the food supply to
cities, reducing costs and waste, but, it does not imply automatically meeting the food
requirement per capita. Socioeconomic conditions as: the prevalence of poverty —topic
considered in the goal but absent of FAO’s policies-, ecological conditions as the
availability of resources and land or urban conditions as the urban space and
infrastructure will get the system working in misbalance with its capacity. In this sense,
we want to stress that as production and distribution processes will have to be improved,
19
other big issues of the system we inhabit will be have to acknowledge. It is irrelevant to
analyze FSDS without considering the important stocks that make them possible.
As summary, the next key points were elicited of the SD updates to FFFA:
1. The FSDS mechanics are embedded in the field of Urban Dynamics: population,
infrastructures growth and urbanization highly impact the FSDS organizational
capability to provide food.
2. Conceiving population growth as the main problem in effectively feeding cities
(thus given the raise in food demand) is only partially correct, hence it is wrong.
The way most cities are conceived structured and reproduced is what contributes
to the system being unsuccessful on meeting urban food needs.
3. Supply and Distribution Systems are just a part of a wider system. Urban, rural
and peri-urban dynamics cannot be longer treated in isolation if the aim is to meet
population food needs for the next decades.
4 CONCLUSIONS & AGENDA
The System Dynamics updates to FFFA by SYDIC, developed using System Thinking
(ST) and System Dynamics (SD) approaches, have consisted on:
1) The development of an epistemic ground to understand FSDS assessing their
characteristics and properties as complex systems, in order to evaluate the feasibility of
using complex system methodologies to analyse them.
2) The analysis of the FFFA and other documents from the Food into the Cities
collection using ST and SD. Which includes the generation of system archetypes
analyses, to qualitative characterization of FSDS by two group model building sessions
with FAO experts, the creation of a framework setting to analyse the main FSDS
dynamics (presented in this work) and a first simple quantitative model to analyse the
population growth and urban dynamics impact on food supply and distribution system
efficiency to eliminate the gap among food demand and food availability in a city (still
on progress).
20
This work allowed us to propose a clarification on the FFFA statements about the
Problem, Goal and Feasible and effective solutions. This clarification is useful to grasp
FAO’s problem understanding; clarify its competence as international organism in the
possible solutions by identifying what actions could be useful for decision makers and are
possible to execute by FAO.
The improvement of the FSDS is the solution proposed by FAO but its recommended
policies are not free of conflict or contradiction with others. Interventions involve a
stakeholder’s variety with specific interests where an agreement is unlikely to happen. An
advice for FAO from system thinking is to /ook beyond the players, the rules; given that
solving specific needs of agents, as numerous policies proposed, might not be as
significant as analysing interconnections among the system elements and identify the
information and material flows that makes them operate in certain way (Meadows, 2008).
Opportunities of this research are in line with FAO’s competence. As international
organization it lacks of coercive capacity but has a potential role in disseminating useful
information for better decision-making. Specifically, it could promote the FSDS
dynamics understanding with its internal and external constrains for improving
organization capacities and, finally, positively impact food security.
The ST and SD approaches applied to the FFFA resulted useful to explain the importance
of overcoming the focus on the urban population growth as the main FSDS problem
without being critical to way the cities are structured, which was the hypothesis managed
in this paper. In the current work it is shown how urban population increase represents a
pressure on the system to provide food, yet, the urban dynamics closely related to the
urbanization and its consequences highly impact the natural resources (land, water,
production and distribution assets on which FSDS depend) and is this urbanization
paradigm what keeps pushing the increase of urban population. The ST and SD have
allowed for a critical perspective also on the solutions proposed as any improvement on
the food production or distribution processes might not imply automatically meeting
urban food needs. An additional important finding is the consideration of the poverty
condition, as a necessary issue to address to meet food needs.
vA
The current paper findings present the limitation of being done under qualitative
assessment. The goal for the next part of SD updates to FFFA, throughout quantitative
modelling out of the ST/SD approaches, will focus on providing instruments to urban
policy makers to:
= Estimate the urban food demand;
= Estimate the food supply to the cities;
= Dimension the infrastructures that maximize food availability, food quality and
health safety of food chains;
= Dimension the interventions to reduce food gaps at market level acting on urban
planning and land use planning and suitability;
= Reduce the environmental impact of food systems in order to increase
sustainability.
= Discern between the most significant policies for their implementation and
explore their priority according to the outcomes desired, through simulation.
Among the scientific community, multidisciplinary and co-disciplinary approach is
encouraged to develop quantitative models and sub-models of the proposed framework
on the basis of FFFA, background and expertise.
5 ACKOWLEDGEMENT
The authors would like to thank Olivio Argenti, Coordinator of the Project “Meeting
urban food needs”, from the Food and Agriculture Organization, (United Nations, B619 —
AGS, FAO - Rome - Italy) for providing valuable information and prior knowledge on
the FAO’s FSDS framework of analysis, and for the help in the building of the revised
SD framework.
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