Paper presented at the 32" International Conference of the System Dynamics Society
July 20-24, 2014, Delft, The Netherlands
Dynamic thinking about food system vulnerabilities
in highly developed countries: Issues and initial
analytic structure for building resilience
Krystyna A. Stave’, Birgit Kopainsky”
1 School of Environmental and Public Affairs, University of Nevada, Las Vegas.
krystyna.stave@unlv.edu.
2 System Dynamics Group, Department of Geography, University of Bergen, Norway.
birgit.kopainsky@geog.uib.no.
Abstract
This paper presents the first step in applying a system dynamics analysis to the question of food
system vulnerability and resilience in developed countries. Existing literature and models about
food security largely focus either on effects of climate change on agriculture at a global scale or on
specific issues on a regional scale, most often in food insecure countries. While there has been
considerable attention in highly developed countries to specific issues such as organic food, agri-
business, effects of pesticide and fertilizer runoff, or introduction of genetically modified organ-
isms, it is hard to get a clear picture of the ways in which the food system as a whole is vulnerable
and resilient at the national level. Here we synthesize literature about food systems and discuss
differences in food systems between less developed and highly developed countries. Based on the
literature review, we develop a conceptual model. We describe next steps for developing an op-
erational model for raising awareness of vulnerability and resilience in developed country food
systems.
Introduction
Food system vulnerability and resilience in highly developed countries have not been much stud-
ied from a national-level, whole-system perspective, especially for more developed
tries. Most of the literature on food systems focuses on food insecure countries, with good rea-
son. Worldwide, over 850 million people do not have regular access to the minimum calories they
require on a regular basis, and most of those are in Asia and sub-Saharan Africa (Hammond &
Dubé, 2012). Population growth, changing consumption patterns, increasing pressures on land for
uses other than agriculture and changing climatic conditions threaten food supplies (Godfray et al.
2010a). Further, significant impacts of climate change are expected on smallholder farmers be-
cause of their limited access to adaptive technology and agricultural practices (Muller et al. 2011).
A number of studies have examined different aspects of food security in developing countries
(e.g., Quinn 2002, Bach and Saeed 1992 (Vietnam), Bala 1999 (Bangladesh), Coxhead 2000 (Phillip-
pines), Falcon et al. 2004 (India), Holden et al. 2005 (Ethiopia)).
On the global level, the literature about food security primarily concerns effects of climate change
on crop production (e.g., Rosenzweig and Parry 1994, Schmidhuber and Tubiello 2007).
Rosenzweig et al. (2013) describe the AgMIP project, which links climate change models with crop
production and economic models to examine potential effects on food supply in different regions.
Other studies have examined the role of global trade (e.g., Rosegrant et al. 2005). Schmidhuber
and Tubiello (2007) point out, however, that food security is more than just ensuring a sufficient
food supply. It also is a function of stability of food supplies, access (resources or other means to
get food), and utilization of food resources (ability to use food effectively, which is affected by
health conditions.)
In more developed countries, discussions about food systems focus more on food-related health
issues such as obesity and malnutrition than on hunger (Hammond & Dubé, 2012). The U.S. has
long had a high and growing rate of obesity, but other countries such as Australia, U.K., and China
are also showing similar alarming trends, to the degree that the world’s overweight and obese
population now exceeds the world’s underweight population (Popkin 2006; Popkin 2010). Other
issues in the literature include managing greenhouse gas emissions from the food chain (Garnett
2011), evaluating whether the movement toward local food sources promotes environmental
health (Edwards-Jones 2010), how convenience stores affect food choices (Sharkey et al. 2013),
and how consumption patterns affect land use requirements (Gerbens-Leenes and Nonhebel
2002). There is a lack of system-wide analysis of the links and feedback mechanisms between dif-
ferent aspects of the food system.
The aim of this paper is to establish a framework for critically analyzing system vulnerabilities, op-
portunities to build resilience and tradeoffs between policy options to promote resilience. In de-
veloped countries, consumers in the system have the potential to influence system resilience
through their choices, so one goal is to present a system-wide perspective for communicating sys-
tem tradeoffs to the general public. Another goal is to illuminate potential vulnerabilities that
might require national policy interventions.
For this purpose, we review the literature about food system issues, differences between develop-
ing and more developed country food systems, concepts of vulnerability and resilience, existing
models, and food system structure to distill an initial causal structure. We then describe the
framework for developing a simple operational model from this structure and the questions to be
examined.
Structure and Vulnerability of Food Systems in Developed
Countries
Food systems, at a minimum, comprise the sets of activities involved in food production; pro-
cessing and packaging; distribution and retail; and consumption (Ericksen, 2008). Each of these
activities is influenced by, and influences a range of social, economic, political, health and envi-
ronmental factors such as agricultural technology, land ownership, capital markets, infrastructure,
distribution of disease, access to clean water, and social norms and practices related to hygiene
and food preparation (Hammond & Dubé, 2012; Muller et al. 2011). Activities cross multiple levels
of scale, from individual producers (farmers) and consumers to regional, national, international
markets, and global supply chains. Agri-food systems with their respective food system activities
are the manifestations of political, biophysical, socioeconomic and other drivers that lead to sus-
tainable or unsustainable outcomes, depending on the perspectives, scales, valuations of trade-
offs and time frames considered (Neufeldt et al., 2013).
A food system can be conceived as including the determinants and outcomes of its activities. The
determinants describe the bio-geophysical as well as the social, economic and political environ-
ments that determine how food system activities are performed (food system drivers). These ac-
tivities lead to a number of social, environmental and food security outcomes. Food system activi-
ties and outcomes eventually result in processes that feed back to environmental and socioeco-
nomic drivers (Ericksen, 2008; FAO, 2008; cf. Figure 1).
Figure 1: Food systems, their drivers and feedback (a); components of food systems (b) (Ingram et
al., 2010: 28)
Food System ACTIVITIES
Producing food: natural resources, pus, markets,
standards,
Distributing & retailing food: transport, markeling, advertising, .
preparation, customs,
Food System OUTCOMES Contributing to:
‘Social Welfare Food Security, ie. stability over time
tect) for:
Employment
Social capital
Poltical capital jee
Human capital
While the basic structure of food systems is similar everywhere — food is produced, processed,
distributed, and consumed — the details are considerably different between developing and devel-
oped countries. The length of the food chain is greater in developed than developing countries,
production depends more on technology and external inputs of fertilizers and pesticides in devel-
oped countries and more on labor in developing countries, there is more diversity in foods availa-
ble for consumption in more developed countries, and more highly developed countries consume
more meat, for example. Godfray et al. (2010b) note that 30-40% of food in most countries is lost
to waste, but where the waste occurs is different. In developing countries, most is lost on the
farm and in transport/processing, while in more developed countries, most is wasted after initial
processing, in retail, food service, and homes and municipalities (Gustavsson et al., 2011). Reduc-
ing waste thus requires different strategies. In the developing world, better storage and transport
technologies would help, while in developed countries, waste reduction may require changes to
food expiration date regulations, or education to change food handling behavior or consumer atti-
tudes.
Other differences include the relative importance of energy for storage and distribution, effects of
pesticide and fertilizer use on the environment, and consumer preferences. In the U.S., as distri-
bution networks have increased, the amount of storage has decreased, raising questions about the
effect of a prolonged fuel shortage or disruption in transportation. By one estimate, the popula-
tion of Hawaii needs 2,000,000 meals-ready-to-eat (MREs) to survive one month without electrici-
ty but the emergency management agency in the state has only 35,000 stored (Marten, pers.
comm.). Greater emphasis on local food sources is being promoted as a solution to what some
consumers perceive as too great a separation between themselves and the source of their food
(Seyfgang 2006). Decreasing ‘food-miles’ is thought to increase food quality and decrease envi-
ronmental pollution. However, decreasing food-miles may not have the benefits envisioned (Ed-
wards-Jones 2010, Edwards-Jones et al. 2008), and may even have unintended negative conse-
quences. Food system analysts in Australia are concerned that shifts to local food sources may
erode regional infrastructure and leave local communities vulnerable if local sources fail (Larsen,
pers. comm.). In Switzerland, where a large part of the calories consumed are imported, changes
to consumption patterns can have far-reaching impacts (Kopainsky et al., under revision).
Compared with the problem of providing enough food to people in food insecure parts of the
world, food system issues in the developed world seem at first glance to be less significant. But
complex connections between food storage, food-miles, nutrition, and environmental and human
health raise questions about how the system might respond to different kinds of disturbances
such as climate change, energy shortage, prolonged water stress, pest infestation or animal dis-
ease, or pandemic. Another key challenge is that of aligning food security outcomes (e.g., provid-
ing the population with at minimum sufficient and at maximum desired amounts of food) with
environmental outcomes (e.g., enhancing and maintaining the provision of ecosystem services).
This challenge is exacerbated by processes such as intensification, specialization, distancing and
concentration/homogenization, which make it difficult to relate concerns about the quality of food
and about environmental risks to consumer choices and to food production methods (Sundkvist et
al., 2005).
Vulnerability of a food system is a function of exposure, sensitivity and adaptive capacity (Ericksen
et al., 2010). Food systems can be vulnerable to a variety of stressors (Adger, 2006; Leichenko &
O’Brien, 2008), and they often constitute a combination of environmental stresses and institution-
al as well as policy failures. Analyzing the vulnerability of an agricultural region fails to adequately
capture the complex spatial and temporal interdependencies and feedbacks that determine how
food security outcomes are achieved. Attention is thus required not only on the vulnerability of
4
food availability but also of food access and utilization (Eakin, 2010). For example, this implies de-
voting attention to the vulnerability of food storage, processing, distribution and retailing, as these
activities link production and consumption across space and time (Eakin, 2010). In the USA, for
example, the railway system and sea ports are thought to be vulnerable to a variety of climatic
disturbances, including flooding, increased severe hurricane activity and sea-level rise (NRC, 2008).
In this analysis, we examine the kinds of disturbances to which developed country food systems
are most vulnerable, how might they be made more resilient to those disturbances, and what po-
tential unintended consequences might result from popularly advocated policies such as greater
localization of food sources.
Resilience thinking has its origin in ecology (Holling, 1973) but has since been expanded to social-
ecological systems (Adger, 2000; Adger et al., 2005; Carpenter et al., 2001; Folke, 2006). Resilience
can be defined as the ‘capacity of a system to experience shocks while retaining essentially the
same function, structure, feedbacks, and therefore identity’ (Walker et al., 2006). Resilience is al-
ternatively used to describe the disturbance that can be absorbed before a change in state (e.g.,
Holling, 1996) or the rate of recovery from perturbation (e.g., Adger, 2000). Resilient systems are
less vulnerable to stressors.
In system dynamics terms, we might characterize the resilience of a food system, or a portion of
the food system, by examining the stocks. Any given stock might be sustainable if the flows in and
out of the stock are the same. A high level of food available for distribution, or a high level of food
storage can be maintained by managing the inflows and outflows to match each other. But that
sustainable condition can also be met with low levels in the stock. A disturbance to the system
might be represented by a sudden, large change to the outflow. For example, a failure in the elec-
tricity grid that affected food storage cooling systems in a way that some portion of food in stor-
age that’s perishable is spoiled. If the disturbance occurred with a large amount of food in stor-
age, the sudden increase in outflow might reduce the amount significantly, but still leave food
available to be consumed. If the system is being maintained with a very low level of stored food,
however, a shock might decrease this to zero, which then might increase the number of deaths in
the population, potentially decreasing the labor force and ultimately decreasing the amount of
food we could continue to produce. These different conditions for sustainability have different
implications for the resilience of the system to withstand a shock to the system.
Existing Food System Models
While the food system representation in Figure 1 gives a conceptual overview of the key sectors
involved in food systems, it fails to explicitly illustrate the feedback mechanisms that might lead to
counterintuitive system responses to initiatives such as local food movements or how food storage
issues might make the system vulnerable to particular disturbances. Here we briefly review ef-
forts to model food system dynamics and then discuss how system dynamics tools can add value
to modeling efforts.
Existing models of large-scale food systems, i.e., at the national or global level, incorporate few, if
any, feedback mechanisms. Decision support tools such as general equilibrium models or optimi-
zation models are widely used for an integrated analysis of the environmental and (socio-) eco-
nomic impacts of development trends and policy interventions in SES (Janssen et al., 2011; Rossing
et al., 2007; Schliter et al., 2012). These tools allow for the simultaneous assessment of environ-
mental and economic impacts in different sub-systems and over time to capture dynamics and
emerging trade-offs. The IIASA Basic Linked System (BLS) model presents one major effort to link
national agricultural systems with each other to examine how they affect one another (Fischer et
5
al. 2002). The BLS model is a general equilibrium model. It does include some environmental fac-
tors such as variables that are affected by climate change. However, there is no feedback from the
economic part (production amount and value) to the environment and this is a limitation for the
purpose of examining vulnerabilities and tradeoffs within a given national system.
Other approaches for agricultural sector modeling are Life Cycle Assessment, Material Flow Analy-
sis, and Environmentally Extended Input-Output Models (Minx et al., 2009; Stoessel et al., 2012;
Carlsson-Kanyama & Gonzalez, 2009; Corson & van der Werf, 2012; Jungbluth, 2000; Nemecek &
Gaillard, 2008; Xue & Landis, 2011). While these provide a detailed overview of physical as well as
economic flows and dependencies, they do not explicitly represent the agricultural production
processes. A wide range of agricultural sector modeling studies address the effects of policy
changes in Switzerland on agricultural production. These studies demonstrate the complex inter-
actions between farm decision-making and environmental outcomes at the sectoral or farm gate
level (Briner et al., 2012; Huber et al., 2013; Schiipbach et al., 2008). However, these studies do
not take into account the environmental and economic impact in the supply chains of agriculture.
Simulation models such as agent-based models and system dynamics offer powerful tools to mod-
el food systems interactions and feedbacks (Hammond & Dubé, 2012). Simulation models help
unraveling the complex and dynamic interactions and feedbacks among biophysical, socio-
economic and institutional components across levels and scales (Dent et al., 1995; Feola et al.,
2012; Janssen & van Ittersum, 2007; Schiere et al., 2012). Agent based modeling approaches are
widely used to model social-ecological systems such as food systems (Heckbert et al., 2010; Huber,
et al., 2013; Le et al., 2010; Le et al., 2008; Le et al., 2012; Li, 2011; Miller & Page, 2007; Schliiter,
et al., 2012; Tesfatsion & Judd, 2006). By representing flexible feedback loop structures (built on
emerging interactions rather than fixed cause-effect relationships) across levels, the approach is
valuable for explaining structural/organizational adaptation to changes in system drivers. Though
the technique can theoretically address cross-scale interactions, it is still challenging to do so due
to excessive data requirements.
A growing body of literature studies transformation processes in the agri-food system resulting
from different policy interventions and the interaction of different stakeholders following a system
dynamics approach (Belcher et al., 2004; Georgiadis et al., 2005; Jones et al., 2002; Kopainsky et
al., 2012; Saysel & Barlas, 2001; Saysel et al., 2002; Shi & Gill, 2005).
Causal Structure of a System Dynamics Model
Hammond & Dubé, (2012) propose a high-level conceptual systems model linking the agri-food
sub-system with the environmental and health sub-systems through individual choices and out-
comes (Figure 2). Their model examines effects of these three systems on individual decisions
(such as work, food choices) and individual outcomes (such as health and economic status).
The main processes in the agri-food (production of raw food materials, processing and packaging
for consumption, distribution, use by consumers) are affected by influences from other sub-
systems and contain reinforcing feedbacks such as: poor nutrition > poor health > decrease in
nutrient absorption > poor nutrition.
Figure 2: A systems framework for food and nutrition security (Hammond & Dubé, 2012: 2)
Pousion ! Caan Water Avainbity &
Food Aminty
Muller et al. (2011) suggest the following key elements that should be part of a comprehensive
food systems model.
System structural issues that might affect vulnerability and resilience include: the length of the
food supply chain, connection to global demand and supply patterns and trade, input require-
ments of the agricultural value chain, degree of centralization of the food system, existing cli-
mate variability, existing mechanisms to cope with variability, types and diversity of cropping
systems, efficiency of market access.
Some of the likely stresses on the system beyond climate change include population growth
and shifting patterns of food intake due to urbanization and development. Indirect effects of
climate change and other stressors include changes in pests and pathogens.
Possible adaptations include changing the types of agricultural production (from livestock to
crops, or from crops to trees) or changing agricultural inputs required. They suggest tradeoffs
be explored between agricultural expansion, intensification and trade.
Figure 3 shows an initial stock and flow structure that aims to simplify the system as much as pos-
sible for explanatory purposes while still incorporating key components. It also identifies entry
points for disturbances that increase vulnerability. Thus, it provides an initial representation of
system structural issues and of some of the likely stresses from the list above.
Figure 3: Initial stock and flow structure exploring structural issues of and stresses on food systems
Notes:
Variables in red: sources of vulnerability
The stock and flow structure in Figure 3 shows the following entry point for disturbances or
stressors (representing sources of vulnerability):
¢ Energy prices/fossil fuel prices (e.g. “PULSE crisis that affects energy production”): These
stressors on the one hand affect the high external input farming systems with their high relia-
bility on mineral fertilizers and labor-saving mechanization and on the other hand the trans-
portation costs involved in moving food and food products from one food system activity to
the other, especially the movement of imported products.
* Disruptions of storage, processing and transportation infrastructures (e.g., “PULSE crisis that
affects transportation infrastructure”). Such disruptions affect the movement of food and food
products from agricultural production to consumers. The length of a supply chain can increase
their vulnerability to global disruptions. Entirely local supply chains on the other hand can be
vulnerable to local disruptions. Effective supply chains thus rely on diversity in the length and
kind of supply chains.
* Destruction of productive resources resulting from, e.g., droughts or floods (e.g., “PULSE crisis
that affects land available”).
¢ Indirect effects of climate change such as changes in pests and pathogens affect the success of
the two main farming systems differently. The diversity inherent in low external input farming
systems makes it more likely that the farming system can cope with such changes.
* Stresses on the system beyond climate change include population growth and changes in con-
sumption patterns but also the potential impacts of pandemics and other disturbances on the
availability of agricultural labor force necessary for (domestic) production (e.g., “PULSE crisis
that affects health”).
In addition to these exogenous sources of vulnerability, the representation of a food system in
terms of stocks and flows indicates additional sources of vulnerability. Reliable provision of food
for a more demanding and potentially growing population requires food to move through the var-
ious stocks in the food system, that is, from the stock of raw food produced all the way to food
available to consumers. The reliability of this process not only depends on the flows linking the
various stocks but also on the average residence time of food in each of the stocks. Low residence
times indicate fast movements of food through the chain. This makes the food system efficient as
long as flows are left uninterrupted. Low levels of stocks, however, can affect food provision very
negatively in situations where inflows are interrupted or stocks are depleted in the course of envi-
ronmental, economic and social shocks.
A key challenge for national food systems in developed countries is that of aligning food provision
with environmental welfare. Figure 4 aggregates the stock and flow structure from Figure 3 but
adds a series of mechanisms that explain the relationships between food provision and environ-
mental welfare. It picks up the third element of a comprehensive food systems model from the list
by Muller et al. (2011) by sketching initial structures for exploring tradeoffs in food systems.
Figure 4: Initial structure for exploring tradeoffs in food systems
Environmental welfare and food provision are driven, among others, by:
¢ Astock of productive resources such as land, water, nutrients, soil organic matter and plant
genetic variety. In Figure 3, this stock was labeled “soil quality”. Here, we use a more generic
term that includes a variety of productive resources. The level of this stock determines the
health of natural resources and thus provides information about environmental welfare.
¢ The productivity of this stock, that is, the amount of food that can be produced with one unit
of productive resources. For plant production, productivity is equivalent to yield. The total
productivity variable provides information about the capacity of the agricultural system to
produce food and thus information about food provision.
The flows affecting the stock of productive resources are determined, among other things, by the
implemented agricultural production system. Agricultural production systems are characterized by
the type and amount of external inputs necessary to support it, by its productivity, and by its envi-
ronmental impacts. We differentiate between two main farming systems:
¢ High external input production systems: Commercial market orientation; Use of improved
high-yielding varieties; Mechanization with low labor intensity; Almost complete reliance on
external synthetic inputs (fertilizers, pharmaceuticals, etc.). High external input systems tend
to overexploit, that is, degrade productive resources, e.g., through the dispersion of pesticides
into the environment.
¢ Regenerative production systems: Largely subsistence focus and less market orientation; Use
of traditional cultivars; High labor and knowledge intensive techniques; No or very little appli-
cation of external nutrients, no use of synthetic chemicals for pest and disease control, but
high emphasis of on-site nutrient cycling. Regenerative agricultural production systems tend to
be characterized by diverse cropping systems that help rebuilding and maintaining stocks of
productive resources.
The extent to which each production system is implemented follows the logic of the success to the
successful archetype (Senge, 1990):
¢ The variables “resources to high external input”/”resources to regenerative agriculture” repre-
sent the potential yield of the two production systems.
¢ The variables “success of high external input”/”success of regenerative agriculture” represent
the realization of the potential yield (with different polarities, the variables could also repre-
sent the remaining gap between realized and potential yield).
With the availability of cheap fossil fuel, external inputs such as fertilizer and pesticides have been
used over the last decades to boost crop yields (Sundkvist, et al., 2005). The success of this high
external inputs production paradigm reinforces itself and forces the alternative production para-
digm, one that treats natural resources in a regenerative way, into a vicious cycle.
One of the problems with this success to the successful archetype is that once the costs of exter-
nal inputs increase or if external inputs are not available at all, it takes a long time for the regener-
ative agriculture paradigm to be effective. The dominance of the high external input paradigm
erodes the knowledge base necessary for a successful implementation and continuous adaptation
of the regenerative agriculture paradigm. This indicates a hidden source of vulnerability of food
systems and especially one for which it takes a lot of time and effort to build resilience.
The health of natural resources is a proxy for options for adaptation and alternative solutions in
case of changes in the environment such as climatic shocks or pests and pathogens. Low health of
natural resources indicates low diversity and diversity is important for absorption of shocks, adap-
tation and alternative solutions (Berkes et al., 2003).
It is important to note, however, that the attractiveness of low external input farming systems,
especially under low-disturbance circumstances and as long as external costs are not internalized,
is bounded by higher labor and product costs caused by the diversity of products and practices.
The same applies to costs related to the diversity of processing and retailing facilities as well as the
diversity of imports. There is thus an inherent tension or an inherent trade-off between the adap-
tive capacity characteristic for low external input farming systems and the efficiency characteristic
for high external input farming systems (Darnhofer, Bellon, et al., 2010; Darnhofer, Fairweather, et
al., 2010).
10
Policy Analysis
In this section, we interpret strategies proposed to foster the achievement of food security out-
comes from the perspective of the conceptual model developed in the previous section.
The literature describes a variety of strategies proposed to meet the challenge of feeding an ever-
growing population with growing consumption needs while protecting the natural resource base
of agriculture and food systems (e.g., Foley et al., 2011 ; Godfray et al., 2010; Pretty et al., 2010;
Tilman et al., 2002; Tilman et al., 2011). Such strategies include:
¢ Sustainable crop production intensification, that is, producing more from the same area of
land while conserving resources, e.g., through the application of conservation agriculture prac-
tices, the use of good seed of high-yielding adapted varieties, integrated pest management,
plant nutrition based on healthy soils, efficient water management, and the integration of
crops, pastures, trees and livestock. This strategy is knowledge-intensive so that policies for
sustainable intensification should build capacity through extension approaches (FAO, 2011).
¢ Increasing production limits, mostly through genetic modification of crop and livestock species.
¢ Reducing waste and losses.
* Changing diets.
¢ Expanding aquaculture.
In subsequent versions of this paper and based on a revised conceptual framework, we will be
able to discuss in detail the dynamic implications of such strategies and to interpret them in terms
of feedback loops and potential unintended consequences resulting from the interaction of these
loops. Increasing production limits through either conventional breeding or genetic modification,
for example, aims at increasing the potential yield. However, such strategy is depends on the
availability of external inputs such as fertilizers, herbicides and pesticides. Our conceptual model
shows that high external input farming systems degrade the stock of productive resources, which,
in the long run, increases the vulnerability of the food system. This strategy is thus likely to result
in better-before-worse behavior. It is also in itself vulnerable as its attractiveness is highly de-
pendent on the costs and availability of external inputs.
Discussion and outlook
The objective of this paper was to develop a system dynamics model for raising awareness of the
vulnerabilities of national food systems in developed countries and for identifying entry points for
increasing their resilience. For this purpose, we synthesized the existing literature on food system
frameworks and food system modeling into a conceptual model that describes the main causal
structures responsible for the generation of food security and environmental welfare outcomes.
Based on this dynamic representation of national food systems, we provided an initial discussion
of the dynamic implications of various strategies proposed to improve food system outcomes. This
discussion highlighted that the vulnerability of a national food system does not automatically re-
sults from exogenous shocks that might affect the country. Instead, it is either exacerbated or
dampened by the interaction of the various feedback loops in the national food system. Thinking
about the vulnerabilities of food systems therefore needs to go beyond an event-based view and
include an endogenous representation of the processes governing national food systems.
11
In subsequent versions of this paper, we will further develop this line of research and describe
next steps for developing an operational model that illustrates our current qualitative reasoning
and quantifies the tradeoffs involved in reducing vulnerability and increasing resilience in devel-
oped country food systems.
Acknowledgements
We would like to thank the 2013/2014 cohort of the European Master Students in System Dynam-
ics for their contribution to the initial stock and flow structure (Figure 3). One of the authors (BK)
was supported by the Norwegian Research Council through the project “Simulation based tools for
linking knowledge with action to improve and maintain food security in Africa” (contract number
217931/F10). The views and conclusions expressed in this paper are those of the authors alone
and do not necessarily reflect the views of the Norwegian Research Council.
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increased, or system understanding is decreased, when questions about the system focus on isolated
system elements and thereby induce local processing of the system.
Second, our results deliver an explanation of why scholars in previous research have not
found effects of data display on SF-performance, albeit it was argued that different displays should
exert an effect on system understanding (Cronin, et al., 2009). Specifically, in the example of the
Department Store task, Cronin, et al., (2009) argue that the numerical display should be easier to
understand since the “line graph may conflict with participants’ conception of the discrete event of
a person entering or leaving a store” (p. 120). While this reasoning is highly plausible, our results
suggest that the effect did not occurr because the questions were formulated locally by asking for
specific and isolated system-elements. We found that when questions are framed globally, data
display did exert an effect. Moreover, the direction of the effect was in line with Cronin et al.’s
(2009), expectations: Participants performed better in the numerical (depicting discrete events) than
the graphical display.
As the global presentation of the system in our specific SF-task not only led to higher
solution rates of the task but also enabled the numerical data display to have a significant impact,
the question arises whether something similar could hold for the remaining category of SF-failure,
namely the impact of previous knowledge. Future research will test the effectiveness of the global
presentation and the numerical data display with the same diversity of participants’ background and
in SF-tasks that are structurally identical to the one used in this study but situated in different
application domains.
Conclusion
We argue that, due to the hierarchical structure of dynamic systems, cognitive processing of
these systems should take place on different cognitive levels as well. In line with this expectation,
our results show that a local focus on lower-level system elements is beneficial for answering
16
questions about inflows and outflows, whereas a global focus on higher-level system elements is
beneficial for answering questions about the stock. Moreover, when the questions on the system are
formatted in such a way that they highlight interrelations between system elements (i.e., global
processing), as opposed to highlighting the system elements in isolation, we found an effect of the
display format such as it was anticipated previously by Cronin et al. (2009), but was not found
empirically in previous research. In sum, our results underline the importance of a match between
the hierarchical level that the format of a dynamic system focuses on, and the hierarchical level of
the system that problem-solvers and policy-designers need to understand.
The ability to infer the behavior of the system as a whole is relevant in a multitude of
systems, with the world’s climate system being just one example. Sterman et al., (2012)
demonstrate that the general public as well as policy makers have difficulties understanding the
dynamics between CO» emissions and CO accumulation in the atmosphere, the impact of time
delays, accumulations, feedback, and non-linearities. Publicly available information on this topic is
published by the Intergovernmental Panel on Climate Change (IPCC) and includes Assessment
Reports and the Summary Report for Policy Makers. Even the latter (IPCC, 2014), that addresses
the wide public untrained in climatology and physics, provides a multitude of detailed information
about the various aspects of climate change. It includes many specifics about scientific concepts and
their underlying mathematical details, including confusing units of measure like ‘GtCO -eq’ and
‘Wm’, for example. It also details likely human and possible natural causes of climate change,
their impacts on global mean temperature and sea level rise and possible future warming scenarios.
The Summary Report also lays out possibilities for adaptation and mitigation. Even though it
illustrates how some of the drivers of climate change interact with each other, the overall picture is
missing. The report focuses on great detail, without explaining the high-level insights with the
highest impacts and their interrelations.
The implications of our findings in the context of the world’s climate system are, for
example, that the effectiveness of communication of results by the IPCC could be improved. One
17
way for doing so is the approach that ClimateInteractive (cf. http://www.climateinteractive.org/)
and the MIT LearningEdge (cf.
https://mitsloan.mit.edu/LearningEdge/simulations/Pages/Overview.aspx) use to simplify
communication of climate change with the C-ROADS (Climate Rapid Overview And Decision
Support) model, a system-dynamics-based management flight simulator. One of the main purposes
of system-dynamics-based management flight simulators (Sterman, 1989, 2014a, 2014b) is to
provide users with the possibility to explore the behavior of a system as a whole, as it results from
the interplay of lower-level elements. C-ROADS (Sterman et al., 2013) helps users understand the
relationship between greenhouse gas emissions, atmospheric CO2, global mean temperature, sea
level rise, and ocean ph-level, for example (http://www.climateinteractive.org/tools/). When there is
interest, users may travel along the cone shown in Figure 1 deeper into details of mathematical
equations of the simulator, the lower-level system elements. Yet, on a higher level of abstraction, C-
ROADS sheds light on the fundamental insights of climate change. Based on the findings from our
study, it is important that management flight simulators are designed in such a way that they
facilitate global rather than local processing of the information provided in the user interface. The
same holds for briefing and debriefing of these management flight simulators.
18
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