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S. Namany and T. Al-Ansari
biofuel production, food production and raising of livestock. Outcomes indicate a
negative effect on food security, as investing in biofuels reduce lands available for
agriculture causing food shortages and therefore causing a surge in food prices [84].
DS modelling can be also used in assessing social concerns associated with food
security. For instance, Galli et al. investigated the relationship between the disproportionate food security across the globe due to poverty in low-income countries,
and excessive food waste in wealthier nations and developed regions. In order to alleviate these two social issues, a holistic framework based on a DS model that depicts
the behaviour of diverse stakeholders involved in the food sector stream, including
consumers, retailers, farmers and caterers, is used to map the links between waste
generation, food redistribution for social causes and food poverty [85].
4.1.3 Agent-Based Modelling
Considering an agriculture farm that involves a fodder production entity and livestock
farming, as an example, making a decision to grow a specific fodder type in a particular land under certain climatic conditions would require a deep and comprehensive
understanding of the requirement of the crop in terms of water consumption and
nutrient intake. Considering that SD is a top-down approach that analyses a system
using an aggregated perspective would not be effective in capturing the individual
characteristics of each crop. Instead, it would provide an overview on the impact
of cultivating that fodder on the raising of livestock, as a different component of
the analysed system. SD is efficient in depicting the relationships between diverse
elements of the system; however, it does not clearly depict the inner functionalities
for each unit of the system [86]. Agent-based modelling (ABM) is an alternative
approach that can simulate the system while intricately capturing the functionalities
of its smallest components. The ABM follows a bottom-up approach that allows the
communication between independents entities known as agents. It is characterised
by modularity in simulation, which enables the aggregation of complex and largescale problems into a set of sub-systems that are studied individually and are then
aggregated to form a complete and dynamic solution [87].
ABM is usually applied to depict the interactions between independent agents
coexisting in an instable environment. It simulates the interactions amongst agents
in addition to the interactions between agents and their surrounding environment.
Agents in an ABM are independent entities represented by some exclusive attributes
and behaviours. The interactions between agents and the responses to the environment are governed by a set of behavioural rules established by agents or inflicted
by external influences [88]. ABM offers a flexible and dynamic ground to solve
complex system involving multiple stakeholders with divergent or convergent targets
by allowing them to set their rules and interact freely to achieve their goals. This
dynamic property in the ABMs is very efficient to direct decision-making in complex
and multi-disciplinary problems, principally those comprising of multiple levels and
stakeholders, such as EWF nexus resource systems. As such, food security, which
S. Namany and T. Al-Ansari
biofuel production, food production and raising of livestock. Outcomes indicate a
negative effect on food security, as investing in biofuels reduce lands available for
agriculture causing food shortages and therefore causing a surge in food prices [84].
DS modelling can be also used in assessing social concerns associated with food
security. For instance, Galli et al. investigated the relationship between the disproportionate food security across the globe due to poverty in low-income countries,
and excessive food waste in wealthier nations and developed regions. In order to alleviate these two social issues, a holistic framework based on a DS model that depicts
the behaviour of diverse stakeholders involved in the food sector stream, including
consumers, retailers, farmers and caterers, is used to map the links between waste
generation, food redistribution for social causes and food poverty [85].
4.1.3 Agent-Based Modelling
Considering an agriculture farm that involves a fodder production entity and livestock
farming, as an example, making a decision to grow a specific fodder type in a particular land under certain climatic conditions would require a deep and comprehensive
understanding of the requirement of the crop in terms of water consumption and
nutrient intake. Considering that SD is a top-down approach that analyses a system
using an aggregated perspective would not be effective in capturing the individual
characteristics of each crop. Instead, it would provide an overview on the impact
of cultivating that fodder on the raising of livestock, as a different component of
the analysed system. SD is efficient in depicting the relationships between diverse
elements of the system; however, it does not clearly depict the inner functionalities
for each unit of the system [86]. Agent-based modelling (ABM) is an alternative
approach that can simulate the system while intricately capturing the functionalities
of its smallest components. The ABM follows a bottom-up approach that allows the
communication between independents entities known as agents. It is characterised
by modularity in simulation, which enables the aggregation of complex and largescale problems into a set of sub-systems that are studied individually and are then
aggregated to form a complete and dynamic solution [87].
ABM is usually applied to depict the interactions between independent agents
coexisting in an instable environment. It simulates the interactions amongst agents
in addition to the interactions between agents and their surrounding environment.
Agents in an ABM are independent entities represented by some exclusive attributes
and behaviours. The interactions between agents and the responses to the environment are governed by a set of behavioural rules established by agents or inflicted
by external influences [88]. ABM offers a flexible and dynamic ground to solve
complex system involving multiple stakeholders with divergent or convergent targets
by allowing them to set their rules and interact freely to achieve their goals. This
dynamic property in the ABMs is very efficient to direct decision-making in complex
and multi-disciplinary problems, principally those comprising of multiple levels and
stakeholders, such as EWF nexus resource systems. As such, food security, which