Developing an ABM-driven Decision Support System
in the Emergency Services
Stephen Dobson
, Mark Burkitt
, Dermot Breslin
and Daniela Romano
International Centre for Transformational Entrepreneurship, Coventry University, Coventry, U.K.
Department of Computing, University of Sheffield, Sheffield, U.K.
Management School, University of Sheffield, Sheffield, U.K.
The Department of Computing, Edge Hill University, Lancaster, U.K.
Keywords: Decision Support System, Co-evolution, Connectivity, Agent-Based Model, Domestic Fire Risk Behaviour.
Abstract: The research presented here outlines an application of Agent-Based Modelling (ABM) used to support
strategic decision-making in the emergency services. Here the resulting ABM-driven Decision Support
System (DSS) (called ‘Premonition’) is designed to help practitioners engage with a complex and dynamic
environment of co-evolving fire risk behaviours through time. Social change is presented here as a process by
which behaviours co-evolve within connected networks of agents. ABM is identified as a beneficial approach
to simulate changing household behaviours and the influence of social networks, environmental factors, and
also fire service interventions within the Sheffield City region, UK. This project seeks to develop a DSS which
supports the stages of ‘intelligence’, ‘design’, and ‘choice’ as the decision-maker moves from identifying
problem areas, establishing possible strategies for intervention, and predicting possible outcomes of
alternative courses of action.
As public services face budgetary cuts in many
European countries, a ‘more-for-less’ mantra pushes
public sector managers to look for greater efficiencies
and value added decision-making. This is particularly
so with emergency services who are faced with
frontline realities on a daily basis. Strategic decision-
making in this arena aims to support the protection of
some of society’s most vulnerable, whilst balancing
decreasing budgets. Decision Support Systems (DSS)
may be seen as an ever more valuable part of the
effective management of resources in this public
service context. DSS have gained increasing
relevance in business and industry since the 1970s,
and particularly with the advent of data warehouses,
on-line analytical processing, data mining and the
Web in the 1990s. However, this paper focuses on
current developments in model-driven DSS as an
important support tool for managers to embrace a
more dynamic and evolving (co-evolving) problem
environment. The paper focusses on Agent-Based
Modelling (ABM) as an emerging approach in DSS
to help develop decision-makers’ understanding of
the complex social environments through which they
Understanding and predicting the behaviours of
households within a community is an important factor
in the planning and operationalisation of activities in
public service. For example, an important part of the
South Yorkshire Fire and Rescue, UK (SYFR)
Integrated Risk Management Plan 2013-17 has
involved the exploration of community fire risk.
Through the geo-demographic mapping of socio-
demographic lifestyle profile data (MOSAIC) the
service aims to improve their information provision
to the public and fire prevention work. Previous
academic studies can support our understanding of
community behaviours in relation to fire risk so as to
help services work on prevention measures in both a
strategic and targeted manner. For example, research
has shown that fire risk due to the behaviours of some
households differ for particular demographic and
socio-economic groups (Smith et al., 2008; Taylor et
al., 2012). By using socio-economic factors to
classify households into low, medium, and high fire
risk groups, preventative measures in the form of
information and support may be targeted at those
most in need. The expectation therefore is that
aggregate risk may be reduced over time in these
localities thus reducing the frequency of emergency
interventions in those geographic areas.
Dobson, S., Burkitt, M., Breslin, D. and Romano, D.
Developing an ABM-driven Decision Support System in the Emergency Services.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 151-157
ISBN: 978-989-758-187-8
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Secondly, statistical approaches may be used to
carry out detailed historical analyses of changing
patterns of behaviour. This enables resource planning
and allocation and for decision-makers to anticipate
need. This kind of method represents a simple model-
driven approach to DSS but it is not without
limitations. By classifying households into groups
based upon socio-demographic factors there is an
assumption that behaviours are static. In this sense,
membership is ascribed based on a notion of shared
characteristics and therefore fire risk by association is
intrinsic. The model does not account for changing
behaviours or the influence of one’s environment or
social connections. Using a linear model for
projecting historical data forward to predict future
outcomes will again assume a stable (static) problem
This paper outlines research into the development
of a practical model-driven DSS for implementation
in the emergency services with SYFR. It uses ABM
to develop a dynamic understanding of changing
behaviours within a community. Whilst the model
draws upon socio-demographic and historical data as
used in the approaches outlined above, the research
also explores the interacting mechanisms which drive
behaviour change. As such the authors present a
simple framework for modelling social change seen
as a process of co-evolution between community
networks (Breslin et al., 2015; Dobson et al., 2013).
This approach shifts the focus of attention from
individual households to the connected and co-
evolving sets of behaviours that they represent. To
further advance this approach, we have developed an
ABM-driven DSS to simulate changing household
behaviours within the Sheffield City region. This
DSS draws on historical data of fire incidents and
direct community interventions undertaken and
collated by SYFR over the last 5 years (e.g. home
safety checks, fire safety campaigns etc.). The project
aims to improve organisational decision-making
through the three stages outlined by Simon (1960).
These are: a) the 'intelligence’ stage of searching for
problems (i.e. the identification) by helping to
identify areas of risk; b) the ‘design’ of inventions
aimed at reducing risk; and c) ‘choice’ of a course of
action based upon predicted outcomes of
interventions (i.e. the exploration of alternatives). As
such it is anticipated that the developed DSS will
become a valuable tool in optimisation of resource
allocation planning of operations as well as
community prevention work.
Decision Support Systems (DSS) can be defined as
“computer-based tools that help users in a problem
solving environment to improve their productivity
and decision-making ability” (Bayraktar and Hastak
2009, p1357). Since the 1970s DSS have evolved and
gained greater prominence in organisational strategic
and operational decision-making (Lauria and
Duchessi, 2006; Chung et al 2004; Shim et al., 2002;
Keenan 1998; Jagielska, 1993; Jensen, 1990). Shim
et al (2002) outline that it was Gorry and Scott Morton
(1971), whose integration of Anthony’s (1965)
categories of management activity and Simon’s
(1960) description of decision types, were perhaps
most influential in defining the concept of DSS. For
Anthony (1965), management activities involved
strategic, management (or tactical), and operational
controls; layers of organisational decision-making
which are now universal tenets in management
thinking. Simon (1960) presented problems for
decision-makers as existing on a continuum from
‘programmed’, which are routine and repetitive
problems that are well structured and easily solved, to
‘non-programmed’. Non-programmed problems are
unique, ill-structured and more difficult to solve, i.e.
'wicked problems’ (Rittel and Webber 1973). To
represent this continuum Gorry and Scott used the
terms ‘structured’, ‘semi-structured’, ‘unstructured’
to develop a framework based upon Simon (1960)
incorporating the search for problems (intelligence),
the development of alternatives (design), and the
analysis of alternatives (choice) (Shim et al 2002).
DSS can be thought of as incorporating a wide
range of technics and applications to help decision-
makers with the processes of 'intelligence', 'design'
and 'choice' such as artificial intelligence, expert
systems, database querying and analytical predicative
modelling. However, the purpose of DSS is not to
provide a direct solution, but simply to add value to
any system output when reaching a decision
(Bayraktar and Hastak 2009).
From the early 1990s DSS benefitted from
emerging developments such as data warehouse, on-
line analytical processing (OLAP), data mining, and
the Web. Lauria and Duchessi (2006) outline that
DSS tends to fall into two main categories comprising
of either narrow or broad definitions. The narrower
or data-driven definitions include these emerging
applications, such as OLAP and data mining, which
enabled users to combine numerous databases
through explicitly defined ontological relationships
so as to enable multiple combinations of data
elements for analysis over time. Data was able to be
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
presented in various graphical formats to support
decision making. “Data mining applications identify
specific, unknown patterns in databases and data
warehouses that typical queries cannot reveal” (Ibid
2006, p1574).
Broader, model-driven DSS aimed to expand
upon techniques to solve complex and sometimes
unstructured problems using quantitative and/or
qualitative models. A model-driven DSS comprises
of three components (Shim et al., 2002) which
integrate, 1) data management/database; 2) model
management involving one or more models applied to
the problem, and; 3) dialogue management which
enables users to change some input variables and
initiate database and model management elements of
the DSS: “via the dialog module, users interact with a
DSS and can perform sensitivity, or “what-if,”
analyses to gain more insight into the problem and its
potential solutions” (Lauria and Duchessi, 2006,
Numerous model-driven methods of DSS have
supported the management of risk in decision-making
such as Decision Tree Analysis (Apolloni 1998),
Cause-Consequence Analysis (De Meaux and
Koornneef 2008; Lee et al 2008), Analytic Hierarchy
Process (Andreica 2009), Monte Carlo method (Hart
2008; Horstmann 2006) and Bayesian Networks
(Xiacong and Ling 2010). The modelling which
supports DSS has been subject to much research and
Cho (2007) identifies three generic strategies which
have shaped development in organisational and
decision-making arenas.
Initially reductive and simplistic computational
models have aimed to isolate key causal relationships
between data (Dodin and Elmaghraby 1985;
MacCrimmon and Ryavec 1964). To address the
limitations of these approaches, scholars have
pursued a second, more computationally intensive,
means of increasing predictive accuracy. Cho (2007)
identifies broad approaches (which include some of
those described above) within this second strategy,
these are the one-time update approach; the Markov
Chain Monte Carlo (Virto et al 2002), and Bayesian
networks. The Bayesian approach (Covaliu and
Soyer 1996) involves developing an acyclic network
of sequential and causal activities or elements in a
model with assigned probabilities. As elements
become known through observation within the
network, probabilities of outcomes are updated
according to Bayesian statistical inference. A third
strategy lies between these two ultimately static
approaches, which aims to maintain both levels of
simplicity whilst also accounting for complex
cyclical and dynamic dependencies and interactions
of feedback between activities.
DSS in the 21
century is described by scholars as
being characterised by collaborative decision-making
and collaboration platforms to support increased
connectivity and data sharing. Here we see a
migration of decisions made by individuals to ones
made by diverse groups or even multiple firms (Shim
et al 2002). However, as Keen (1987) identified in
the late 1980s, there is also a need to explore increases
in computing power to extend model-driven DSS to
embrace much more fluid, emergent and nuanced
picture of the decision environment. Mitroff and
Linstone (1993) suggest that this kind of shift would
include consideration of much broader organizational
and cultural factors than have featured in past DSS.
In the work presented here we present DSS in the
public service environment as needing to reflect
changing and dynamic processes of co-evolving parts
within a social system. The emergency services is
used here as an example of how a more responsive
and dynamic decision-making context can benefit
from an ABM-driven DSS approach.
“Agent-based modelling is a computational method
that enables a researcher to create, analyse, and
experiment with models composed of agents that
interact within an environment”. (Gilbert 2008, p2)
Agent-based models (ABM) consist of agents
modelled to interact with each other and the
environment through a set of predefined rules or
heuristics. Agents are broadly defined and are
distinct parts of a program representing social entities
such as individuals, groups, organisations or wider
social, political or economic institutions. Agents may
be defined as interacting and responding to physical
feedback through movement, as explored through the
analysis of swarm behaviour, or as in the case of this
research may be purely social entities interacting on a
behavioural level. As such, broad ranges of complex
adaptive systems have been modelled using ABM
frameworks. A Flexible Large-scale Agent
Modelling Environment on the Graphics Processing
Unit (FLAME GPU) (Richmond et al 2010) has been
used for this project. The technology merges the
modelling power of ABM with that of 3D graphics.
The resulting framework enables very large-scale
simulations with a massive number of agents to be
processed and visualised in real-time with both 2D
and 3D representation of the environment. Examples
Developing an ABM-driven Decision Support System in the Emergency Services
of FLAME GPU ABM research range from
modelling the illegal drug market (Romano et el
2009), to investigating innovative methods for
training public service staff working within the
community, to a simulated social crowd for the
training of CCTV operators to spot malicious
3.1 ABM-driven DSS
Given the complex and dynamic nature of decision-
making environment through which SYFR operate
(due to shifting household behaviours over time) it is
suggested here that the co-evolutionary process
through which change occurs may be further
understood through models. As described above,
previous approaches have used models to estimate
fire risk which have remained statically defined and
therefore unresponsive to change. By extending these
methods through the development of an ABM-driven
DSS we propose that the dynamics of change are
better reflected to the decision-maker. In ABM the
changing behaviours of specific households (or
‘agents’) are modelled computationally. These agents
are viewed as interacting heuristically within a
network of other households which define the
geographic community. The heuristics and agents for
the model are broadly outlined in the next section, and
also underpinned by literature reported on more fully
in a forthcoming publication dedicated solely to the
model specification. These include the influence of
connections upon household behaviour within a close
social network in relation to changing domestic
behaviours (i.e. smoking, consumption of alcohol,
use of electrical appliances and cooking practices). A
target community in South Yorkshire (United
Kingdom) was simulated over time through an ABM
approach to illustrate the emergence of patterns of
behaviour within a complex system of interacting
parts. A key advantage of ABM for DSS is that it
provides a simulated dynamic environment enabling
the decision-maker to carry out ‘what-if’ experiments
which would otherwise be impossible in live
scenarios. This helps the decision-maker to develop
approaches and design choices which maximise
opportunities for positive change.
The purpose of the model is to investigate the impact
that different intervention methods are likely to have
on fire risk in different areas, based on knowledge
about the people who live in the area. Each individual
household in the area is explicitly modelled, and
includes details about the individual behaviours and
fire risk factors of the people living in the household,
as well as a representation of their household level
social network. The simulation represents a set of
different intervention methods, which can be used to
predict which interventions are likely to have the
most impact under different conditions. Each
intervention can have both a direct influence on a
household, and an indirect influence via the
household’s social network.
Data on fire service callouts has been provided for
the development of the DSS ranging from 1st April
2009 until 1st December 2014. This contains
deliberate, accidental and unknown incidents, both
dwelling and non-dwelling. Each record contains
detailed information about the incident, along with an
Ordnance Survey grid reference.
Data from the Home Safety Check (HSC) reports
has also been provided for the period between 2009
and 2015. Each record in the data set contains a street
address, date, a single question and corresponding
answer. There are 70103 data rows, which
correspond to roughly 1589 households, which have
one or more repeat checks for different years.
Finally, MOSAIC demographic data containing
household level classifications for every household in
the area was also used to create the agent-based model
of fire-risk behaviours and comprised of 4723 unique
records in total for the area.
4.1 State Variables and Scales
Three different agent types have been identified;
these agents represent an individual household, fire
incidents and interventions.
The Household agent represents an individual
household within the area. This can be the occupants
of an individual house, or a flat within a larger
building. The Household Agent contains all the
parameters that define an individual household in the
area. Each household has a unique identifier and a
location which determines its physical location in
relation to other households. A household also has a
set of Risk Markers, which are used to determine the
Risk Factor, which is the relative risk that an
individual household will have a fire. The Risk
Markers are derived using all available information
about the households in the area. The Risk Factor
fluctuates in response to external influences and the
gradually decays over time back to the baseline.
As individual households are influenced by both
interventions and the influence of social connections,
the Household agent maintains an individual social
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
network, which connects it to other households in the
area. These relationships are modelled as properties
of a Household, each with a directionality and
weighting, representing the amount of influence that
one household has over another.
The households are influenced both directly and
indirectly via their relationships by interventions and
fire incidents. Intervention agents can have different
types, representing the range of different
interventions that can be performed, such as leafleting
campaigns, home safety visits and shock campaigns.
An intervention will typically have an area of
influence, a strength, representing its effectiveness,
and duration, representing how long the influence of
the intervention will last. A FireIncident agent
represents an individual household fire, which could
potentially spread to other households, and cause
fatalities in one or more households. For historical
time periods, fires are based on the actual fire
incidents. For future time periods, fires are random,
based on statistical analysis of the available data.
When a fire occurs, it is assumed that the occupants
won’t change. However, there will be an initial area
effect of reduced risk in the surrounding households.
The magnitude and radius of the effect will be related
to the incident severity. Changes to the
neighbourhood are not modelled, and the households
remain the same for the duration of the simulation.
A general systems diagram illustrating the DSS
(called ‘Premonition’) can be seen in figure1. Here
the inputs may be regarded as both data and also
management decisions. Management decisions
comprise of the levels suggested by Anthony (1965).
These are operational (planned operational
interventions and infrastructure), tactical
(performance measures) and strategic (policy and
budgetary considerations). The ‘Premonition’ ABM-
driven DSS combines these data and organisational
knowledge inputs through a model management and
processing component and dialogue management
(graphical user interface, GUI). The key output is in
the form of GIS mapping presented sequentially with
Figure 1: General system diagram for the ‘Premonition’ ABM-driven DSS.
Social network
Policy /
Operations and
Management Decisions
Visual GIS
Household risk
Area risk rating
Developing an ABM-driven Decision Support System in the Emergency Services
1 day time steps illustrating incidents, interventions
and shifting household fire risk over time.
This work adds to ongoing research which considers
the dynamics of networking processes and the nature
of connected behaviours which change and develop
over time. As such the resulting model aims to
‘embed’ the decision-maker in this process to provide
a richer and more nuanced picture of the ever
changing environment that they are working in. The
more cohesive and close-knit the network, the more
interpretive heuristics are shared between households
(Breslin, 2011; Dobson et al., 2013), and as a result
the fewer the opportunities for different
interpretations and with this possibilities for variation
and innovation. In these close-knit communities,
local authorities and service providers need to be
closely engaged and embedded in order to affect
change. Interventions should therefore be targeted at
key thought leaders, and positioned in terms of local
issues. On the other hand, in more sparsely connected
networks opportunities for change are increased.
However, interventions here would tend to be more
costly given the difficulty in reaching such diverse
groups. In both instances households which are more
socially isolated present the greatest challenge.
Validation is key to developing the predictive
power of ABM. In a sense the validation process
involves comparing and then fine-tuning the model to
reflect actual recorded behaviours of households. In
this study data will be drawn from three key sources.
In the first instance, a generic model of changing fire
risk behaviours is constructed drawing from
extensive academic and industry-focused research
noted above. Second, past research on fire risk
behaviours and trends, including both published
reports and data obtained by SYFR, is used to adapt
the generic model. Finally, region-specific data
collated by SYFR is used to fine tune the model. This
final process allows both historic data trends and the
local experience and expertise of SYFR staff to be
incorporated into the model. Once validated, the
model can be expanded to include other regions both
locally and nationally, to predict changing trends in
fire risk behaviours. In addition the model can be
further developed to consider other types of
household behaviours, for example related to health
and social care. In sum, once validated, the co-
evolutionary model can be generalised to simulate
changes in other household behaviours using the
developed understanding on connected social
Understanding and predicting the behaviours of
households within a community is an important factor
in the planning and operationalisation of activities for
the SYFR emergency service. To help support
decision-makers in this environment the paper has
focussed on the development of an ABM-driven DSS
to help develop decision-makers’ understanding of
the complex social environments through which they
operate. Whilst the model has drawn upon socio-
demographic and historical data as used in existing
approaches, the research has also explored the
interacting mechanisms which drive behaviour
change. The framework for modelling social change
is seen as an ever shifting interaction between agents
within a system. Three key agent types have been
identified in this study representing individual
households, fire incidents, and interventions. The
DSS is able to provide decision-makers with an
historical view of fire events and interventions in the
target area but also model changes in fire risk
behaviours. Whilst socio-demographics play an
important part in the baseline risk of households,
interventions, fire events and the influence of primary
and secondary social networks all combine to
influence changing fire risk behaviours over time.
Using this tool it is anticipated that decision-makers
may calculate the modelled aggregate risk for an area
and explore ‘what-if’ scenarios of various possible
planned interventions such as targeted information
provision and community support.
The authors would like to thank Steve Chu, Steve
Fletcher, Graham Howe, Mick Mason, Nicola Smith,
Ian Standeven and colleagues at South Yorkshire Fire
& Rescue for help and support in the development of
this research.
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Developing an ABM-driven Decision Support System in the Emergency Services