Systemic Business Process Simulation using Agent-based Simulation
and BPMN
Jácint Duduka
and Sérgio Guerreiro
INESC-ID, Lisbon, Portugal
Universidade Aberta, Lisbon, Portugal
Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
Keywords: Business Process Simulation, Agent-based Modelling, BPMN, Socio-technical Systems, Distributed
Abstract: The current paradigm of a business process model is that it is a representation of a sequence of tasks that act
upon some data input, to produce an output, aiming the production of a new service or product to be delivered
from a producer to a customer. Although this is a valid way of thinking, it neglects to consider in enough
detail the influence of some phenomenon on inputs, e.g. human behaviour, communication, social interactions,
the organisational culture which can have a significant effect on the output delivered by a business process.
As the dynamics of these phenomena are non-linear, they can be interpreted as a complex system. This holistic
way of thinking about business processes opens the doors to the possibility of combining different simulation
methods to model different aspects that influence a process. A BPMN engine and an agent-based simulation
(ABS) engine are chosen to serve the basis of our framework. In its conception, we not only consider the
technical aspects of the framework but also delve into exploring its management and organizational
dimensions, with the intent of facilitating its adoption in enterprises, as a tool to support decision support
systems. We analyse how accurate the simulation results can be when using these two tools as well as what
considerations need to be considered within organizations.
Today rapid technological change is being driven by
the information revolution, as we live in
environments that are increasingly technology-
saturated (Kadar et al., 2015). This saturation makes
the question of the relationship between people and
technology more explicit than ever, to the extent that
this relationship is widely reported and extensively
studied in the literature in the domain of socio-
technical systems (Bider, n.d.; Gregoriades &
Sutcliffe, 2008; Henda et al., 2016; Ibl & Čapek,
2017; Norta et al., 2014; The-Evolution-of-Socio-
Technical-Systems-Trist.Pdf, n.d.; Tropmann-Frick
& Thalheim, 2015; Vespignani, 2012). Socio-
technical systems are an approach to the
understanding and design of complex organisations
and technologies that recognise the interplay between
people and technology (Kloeckner & Birkmeier,
Despite the realisation of the importance of
humans in business processes, as far as we know,
there has been little focus on how agent-based
simulators(ABS) can be used to enable business
process simulation in enterprises. The majority of the
studies (Haiyan Zhao & Jian Cao, 2007; Halaška &
Šperka, 2018; Liu & Iijima, 2015; Sulis & Di Leva,
2018; Tan et al., 2009) focus on integrating ABS with
discrete event simulation(DES), Petri-nets and other
workflow engines. This choice can pose some
challenges for organizations due to extra investment
required to procure software, hire specialized
workforce with DES knowledge and time to convert
existing business processes to DES models. Although
this approach is suitable in some cases, it is less likely
to be adopted in organizations because of the time and
effort commitments it requires. Our assumption of
what constitutes a successful information system
implementation is based on the information systems
analysis framework depicted by Laudon & Laudon,
2013 that state that there should always be three
dimensions to any successful information system.
The first is management, where there should be
tasks performed at a management level of the
Duduka, J. and Guerreiro, S.
Systemic Business Process Simulation using Agent-based Simulation and BPMN.
DOI: 10.5220/0010177001220130
In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD, pages 122-130
ISBN: 978-989-758-474-9
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
organization for the implementation of the system.
These include but not limited to reflecting about what
knowledge acquisition, retention strategies, training
strategies and budget plans are suitable for the
project. The second dimension is organization, where
they state that there should be a reflection on how
issues such as organizations hierarchy, functional
specialties, business processes, organizational culture
and pollical interest groups impact an information
system. Lastly there is the technology dimension,
where hardware, software, data management and
networking issues should be considered.
We observed that current simulation frameworks
involving ABS and BPS, do not take the management
and organization dimensions into account, focussing
more on technology, and therefore to solve this
problem, we ask the following question: "How to
implement a systemic simulation interoperability
framework, between an agent-based simulator and a
business process engine?"
To address the issue, we frame a solution that
approaches the problem from a holistic view. Some
authors also share the view of a holistic approach to
this issue (Wu, 2015), in the sense that holistic
modelling plays a vital role in developing socio-
technical systems(STS), because of the interplay
between social and technical elements within these
systems and the resulting emergent behaviour.
The current state of the art in the topic focusses
on two main categories. In the first category, we can
find solutions that try to conceive a concept
equivalency framework between ABM to business
process modelling notations or vice-versa.
there is some success in doing this (Aksyonov &
Aksyonova, 2013; Dam et al., 2015; Endert et al.,
n.d.; Ghlala et al., 2017; Laroque et al., n.d.), they
agree that there will be concepts that are merely
difficult or even impossible to convert.
The second category consists of integrating ABS
with process simulation engines that do not take into
account the complexities occurring in enterprises,
such as budget limits, training, project deadlines,
skillset availability in the workforce. Between these,
we find mainly DES, Petri-nets and some other
generic process engines.
Instead, in our solution, we propose designing
agent-based models and business processes
separately and let the software agents drive the
business process engine as if they were real users. To
address the management and organizational
dimensions of our solution, we compared usage
trends of business process modelling languages, for
the past 16 years worldwide. Although the
comparison is not exhaustive of all languages, we
focussed on the main ones and collected data using
Google Trends.
Figure 1: Numbers represent search interest relative to the
highest point on the chart for the given region and time. A
value of 100 is the peak popularity for the term. A value of
50 means that the term is half as popular. A score of 0
means there was not enough data for this term. Source:
Google Trends. (2020). Retrieved September 22, 2020,
Choosing a highly available modelling language
is how we intended to fulfil the management and
organizational dimensions of our framework, the
assumption being that a highly adopted language
requires less investment to implement, less workforce
training, less time to convert models, and more
skillset reusability encourages collaboration. Given
the overall trend of BPMN, we chose it as our
business process modelling language.
2.1 The Simulation Process
Our engine of choice, Camunda, continues
functioning with the purpose for which it has been
designed, and is not aware of the change of process
actors, from real humans to software agents.
The same can be said for the software agents and
the ABS, which continue working and following the
rules defined in its model. Agents send messages to
the BPMN engine when specific pre-set criteria are
met via REST and are not aware of the purpose of
those messages.
This approach is different from the current
mapping approaches, in that it avoids any sort of
concept equivalency problems altogether because
models are not converted, they interact with each
other during the simulation runtime.
One of the challenges in conceiving such
Systemic Business Process Simulation using Agent-based Simulation and BPMN
integration between the two systems is that multiple
agent instances will be created during the simulation
process, as well as multiple process instances in the
BPMN engine. The consequence is that messages
may be routed to the wrong process instance if no
attention is paid to the way messages are transferred
between systems. Therefore, the question is: "How
does a system that has received a message know
which request this is the reply for?"
The solution concept we adopted is demonstrated
by Hohpe & Woolf, 2012, where it is suggested that
"the requestor add a Request ID to the request
message, have the replier copy the Request ID to the
Correlation ID field of the response message so that
the requestor can correlate the reply message to the
request message."
In our case, the "Agent ID" is used
as the identifier of the message.
This separation of concerns also has other
advantages, which for instance, opposed to BPMN
integration approaches proposed by some authors
(Onggo et al., 2017), this one does not suggest any
extension artefacts to the BPMN standard. The
ability to bypass these difficulties would significantly
enhance the process of creating better models because
the BPMN standard itself does not need to be
modified or extended in any way and no significant
investment of time is required to train staff in
organisations about features of new extensions,
instead, already existing tools are reused.
2.2 Scope of Work
The focus of our research is oriented towards
verifying functional aspects of the distributed
simulation method being proposed. It intends to
understand only aspects deemed fundamental for the
operation of such a way of simulating socio-technical
systems and ignores testing the broader context of
applicability in the industry. A detailed analysis of
those aspects is outside of the scope of this work, and
hence, they are only briefly outlined here.
Our first objective was to determine whether it is
possible to perform web requests from an ABS engine
to a BPMN engine using the REST protocol. A
review was conducted to understand what support is
provided within the leading agent-based simulators
for performing web requests. Then one engine is
chosen based on the effort required to implement
models and how easily it can be scaled. The rationale
behind this criteria is to find an ABS engine for our
work, that has potential to study social systems of a
large size, with low investment requirements to
develop those models, relative to other engines.
The second objective was to understand if time
intervals between events occurring in the ABS are
conserved in BPMN. This was mainly an essential
step of our research as it has been highlighted several
times in the literature (Baker, n.d.; Brodsky, n.d.; Lin
& Guo, 2010; Tolk, 2013), that time management is a
usual problem to address in distributed simulation
The experimental method was used for this
purpose because our primary goal is to determine
whether our proposed solution works at a functional
level. It means that to determine whether time
intervals between events vary between the two
engines, we needed to understand how those time
intervals change over time between the two engines,
and an experiment would give us the control needed
to set up those conditions and test our theory. Its been
noted in the literature(Dennis & Valacich, 2001) that
the objective of experimental research is to enable
testing and extending a theory. Also, Williamson &
Johanson, 2017 proceed in stating that it is a method
that seeks to establish a cause-and-effect relationship
between variables, which is the case in our study.
By no means, experimental research is the best or
worst method, yet it is the most adequate for some
A cause-effect relationship needed to be
understood. Specifically, we wanted to
understand whether time intervals between
events occurring in an ABS are kept constant
upon triggering equivalent events in a BPMN
A specific set of conditions are being studied.
We only want to verify that REST API
requests can be transmitted between the two
systems and that the intervals between two
events are respected between the two systems.
3.1 Choosing a Real-life Inspired
Business Process
It was important to inspire our experiment in a real
business process, as some authors point out (Guala,
2002) there is higher confidence in an experiment if a
real component is used. Thus metrics and business
process model from a real case study(RCS) had been
chosen(Bhat et al., 2014) which is a Lean Six-
Sigma(LSS) process improvement study conducted
in the Health Information Department (HID) of a
Medical College Hospital in India which consisted in
using LSS to improve the patient registration process
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
of the hospital.
The RCS concluded that the mother tongue
patients and receptionists spoke, had an impact on the
process cycle time. This variable was adequate for
this experiment because it satisfies the criteria for the
case study selection which was that it had to describe
the impact of user behaviour in a business process
output, in this case, it was communication between
patients and receptionists, given that they spoke
different languages and how this impacts the number
of patients registered per unit of time.
3.2 Modelling Communication between
Communication between actors is a relevant aspect to
be modelled in business processes (Gregoriades &
Sutcliffe, 2008) and some authors have already
studied it (Barange et al., 2014; Elleström, 2019;
Frieder et al., n.d.; Kennedy, 2012; March, 2004)
within the context of agent-based modelling.
In our RCS all the staff were proficient in the local
languages, namely Kannada and Tulu, in addition to
English. The study also observed that out of 16 staff
working in the department, only two of them knew
Malayalam, five knew Konkani, six knew Hindi, one
knew Malayalam and Konkani, and the only one
knew all three languages in addition to the local
language. Thus, six staff with a different combination
of language expertise were selected for the study. The
cycle time in handling patients, who were proficient
in only local languages, only Malayalam, only
Konkani and only Hindi was observed for ten patients
in each group(Bhat et al., 2014).
The study concluded that cycle time for
registering patients, who only spoke Malayalam,
Konkani and Hindi was significantly larger than those
who knew local languages and therefore, that is the
behaviour we model in our ABS, more specifically,
which is difficulty in communication between agents
as a function of their fluency in certain idioms.
3.3 Modelling the Business Process
Once the behaviour above is configured in the ABS,
the business process below is modelled in BPMN.
Due to its versatility and ease of use, we chose
Camunda Modeler V4.0.0 to design our model and
Camunda BPM 7.12 server as the business process
engine for our experiment.
When converting the activity diagram to BPMN,
some tasks were omitted as those played no active
part in the experiment because they did not send or
receive messages from or to agents.
Figure 2: Activity diagram of a chosen business process
Source: (Bhat et al., 2014).
Figure 3: Simplified BPMN model of the RCS.
Systemic Business Process Simulation using Agent-based Simulation and BPMN
The ABS engine begins execution, by having agents
communicating with each other and invoking the BPMN
engine when the PR pair finishes communicating, by
sending messages using REST. Cycle time results are
collected and stored in a database for posterior analysis.
Netlogo had been highlighted by several authors
(Abar et al., 2017; Lytinen & Railsback, n.d.;
Railsback et al., 2006) as being versatile enough for
small and large experiments, as well as presenting a
low learning curve. These characteristics were
relevant for our choice as we needed to find an engine
that is not only robust but also readily available in the
industry to facilitate adoption within organisations,
and also if further studies are conducted in the future.
Looking at our first objective, we were able to
gather data about existing ABM systems concerning
the programming languages they use to create their
model. Understanding which programming language,
they use was fundamental as we assumed that it
would be the primary vector by which the ABM could
send web requests, the assumption being that if the
underlying modelling language supports web
requests, then the engine supports them too.
Table 1: Agent-Based Modelling engines vs Programming
language they use (‘Comparison of Agent-Based Modeling
Software', 2020).
Name Programming Language
AnyLogic Java
Cougaar Java
Framsticks Framscript
Netlogo Netlogo,
Repast Java, .Net, Python
From the short review above, key findings
emerge: 100% of the ABM engines support
programming languages that can submit web requests
or support extensions that allow for external scripting
engines to be embedded in the agent-based model,
which in turn supports sending web requests.
Besides, we specifically studied the
documentation of the ABM of choice, Netlogo 6.1.
We found that it does not support any capability to
perform web requests natively, although there were
some attempts (NetLogo/Web-Extension,
2012/2020) to introduce similar functionality using
extensions, however not to send generic web requests.
On the other hand, it was also found that one of the
extensions supported is the Python scripting
engine(Jaxa-Rozen & Kwakkel, 2018) through the
PyNetLogo extension. As Python is a generic
scripting language, it not only allowed to make web
requests via REST protocol but also to establish full
integration between the two applications, control
message correlation, transformation and logging.
Although the results above confirm that majority
of ABM engines support web requests, our method
also relies on the BPMN engine supporting a REST
API that allows a consumer to start a process.
Table 2: List of major BPMN engines vs support for process
invocation through REST.
Engine Support REST process
ActiveVOS Y
Activiti Y
Bizagi BPM Suite Y
Bonita BPM Y
Camunda Y
Flowable Y
Imixs-Workflow Y
Orchestra N
Sydle SEED Undetermined
From this, we can understand that the majority of
the BPMN engines do provide support for a REST
API that allows invocation of processes, and in our
experiment, we used Camunda Server 7.12.
These results together demonstrate the adequacy
for implementing our method using the majority of
ABM and BPMN engines.
With regards to verifying the accuracy of our
simulation results, we had two objectives:
O1: Determine the correlation between engine
type and task execution interval;
O2: Determine whether the engine type has a
significant effect on the task execution interval
of different groups of agents.
Regarding O1, the intension was to analyse how
event intervals varied between the two engines,
considering agent behaviour individually. For this,
we collected and compared time deltas between
events in each engine. A Pearson Correlation
Coefficient was then calculated to understand if event
intervals vary significantly between engines. The
Pearson correlation analysis was conducted between
DeltaABS and DeltaBPMN variables. Cohen's
standard was used to evaluate the strength of the
relationship, where coefficients between .10 and .29
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
represent a small effect size, coefficients between .30
and .49 represent a moderate effect size, and
coefficients above .50 indicate a large effect
size(Cohen, 1988). One of the assumptions made in
this work when estimating the Pearson correlation is
that a Pearson correlation requires that the
relationship between each pair of variables is linear
(Conover & Iman, 1981). This assumption is violated
if there is curvature among the points on the
scatterplot between any pair of variables.
Figure 3: Presents the scatterplot of the correlation. A
regression line has been added to assist the interpretation.
The result of the correlation was examined based
on an alpha value of 0.05. A significant positive
correlation was observed between DeltaABS and
DeltaBPMN (r
= 1.00, p < .001, 95% CI [1.00,
1.00]). The correlation coefficient between DeltaABS
and DeltaBPMN was 1.00, indicating a large effect
size. This correlation indicates that as DeltaABS
increases, DeltaBPMN tends to increase. Table 3
presents the results of the correlation. Note. n = 7137.
Table 3: Pearson Correlation Results Between DeltaABS
and DeltaBPMN.
Combination r
95% CI p
1.00 [1.00, 1.00]
The results of our experiment suggest the
correlation coefficient between DeltaABS and
DeltaBPMN was 1.00, indicating a large effect size.
This correlation indicates that event intervals are kept
constant between the ABS and BPMN engines. It
confirms our suspicion that the messages flow
between systems without significant changes in task
execution intervals.
For O2, the intension was to analyse how event
intervals varied between the two engines, considering
the collective behaviour of agents. This analysis was
deemed relevant because it is likely that in real-world
agent-based models, the behaviour is modelled for a
collection of agents. Many agent-based simulation
tools provide support for the concept of "breed"
which allows a modeller to create different varieties
of agents that behave differently. We segregated our
agents by communication difficulty, i.e. each group
of agents took a different amount of time to fill in the
registration form, and it varied according to time
ranges the table below:
Table 4: Agent Group VS Delay Range.
Patient Language Time Range(ticks)
Hindi 450-550
Konkani 150-250
Malayalam 350-450
Other 0-50
To evaluate point number two, a multivariate
analysis of variance (MANOVA) was conducted to
assess if mean differences exist on task execution
interval for Hindi, Konkani, Malayalam and Others
between the different source engines. The MANOVA
test is an appropriate statistical analysis when the
purpose of the research is to assess if mean
differences exist on more than one continuous
dependent variable by one or more discrete
independent variables (DeCarlo, 1997).
The main effect for source engine was not
significant, F(4, 899) = 0.00, p = 1.000, η
p = 0.00,
suggesting the linear combination of Malayalam,
Konkani, Other, and Hindi was similar for each level
of source engine. The MANOVA results are
presented in Table 5.
Table 5: MANOVA Results for Malayalam, Konkani,
Other, and Hindi by source engine.
Variable Pillai F df
p η
Source 0.00 0 4 899 1 0
The results indicate that the linear combination of
Malayalam, Konkani, Other, and Hindi was similar
for each level of source engine, which leads us to
conclude that even if the agents were operating in
groups, those differences would not be affected
during message transmission between systems.
We intended to investigate how could we integrate an
ABS engine with a BPMN engine, to perform
business process simulations and obtain statistically
Systemic Business Process Simulation using Agent-based Simulation and BPMN
significant results. The main aim of such integration
is to create a mechanism to simulate and study
complex phenomena within business processes.
Based on the quantitative analysis of event
intervals between the two systems and also based on
the event intervals of groups of agents between the
two engines, it can be concluded that integrating an
ABS engine with a BPMN engine, produces
statistically coherent simulation results with respect
to time management between the systems.
Despite the success demonstrated, some
significant limitations should be highlighted. We
could not evaluate how well our ndings apply in a
real implementation project within an organisation as
our experiment has firmly focussed on addressing
functional and simulation results significance aspects.
It is possible that the practical implementation
constrains of our technique outweigh the benefits of
using it, so, therefore, it is suggested that further
research is undertaken to look into those aspects.
Due to the novelty of the simulation framework
proposed, we also encountered difficulties in
determining how it compares to other studies in the
same field. On the one hand, this can be a significant
step forward for a holistic business process simulation
paradigm, but on the other, for the time being, it
leaves some gaps in knowledge that can only be filled
in by further research.
The main achievements, including contributions,
may be summarised as follows. First, we created a
new way of simulating business processes. The
innovation in our method is that it allows for a holistic
simulation to happen, where complex phenomena can
be made part of the business process simulation. We
did consider not only the technical aspects of the
solution but also its organizational and management
contexts. This, in turn, opens doors to study more
complicated problems within enterprises, that are
difficult to study analytically, such as the effect of
emergence, feedback loops and self-organization on
process performance and at a broader sense, it
enriches our scientific knowledge base in process
optimisation methodologies.
It has been shown that it is possible to use and
reuse existing simulation tools to enable this holistic
type of simulation. It remains unclear to which degree
our framework is related to low implementation costs,
for instance, we speculate that our approach requires
low investment in purchasing new tools and training
staff as tools we propose are readily available in the
market. It is suspected that this is an attractive
proposition not only for large organisations but also
for small to medium businesses that cannot afford
expensive software solutions. Finally, we
contemplate whether our approach is simple and easy
to be adopted in academia or for individual
researchers, as a tool to study conservation laws in
business processes. All data collected and analysis
results were made available online: Duduka, Jacint.
(2020) 00xE8/BPABSIF: Business process & Agent-
based Interoperability Framework. Retrieved
September 22, 2020, from
The author identified two categories of work to be
proposed based on the experiences collected during
the research. The first category is related to problems
identified during the work undergone, and the second
relates to further areas of research that would expand
the scope of the work and enrich the features of our
Regarding problems encountered during the
experiment, we found that although our results point
in the direction that our proposed method can be used
to simulate complexity in business processes, the
author feels that further investigation should be
conducted into some aspects that came to light during
the current study:
1. Netlogo and many other agent-based
simulation engines are synchronous systems. This
means that agents perform actions in sequence
without true parallelism, and therefore if the business
process being simulated require messages to be sent
in parallel, this may create challenges. We are
proposing further studies to understand the extent to
which this can create issues;
2. Impact of errors in simulation results. It is
understood that there is a margin of error in every
experiment; however, the author suggests a broader
study that looks are factors that can cause Netlogo to
behave abruptly and understand how these can
influence simulation results. These factors could be
hardware, software, resource availability;
3. The implementation of functionality within
BPMN to handle incoming and outgoing messages. It
has been found that custom scripts embedded in to
"receive the message" tasks in BPMN are not invoked
when messages arrive but straight after the token
arrives in the task. This can influence cycle time
results and other problems, and a better way to handle
messages in BPMN should be studied;
In terms of improvements to be made to our
method, it is proposed that future work consists in
exploring other simulation methods that are best
suited to stimulate different types of factors that
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
influence a business process. More specifically,
system dynamics is a method well suited to study how
quantitative variables are impacted by the overall
dynamics of the process and thus, variables such as
costs and budgets, can be included in the simulation
to create an even richer understanding of the overall
dynamics of the business process.
In order to better comprehend the suitability of
this simulation approach in real-world situations,
there is a need to employ it in a project from the
design phase, so that aspects as the influence of
process designer skills and time to create models can
be factored into the effectiveness. These are aspects
not covered in this study, as we only focus on
understanding the feasibility of building a solution
that supports such a simulation approach and whether
simulation results are reliable enough compared to
real ones. Therefore, a case study employing our
approach is another suggestion for future work.
This work was supported by the European
Commission program H2020 under the grant
agreement 822404 (project QualiChain) and by
national funds through Fundação para a Ciência e a
Tecnologia (FCT) with reference UIDB/50021/2020
A special thanks also goes to Liaison Group
supported our research.
00xE8. (2020). 00xE8/BPABSIF.
00xE8/BPABSIF (Original work published 2020)
Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O’Hare,
G. M. P. (2017). Agent Based Modelling and
Simulation tools: A review of the state-of-art software.
Computer Science Review, 24, 13–33.
Aksyonov, K. A., & Aksyonova, O. P. (2013). Application
of BPMN and EPC graphical notations for multi agent
simulation of business processes. 2013 23rd
International ….
Baker, T. M. (n.d.). Time Management in Distributed
Simulation Models. 5.
Barange, M., Kabil, A., Keukelaere, C. D., & Chevaillier,
P. (2014). Collaborative Behaviour Modelling of
Virtual Agents using Communication in a Mixed
Human-Agent Teamwork. 17.
Bhat, S., Gijo, E. V., & Jnanesh, N. A. (2014). Application
of Lean Six Sigma methodology in the registration
process of a hospital. International Journal of
Productivity and Performance Management, 63(5),
Bider, I. (n.d.). Functional Decomposition of a Socio-
Technical System: What is Missing? 8.
Brodsky, Y. I. (n.d.). Fundamentals of Simulation for
Complex Systems. Complex Systems, 6.
Cohen, J. (1988). Statistical power analysis for the
behavioral sciences (2nd ed). L. Erlbaum Associates.
Conover, W. J., & Iman, R. L. (1981). Rank
Transformations as a Bridge between Parametric and
Nonparametric Statistics. The American Statistician,
35(3), 124–129.
Dam, H. K., Ghose, A., & Qasim, M. (2015). An Agent-
Mediated Platform for Business Processes. In
Issue 2, pp. 43–61).
DeCarlo, L. T. (1997). On the meaning and use of kurtosis.
Psychological Methods, 2(3), 292–307.
Dennis, A. R., & Valacich, J. S. (2001). Conducting
Experimental Research in Information Systems.
Communications of the Association for Information
Systems, 7.
Elleström, L. (2019). Modelling Human Communication:
Mediality and Semiotics. In A. Olteanu, A. Stables, &
D. Borţun (Eds.), Meanings & Co. (Vol. 6, pp. 7–32).
Springer International Publishing.
Endert, H., Kuster, T., Hirsch, B., & Albayrak, S. (n.d.).
Mapping BPMN to Agents: An Analysis. 17.
Frieder, A., Lin, R., & Kraus, S. (n.d.). Agent-human
Coordination with Communication Costs under
Uncertainty. 8.
Ghlala, R., Aouina, Z. K., & Said, L. B. (2017). Multi-
Agent BPMN Decision Footprint.
… Symposium on
Agent and Multi-Agent ….
Gregoriades, A., & Sutcliffe, A. (2008). A socio-technical
approach to business process simulation. Decision
Support Systems, 45(4), 1017–1030.
Guala, F. (2002). Models, Simulations, and Experiments. In
L. Magnani & N. J. Nersessian (Eds.), Model-Based
Reasoning (pp. 59–74). Springer US.
Haiyan Zhao, & Jian Cao. (2007). A business process
simulation environment based on workflow and multi-
agent. 2007 IEEE International Conference on
Industrial Engineering and Engineering Management,
Halaška, M., & Šperka, R. (2018). Is there a Need for
Agent-based Modelling and Simulation in Business
Systemic Business Process Simulation using Agent-based Simulation and BPMN
Process Management? Organizacija, 51(4), 255–269.
Henda, H. B. G., Sayed, Y., & Ibtissem, F. (2016). Using a
Multi-Perpectives Approach for Building a Socio-
Technical Information System: Proceedings of the
Sixth International Symposium on Business Modeling
and Software Design, 217–220.
Hohpe, G., & Woolf, B. (2012). Enterprise Integration
Patterns: Designing, Building, and Deploying
Messaging Solutions. Addison-Wesley.
Ibl, M., & Čapek, J. (2017). A Behavioural Analysis of
Complexity in Socio-Technical Systems under Tension
Modelled by Petri Nets. Entropy, 19(11), 572.
Jaxa-Rozen, M., & Kwakkel, J. H. (2018). PyNetLogo:
Linking NetLogo with Python. Journal of Artificial
Societies and Social Simulation, 21(2), 4.
Kadar, M., Muntean, M., Cretan, A., & Jardim-Gonçalves,
R. (2015). Automated Negotiation with Multi-agent
Systems in Business Processes. In P. Angelov, K. T.
Atanassov, L. Doukovska, M. Hadjiski, V. Jotsov, J.
Kacprzyk, N. Kasabov, S. Sotirov, E. Szmidt, & S.
Zadrożny (Eds.), Intelligent Systems’2014 (pp. 289–
301). Springer International Publishing.
Kennedy, W. G. (2012). Modelling Human Behaviour in
Agent-Based Models. In A. J. Heppenstall, A. T.
Crooks, L. M. See, & M. Batty (Eds.), Agent-Based
Models of Geographical Systems (pp. 167–179).
Springer Netherlands.
Kloeckner, S., & Birkmeier, D. (2010). Something Is
Missing: Enterprise Architecture from a Systems
Theory Perspective. In B. J. Krämer, K.-J. Lin, & P.
Narasimhan (Eds.), Service-Oriented Computing –
ICSOC 2007 (Vol. 4749, pp. 22–34). Springer Berlin
Laroque, C., Himmelspach, J., Pasupathy, R., Rose, O., &
Laudon, K., & Laudon, J. P. (2013). Management
Information Systems, Global Edition. Pearson.
Lin, Q., & Guo, J. (2010). Accuracy analysis of distributed
simulation systems. 75446I.
Liu, Y., & Iijima, J. (2015). Business process simulation in
the context of enterprise engineering. Journal of
Simulation, 9(3), 206–222.
Lytinen, S. L., & Railsback, S. F. (n.d.). Agent-based
Simulation Platforms: An Updated Review
. 10.
March, O. (2004). Natural language as an agent
communication language. http://minerva-
NetLogo/Web-Extension. (2020). [Scala]. Center for
Connected Learning. (Original
work published 2012)
Norta, A., Mahunnah, M., Tenso, T., Taveter, K., &
Narendra, N. C. (2014). An Agent-Oriented Method for
Designing Large Socio-technical Service-Ecosystems.
2014 IEEE World Congress on Services, 242–249.
Onggo, B. S. S., Proudlove, N. C., D’Ambrogio, S. A.,
Calabrese, A., Bisogno, S., & Levialdi Ghiron, N.
(2017). A BPMN extension to support discrete-event
simulation for healthcare applications: An explicit
representation of queues, attributes and data-driven
decision points. Journal of the Operational Research
Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006).
Agent-based Simulation Platforms: Review and
Development Recommendations. SIMULATION,
82(9), 609–623.
Sulis, E., & Di Leva, A. (2018). An Agent-Based Model of
a Business Process: The Use Case of a Hospital
Emergency Department. In E. Teniente & M. Weidlich
(Eds.), Business Process Management Workshops (Vol.
308, pp. 124–132). Springer International Publishing.
Tan, W., Xu, W., Yang, F., Li, S., & Du, Y. (2009). A
Framework for Business Process Simulation Based on
Multi-Agent Cooperation. In S. Ahmed & M. Noh
(Eds.), Multiagent Systems. I-Tech Education and
(n.d.). Retrieved 18 August 2020, from
Tolk, A. (2013). Interoperability, Composability, and Their
Implications for Distributed Simulation: Towards
Mathematical Foundations of Simulation
Interoperability. 2013 IEEE/ACM 17th International
Symposium on Distributed Simulation and Real Time
Applications, 3–9.
Tropmann-Frick, M., & Thalheim, B. (2015). Socio-
technical System Design for Generic Workflows. 2015
26th International Workshop on Database and Expert
Systems Applications (DEXA), 134–138.
Vespignani, A. (2012). Modelling dynamical processes in
complex socio-technical systems. Nature Physics, 8(1),
Williamson, K., & Johanson, G. (2017). Research Methods:
Information, Systems, and Contexts. Chandos
Wu, P. (2015). A framework for model integration and
holistic modelling of socio-technical systems. In
Decision Support Systems (Vol. 71, pp. 14–27).
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