A Simulation-based Aid for Organisational Decision-making
Souvik Barat
1
, Vinay Kulkarni
1
, Tony Clark
2
and Balbir Barn
2
1
Tata Consultancy Services Research, Pune, India
2
Sheffield Hallam University, London, U.K.
3
Middlesex University, London, U.K.
Keywords:
Organisational Decision Making, Enterprise Modelling Languages, Meta Modelling, Simulation.
Abstract:
Effective decision-making of modern organisation often requires deep understanding of various aspects of
organisation such as organisational goals, organisational structure, business-as-usual operational processes.
The large size of the organisation, its socio-technical characteristics, and fast business dynamics make this
endeavor challenging. Industry practice relies on human experts for comprehending various aspects of organi-
sation thus making organisational decision-making a time-, effort- and intellectually-intensive endeavor. This
paper proposes a model-based simulation approach to organisational decision-making. We illustrate how this
is applied to a real life problem from software service industry.
1 INTRODUCTION
Modern organisations need to respond to a variety of
changes while operating in a dynamic environment.
In order to minimise undesirable consequences such
as prohibitive costs of erroneous decisions and lack
of opportunities for later course correction, there is
a need for a-priori judicious evaluation of the avail-
able courses of action. The decision-makers are thus
expected to understand, analyze and correlate exist-
ing information about various aspects of enterprise
such as organisational goals, organisational structure,
operational processes, change drivers and their influ-
ences on overall organisation. Large and complex
organisational structure, and inherent socio-technical
characteristics of the organisation (McDermott et al.,
2013) all contribute to the complexity of organisa-
tional decision-making.
Current industry practice relies mostly on human
experts for decision-making with spreadsheet, word
processors, and diagram editors being the most pop-
ular tools used for capturing the relevant information
about enterprise. The informal nature of the informa-
tion means the power, rigour, and speed of sophisti-
cated analysis due to automation cannot be brought
to bear upon the decision-making problem. As a re-
sult, the quality of the solution is largely dependent
on knowledge and experience of human experts in-
volved in the decision-making process. When this is
coupled with the sheer volume, heterogeneity of the
information, and the complexity of a dynamic envi-
ronment then the analysis is further untenable. Pro-
vision of solutions that are able to stitch together a
coherent, consistent and integrated view from these
pieces is challenging for decision-makers.
Enterprise Modelling (EM) tries to reduce the
complexity of organisational decision-making with a
range of concepts, languages and tools for represent-
ing and analyzing the aspects of organisation. For
instance, the Zachman framework (Zachman et al.,
1987) advocates six aspects namely why, what, how,
who, when and where for comprehensive representa-
tion of an organisation. Thus it can be argued that
complete specification of enterprise is possible using
Zachman framework, however, no automated analy-
sis support is available. Examination of existing EM
reveals some interesting observations. Languages ca-
pable of specifying all the relevant aspects of enter-
prise for organisational decision-making lack support
for automated analysis (e.g., Archimate (Iacob et al.,
2012), IEM (Bernus and Schmidt, 2006) and EEML
(Krogstie, 2008)). Languages capable of automated
analysis only cater for a subset of the relevant as-
pects for decision-making (e.g., BPMN
1
, i* (Yu et al.,
2006), and System Dynamics (Meadows and Wright,
2008)). Co-simulation using a relevant subset of EM
languages can be a pragmatic solution (Barjis, 2008).
For instance, i* (to specify the why aspect), BPMN (to
1
www.omg.org/spec/BPMN/2.0/
Barat, S., Kulkarni, V., Clark, T. and Barn, B.
A Simulation-based Aid for Organisational Decision-making.
DOI: 10.5220/0005992401090116
In Proceedings of the 11th International Joint Conference on Software Technologies (ICSOFT 2016) - Volume 2: ICSOFT-PT, pages 109-116
ISBN: 978-989-758-194-6
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
109
Figure 1: Abstraction of organisational decision-making.
specify the how aspect) and Stock-n-flow (to specify
the what aspect) can be used to come up with the nec-
essary and sufficient specification which is amenable
for analysis albeit in parts (Kulkarni et al., 2015). Hu-
man expertise is still required for the analysis of the
problem, selection of the appropriate EM technique
and the integration of the technology into a consis-
tent whole (Barjis, 2008). This intellectual challenge
is further exacerbated due to paradigmatically diverse
nature of the three languages and issues of interoper-
ability of the various tools.
A simulation-based approach to organisational
decision-making may offer a pragmatic solution.
However, simulation is known to deliver in situations
where mechanistic world view holds (Barjis, 2008)
whereas modern enterprises are socio-technical sys-
tems (McDermott et al., 2013) bringing additional
dimensions such as uncertainty, autonomicity and
adaptability to the problem space.
We propose a pragmatic model-based simula-
tion approach for analyzing organisations as socio-
technical systems. This analysis centric approach
hinges on: (i) the necessary and sufficient information
for decision-making to exist in machine processable
manner, (ii) machinery for effective processing of this
information, and (iii) a method to enable repetitive
use of the machinery at the hands of knowledgeable
users. This paper addresses tenets (i) and (ii) while
hinting at (iii). This paper also describes a model-
based realization of the proposed approach. The ap-
proach is illustrated using an example from the soft-
ware services industry. The principal contributions of
this paper are: (i) a modelling abstraction for precise
and comprehensive representation of organisations as
socio-technical systems, (ii) application of a simu-
lation technique to support organisational decision-
making through repeatable scenario playing.
2 MOTIVATION
Organisational decision-making activity is an iterative
process for selecting appropriate course of actions for
achieving the desired goals of organisation.
The process starts with identifying possible goals
or course of actions or both. The successive steps
deal with validation of selected course of actions
against goals, ranking alternatives options, and selec-
tion of alternatives over the other. Our visualization
of decision-making is depicted in Fig. 1. As shown
in the figure, an organisation (O) comprises four ba-
sic elements Data (D), Structure (S), Processes (P)
and Goals (G) i.e., O = <D, S, P, G>. The data D
describes the current state of organisation and a set of
past states of interest, process P describes collection
of Business As Usual (BAU) behaviors, and goals G
specify the desired intention of organisation. Struc-
turally an organisation (i.e., S of O) is a composition
of interacting socio-technical units where each unit
can further be visualized as <D, S, P, G>tuple. An
organisation or its constituent unit manages its goals
G; goals G affect organisational BAU behaviours P;
and organisational BAU behaviours P is accountable
for state change leading to data update D. The avail-
able data D determines whether the stated goals G
are achieved or not. The meaningful state variables
used for evaluation of goals are called the Measures
(M). The Levers (L) are appropriate course of actions
the decision-maker can take for achieving the stated
goals. A lever is essentially the specification of a
change to structure, process, goals or any combina-
tion thereof.
Thus, in this formulation, decision-making is
human-guided exploration of design space wherein
a set of levers L
select
from the available levers L are
selected for application, their effect on the relevant
set of measures is observed, the desired goals are
(re)evaluated using the new values of measures - this
loop continues till either the desired goals are met
or the desired goal is changed thus starting off an-
other iterate-till-saturate process. Critically, the abil-
ity to specify influence of a lever on a set of measures
is the key. The socio-technical nature of an enter-
prise and the inability to have complete understand-
ing of the problem space make specification of lever-
to-measure influence in pure mathematical terms very
hard. Therefore, simulation seems to be the pragmatic
recourse.
2.1 Tenets of Decision-making
We argue that an organisation can be understood well
by knowing what an organisation is, how it operates
and why it is so (Kulkarni et al., 2015). Further clarity
can be obtained by considering organisational respon-
sibilities and understanding the who (i.e., responsible
stakeholders) aspect of the organisation. Therefore,
we consider the four aspects, namely what, how, why
and who, as necessary and sufficient for specifying
ICSOFT-PT 2016 - 11th International Conference on Software Paradigm Trends
110
Figure 2: Conceptual model of organisation.
data, structure, process and goals of an organisation.
This is broadly aligned with the Zachman framework
(Zachman et al., 1987) except for the where and when
aspects which we believe are mostly subsumed within
what and how aspects.
This boils down to two primary requirements for
supporting organisational decision-making: (i) the
ability to capture why, what, how and who aspects of
an organisation, in a formal manner and (ii) the ability
to perform what-if and if-what analyses of the formal
specification.
Decision makers expect help not only in identi-
fying the candidate set of levers to be applied at a
given state but also with quantitative as well as qual-
itative estimation of application of the selected levers
towards achievement of the stated goals.
3 PROPOSED SOLUTION
We propose a pragmatic approach to improve the cur-
rent state of organisational decision-making to help
decision-makers to analyze various what-if scenario
of a decision-making problem. We now describe a
model-based realisation of simulation-based analysis
approach. Firstly, we propose a conceptual model for
representing the why, what, how and who aspects of
organisation in a localised relatable manner. Further
we refine this conceptual model to an implementa-
tion model and provide simulation semantics to en-
able what-if scenario playing thus enabling human-
guided exploration of solution space with enhanced
certainty. We argue how the proposed implementa-
tion model meets the desired tenets, describe an im-
plementation strategy, and outline a packaging that
practitioners may find effective in real-life industry-
scale situations.
3.1 Conceptual Model
From an external stakeholder perspective, an organ-
isation can be viewed as something that responds to
a set of events as it goes about achieving its stated
goals. Organisations consist of many autonomous
units, organised into dynamically changing hierar-
chical groups, operating concurrently, and managing
goals that affect their behaviour. We describe struc-
ture and behaviour of an organisation using a small
set of concepts and their relationships as depicted in
Fig. 2.
A Unit is an autonomous self-contained functional
unit with high coherence and low external coupling.
It exposes Goals stating its intention, and it interacts
with environment through a set of In-Events and Out-
Events. Internally it contains a Behaviour, a set of
Internal Events and a Type Model. The type model
describes the schema for representing current and pre-
vious states of the organisation, i.e. Data and History.
A Unit may make use of several contained Units in or-
der to meet the promised goals. The contained units
can interact with each other to delegate their respon-
sibilities to others; a unit can also participate in hi-
erarchical composition structure to accomplish wider
goals of the organisation, e.g., a larger unit or an
organisation. A Unit has a set of Levers and Mea-
sures where levers are parameters that can be used
for configuration purposes, and measures are mean-
ingful state variables that are exposed to the environ-
ment. Conceptually, the elements Unit, unit relation
and nesting capability represent the structure S, the
Event and Behaviour represent process P, Data and
History represent data D, Goal represent the goal G
of organisation O. On the other hand elements Unit,
Event, Data, History and nesting capability of Unit
are capable of specifying the what aspect, Goal spec-
ifies the why aspect, Behaviour specifies the how as-
pect and Unit, as individual, specifies the who aspect
of an organisation. Event helps to capture reactive na-
ture of Unit, the intent is captured using Goal, mod-
ularity is achieved through Unit, autonomy is possi-
ble due to the concept of Internal Event, and compo-
sition can be specified using nesting relation. Also,
Unit is adaptable as it can construct and reconstruct
its structure; modular as it encapsulates the structure
and behaviour of an organisation; intentional as it has
its own goals; and compositional as it can be an as-
sembly of Units.
We draw from a set of existing concepts to come
up with the unit abstraction. Modularization and re-
flective unit hierarchy are taken from fractal compo-
nent models (Barros et al., 2009). Goal-directed reac-
tive and autonomous behaviour can be traced to agent
A Simulation-based Aid for Organisational Decision-making
111
Figure 3: Implementation Meta Model.
behaviour . Defining states in terms of a type model
is borrowed from UML
2
. An event driven architec-
ture (Michelson, 2006) supports flexible interactions
between components, and the concept of intentional
modelling (Yu et al., 2006) is adopted to enable spec-
ification of component goals.
3.2 Implementation
Fig. 3 describes the specialisation of the conceptual
model of Fig. 2 for implementation and simulation se-
mantic purposes. It can be read as follows.
Organisation is a Unit that comprises a set of Units
and strives to accomplish its stated Goal. It does so by
responding to Events taking place in its environment
(In-Events), processing them (as specified in Spec),
and by interacting with other external Units in terms
of Events raised/responded (OutEvents). A Unit may
choose not to expose all events to the external world
(InternalEvents). Spec (associated with Unit through
behavioralSpec) is a declarative specification of event
processing logic i.e., behaviour of the Unit. Thus,
looking outside-in, a Unit is a Goal-directed agent that
receives events (InEvents), processes them, and raises
events (OutEvents) to be processed by other Units.
Also, Unit is a parameterized entity whose structure
and behaviour can be altered through Levers. The
lever specification is a Spec that connects Unit with
leverSpec. Internally, a Unit has current and historic
states that comprise the instances of Event, Unit, As-
sociations and Attributes. The model provides two
abstractions namely Snapshots and Value to encapsu-
late instances. A Unit may choose not to expose the
entire state to the external world (InternalState). A
Unit interacts with other Units in a-priori well-defined
2
http://www.omg.org/spec/UML/
Role-playing manner. TypeModel provides a type
system for structural as well as behavioural aspects of
a Unit. A language defined using the metamodel from
Fig. 3, is termed as ESL (Enterprise Simulation Lan-
guage) and has a meaning that is defined with respect
to trace semantics expressed as a sequence of Snap-
shots. Sequence of snapshots describes the history
and current state (i.e. D) of the organisation (O). Each
unit has a specification that describes the organisa-
tional behaviour (P) and a unit conforms to a structure
(S). A behavioural specification (Spec associated with
behaviouralSpec) is a predicate over traces that must
be true for the projection of the overall organisation-
trace that relates to the appropriate unit. Each unit has
a goal (G) that governs its intent and behaviour. The
semantics of goals are predicates over state traces or
snapshots (i.e. D). Selected snapshots and slots can
be marked as measure (M) for quantitative measure-
ments. Unlike specifications, goals need not be true
for every legal behavioural-trace: a goal may fail and
it is the job of each individual unit to perform actions
in order to achieve its goal. In a simulation scenario,
the lever l L can be selected though appropriate pa-
rameter value to lever Specs or modifying lever Specs.
The observation of measures (M) is evaluation of ap-
propriate snapshot values. Thus the scenario playing
formulation i.e., M = P
<t0t1>
(D
t0
, L
select
(O)), is es-
sentially setting initial simulation value (D
t0
), select-
ing L
select
from possible levers L, executing BAU pro-
cess P for duration t1-t0 and evaluating of appropriate
snapshot values.
ESL was prototyped by extending an existing
event-driven language LEAP (Clark and Barn, 2013)
with the concepts borrowed from actor model of
computation (Hewitt, 2010), multi-agent systems
(Van Harmelen et al., 2008), and goals (Yu et al.,
2006). These concepts and their augmentation with
ICSOFT-PT 2016 - 11th International Conference on Software Paradigm Trends
112
Figure 4: Models of Software Service Provisioning Organisation (SSPO).
conventional class models and temporal logic closely
match the required features specified in Fig. 3. The
ESL and simulation engine for ESL is implemented
using DrRacket
3
.
4 ILLUSTRATIVE EXAMPLE
In this section we evaluate our approach by present-
ing a modelling and sample decision-making scenario
3
http://racket-lang.org/
of a software service-provisioning organisation. We
consider an organisation that earns revenue by devel-
oping bespoke software for its customers. The or-
ganisation bids for various software projects in re-
sponse to request for proposals (RFPs). Once a bid is
won, the organisation initiates and executes projects
using tried-and-tested process. This business as usual
(BAU) scenario of the organisation is driven by high
level goals of securing leadership position in terms of
business volume, profitability and customer satisfac-
tion.
The implementation of this case study example is
A Simulation-based Aid for Organisational Decision-making
113
detailed and can be seen to approximating to real life.
The detail has considered various kinds of projects,
different execution strategies and resource catego-
rization derived from industry. But in the interest
of size, here we consider a part of the case-study
by limiting to a simple project classification and a
relatively non-disruptive strategy for illustration pur-
poses. Hence, the environment is characterised us-
ing a representative classification with customers of-
fering four different kinds of projects High Mar-
gin High Risk (HMHR) project, Low Margin Low Risk
(LMLR) project, Medium Margin High Risk (MMHR)
and Medium Margin Low Risk (MMLR). The strategy
is implemented using four levers namely “Increase
Win Rate”, “New Opportunity Stream”, “Increase Re-
source Strength” and “Improve resource Skill”. Mod-
els and decision-making process for selecting the
levers with best potential for achieving the desired or-
ganisational goal are illustrated below.
4.1 Models
The model of the software service provisioning or-
ganisation is illustrated in Fig. 4. The organisation is
visualized as a unit (SSPO unit) with four in-events,
four out-events and an organisational goal as shown in
Fig. 4 a. The key elements of the model are illustrated
below:
Goal specification: SSPO unit targets a primary
goal namely “Securing Leadership Position”. This
goal is decomposed into three sub-goals namely
“Increase Business Volume”, “Increase Profitability”
and “Improve Customer Satisfaction” to support bet-
ter qualitative and quantitative measurements. The
“Increase Profitability” sub-goal is further decom-
posed into two sub-goals namely “Increase Revenue”
and “Reduce Expenditure”. These goals and sub-
goals are described using predicates where terms
are finally associated with TypeModel shown in
Fig. 4 c. For instance, “Increase Business Vol-
ume” is associated with “business volume” attribute
of “Sales Record” class, “Improve Customer satis-
faction” is associated with two attributes of “Project
Delivery” class - “project completed ontime” and
“project completed with delay” (where these two at-
tributes contribute positively and negatively respec-
tively towards “Improve Customer satisfaction”), “In-
crease Revenue” is associated with “revenue” of “Ac-
count” class and “Reduce Expenditure” is associated
with “expenditure” of Account class of the Type-
Model.
In and Out Events: The SSPO unit inter-
acts with environment by receiving “rfp(RFP)”
event, “bidResponse(BidResponse)” event, and “pay-
ment(Payment)” event from environment (in partic-
ular from customers), and responding “bid(Bid)”
event and “deliver(Deliverable)” event to the cus-
tomers. It also receives “join(Resource)” event from
various sources, sends “offers(Offer)” event to the
Resources who are outside of SSPO organisation,
and send Resources to the environment using “sep-
arates(Resources)” event for resign, termination and
retirement. Events use TypeModel for specifying
their parameters.
Internal events and organisation structure: In-
ternal units, internal events and their interactions
are depicted using component model in Fig. 4 b.
The figure shows four sub-units namely “Sales”,
“Delivery”, Account” and “Resource Management”
units with their in and out events. Interactions be-
tween SSPO and sub-unit, and between sub-units are
also illustrated. For example “allocation(Resource)”
and “deAllocation(Resource)” events are the in-
teractions between “Delivery” unit and “Resource
Management” unit whereas the delegation of event
“rfp(RFP)” is the interaction between SSPO and sub-
unit. The interaction and structure can be static or dy-
namic. For example the “CustomerProject” is a unit
is created once a Bid is won by SSPO unit and it re-
solves after producing “deliver(Deliverable)” event.
Behaviour: a simplified behaviour of SSPO unit
is depicted using state diagrams in Fig. 4 d. The
behaviour shows the transformation and life-cycle
RFP. SSPO unit receives “RFP” through “rfp(RFP)”
event; it then delegates to “Sales” unit; “Sales”
unit works on “RFP” and transforms it to “Bid”;
the “Bid” is transformed into a “CustomerProject”
when a bid is won by SSPO (the intimation re-
ceives through “bidResponse(Bidresponse)”); inter-
nally the “CustomerProject” goes through many
states and finally dissolves by responding event “de-
liver(Deliverables)” and deallocating resource using
“deAllocate(Resource)”.
Measures: measures are state variables or condi-
tion over state variables. The attributes which are
used in measures are highlighted in TypeModel of
Fig. 4 c. The measures within SSPO unit are shown
in Fig. 4 b. We consider 6 measures for SSPO
unit - “Business Volume”, “Revenue”, “Expense”,
“Profitability” and “Customer Satisfaction”. “Busi-
ness Volume” measure represents the slot value “busi-
ness volume” attribute of “Sales Record” class, “Rev-
enue” measure represents the slot value of “revenue”
attribute of “Account” class, and “Expenditure“ mea-
sure represents the slot value of “expenditure“ at-
tribute of Account” class of TypeModel shown in
Fig. 4 c. Similarly, the “Profitability” represents the
conditions on the slot values of “revenue” and “ex-
ICSOFT-PT 2016 - 11th International Conference on Software Paradigm Trends
114
Figure 5: Decision Making and Simulation Results.
penditure” attributes of Account” class, and “Cus-
tomer Satisfaction” is for representing the condition
on the slot values of “project completed ontime” and
“project completed with delay” attributes of “Project
Delivery” class.
Levers: the levers are the condition over events
and its parameters in the context of behaviour. In
this example, we consider 4 basic levers “Increase
Win Rate”, “New Opportunity Stream”, “Increase Re-
source Strength” and “Improve Resource Skill”.
4.2 Decision-making
The decision-making process is about finding possi-
ble levers (L), evaluating them with respect to organ-
isational goals and sub-goals (G), and selecting a set
of levers (L
select
L) that have the best potential to
achieve the goal G. Simulation-based what-if scenario
playing forms a cornerstone of this process.
Fig. 5 a shows simulation needs in a consolidated
form (upper portion) and the rest of the sub-figures in
Fig. 5 represent the simulation outputs from DrRacket
based ESL prototype. Goal and sub-goals G form
columns of the table (depicted in Fig. 5 a) with levers
L forming the rows. Each cell of the table represents a
what-if scenario for a lever l L on goal g G where
the impact of a lever on a goal needs to be computed
using simulation run. For example, first row in the
table corresponds to “Increase Win Rate” lever. As
can be seen, this lever has a positive impact on “Busi-
ness Volume” sub-goal, marginally positive impact on
“Revenue”, “Expense” and “Profitability” sub-goals,
and eventual negative impact on “Customer Satisfac-
tion” sub-goal. As a result, nothing conclusive can be
said about impact of “Increase Win Rate” lever on the
overall goal of “Secure Leadership Position”. The left
half of figure Fig. 5 b depicts initial state of the organ-
isation in terms of RFPs received, RFPs responded,
RFPs won (i.e., “Business Volume”), On-time de-
livery, Delayed delivery, Project execution pipeline
build-up etc without applying any levers. Significant
points to be noted are: all projects are delivered on
time, and there is no project execution pipeline build-
up. Right half of Fig. 5 b depicts details of simula-
tions carried out to determine the impact of “Increase
Win Rate” lever on various sub-goals. As can be seen,
A Simulation-based Aid for Organisational Decision-making
115
“Business Volume” increases by about 30% but there
is significant increase in the number of projects de-
livered with a delay some of which leads to penal-
ties. As a result, profits do not increase in the same
proportion as increase in “Business Volume”. Also,
build-up in project execution pipeline is a concern
that can lead to customer dissatisfaction that can po-
tentially impact overall goal adversely. Fig. 5 c and
Fig. 5 d. depict impact of levers “New Opportunity
Stream” and “Increase Resource Strength” on the var-
ious sub-goals. Comparison of figures Fig. 5 b. and
Fig. 5 c. shows the “Profitability” of “New Opportu-
nity Stream” is much higher than the “Profitability”
of “Increase Win Rate” however the factors associ-
ated with negative “Customer Satisfaction” are also
high. On other hand, “Increase Resource Strength”
shows positive impact on “Customer Satisfaction” but
with an additional cost that brings down “Profitabil-
ity”. Thus, as can be seen from the first four rows of
figure Fig. 5 a, no lever individually can ensure the
overall goal of “Secure Leadership Position” can be
achieved. As a result, one has to explore what impact
a combination of these levers can have. For example
one can evaluate the combination of levers “Increase
Win Rate” and “Increase Resource Strength” or levers
“New Opportunity Stream” and “Increase Resource
Strength”. Fig. 5 e. shows impact of levers “Increase
Win Rate”, “New Opportunity Stream” and “Increase
Resource Strength” applied together. As can be seen
from Fig. 5 a, this conclusively leads to achievement
of the overall goal. Further simulation can be done to
fine tune the options by deciding quantitative figures.
5 CONCLUSION
Organisational decision-making practice today relies
excessively on human expertise. This is primarily
due to unavailability of suitable technology support.
Available technology support is found inadequate ei-
ther in completeness of specification of all relevant
aspects of decision-making or in analysis rigour or
both. This paper has presented a conceptual model,
the accompanying implementation model that forms
the basis of a high-level language and its simulation
semantics.
The approach has been illustrated with a substan-
tive example from the software services domain. We
have shown the example can be modeled and sim-
ulated leading to the ability to influence the strate-
gically selected measures. However, we recognise
that the current implementation model (Fig. 3) of
ESL is not sufficiently high-level for direct adop-
tion by decision-makers. Our immediate next step
is to develop high-level abstractions to support the
core concepts of Fig. 3 in a business facing manner.
In doing so, we will adopt language processing and
model transformation technology to enable support
for defining domain specific languages geared for spe-
cific problems.
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