Next Generation Enterprise Modelling
The Role of Organizational Theory and Multi-Agent Systems
Balbir Barn
, Tony Clark
and Vinay Kulkarni
Department of Computer Science, Middlesex University, The Burroughs, London, U.K.
TRDDC, Tata Consulting Services, India
Organization Theory, Multi-Agent Systems, Actor Theory, Enterprise Modeling.
This position paper proposes that the current enterprise modeling approaches are overly reliant on the know
how or tacit knowledge of enterprise architects for addressing organizational challenges such as business-
IT alignment. Furthermore, current modeling languages only encourage linear thinking. By drawing upon
existing research on (computational) organization theory and multi-agent systems, we propose implementation
requirements for a next generation enterprise modeling language that supports agent based simulation.
The modern enterprise is faced with the tricky chal-
lenge of responding to external drivers such as merger
and acquisitions, or potential new markets, by adapt-
ing and managing internal change particularly with
respect to business-IT alignment. Up to now, such
a response has been dependent upon human expertise
based on tacit knowledge and experience or “know
how”. Such a position is not sustainable with the
rapid pace of change attributed to technology and
globalization. This is confirmed with research that
indicates that Strategic business-IT alignment has re-
mained an ongoing concern for organizations (Luft-
man, 2004) and researchers have addressed the im-
portance of alignment and in particular the need for
congruence between business strategy and IT strategy
(Chan and Reich, 2007).
One specific approach that has been used to bear
upon the problem of business alignment is the role
of Enterprise Architecture (EA) (Lankhorst, 2005).
However, the predominant theme has focused on de-
veloping enterprise models that are descriptive in na-
ture and hence needing human expertise for their in-
terpretation (see ((Veken, 2013), (Zachman, 1987))
for two obvious examples). As a result, current ap-
proaches to Enterprise Modeling (EM) exhibit a high
degree of latency in meeting key objectives such as
alignment, adaptation etc. Thus EA in its current
state does not readily lend itself to supporting the type
of analysis that key decision makers typically utilize.
Such stakeholders demand: ease of comprehension of
the entire business so that decision-making can lead
to efficient and effective change. In particular they
require the ability play out various what-if (i.e., what
will be the consequences of such and such action) and
if-what (i.e., what would have led to such and such sit-
uation) scenarios to arrive at the right response, estab-
lish feasibility of the response, and estimate a ROI of
the response. Thus ways of simulating an enterprise
are needed and currently EA modeling approaches do
not readily support this requirement.
Away from the EA modeling community, orga-
nizational theory and in particular computational or-
ganizational theory manifested in technologies such
as multi-agent systems provides an opportunity to re-
purpose existing research outcomes to address the EA
simulation and alignment conundrum. In doing so,
this position paper proposes that next generation En-
terprise Modeling languages should draw upon con-
cepts from organizational theory and multi-agent sys-
tems in order that appropriate machinery can be im-
plemented to support the simulation requirement.
The remainder of this paper is structured as fol-
lows: Section 2 presents key concepts from organiza-
tional theory and multi-agent systems. Specifically
it draws upon the established research relationship
that exists between the two areas. Section 3 presents
the main contribution of the paper and proposes a
novel language based approach to enterprise model-
ing for simulation. The proposal draws upon compo-
nents, goal modeling and agent technologies. Section
4 discusses our plans for addressing the research chal-
lenges raised by the approach.
Barn B., Clark T. and Kulkarni V..
Next Generation Enterprise Modelling - The Role of Organizational Theory and Multi-Agent Systems.
DOI: 10.5220/0005097404820487
In Proceedings of the 9th International Conference on Software Engineering and Applications (ICSOFT-EA-2014), pages 482-487
ISBN: 978-989-758-036-9
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Our starting premise is the view that there is a press-
ing need for next generation EM languages to ad-
dress the requirements described in section 1 which
can be broadly summarized as languages that: are ma-
chine manipulatable, support simulation through exe-
cutability, and are model based. Such requirements
raise two questions: What needs to be simulated?
Secondly, what technologies can we deploy to sup-
port execution? In answering the first question we
need to revert to Organizational Theory to understand
the meaning of Organization and its constituting ele-
ments. Human organizations in particular have been
the subject of detailed analysis from a range of disci-
plines including engineering, economics, psychology
and sociology (Scott, 1992). The resulting analysis
of the literature for organizational theory leads to a
persuasive argument for the use of agent technology,
particularly multi-agent systems as candidate tech-
nology for supporting the simulation/executability re-
quirements identified.
Ours is an organizational society such that orga-
nizations are the dominant characteristic of modern
societies. One rationale for the existence of organi-
zations posited by Carley and Gassser is that they ex-
ist to overcome the cognitive, physical, temporal and
institutional limitations of individual agency (Car-
ley and Gasser, 1999). While there are many ways
in which these limitations can be overcome and the
structure, form or architecture of an organization con-
tributes to such efforts, decades of research indicate
that there is no optimal organizational design. In-
stead, the challenge morphs into one of adaptability
and response to change. First we present here a nec-
essarily brief overview of some of the key definitions
and perspectives on organizations that underpin how
we intend to articulate the concept of an organization
in the context of the model driven enterprise. We first
begin with a definition of the term organization rec-
ognizing that there are multiple definitions depending
upon the perspective taken. The definition is reported
from (Parsons and Jones, 1960):
Organizations are social units (or human
groupings) deliberately constructed and re-
constructed to seek specific goals.
We explore this definition further by considering how
the study of organizations has generally investigated
the constituent elements of an organization and three
dominant theoretical perspectives informing research.
Leavitt identifies some core features of organizations
(Leavitt, 1964):
Social: Structure regularized aspects of relationships
among participants in an organization that may be
both normative (embodying what ought to be) or
factual order (actual structures).
Participants: individuals who in return for a variety
of inducements make contributions to the organi-
zation. Participants may belong to more than one
Goals: An organizational goal is a desired state of
affairs which the organization attempts to realize.
Goals are central to how an organization functions
and are often vague or very specific.
Technology: This is the means by which work is
performed in an organization. Technology can
be interpreted as a manufacturing plant, the soft-
ware systems enabling workers to perform work
or even technical knowledge and skills of partici-
Environment: Organizations exist in a specific
physical, socio-technical and cultural environ-
ment to which they must respond and adapt. All
aspects of an organization are influenced and
contextualized by the environment. For exam-
ple, software systems are purchased from external
providers or developed by technicians trained in
some other organization.
These features are generic to organizations and can
form the basis for extracting key concepts of an orga-
nization. Carley (Carley and Gasser, 1999) presents a
similar set. Note that these features may vary in some
way when viewed through a particular perspective or
metaphor. The last century has seen three dominant
perspectives (and overlaps) dominating research in
organization theory: Organizations as Rational Sys-
tems; Organizations as Natural Systems and Orga-
nizations as Open Systems. A rational system per-
spective denotes a focus on efficiency and optimiza-
tion and ultimately presents a reductionist model. The
open systems perspective is of most relevance to us as
it ranges from a simple clockwork view (a dynamic
system with predetermined motions), cybernetic view
(a system capable of self-regulation in terms of exter-
nally prescribed criterion such as a thermostat) to an
open system (a system capable of self-maintenance
based on throughputs of resources such as a living
cell) (Buckley, 1967).
These views are categorized by Gazendam
(Gazendam et al., 1998) and conform to essentially
two categories: Classical Organizational theories and
Systems theories. He suggests that classical the-
ories have a strong correspondence to a machine
metaphor where the organization as a whole con-
sists of agents performing tasks in fixed structures
consisting of agent tasks, communication paths and
spatio-temporal orderings. Systems theories on the
other hand view the organization comprising of sub-
organizations fulfilling a specific function. Gazendam
furthermore notes that: “System theories of organiza-
tion are relatively poor because they only pay atten-
tion to the system level, and remain rather abstract.
Theories based on the machine metaphor have
formed the basis of research on (multi-) agent-based
system in the late 1980s and 1990s to study alternative
viewpoints for describing organizations (Wooldridge,
2009). Here an agent is an autonomous and intelli-
gent being such as a human or a simulator of a human
realized by software (a computer agent) (Gazendam
et al., 1998). Systems that are comprised entirely of
computer agents have been used as simulations of or-
ganizations and correspondingly offer interesting per-
spectives on the study of organizations. Multi-agent
systems (MAS) and the associated Computational Or-
ganizational Theory (Carley and Gasser, 1999) pro-
vide the collective apparatus for investigation.
Computational Organizational Theory (COT)
aims to understand and model both human organi-
zations and artificial organizations (multi-agent sys-
tems) that exhibit collective organizational properties
such as the need to act collaboratively. Typical out-
puts of such research are the generation of new con-
cepts and theories about organizing and organizations.
Historically many applications and models have been
constructed but our review of the current enterprise
modeling literature indicates that COT has not yet
been applied to some of the tricky problems of en-
terprise modeling such as Business-IT alignment dis-
cussed in the introduction.
There are immediate information processing re-
quirements that are deducible from the definition of
organization such as: information ubiquity, tasks,
uncertainty distribution of organizational intelligence
and necessity of communication through a model-
based perspective. COT also suggests that: organi-
zations are modelable, and so are manipulatable; are
able to be designed to fit specific needs and there is an
assumption that the costs of modeling and researching
organizations in simulation mode rather than in vivo
are lower (Carley and Gasser, 1999).
Key characteristics of organizations such as that
described by Leavitt and Carley emphasize structure
and behavior. (Hoogendoorn et al., 2007) propose
these two aspects as necessary pre-requisites for mod-
eling change when using MAS. In their proposal, or-
ganizations are described solely by the way groups
and roles are arranged to form a whole. Related to
this, Giorgini et al. use the i* framework (Yu, 1997)
to define a series of architectural organizational styles
which act as metaclasses and offer a set of design pa-
rameters for coordinating goals, actions and behavior
and therefore govern how an organization functions
(Kolp et al., 2006; Argente et al., 2006). Our posi-
tion contributes to enterprise modeling technologies
by drawing upon research outputs from COT to meet
the needs of an adaptive organization located in a sys-
tematic understanding of socio-technical nature of an
organization (Bean, 2010).
If MAS and COT are an appropriate way forward,
then there are additional requirements for methods
that can support COT based approaches. Those tasked
with modeling enterprises need guidance that: “al-
low the description of social structures, permit the use
of tools to perform project management, and include
IDE or CASE tools that facilitate the analysis and
design of MASs (Luck et al., 2005)”. Furthermore,
all methodologies need to contain enough abstrac-
tions to model and support MASs, which are usually
structured as societies of agents that play roles and
exchange information following predefined protocols
(Isern et al., 2011). Isern et al. then go onto review a
range of agent-oriented methodologies by evaluating
their underlying meta models. Analysis of these meta
models guides us toward the essential features of the
language proposed in section 3.
We have posited that current approaches to EM
presents a linear form of enquiry requiring tacit
knowledge based on an Architect’s know how that
prevents scaling up to rapidly address “what if type
of questions. Adopting technologies based on MAS
requires robust models for representing the complex-
ity and dynamic nature of organizations as they re-
spond to external business drivers. In particular then
MAS can be used to provide simulation models for
exploration of complex environments. Simulation
models can be explanatory models that can help iden-
tify kinds of behaviour expected under specific con-
ditions or they can predictive models that determine
more precisely the the kind of behaviour a system will
display in the future (Siebers and Aickelin, 2008).
Luck et al. propose a grouping of the agent-
technologies, tools and techniques that can address
these types of simulation for EM for theory building
about an enterprise at three levels: Organizational-
level (focusing on larger aggregations of structures;
Interaction-level (collaboration, communication and
decision making between agents) and Agent-level
(learning and reasoning (Luck et al., 2005). Cross cut-
ting concerns such as agent programming languages
and methodologies (noted earlier) provide practical
steps towards realization of agent systems.
In the next section, we discuss this partitioning has
been used to influence our proposal.
Figure 1: Component Abstraction (Core Concepts).
We posit that any approach that is derived from ideas
from the previous sections relies on being able to rep-
resent and process an organization that is expressed
in terms of a component-based abstraction. We envis-
age a product-line approach (Reinhartz-Berger et al.,
2013) whereby a suite of tools based on this abstrac-
tion is used to facilitate a collection of different orga-
nization analysis and simulation activities. Each ac-
tivity will constitute a domain, e.g., cost analysis, re-
source analysis, mergers and acquisition, regulatory
compliance. In principle, each new domain will re-
quire a new domain specific language to represent the
concepts. How should such a proliferation of domains
be accommodated by a single component abstraction?
Our proposal is to construct an extensible kernel
language that is used as the target of translations from
a range of domain specific languages (DSLs). Each
DSL supports an organization analysis and simulation
use-case. We then aim to construct a virtual machine
for the kernel language so that it is executable. Model
execution supports organization simulation and some
analysis use-cases. Links to external packages such as
model-checkers will complete the analysis use-cases.
The use of a single kernel language provides a sin-
gle focus of development effort and can help min-
imize the problem of point-to-point integration of
analysis methods. Our proposal is that a small core
collection of concepts, including component, inter-
face, goal, event, function as shown in figure 1, are
a suitable basis for most types of analysis and simu-
lation use-case and therefore the kernel language will
be defined in terms of these concepts.
Given its ability to accommodate multiple simula-
tion and analysis use-cases, we envisage the language
being the basis of a suite of organizational modeling,
simulation and analysis tools, presented in the form of
a single integrated extensible meta-tool EA Simula-
tion Environment (EASE-Y) shown in figure 2. Since
organizational information is likely to be very large
(at least many tens of thousands of model elements)
Figure 2: The EASE-Y Architecture.
it is important the tool is implemented efficiently, is
scalable, supports distributed concurrent development
and is flexible in terms of its architecture. To this end
we aim that the kernel language should be compiled
to a machine language running on a dedicated kernel
engine, the language integrates with standard repos-
itory technology, and can run equally well on single
machines, networked machines and via the cloud.
Organizations consist of many autonomous com-
ponents. Components are organized into dynamically
changing hierarchical groups, operate concurrently,
and manage goals that affect their behavior. We aim
for the kernel language to reflect these features by
having an operational semantics based on the Actor
Model of Computation (AMC) (Hewitt, 2010) and its
relation to organizations, or iOrgs (Hewitt, 2009). Ac-
tors have an address and manage an internal state that
is private and cannot be shared with other actors in the
Execution proceeds by sending asynchronous
messages from a source actor to the address of a tar-
get actor. Synchronous messages can be achieved
by sending an actor in an asynchronous message to
which the result should be sent. Each message is han-
dled in a separate execution thread associated with the
target of the message and the message itself (collec-
tively referred to as a task). During task-execution an
actor may choose to change its state and behavior (be-
coming a new actor) that is immediately available to
process the next message sent to the target address.
Our claim is that the AMC provides a suitable
basis for execution and analysis of the concepts dis-
cussed in section 2. Actors, sometimes individually
and sometimes collectively, can be used to represent
the features of a component. The rest of this section
lists the key features that must be supported by the
kernel language and how the actor approach can sup-
port them:
[adaptability] This is required because organiza-
tional components may change dynamically during a
simulation. Resources, individuals, and even depart-
ments may move location, and have an affect on re-
sults. Furthermore, the behavior of a component may
change over time as information changes within the
system. An actor can, in principle, change behavior
as a result of handling each message.
[modularity] Each part of an organization is in-
tended to perform a business function that can be ex-
pressed in terms of a collection of operations. The
internal organization in terms of people, IT systems
and the implementation of various business processes
is usually hidden. The AMC provides an interface
of message handlers for each actor. Both the state
and the implementation of the message interface are
hidden from the outside. The specification of an ac-
tor in terms of its external interface can be expressed
in terms of LTL formulas that constitute the external
goal for a component.
[autonomy] A key feature of an organization
is that the behavior of each sub-component is au-
tonomous. A particular department is responsible for
its own behavior and can generate output without the
need for a stimulus. The AMC is highly concurrent
with each actor being able to spawn multiple threads
and over which other actors have no control (unless
granted by the thread originator).
[distribution] An organization may be distributed
and this may be an important feature of its simulation.
Furthermore, we have a requirement that the tooling
for organizational analysis and simulation should sup-
port distributed concurrent development. The AMC
associates actors with addresses to which messages
are sent. Execution does not rely on the particular
location of the actor (i.e. the mapping between the
address and the actor behavior) that can be in the
same address space, via a network connection or in
the cloud.
[intent] In addition to autonomous behavior, an
organization component exhibits intent. This might
take the form of an internal goal that guides the be-
havior of the component to ensure that it contributes
to the overall mission of the organization. Although
actors do not directly provide support for such goals,
we intend to use results from the field of Multi-Agent
Systems (Wooldridge, 2009) where support for goal-
based reasoning is provided within each agent when
determining how to handle messages.
[composition] An organization is an assembly of
components. As noted above, the topology of an or-
ganization may be static or dynamic. Actors can be
nested in more than one way. Actor behaviors are de-
clared and new actors are dynamically created with an
initial behavior (much like Java classes). The scope
of actor behaviors can be nested to provide modular-
ity. Adding a dynamically created actor to the state
of a parent actor provides composition. Such actors
can be sent as part of messages. If the source actor
retains the address, then the communicated actor be-
comes shared between the source and the target of the
[extensibility] Our aim is to support a number of
simulation and analysis use-cases. As such the kernel
language will need to support a collection of indepen-
dent domains. Whilst we expect the DSLs to target
the kernel language it is likely that each domain will
have its own fundamental concepts and actions (so-
called Therbligs, (Stanton, 2006)). We envisage such
domain-specific features being defined in the kernel
language and then pre-loaded to form an augmented
target language for DSL translations.
[event-driven] Organizational components can-
not rely on when communications occur and where
they originate. In addition, a component may sim-
ply cause an event to occur without knowing who
will consume the event. This is to be contrasted with
message-based communication where the target is al-
ways known to the source and where sometimes the
message carries information about the source that be-
comes available to the target. The AMC is based on
message passing where the source knows the address
of the target. Given that the kernel language is the tar-
get of DSL transformations, support for event-based
communication becomes an architectural issue where
events are simply messages that are sent to an ac-
tor container that is responsible for delivering event-
messages to dynamically changing collections of ac-
tors. Providing that the transformation establishes the
correct assembly of actors and conforms to an ap-
propriate message passing protocol then component
events are supported without needing to make them
an intrinsic part of the kernel.
This position paper has proposed that current genera-
tion enterprise modeling languages and technologies
support a linear form of enquiry that requires tacit
knowledge based on an Architect’s know how. Such
an approach prevents scaling up to rapidly address
“what if type of questions that face organizations
as they seek to adapt to respond to ongoing change.
At an abstract level, these types of requirements
have been studied in other disciplines, including eco-
nomics, political science, philosophy and linguistics
leading to computation based organizational theo-
ries and technologies for describing agent interaction,
communication and decision-making. We have pre-
sented an argument that traces a route through (com-
putational) organization theory to propose that next
generation enterprise modeling languages should ad-
dress COT and multi-agent system approaches to pro-
vide a rich simulation platform that supports both ex-
planatory models and predictive models for the “what
if” question. In doing so, we recognize that there are
open-ended research questions around methodology
and proposed the simulation platform. We plan to
validate our proposition in a number of ways. We
are currently developing a collection of representa-
tive case studies based on real-world data in a lab-
oratory setting. One case study illustrates how the
proposed ideas and techniques can help data-driven
decision making in an IT services providing organi-
zation. Another case study will address merger and
acquisition problem in wealth management domain.
We intend to run co-design workshops with Business
Management domain experts in order to evaluate their
response to our proposals. We are currently extending
µLEAP (Clark and Barn, 2014) to be the target kernel
language. We plan to design and implement the ker-
nel language meta-model as a virtual machine, pos-
sibly using multiple Java VMs as targets. Scalability
of the platform in terms of the number of runtime vir-
tual machines is a critical factor for acceptability of
the platform in practice. Recent research by Bolz and
Tratt (Bolz and Tratt, 2013) seems a promising direc-
tion in this regard.
Argente, E., Julian, V., and Botti, V. (2006). Multi-
agent system development based on organizations.
Electronic Notes in Theoretical Computer Science,
Bean, S. (2010). Re-thinking enterprise architecture using
systems and complexity approaches. Journal of En-
terprise Architecture, 6(4):7–13.
Bolz, C. F. and Tratt, L. (2013). The impact of meta-tracing
on vm design and implementation. Science of Com-
puter Programming.
Buckley, W. (1967). Sociology and modern systems theory.
Carley, K. M. and Gasser, L. (1999). Computational or-
ganization theory. Multiagent systems: A modern
approach to distributed artificial intelligence, pages
Chan, Y. E. and Reich, B. H. (2007). It alignment: what
have we learned? Journal of Information technology,
Clark, T. and Barn, B. S. (2014). Outsourcing service pro-
vision through step-wise transformation. In Proceed-
ings of the 7th India Software Engineering Confer-
ence. ACM.
Gazendam, H. W., Jorna, R. J., et al. (1998). Theories about
architecture and performance of multi-agent systems.
University of Groningen.
Hewitt, C. (2009). Norms and commitment for iorgs
(tm) information systems: Direct logic (tm) and
participatory grounding checking. arXiv preprint
Hewitt, C. (2010). Actor model of computation: scal-
able robust information systems. arXiv preprint
Hoogendoorn, M., Jonker, C. M., Schut, M. C., and Treur,
J. (2007). Modeling centralized organization of orga-
nizational change. Computational and Mathematical
Organization Theory, 13(2):147–184.
Isern, D., S
anchez, D., and Moreno, A. (2011). Organiza-
tional structures supported by agent-oriented method-
ologies. Journal of Systems and Software, 84(2):169–
Kolp, M., Giorgini, P., and Mylopoulos, J. (2006). Multi-
agent architectures as organizational structures. Au-
tonomous Agents and Multi-Agent Systems, 13(1):3–
Leavitt, H. J. (1964). Applied organization change in indus-
try: structural, technical and human approaches. New
Perspectives in Organizational Research, 55:71.
Luck, M., McBurney, P., Shehory, O., and Willmott, S.
(2005). Agent technology: computing as interaction
(a roadmap for agent based computing).
Luftman, J. (2004). Assessing business-it alignment ma-
turity. Strategies for information technology gover-
nance, 4:99.
Parsons, T. and Jones, I. (1960). Structure and process in
modern societies, volume 3. Free Press New York.
Reinhartz-Berger, I., Cohen, S., Bettin, J., Clark, T., and
Sturm, A. (2013). Domain Engineering. Springer.
Scott, W. R. (1992). Organizations. Prentice-Hall Engle-
wood Cliffs, NJ.
Siebers, P.-O. and Aickelin, U. (2008). Introduc-
tion to multi-agent simulation. arXiv preprint
Stanton, N. A. (2006). Hierarchical task analysis: Devel-
opments, applications, and extensions. Applied er-
gonomics, 37(1):55–79.
Veken, K. V. d. (2013). Enterprise architecture modelling
to support collaboration-the archimate language as a
tool for communication.
Wooldridge, M. (2009). An introduction to multiagent sys-
tems. John Wiley & Sons.
Yu, E. (1997). Towards modelling and reasoning support
for early-phase requirements engineering. In Require-
ments Engineering, 1997., Proceedings of the Third
IEEE International Symposium on, pages 226–235.
Zachman, J. A. (1987). A framework for information sys-
tems architecture. IBM systems journal, 26(3):276–