Lourival Paulino da Silva (2005)
A Formal Model for the Fifth Discipline
Journal of Artificial Societies and Social Simulation
vol. 8, no. 3
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In this paper we present the main results of our research concerning the development of a formal model for the theory called The Fifth Discipline. Our model is based on a Multi-Agent Systems framework. The contributions of this work include a formal model for the Fifth Discipline, and analyses that highlight key features of that theory, namely the pressupositions that agents must be honest, cooperative, tenacious, and that trust is fundamental in the agents' interactions.
Multi-Agent Systems, Formal Methods, the Fifth Discipline, Organizational Modeling, Learning Organization, Organizational Learning, z
In this paper we outline a formalization, from a Multi-Agent Systems
(MAS) perspective, of an Organizational Theory (OT). Based on this
formalization, we highlight some relevant features of this OT: the
models and underlying assumptions about an organization that
implements this OT; their members and the interactions among members;
and the interactions among the organization as a whole and its
The OT selected for this work is Senge's Fifth Discipline
[Sen90], and the MAS formal framework that is adopted is SMART
[dL01], which in turn uses the Z [Spi92]
specification language to formally define MAS related concepts and terms.
The formal model outlined in this paper corresponds to our personal
interpretation of Senge's work and depicts some characteristics of his
theory, thus is not intended to be a full detailed translation of the
Fifth Discipline theory into a formal model. Furthermore, due to space
limitations we present here just an overview of our model. The
interested reader should refer to Part II - A Detailed Version,
where we describe this model in more detail.
The contributions of this work include not only a formal model for
Senge's theory, but also analyses that indicate that several
individual features play an important role in Senge's theory, namely
agents must be honest, cooperative, tenacious, and trust is
fundamental in the agents' interactions.
We intend our formalization also to be an instance of what can be done
to check an organization with respect to a specific organizational
In the next section, we present an overview of the Fifth Discipline theory.
Some excerpts of our formal model for this theory are outlined in section 3.
In section 4, we discuss some properties of our model. We briefly
discuss work that relates to our research in section 5. Finally, in
section 6, we present our concluding remarks and discussion.
Overview of the Fifth Discipline Theory
The field of OT relates to the structure, design and performance of
organizations. The improvement of organizational effectiveness is the
main goal of this field.
Different approaches have been developed through the last hundred
years of research in OT, from the work of
Taylor [Tay11] and Fayol[Fay49]
to the Human Relations School, Theory of Behavior,
Systems Theory and Contingency Theory.
In this path, it is noticeable a change from prescriptive
to descriptive and explanatory paradigms,
as well as a change from the concept of man as homo economicus,
motivated by material and monetary incentives, to more complex models
that also include social
Following this trend, Organizational Learning has been growing in the
last 40 years. This school focuses in the processes of learning,
innovation, and the creation, storage, manipulation and transfer of
knowledge. The purpose is the creation of increased organizational
performance and adaptability. Differently from other schools, here,
greater attention is paid to the management of intangible assets which
may provide competitive advantage to organizations.
In our research we deal with one of the most recent and influential
contributions to the school of Organizational Learning: The Fifth
Discipline, presented by Senge in [Sen90],
and referred in this work as the LO theory.
According to Senge, a learning organization is
the one that is "continually expanding its capacity to create its
future". Here we outline this theory. A more detailed description
can be found in Part II - A Detailed Version.
The LO theory focuses in ways to overcome learning deficiencies, which
are identified as the main reasons for the relatively short life span
of most organizations. The "methods, techniques and principles"
recommended by Senge must be continually developed and are structured
into five learning disciplines: Systems Thinking, Personal Mastery,
Mental Models, Shared Vision and Team Learning.
Systems Thinking involves the development of the capability to
understand patterns of events in organizations, viewed as complex
systems where inter-related actions fully develop their effects only
after some period of time.
Personal Mastery involves the individual commitment to
reducing the distance
between the individual's goals
and current reality, so that
organizational learning is founded
on each person's
commitment with self-development.
Mental Models are hypotheses and generalizations that
influence both the persons'
comprehension of and interaction with the world.
Shared Vision corresponds to the development of
goals, values and mission that are profoundly
shared through all organization so that
the organization may unite its members around
a shared identity and a sense of destiny.
Team Learning involves the development of the capability of team
members to suppress their individual assumptions, making possible
the free flow of meaning among members, and enabling the collective
discovery of insights.
Senge states that the five disciplines must be developed in
consonance, in a coherent body of theory and practice, which is united
by the Systems Thinking discipline.
However, Systems Thinking also depends on the other
four disciplines in order to develop its full potential.
The LO theory is a modern, influential and successful OT. Many
important organizations have used it very successfully, including
Shell, Harley-Davidson, Kyocera, and Federal Express.
Modeling the Fifth Discipline as MAS
The area of MAS has gained increasing attention during the last
decade. In order to provide a sound conceptual foundation to concepts
involving agency and agent-based development,
[dL01] built the SMART conceptual framework.
SMART was chosen in our research because
it is a formal, unambiguous, structured, and extensible
framework created to serve as a foundation for
studying and building different agent oriented architectures.
For example, in [BCV98,
different models based on SMART are presented.
Similarly, in our work we have taken advantage of such SMART's
features and have extended it to include new layers that specify
agents that aggregate new capabilities and present increasing
SMART is specified in Z. Therefore,
in appendix A of Part II - A Detailed Version
we present introductory information concerning this notation.
However, this basic introduction may not
provide sufficient information for the reader with no previous
experience with Z.
In this case, we suggest
for an introduction to this notation.
In SMART, the world is made of entities, which are
specified as a collection of attributes.
Moreover, entities are classified according
to additional features they have: objects are entities with
an associated collection of capabilities; agents have a set
of goals; and autonomous agents have motivations.
For example, specifications for
objects and agents in SMART are presented below.
Learning Organization Agents
In our work we apply the same reasoning presented above
and specify more complex agent types:
agents that are capable of planning,
performing roles in organizations,
and agents that can be members of a
Furthermore, interactions among agents can be described. Thus, the
formal specification of individual, collective,
and organizational aspects can be performed.
The construction of these types is based on those ones
previously defined in SMART.
We note that this section contains only some excerpts
of our model for the Fifth Discipline, just to provide
an overview of what has been produced. Due to
space limitation, most types and concepts are not
presented in this work. The interested reader
should refer to Part II - A Detailed Version,
where we describe this model in more detail.
The diagram 
in Figure 1
represents the basic
schema structure of our formal model for the Fifth Discipline.
diagram we have characterized the different layers that our model
incremental definitions via
refinement of agent types. Consequently, features that
are present in the lower layers are incorporated by
the types defined in the upper layers.
In our model, the first layer that is capable
of storing internal representations of the environment
is the type
In the next layer we introduce the type
that corresponds to an autonomous agent that is also
capable of capturing and storing information
as internal state. Moreover, since it has the ability
to generate its own goals according
to its motivations, it is an autonomous agent.
The perception of this agent is influenced
by its goals and motivations, and its behavior
is influenced by the environment and by its motivations.
On top of this layer we define
an agent with a set of plans associated with a set of goals:
a planning agent.
This agent type has also a set of resources that are required
to instantiate an action within a plan.
The actions of a planning agent are influenced by its motivations,
the environment, and its plans.
In the next layer we present the type organizational agent
which participates in a set of organizations,
performs some roles and has a set of plans that include both
its personal plans and those related to the its roles.
Similarly, the agent's set of goals include both the
agent's goals and the goals associated with
each of its roles.
In summary, on top of the SMART layers we have specified layers
related to organizational capabilities, and on top of these, we have
defined the layer related to the agent type that is capable of being
member of a Learning Organization. Therefore, in this incremental
definition we define a more specialized type of agent which
corresponds to a formal specification of the requirements described in
Senge's theory. The Learning Organization Agent -
is an organizational agent, develops personal mastery, has mental
models and develops systems thinking skills. Among the motivations of
this agent we have: reducing creative tension and commitment to a
clear perception of reality.
Figure 1. Formal model's basic schema structure.
Consequently, we define that a LearningOrgAgent develops all of
Senge's disciplines. Based on its motivations, on the principles and
actions related to each discipline, a given agent develops new
capabilities to act.
Moreover, the agent's guiding ideas are
consistent with all of its goals.
In our model we divide Senge's
disciplines in two groups: intra-personal and inter-personal,
according to the type of skills that such disciplines allow the agent
to develop. The first group mainly deals with the agent's internal
states and representations, and the second deals with interactions
among Learning Organization Agents.
The Learning Organization Model
Initially, we discuss how some basic types used in the
definition of a Learning Organization,
e.g.: groups, teams organizations and
formal organizations, are defined in our model.
A group is a set of individuals that share a set of resources.
Thus, in such a group there is no need for a joint commitment
towards a joint goal.
A team is a group where there is a set of goals that is shared among
In a learning team all members are agents of the type
and it is possible to develop team's
guiding ideas that are shared among its members.
comprises an organizational structure,
sets of teams, roles, norms, and organizational goals.
The structure of an organization is specified as a connected graph in
which the node set corresponds to teams and the edge set corresponds to
relationships among the teams.
Roles are specified as sets of actions that must be performed, sets of goals,
sets of resources that an agent,
while performing this role, has permission to manage, and a level
of organizational autonomy. This level defines whether the agent performing
such role has some freedom to define new tasks in order to achieve
the goals associated with the role (as stipulated in norms), or
even to change part of the goals, or no freedom at all.
We also define that an organization is formal if
there are defined organizational positions and norms defined in
its context, and each member in the organization performs at
least one role.
We specify a Learning Organization as a type of
in which all teams are learning teams.
In summary, the LearningOrg schema specifies, via schema inclusion, that a
Learning Organization is such that it has structure, roles, norms that permit
that all teams develop the team learning discipline.
Moreover, every agent in the organization develops its personal mastery,
reflects on their mental models, have systems thinking skills,
collectively develop shared visions, and perform roles in the
The reference to
gives access to the organizational shared vision.
We require that this set is non-empty, ie, a
Learning Organization must develop shared visions.
is restricted so that the teams that produce such visions
are exactly the teams that constitute the organization.
Interactions, Knowledge and Mental Models
In Part II - A Detailed Version we present a formalization for
some of the processes involving interactions among agents of type
and how these interactions affect their knowledge
and mental models. Here we just present a brief overview of these
In our model agents communicate using messages that may carry
information about some specific entity or about the environment in
general. We assume that all communications occur under a given
conversational context that may involve agents' beliefs or goals. In
addition, these messages are interpreted by the agents in accordance
with their mental models, thus producing internal views. An agent is
able to recall views from its memory or to infer views that were not
previously stored in it. Agents are also capable of producing
messages that are influenced by their mental models.
We assume that an agent's mental models include its knowledge and
beliefs. In our model an agent knows a view if it can be recalled from
its memory or if the agent can infer it from the views contained in
such memory. Furthermore, an agent capable of interacting has
knowledge, extracts, interprets, produces, and exposes messages.
We also define that the agents interact in sequences of messages
and that conversation sessions are composed of sequences of interactions.
As we are studying Senge's theory, there are two types of sessions that
we are interested in modeling: dialogs and discussions. In our model
the agents involved in these sessions are members of learning teams.
They perform two alternate roles in these interactions: sender or
receiver. This is a broadcast model, so that at a given interaction
just one agent may perform the sender role, while the others perform
receivers roles. We also require that all members perform the sender
role at least once in a given session.
Properties of the Formal Model
In this section we investigate some characteristics
of the model that we have built for Senge's theory
TRUST Is Necessary for the Formalized Fifth Discipline
We argue that it is not possible to build a shared set of goals
(similarly for visions, values, purposes) if the agents involved in
the process do not trust each other.
First we need some definitions.
- An agent ai is trustAgent if it exposes only information
that it believes or knows, otherwise it is a nonTrustAgent.
- Agent ai is believer if, in the absence of previous
interactions with a particular agent aj,
ai assumes by default a trustAgent model of aj.
- An interaction among agents ai and aj
is TRUST if, during this interaction, ai has in its
mental models a trustAgent model of aj and,
reciprocally, if aj has in its
mental models a trustAgent model of ai.
- An agent may revise its trustAgent model of its partner
to nonTrustAgent if it is able to perceive
that there is no consistency between its partner's exposed mental models,
proposals (messages), agreements, and performed actions.
A dialog/discussion session involving agents ai and aj
successfully produces a set goals of shared goals if at the end of the
session all these holds:
- ai believes in goals and
- aj believes in goals and
- ai believes that aj believes in aj's interpretation of goals and
- aj believes that ai believes in ai's interpretation of goals
The context of analysis involves two agents ai and aj
and an initial sequence of sending and receiving messages performed by both
agents in order to formulate a set of goals which both agree upon.
Our initial hypotheses include:
(h0). We suppose that there are no records of previous interactions among
ai and aj.
(h1). We suppose that both ai and aj are believer agents.
Let us consider the following process:
Agent ai sends message mi. Thus, first, ai has exposed message mi.
Then, the received message is interpreted by aj.
According to (h1), aj is a believer agent, therefore
it believes that ai believes in mi.
Thus, aj believes that ai believes in mi, ie,
that ai believes that the state of affairs in the environment corresponds
to the information contained in mi and aj may proceed and drive its
reasoning based on this belief. After this step, aj may send message mj
regarding this dialog/discussion session.
Similarly, ai believes that aj believes in what
is stated in mj. We may assume that this process proceeds through several interactions
involving negotiation/argumentation, so that when the session is considered finished by
both ai and aj, each agent will have a richer model about the beliefs of the
corresponding partner, in particular concerning goals.
Regarding the set of shared goals, there are two possibilities at the end of the session:
a set was built, or there was no agreement about shared goals.
Here, we suppose that there was an agreement about a set of goals goals.
if ai and aj at the end of the session still model the corresponding
partner as trustAgent
we can affirm that ai believes that aj
believes in its interpretation
of the goal goals, and
that aj believes that ai
believes in its interpretation
of the goal goals.
If, however, at the end of the session, at least one of ai or aj
has revised its partner's trustAgent model to nonTrustAgent, then
one of our two previous assertions will not hold,
and therefore it will not be possible to build a goals set.
Agents Must Be trustAgent
One formal formal
term that we present in detail
in Part II - A Detailed Version
is ExposedBelief, which
is a type of the agent's beliefs.
These are stored in the agent's memory.
In addition, in our formal model for Mental
Models, we introduce the function advocacy.
According to this function,
the agent has motivations and performs actions so as to
expose its beliefs and reasoning.
Therefore, the agent in our model is a trustAgent, ie,
it exposes only information that it believes or knows.
Agents' Motivations and Disciplines Must Be Consistent
First, in our formalization of Mental Models, the
reflection and advocacy functions depend on the agent's motivations.
In the case of the Personal Mastery discipline, the agent's
creativetension functions are
dependent on the agent's motivations.
The formalization of the
Team Learning discipline introduces a set of actions and protocols,
among them inquiry and advocacy which, as showed above in the considerations
about Mental Models, are influenced by the agent's motivations. In addition, there
is also the function
developdialogdiscussactions, which also depends
on the motivations of the agent.
As for the Systems Thinking
discipline, we have the
potentialstatesanalyser function which
PersonalVision which, in its turn, depends on the agent's motivations.
Finally, the Shared Vision discipline is developed by teams of
agents that construct agreements via dialogdiscuss function, which
uses dialogdiscussactions actions. The development of these actions
depend on the agents' motivations.
Thus, in summary, an agent that develops the five disciplines must have
motivations that are consistent with the actions and protocols defined in each
of the mentioned disciplines.
Agents Must Be tenacious
Following the specification of the
such type of agent develops the five disciplines independently
of any temporal considerations, ie, the disciplines
determines the way the agent thinks, acts and interacts.
This is a consequence of the fact that for every interaction
(with the environment or with another agent) the
function is used to produce the agent's percepts. This function
is affected by the agent's mental models and guiding ideas.
Guiding ideas result from the agent's personal mastery and include
vision, purpose and values.
Accordingly, the agent's action selection function,
learningorgact, is affected by Mental Models, Personal Mastery,
and Systems Thinking.
In addition, the learning organization model, presented in
LearnOrg, requires that every team in the organization is
a learning team
and the organization must build a set of
shared visions. Therefore, all disciplines must be
continuously developed by the agents.
Agents Must Be cooperative
According to [Wei01], a cooperation is a type of coordination
among non antagonistic agents in which the participants succeed
or fail together. In contrast, in a competition the success of one participant
implies the failure of others.
In our formalization of the Mental Models discipline we require that the agent
exposes its beliefs so that, in a team of agents, each agent
learn the others' mental models. This is also true for building
If we consider that an "index" of success of a
learning organization is to what degree it is able to develop
shared visions, and that this is a collective achievement, then
we conclude that, in fact, the learning organization represents a
cooperative scenario: agents succeed (or fail) together in the
process of shared visions construction.
In a competitive scenario an
agent exposes its mental models only if it believes that by
doing so it will have some type of benefit.
Organizational Change Emerges from Individual's Motivations
According to our model, all agents in the organization develop the
five disciplines and the disciplines determine how the agents think,
act and interact. We also know that the development of the disciplines
by a given agent is influenced by its motivations. Moreover,
in Part II - A Detailed Version we show that shared goals,
values, and visions, may emerge from
interactions among agents and also depend on the agents development
of the disciplines.
Taking into account that one of the indicators of a given organizational
state is its current set of shared goals, values, and visions; and that the
outcomes produced by the organization result from agent's actions,
we conclude that organizational change emerge from individual motivations.
Low Turn-Over Is Required in the Organization
The interaction process described in our model presents
a situation where there are no changes in the agents that are members
of the team during all the process. However, if we take changes in
team membership into account,
it is possible to observe that the interaction
process underlying the construction of shared vision and shared
mental models is affected as follows.
Let us consider a team with n members.
We show in Part II - A Detailed Version that at
least n rounds of interactions are required so that each
agent has the opportunity to play the role of sender.
that in round k, with k < n , k agents have already played
the sender role when a new agent enters the group. Now the group
has n + 1 members and at least n + 1 rounds would be required to
allow the new member to play the sender role.
However, in this case the new member also needs to play the
receiver role for all the k presentations that occurred before
he was a member of the team. Thus, at least k + n + 1 rounds
are required for each new agent that becomes a member of the
team in a given round k.
Now, considering the case where the
new agent substitutes a former member of the team, we notice
that k + n rounds are required for each new agent that substitutes
a member of the team in a given round k.
Hence, the more frequent inclusions or substitutions of members
of a team, the less efficient is the process of producing
common knowledge via interactions.
In fact, for each agent in the team
a certain amount of time is spent in modeling the others
mental models and visions. If there is a high frequency of turn-over
in the teams, all this effort may be wasted.
Bounded Number of Members in Learning Teams
In order to improve the efficiency of the interaction process,
the number of members in a given team must be
bounded. Otherwise, teams
with a large number of members
will have to spend too much time
deliberating, even if the agent population is constant
order to cope with this complexity, the learning organization must
be subdivided in a number of teams and must also
define an upper limit for the size of its teams.
Formalizations for the organizational theory
Organization in Action (OA) [Tho67]
are presented in [KP99] and [MH96].
Similarly to the work reported in this paper,
parts of a discursive theory are revised using
formal methods. In [KP99], first order predicate
logic is used to study the underlying argumentation
structure for the propositions of the OA theory.
On the other hand, a multi-agent modal logic
developed by the authors in [MH96], is used to achieve
goals that are similar to the ones mentioned above,
and, additionally, to investigate the expressive power
and applicability of this logic for the formalization of
a discursive theory.
The formal model presented in this paper uses the Z notation
to build a structured framework that can be used to
study Senge's theory and also to investigate hybrid
organizations, involving both human and computational agents.
In [Lin98] the investigation and design of organizations
that require a high level of reliability is presented.
The approach presented in that work is a generalization of the
Contingency Theory [LL67] of administration: the effect of
on the organizational performance and the corresponding
implications on its structure. Differently,
the investigation of organizational structures
is beyond the scope of this work, as Senge does
not specify them in detail in his theory. In the
model presented here, we decided to specify the
organizational structure as a graph in which each
team is represented by a node, and relationships
among teams are represented by edges.
However, in our model different organizational
structures could have been specified instead.
The social dilemma involving voluntary cooperation among
individuals confronted with conflicting time and effort
options is the focus of the work reported in [HG98].
The individual can contribute to build a common good
or, alternatively, it may decide to take advantage of the
efforts of the other individuals.
In the model presented in this paper, the shared vision discipline
involves a process in which shared high level goals have to emerge.
In this process the motivation of each agent underlies its decision
to adopt a set of shared goals.
The existence of conflicting time and effort options for a given
agent could imply the absence of a motivation for this agent
to adopt a shared goal or shared vision. However, in our formal
model a learning organization has to develop a shared vision, in a
process that involves all agents in the organization.
Therefore, confronted with such dilemmas, in our model for
the Fifth Discipline the agents choose to cooperate.
In [CP98] a type of agent, called WebBot is investigated.
This agent performs tasks autonomously, acting as an assistent for
humans or other WebBots). That work studies the effect of
the honesty of the WebBot concerning the individual and
collective organizational behavior.
Similarly, in our model one of the features that plays an important
role is the honesty of the agent in its interactions in a
learning organization. In fact, we show that in this model
trust interactions among agents are necessary to build a
The goal of the work presented in [YS99] is to study a
role based agent-oriented conceptual framework in order to
model workflow. In that case, business processes are viewed
as a collection of problem solvers autonomous agents that
interact when faced with interdependencies.
Workflow coordination is achieved via agent communication.
In that work, the organizational model results from the
definition of organizational roles and description of the
coordination and agent performance while performing a
given role. Therefore, that model focuses in organizational
processes, while our model is based on a specific organizational
In [K. 99] multi-agent learning is investigated.
That work is based on an Organizational Learning approach
based on [Arg77] in which four types of learning are
considered. The first is called individual single loop learning and
enhances performance in the scope of an individual norm.
The second, individual double loop learning, enhances performance
via changes in an individual norm. The third, known as organizational
single loop learning improves performance in the scope of an
organizational norm. Finally, the fourth is named organizational
double loop learning and improves performance via changes in an
Similarly to the work presented in this paper, [K. 99]
also presents a computational model that is inspired by an
organizational theory associated to the Organizational Learning
approach. In that case, however, Machine Learning and genetic
algorithms techniques are used instead, to investigate
the computational performance in processes involving
multi-agent learning via implementation of
concepts defined in an organizational theory. Nonetheless,
we note that some of the concepts of [Arg77] may be
mapped to concepts in Senge's theory. For example, individual
and organizational double loop learning may be compared to
personal mastery and shared vision, respectively,
in the Fifth Discipline.
In [HSB02] a model for the specification of multi-agent
organizations is presented. This model focuses on functional,
structural and deontic aspects. The structure is related to the
concepts of role, relationships among roles and groups. The
functional aspect includes the concepts of global plans and
missions that are structured in a type of goal
decomposition tree. Functional and structural aspects are independent
so that changes in the functional dimension do not require
changes in the organizational structure. The only dimension that
has to be adapted is the deontic, in order to reflect the
modifications on the other two dimensions.
Considering the work reported in this paper we note that the concepts
of global missions and goals are similar to the concept of shared
guiding ideas in our formal model. However, [HSB02]
specifies a structure that associates global missions and plans
to lower level goals. Instead, in our model, the development of
shared guiding ideas is dynamic and results from interactions
In summary, some of the papers mentioned above deal with
computational tools in general, and MAS technology in
particular to model or simulate organizations, or use
formal methods to investigate characteristics of a particular
Other papers use organizational theories concepts to design
models that can be used to implement computational systems or
build MAS frameworks.
Therefore, there are similarities between our work and the above
mentioned research. However, the novelty of our work is the use of
a Software Engineering method to build a formal model based on an
organizational theory. Moreover, our model is founded on a MAS
formal framework, so that our model represents a perspective on the
implementation of the Fifth Discipline in the context of
a MAS framework. This model presents a formalization for
the main concepts of Senge's theory and, as a consequence, it is
not restricted to particular organizational aspects like: coordination,
team work, cooperation.
We also note that in our formal model there are no constraints
concerning the representation of different types of agents, so
that organizations composed of human agents,
computational agents, or both, can be modeled.
In this work, we presented an overview of the Fifth Discipline theory
and outlined a formalization
of this theory in the context of a MAS formal framework.
In this section we discuss some issues regarding the formalization process
of Senge's theory and issues concerning the
properties of the formal model presented here.
Initially, we point out that during the formalization process
we observed some situations where the main reference for the LO theory
[Sen90] presented some concepts in an ambiguous way.
For example, Senge states in [Sen90, p. 147] that goals and
objectives are distinct from visions.
However, in another part of the book [Sen90, p. 149] he affirms
that a vision corresponds to a specific destination, a concrete image
of the desired future.
In our work clear definitions are required for every
concept, thus we consider that visions, either personal or shared,
correspond to goals.
Additionally, some concepts (or types) used in this formalization
are not explicitly specified in Senge's theory, for example there
are no details in [Sen90] or [SKR+94] concerning an
agent's plans or its capabilities.
Therefore, the formalization of these concepts follows an
interpretation of the LO theory and its (plausible) mapping to
current research in MAS.
Other issues arose involving the concepts of autonomy, creative
tension, and motivation.
It is important to note the different issues involving the concept
of autonomy as used in SMART and in our formalization.
In the former case, autonomy depends on the fact that an agent
is capable of (or has) motivations or not, so that the agent is
able to generate its own goals.
In our case, the
LearningOrgAgent is autonomous but is
also embedded in an organizational environment. Therefore, it
has to adopt the goals associated with a given role in order
to perform this role. Otherwise, as it is autonomous, it can
refuse to adopt that goals, but it will also refuse to
perform that given role in the organization. In addition,
roles have an associated autonomy level (in an organizational sense),
so that the agent is able to generate its own goals in the context
of the role.
Additionally, the concept of creative tension in LO seems to be
closely related to the concept of motivation in SMART.
In fact, as creative tension may be viewed as a measure of the
distance between the current state of affairs in the environment
the reduction of creative tension can be considered as a
Concerning the properties derived from the formal model and presented
in section 4, it is interesting to note the
importance that some
individual characteristics play in an organization that plans to
successfully implement the LO theory. Hence, we observe that the
agents must be honest, cooperative, tenacious and the interactions
among agents must also be honest.
Thus, there are several constraints on individual features of
the agents that are members
of such type of formal organization.
It is important to note that the formal model that we have built
corresponds to our interpretation of Senge's work and
depicts some characteristics of his theory, thus is not
intended to be a full detailed translation of the LO theory
into a formal model. For example, most of the skills/activities
related to systems thinking are encapsulated in functions. However,
the level of abstraction presented in this model is appropriate
to allow us to study important properties of the LO theory and
discuss individual and organizational features that should be taken
into account in an implementation of the LO theory, either in
artificial or in natural organizations.
Additionally, we note that our use of formal methods for modeling systems in general
presents new perspectives and reveals new, not yet explored, potentialities
concerning the use of these methods.
For example, consistency checking of an organization with regard to a specific
organizational theory can be investigated.
All these issues should certainly pose a number of
interesting problems and questions regarding the
construction of formal models for the
Fifth Discipline in particular, and for OT theories in general,
and also for the study of different implementation processes of
such theories in organizations.
A further advancement in this direction is the development of
a test case which is presented in [Sil04]. It
uses parts of the model introduced in
this article and was developed in the form
of an animation of the specification using [GB03].
Our future interest lies in the refinement of our model in order to
computationally implement it using MAS development tools like
actSMART [AL01,dL04] or the SACI [HS00] environment.
We are also interested in investigating some aspects not covered in detail by Senge's
theory, for example: planning, skills, the process of emergence of the shared vision.
In this case, results from other works that present more detailed views of
these aspects will be useful, for example, the process
of emergence of organizational mental models based on individual
mental models, as presented in [Kim93].
We gratefully acknowledge the important comments and suggestions by Nigel Gilbert and two anonymous
1 Creative tension, in Senge's vocabulary.
2 Personal vision, in Senge's vocabulary.
3 This type of diagram was introduced by Luck and D'Inverno [dL01].
4 This perspective on the concept of autonomy is presented in [dL01] and is also used in our model.
5 In the following sections our explanations are based on several formal terms
that are described in detail in Part II - A Detailed Version.
6 The population is constant if turn-over is equal to zero and new members are not admitted.
7 In the PersonalMastery schema, the creativetension function produces goals. See Part II - A Detailed Version.
R. Ashri and M. Luck.
Towards a Layered Aproach for Agent Infrastructure: the
Right Tools for the Right Job.
In Proceedings of the Second International Workshop on
Infrastructure for Agents, MAS, and Scalable MAS, pages 9-16.
Double Loop Learning in Organizations.
Harvard Business Review, pages 115-125, Sept-Oct 1977.
R. H. Bordini, J. A. Campbell, and R. Vieira.
Extending Ascribed Intensional Ontologies with Taxonomical
Relations in Anthropological Descriptions of Multi-Agent Systems.
Journal of Artificial Societies and Social Simulation, 1(4),
V. Cangelosi and W. Dill.
Organizational Learning: Observations Toward a Theory.
Administrative Sciences Quarterly, 10:175-203, 1965.
Modal Logic: An Introduction.
Cambridge University Press: Cambridge, England, 1980.
Introdução à Teoria Geral da Administração.
Editora Campus Ltda, second edition, 2000.
M. Crossan, H. Lane, and R. White.
An Organizational Learning Framework: from Intuition to
Academy of Management Review, 24(3):522-537, 1999.
K. M. Carley and M. J. Prietula, editors.
Computational Organization Theory.
Lawrence Erlbaum Associates, Publishers: Hillsdale, NJ, 1994.
K. M. Carley and M. J. Prietula.
Webbots, Trust, and Organizational Science.
In M. J. Prietula, K. M. Carley, and L. Gasser, editors, Simulating Organizations, pages 3-22. AAAI Press/The MIT Press, 1998.
M. d'Inverno and M. Luck.
Understanding Agent Systems.
M. d'Inverno and M. Luck.
Understanding Agent Systems.
Springer-Verlag, second edition, 2004.
D. Dunphy, D. Turner, and M. Crawford.
Organizational Learning as the Creation of Corporate
Journal of Management Development, 16(4):232-244, 1997.
General and Industrial Management.
Pitman, London, 1949.
C. M. Fiol and M. A. Lyles.
Academy of Management Review, 10(4):803-813, 1985.
D. A. Garvin.
Building a Learning Organization.
Harvard Business Review, pages 78-91, Jul-Aug 1993.
W. Grieskamp and R. Bssow.
The ZETA System.
E. E. Goldberg.
Genetic Algorithms in Search, Optimization, and Machine
B. A. Huberman and N. S. Glance.
Fluctuating Efforts and Sustainable Cooperation.
In M. J. Prietula, K. M. Carley, and L. Gasser, editors, Simulating Organizations, pages 89-103. AAAI Press/The MIT Press, 1998.
C. A. R. Hoare.
Communicating Sequential Processes.
Communications of the ACM, 21(8):666-677, 1978.
J. F. Hübner and J. S. Sichman.
SACI: Uma Ferramenta para Implementao e Monitorao da Comunicao
In M. C. Monard and J. S. Sichman, editors, IBERAMIA/SBIA 2000
open discussion track: proceedings, pages 47-56.
J. F. Hübner, J. S. Sichman, and O. Boissier.
A Model for the Structural, Functional, and Deontic Specification of
Organizations in Multiagent Systems.
In G. Bittencourt and G. L. Ramalho, editors, Proceedings of the
16th Brazilian Symposium on Artificial Intelligence (SBIA'02), LNAI 2507,
pages 118-128, Porto de Galinhas, PE, Brazil, Springer, 2002.
The Way of Z: Practical Programming with Formal Methods.
Cambridge University Press, 1997.
C. B. Jones.
Systematic Software Development using VDM.
Prentice Hall, second edition, 1990.
- K. 99
K. Takadama and T. Terano and K. Shimohara.
Can multiagents learn in organization? - analyzing organizational
learning-oriented classifier system.
In IJCAI'99 Workshop on Agents Learning about, from and other
The Link Between Individual and Organizational Learning.
Sloan Management Review, 35(1):37-50, 1993.
J. Kamps and L. Pólos.
Reducing Uncertainty: A Formal Theory of Organizations in Action.
American Journal of Sociology, 104:1776-1812, 1999.
M. C. Kang, L. B. Waisel, and W. A. Wallace.
Team Soar: A Model for Team Decision Making.
In M. J. Prietula, K. M. Carley, and L. Gasser, editors, Simulating Organizations, pages 23-45. AAAI Press/The MIT Press, 1998.
The Choice Between Accuracy and Errors: A Contingency Analysis of
External Conditions and Organizational Decision Making Performance.
In M. J. Prietula, K. M. Carley, and L. Gasser, editors, Simulating Organizations, pages 67-87. AAAI Press/The MIT Press, 1998.
P. R. Lawrence and J. W. Lorsch.
Organization and Environment.
Harvard University Press, Cambridge MA, 1967.
F. Lopez, y Lopez, M. Luck, and M. d'Inverno.
A Framework for Norm-Based Inter-Agent Dependence.
In Proceedings of The Third Mexican International Conference on
Computer Science. SMCC-INEGI, pages 31-40.
M. Masuch and Z. Huang.
A Case Study in Logical Deconstruction: Formalizing J. D.
Thompson's Organizations in Action in a Multi-Agent Action Logic.
Computational and Mathematical Organization Theory,
Communication and Concurrency.
S. Munroe, M. Luck, and M. d'Inverno.
Towards Motivation-Based Decisions for Worth Goals.
To appear as a paper in The Third International/Central and Eastern
European Conference on Multi-Agent Systems - CEEMAS 2003. Prague, Czech
Republic, 6 2003.
J. Morabito, I. Sack, and A. Bhate.
Prentice-Hall, Inc., 1999.
The Knowledge-Creating Company.
Harvard Business Review, pages 96-104, November-December 1991.
M. J. Prietula, K. M. Carley, and L. Gasser, editors.
AAAI Press/The MIT Press, 1998.
Y. So and E. H. Durfee.
Designing Organizations for Computational Agents.
In M. J. Prietula, K. M. Carley, and L. Gasser, editors, Simulating Organizations, pages 47-64. AAAI Press/The MIT Press, 1998.
The Fifth Discipline - The Art and Practice of the Learning
Currency Doubleday, 1990.
L. P. Silva.
Um Modelo Formal para a Quinta Disciplina.
PhD thesis, Instituto de Matematica e Estatistica, Universidade de
Sao Paulo, 2004.
P. Senge, A. Kleiner, C. Roberts, R. Ross, and B. Smith.
The Fifth Discipline Fieldbook - Strategies and Tools for
Building a Learning Organization.
Currency Doubleday, 1994.
J. M. Spivey.
Understanding Z - A Specification Language and its Formal
Cambridge University Press, 1989.
J. M. Spivey.
The Z Notation: A Reference Manual.
http://spivey.oriel.ox.ac.uk/mike/zrm/, 2nd edition, 1992.
F. W. Taylor.
The Principles of Scientific Management.
Harper, New York, 1911.
J. D. Thompson.
Organizations in Action: Social Science Bases of Administrative
McGraw-Hill, New York, 1967.
E. L. Trist.
The evolution of sociotechnical systems as a conceptual framework and
as an action research program.
In A. H. Van de Ven and W. F. Joyce, editors, Perspectives on
Organization Design and Behavior, pages 19-75, New York, 1981. John Wiley,
G. Weiss, editor.
Multiagent Systems: A Modern Approach to Distributed Artificial
MIT Press, 2001.
L. Yu and B. Schmid.
A Conceptual Framework for Agent Oriented and Role Based Workflow
In Presented at CaiSE Workshop on Agent Oriented Information
Systems (AOIS¥99) - Heidelberg, Germany.
ZTC - A Type Checker for Z Notation.
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