=Paper=
{{Paper
|id=Vol-2843/shortpaper39
|storemode=property
|title=The Basis for System Dynamics and Agent-based Modeling of Strategic Management of Intellectual Capital in Educational Organizations (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2843/shortpaper039.pdf
|volume=Vol-2843
|authors=Edward Ozhiganov,Sergei Nazyuta,Alexander Chursin
}}
==The Basis for System Dynamics and Agent-based Modeling of Strategic Management of Intellectual Capital in Educational Organizations (short paper)==
https://ceur-ws.org/Vol-2843/shortpaper039.pdf
The Basis for System Dynamics and Agent-based
Modeling of Strategic Management of Intellectual Capital
in Educational Organizations*
Edward Ozhiganov, Sergei Nazyuta and Alexander Chursin
RUDN University, 6, Miklukho-Maklaya str., Moscow, 117198, Russia
chursin-aa@rudn.ru
Abstract. This article discusses the problems of using simulation modeling to
improve the management of intellectual capital of educational organizations
based on the methodologies of system dynamics and agent-based modeling in
the context of their impact on reaching strategic goals of an organization. Com-
puter simulation methods using operational indicators and scenario variations
provide management with a practical, adaptable and meaningful toolkit for the
strategic management of the company's intellectual assets.
Keywords: Intellectual Capital, Educational Organizations, agent-based mod-
els.
1 Introduction
Methods of system dynamics and agent-based modeling can be used by educational
organizations, as well as other companies and enterprises (hereinafter referred to as
organizations) to make strategic decisions in order to achieve competitive advantages.
To do this, a computer modeling system should be created and applied that can regis-
ter, analyze and evaluate intangible assets, since these cases are not sufficient for
accounting methods. In addition, this system will allow for an in-depth analysis of an
organization's activities (in terms of intellectual capital) in order to identify potential
opportunities for increasing the competitiveness of the organization.
2 Materials and methods
In modern international scientific literature, intellectual capital is defined as a set of
intangible assets (resources, capabilities, and competencies) that drive an organiza-
tion's performance and value creation [1]. According to the prevailing view in modern
science, the structure of intellectual capital includes human, structural and relational
(client) capital [2].
*
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0).
Accordingly, management of intellectual capital can be represented as a complex
system of autonomous human and institutional agents, whose interaction is influenced
by various stochastic and dynamic factors, which leads to the emergence of new phe-
nomena in the activities of companies in the long term. Therefore, decision-making in
this complex system can be problematic due to limited understanding of probabilistic
and dynamic interactions, contradictions arising from competing goals and indicators,
intermediate information generated by multiple variable feedback loops, etc.
3 Results and Discussion
In cases where strategic planning and cost research in organizations focus on eco-
nomic performance and cost performance, they do not actually take into account the
factors of intellectual capital (knowledge) in any form, which is unacceptable for
strategic planning in knowledge-intensive organizations based on understanding and
determining their perspective on the basis of available knowledge and intangible re-
sources. Therefore, the scientific literature articulates the opinion that "understanding
the potential and limitations of their own model of knowledge strategy, as well as its
assessment and management is the key to developing an organization's competitive
advantage" [3].
In this regard, systems thinking and its various variants are of particular interest
and significance, since they consider the internal complexity of intangible assets and
provide a holistic perspective. Intangible assets can be used as leverage to achieve
business goals when viewed within the framework of a systems thinking paradigm. In
particular, simulation is an effective approach, firstly, to understand and, secondly, to
represent the complexity of an organization's intangible asset system.
For example, A. Nielsen classified three dynamic capabilities (possibilities) of the
knowledge management process, each of which includes, respectively, several types
of actions [4]:
─ Development (creation, acquisition, collection);
─ Combination (assembly, exchange, integration);
─ Use (application, exploitation) of knowledge.
In other words, he presented a diagram of the relationship between dynamic capa-
bilities and knowledge-based resources, stocks and flows of knowledge in a firm,
which is considered as means of creating or updating organizational capabilities of a
firm, contribing to the realization of the existing potential for creating value.
The mathematical principles of system-dynamic modeling make it possible to give
a simple representation of this scheme, since the dynamics of a complex system is
determined by its structural elements (stocks), the main of which, according to A.
Nielsen's terminology, is the “stock of knowledge”. System dynamics equations rep-
resent the evolution of the system over time, the calculation of rates that determine
this evolution, the intermediate results needed to calculate the indicators, and the ini-
tialization of the system.
According to standard practice, the system dynamics (SD) method aggregates dis-
crete processes, representing them in the form of continuous flows. System dynamics
technology was originally developed for the systems whose states change continu-
ously over time. The SD model typically aggregates discrete events of the main sys-
tem at a level where events can be considered as parts of a continuous flow. For ex-
ample, individual events can be considered as a flow of similar events over a certain
period of time.
In the context of SD, intellectual capital can be represented as an accumulated and
expendable "level" (also called a fund or stock in SD models). For example, a project
of UNSW Centre for Business Dynamics combines the groundbreaking work on "in-
tangible assets" by Karl-Erik Sveiby with human capital modeling based on system
dynamics [5].
If the system is represented as a network of "storages" and "flows", then the corre-
sponding system-dynamic model can be implemented as a computer program. Such a
program can be used to conduct pilot testing of proposed management policy changes.
Using the modeling language, systems of linear and nonlinear algebraic and differen-
tial equations containing up to several thousand variables can be effectively solved.
Quantitatively, each "storage" is determined by the level of its content, and each
"flow" is determined by the rate (speed) of movement. The rate of movement is calcu-
lated based on information about the content levels of the "levels". Thus, the simu-
lated object is represented as an information system with feedback. "Storage" de-
scribes quantities that are continuous in their range of values and discrete in time.
They can be defined as system state variables whose values are formed by accumulat-
ing differences between incoming and outgoing flows. "Flows" are analogs of accu-
mulation transformation processes in the system; they move the contents of "storages"
and reflect either material or informational processes. Their intensity (pace) is deter-
mined by management decisions, which are formed on the basis of information about
the state of the levels. Decision functions (or rate equations) are a formalization of
rules that determine how information about levels leads to the selection of current
flow rates.
Flow-type models are dynamic feedback models. Since real systems have inertia,
their structure contains elements that determine the delays in the transfer of changes
along the feedback loop. In each model of system dynamics there is a special discrete
variable − time, the choice of the unit of measurement, which (the time step of model-
ing) is carried out by the developer of the model as well as the modeling interval.
Simulation research is characterized by internal logic, which is expressed in the se-
quence of a number of stages and allows combining the features of the experimental
approach and the specifics of using computer support tools. Simulation research is
sequential and iterative in nature, connected, on the one hand, with a clear order of
changing stages, and on the other − with the cyclicity of individual stages (within
individual stages and between stages).
Visual programming is used when developing models. A model that includes vari-
ous elements is built on a conceptual level.
The cause-and-effect diagram shows how the model variables are related to each
other. It is a marked-up graph whose elements are variables represented by their iden-
tifiers or full names, and arrows go from cause variables to effect variables.
The values of all variables included in the simulation model are calculated at each
moment of the model time. Then, at a certain interval, new values of variables are
calculated based on the old values, etc. Thus, the simulation model develops along a
certain trajectory during a given period of model time.
The variable "time" is primary for the simulation model of a dynamic system: its
value is generated by the system timer and changes discretely, that is, starting from a
certain value, the time for each cycle increases by a predetermined value, which
serves as a unit of model time. The number of cycles and the unit of time are parame-
ters of the model "run" and are determined by the model designer.
From the point of view of the presented approach, it is impossible to study the
processes in the system separately – only the study of the dynamics of the entire sys-
tem with all its connections gives a correct understanding of the problem under study.
All systems, no matter how complex they are, are built on closed feedback loops of
the dependencies of variables in the structure of the simulation model.
To display the set of cause-and-effect relationships, which we call the basic
scheme of the theory of intellectual capital, algorithms of the Powersim Studio Enter-
prise computer program can be used, which serves as an add-on for strategic analysis
in the well-known complex of automated management of large firms and corporations
SAP. The representation of cause-and-effect relationships in the form of connectors
(arrows) that connect subsystems and system elements in a closed loop is displayed
on the computer program interface, which is shown in Figure 1.
Fig. 1. Structure of a fragment of the SD model of intellectual capital Source: Compiled by the
author.
Depending on the characteristics of specific industries and the research and man-
agement tasks put forward, the subsystems of the developed model of intellectual
capital can include as many subsystems and variables as are considered optimal, and
mathematical calculations of varying degrees of complexity can be performed with
these variables.
For example, K. E. Sveiby’s system-dynamic model of intellectual capital is based
on his theory of measuring a company's intangible assets [6]. Three “families” of
intangible assets act as “levels” of the model: external structure, internal structure and
individual competence. The combination of internal structure and individual compe-
tence together can be called the “knowledge capital” of a firm. The external structure
is formed by intangible relationships with customers and suppliers, which form the
basis of a company's reputation (image). The internal structure contains patents, con-
cepts, models, templates, computer systems, and other more or less explicit adminis-
trative tools and processes. The structure of individual competencies consists, on the
one hand, of the competencies of professional and technical personnel, and on the
other hand, of support and management personnel, including employees of the re-
search and development department, employees of the enterprise, sales and marketing
department.
The comparison criteria for choosing and applying system dynamics (SD) or agent-
based methods (ABM) in the field of intellectual capital and intangible assets analysis
may differ depending on the strategic goals.
The scientific literature notes that “one of the undeniable advantages of using
ABM to study the dynamics of a certain system is that you are not limited to defining
the behavior of the system in a mathematically solvable form. First, it allows complex
behaviors to be built using multiple “if-then” rules. This is important because behav-
iors are captured by flowcharts much more easily and naturally than by systems of
equations. Second, the simulation may contain many random elements controlled by
different probability distributions. Applications in economics and finance, unlike
some other areas where ABM is applied, almost always require randomness in the
behavior specifications of the agent or system. Mathematical modeling in such condi-
tions quickly becomes excessively cumbersome [7].
Since the model is created as a computer program, the behavior of agents can be
easily described using a set of “if-then” rules, in fact by placing a flowchart describ-
ing an agent’s behavior in the computer code. A behavioral model is clearly easier
and more natural to capture with the help of flowcharts than through systems of
mathematical equations. Simulations can also contain many random elements driven
by different probability distributions. Social and economic applications almost always
contain randomness in the behavior of agents, and this behavior may not always fall
under well-studied analytically distributions such as the normal distribution.
Agents are reactive, active, autonomous and social software objects, a computer
program or an isolated computer system that is located in a certain environment and is
capable to act flexibly and autonomously in it. Agents have the following properties:
─ are clearly identifiable (have clearly defined borders and interfaces);
─ are located in a certain environment (receive data through sensors and act through
effectors);
─ are capable of flexible actions (react to changes and act ahead of time);
─ have autonomy (control both their internal state and their own behavior);
─ are created to achieve certain goals (try to achieve a goal or solve a problem).
Since an agent consists of both “states” and “rules”, it can perform actions on it-
self, other agents, and the environment, and receive input from other agents and the
environment. An agent's "state" is a specific set of parameters that defines an agent or
all relevant information about it at a given time. The internal, local and global states
of each agent, static or dynamic, affect its overall state. Agent “rules” describe how
agent states are converted into actions or new states. Rules are understood as me-
chanical rules of decision-making or transformation functions, rather than as generally
accepted social concepts of rules as regulations or agreements (Figure 2).
Fig. 2. Structure of an agent-based model. Source: Adapted diagram from the work [8].
When constructing full-scale agent-based models of innovation capital, the specific
mechanisms included in the model structure (agents, their behavior, interactions, envi-
ronmental structures, etc.) depend crucially on the goal and concept of the model. In
our case, we consider two types of agents: agents representing real people, and agents
representing real organizations and/or their divisions, which add several more com-
plex levels to the model.
If we return to the fragment of the system-dynamic model of intellectual capital
from this point of view (see the chart), the difference between system dynamics and
the agent-based approach becomes more obvious. In the first case, a decisive role is
played by the structure of the IC; in the second case, the behavior of these types of
agents in the sectors of formation, positioning, and erosion of intellectual capital and
its components – human, structural, and relational capital is of great importance.
When modeling IC, it is necessary to consider organizations as a set of manage-
ment positions, and, more importantly, as a set of management agents. In any case,
an organization must be a well-defined structure. The organizational design of a com-
pany, its strategy for developing intellectual capital, and mechanisms for managing
intellectual capital are modeled.
The most important aspect of agent-based modeling is the design and inclusion of
social networks in the IC model, since they influence the decisions and behavior of
individual agents, and, consequently, the company in relation to intellectual capital at
both the micro and macro levels. Theoretically, there are two ways to create social
networks in an agent-based model: use existing software to create graphs with speci-
fied characteristics, or write the necessary codes using algorithms for creating net-
work packets in Java or other General-purpose programming languages, if the model
is created in an environment that uses this language (for example, REPAST Sym-
phony [9]).
Networked agent-based modeling algorithms make it possible to deal with the
long-term effects of stochastic and dynamic interactions and trade-offs arising from
conflicting goals. Traditional optimization algorithms that search for optimal analyti-
cal solutions are less effective when analyzing complex real-world systems, since
they do not take into account uncertainties, intangible factors, and dynamic factors of
the decision-making environment, and homogeneous assumptions in conventional
statistical models are not true for a system of heterogeneous agents.
Potentially, modeling allows management to better understand the complexities of
real organizational systems by integrating organizational structure, tangible and in-
tangible aspects of organizational behavior, resources and policies so that a holistic
view of system development can be used. Most importantly, non-material behavioral
aspects can be displayed and modeled, and the impact of feedback loops on system
behavior can be included in analysis that cannot otherwise be reflected in traditional
analytical models. In addition, organizational knowledge is consolidated during the
model conceptualization stage by encouraging stakeholder participation, identifying
structural relationships, analyzing basic assumptions and theories, and evaluating the
values of modeling parameters.
In this context, agent-based modeling is an imitation of a complex human system
as an artificial system of autonomous agents that interact based on a set of theories,
rules, or assumptions. Macro-level behavior of a system arises as a result of repeated
interactions of its subjects at the micro-level of the system. Agent-based modeling is a
microanalysis technique that can be used as a learning tool in decision-making based
on comparing emerging scenarios, rather than as an optimization tool for finding op-
timal analytical solutions.
Current approaches to decision support rely heavily on aggregates, assuming ho-
mogeneity in human resources, and focus on organizational performance at the macro
level. Both static and dynamic assessments are performed using analytical and statis-
tical methods, but the assumption of uniformity and the use of aggregated indicators
limit the ability of such methods to reflect the qualitative heterogeneity of an organi-
zation's staff groups and the actual micro-level of their interaction. In addition, their
behavior may be determined by dynamic internal and external factors of the organiza-
tion itself.
In modern scientific literature, it is believed that approaches based on the methods
of systems dynamics and agent-based modeling provide a more creative understand-
ing of intellectual capital and intangible assets in general. In the usual sense, intangi-
ble assets are passive elements that are not included in the general statements of the
results of operations and balance sheets of companies. Through these systematic ap-
proaches, intangible assets can be presented and reinterpreted as natural elements with
active behavior. This leads to “significant improvements and new contributions to the
area of knowledge management, as well as to the full range of applications of simula-
tion approaches” [10].
4 Conclusion
Detailed, calibrated models using systems dynamics techniques, combined with
agent-based modeling techniques, are powerful tools to support business strategy.
They correspond to modern approaches to planning organizations, serve as a test for
the adequacy of the model as a representation of the intellectual capital system, and
provide accurate estimates of costs and benefits in the trade-offs of alternative strate-
gies
5 Acknowledgments
The reported study was funded by RFBR, project number 19-29-07348.
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