Game Development Reference
In-Depth Information
INTRODUCTION
In this chapter we propose a symbiosis be-
tween two different research areas, Business
Management and Artificial Intelligence, through
the development and use of SIMBA. Business
Simulators offer an accurate framework for the
study of AI techniques, and we show three dif-
ferent examples: multi-agent theory, data mining,
and reinforcement learning. The other direction
of the symbiosis shows that AI techniques can
contribute business administration with new tools,
as multi-agent architectures, behavior prediction
or autonomous decision-making.
This chapter has three goals. The first one is
to describe SIMBA, a Simulator for Business
Administration. We describe SIMBA from two
perspectives. On the one hand, Section 3 shows the
business model, describing the economical model
used to implement it. On the other hand, Section
4 shows the technical point of view, describing
the software architecture. The second goal is to
describe SIMBA as a powerful tool for business
education. Thus Section 5 describes the main
characteristics of SIMBA that make the simula-
tor very useful for students and teachers. The last
goal relates to the research opportunities SIMBA
offers in the application of Artificial Intelligence
approaches in Business Intelligence, and is de-
scribed in Section 6. Last, Section 7 summarizes
the main conclusions. The next section describes
some related studies in business simulators and
intelligence business.
The goal of business simulators is to offer tools,
processes, and best practices for integrating,
warehousing and analyzing business information.
Business simulators are needed in the decision-
making process because they help to identify the
right area to change in an organization, which is
a very important factor in improving the success
of an organization. These tools help managers
to understand their business processes and how
the modification of this process impacts on the
organization. In this way, the dangers involved
in changes are identified before they are applied
(Laguna and Marklund, 2005). Once the risk fac-
tors are identified, the business simulator can be
used to change the desired parameter (Koyamada,
2007).
Economic simulations try to emulate real-
world situations, so they can be used for business
education. Classical simulators teach the students,
in an entertaining way, how to run a global busi-
ness, or a part of it, in a competitive marketplace.
The success or failure of the organization depends
on how the managers take key decisions such as
pricing, marketing, capital investment, etcetera,
following a successful strategy against competi-
tors.
However, business simulators are not only
used for learning and education, they also offer
a complex domain where the researchers can
investigate different areas. Typical uses of busi-
ness simulators in research are financial planning
(quantifying the impact of the decision-making
process), risk management (measuring, managing
and determining the right balance between profit-
ability and risks), forecasting (analyzing historical
data and using the data obtained by the business
simulator to predict the future), business process
modeling (establishing the business process steps
in an easy way), interactive learning (business
simulators can be used in teaching economics),
artificial intelligence research (using Data Mining,
Evolutionary Computation or Intelligent Agents).
RELATED WORK
This section provides an introduction to the
background of business simulation, as well as the
application of business simulators for teaching
and research.
Business Simulators
The use of business simulators or business games
started in the second half of the 20th century.
Search Nedrilad ::




Custom Search