| Notes |
Synthetic Environment for Analysis
and Simulation
Alok R. Chaturvedi
&
Shailendra Raj Mehta
Purdue e-business Research center
http://www.mgmt.purdue.edu/centers/perc
Krannert School of Management
Purdue University
West Lafayette, IN 47906
2001
Introduction
Peering through the clouds
As a marketing manager of a major brand in a global automotive company named CarKing, it’s a
white-knuckle ride to create your latest multi-million dollar ad campaign. The ideal target market is
identified and a value proposition to deliver to that market is created. With millions of dollars in the
balance, what would you give to peer through the clouds of uncertainty obscuring your market and
see how your ad campaign will be received?
This desire is common among business managers everywhere, a desire to have a clearer picture of
how the market will react to action taken by business. Those firms with the clearest picture of
market reaction attain success, while those with consistently cloudy vision lose customers and
ultimately perish.
Business simulations are created with the intent of simulating markets, allowing firms to observe the
results of their decisions in synthetic environments as opposed to real environments where
experience can be an expensive teacher. Such environments can help decision makers attain a clearer
picture of how markets will react to decisions, testing decisions that in turn will generate higher
revenues.
SEAS
Synthetic Economies for Analysis and Simulation (SEAS) is at the forefront of business simulation,
helping companies to see markets clearly and make informed decisions. SEAS is a result of over
eight years of research and development at Purdue University’s Krannert Graduate School of
Management. SEAS seamlessly incorporates all aspects of managerial decision-making to provide a
complete and integrated view of economies, industries, and organizations.
SEAS is a simulated environment that models all aspects of the economy, including the government,
competition, public and foreign policy, and other international economies. SEAS is able to model
this environment accurately through the use of intelligent software “agents”. Agents are distinct
entities such as types of customers and suppliers, citizens, channels, and competitors. Agents can be
used to model any group of people whose actions are important to the organizations outcome.
The agents’ behaviors are defined by actual data that demonstrates how the agents have acted in the
past. SEAS incorporates genetic algorithms that allow the agents to react and learn from its own
actions as well as actions by other agents. It is through the combination of the continual actions and
reactions of all the agents that produces a very real and life-like environment that allows an
organization to test different strategies and analyze the outcomes of executing those strategies - all in
a simulated environment.
Utilizing SEAS removes a great deal of uncertainty with regard to key decisions and strategic
direction faced by every organization. It allows the organization to play out several scenarios and
analyze the outcomes, on many different levels. This analysis then aids the organization immensely
in deciding what strategic direction and decisions to implement.
Using SEAS to optimize decision-making
To better understand how SEAS can be used to benefit firm decision-making, it’s important to
understand:
• The cycle of decision making in actual environments
• The cycle of decision making in synthetic environments
• What constitutes an actual environment
• What constitutes a synthetic environment
The Decision Cycle in an actual environment
Today’s firms run a cycle of decision-making consisting of four parts (Figure 1):
• Confront Business challenge
• Select Alternative
• Implement Alternative in Actual Environment
• Assess Results
Figure 1
The four steps of the Decision Cycle in an actual environment can be demonstrated using our
fictional firm, CarKing from the opening paragraph.
Confront Business Challenge
For CarKing, the challenge to this firm is to create a marketing campaign that will generate enough
incremental sales to produce a campaign ROI of 30%.
Select Alternative
CarKing is weighing two alternatives to entice customers into purchasing a new vehicle: offer a $500
factory rebate to entice purchase, or a $50 gift certificate to participate in a test drive. (CarKing
believes that customers participating in test drives have a higher propensity to buy over those that do
not). Despite uncertainty, the firm must make a decision as to which alternative will be chosen.
CarKing opts to offer the $500 factory rebate and moves forward with the campaign.
Decision Cycle Actual
Confront
Business
Challenge
Select
Alternative
Assess
Results
Implement in
Actual
Environment
Confront
Business
Challenge
Select
Alternative
Assess
Results
Implement in
Actual
Environment
Implement in Actual Environment
CarKing contracts with a fulfillment house to mail out the $500 factory rebates to the customers
identified as part of the target market.
Assess Results
CarKing’s decision to implement the rebate generates a negative ROI; in retrospect, the marketing
manager feels the $50 test drive incentive may have been a better play, but now lacks the resources
for a second campaign.
The Decision Cycle in a synthetic environment
As seen above, decisions in actual environments can feel like an all or nothing proposition, as they do
not allow for a “do over” of a failed decision. Alternatively, the synthetic environment allows each
alternative to be tested before being implemented in an actual environment.
Here are the four steps in the Decision Cycle in a synthetic environment (Figure 2):
• Confront Business Challenge
• Generate Alternatives
• Test Alternative in Synthetic Environment
• Pick the “Winner”
Figure 2
Building on the previous example, CarKing uses a synthetic environment as part of its Decision
Cycle.
Confront Business Challenge
Again, the firm must generate marketing campaign with 30% ROI.
Generate Alternatives
Offer a factory rebate of $500 or $50 test drive incentive.
Test in
Synthetic
Environment
Confront
Business
Challenge
Generate
Alternatives
Pick the
“Winner”
Decision Cycle Synthetic
Test Alternative in Synthetic Environment
CarKing opts to offer the $500 factory rebate and moves forward with the campaign.
Pick the “Winner”
The results of CarKing’s decision to implement the $500 rebate generates a negative ROI causing the
marketing manager opts to return to the testing phase and implement the $50 test drive incentive.
The $50 test drive incentive campaign results in enough incremental sales to generate a 28% ROI.
The manager chooses to go with the test drive incentive for the actual campaign.
Defining actual and synthetic environments
Our example of CarKing’s Decision Cycle requires a deeper look into the composition of the actual
and synthetic environments used in the examples, and the outcomes produced in each scenario.
The composition of an actual environment
To gain a better understanding of what makes up an implementation in an actual environment, we
peel off the cover and take a closer look at its contents (Figure 3).
Figure 3
From right to left, the actual environment consists of the following:
• The alternatives that the firm is faced with
• The human decision makers who weigh the alternatives
• The decisions that are taken
• The human agents who constitute the market
• The reactions of the human agents to decisions
• The results of those reactions
The human decision makers have qualities that are unique. Like our CarKing marketing manager,
these individuals assess the market both quantitatively and qualitatively and make an educated
Confront
Business
Challenge
Select
Alternative
Assess
Results
Implement in
Actual
Environment
Confront
Business
Challenge
Select
Alternative
Assess
Results
Implement in
Actual
Environment
Results
Human
decision
makers
Human
agents
Reactions
Decisions
Alternatives
Actual Environment Confront
Business
Challenge
Select
Alternative
Assess
Results
Implement in
Actual
Environment
Confront
Business
Challenge
Select
Alternative
Assess
Results
Implement in
Actual
Environment
Results
Human
decision
makers
Human
agents
Reactions
Decisions
Alternatives
Actual Environment
decision based upon research, experience, and “gut feeling.” Many times, decisions made by human
agents are unpredictable, much like trying to out guess the strategy of a competitor.
The human agents who, in aggregate, constitute the market are somewhat simpler. These
individuals, thousands or millions of them, will react to the decisions of the decision makers. When
their decisions are analyzed, trends are revealed that make guessing behavior somewhat predictable.
The composition of a synthetic environment
Now, to better understand the synthetic environment, we peel off the cover and take a closer look at
its contents (Figure 4).
Figure 4
From right to left, the synthetic environment consists of the following:
• The alternatives that the firm is faced with
• The human decision makers who weigh the alternatives
• The decisions that are taken
• The software agents who constitute the market
• The reactions of the software agents to decisions
• The results of those reactions
In the synthetic environment, the human decision makers will be the same as in the actual
environment; the difficult to predict “gut feeling”, strategizing, and experience will all be captured in
the decisions that are made these individuals.
Conversely, software agents now replace the human agents who constituted the market in the actual
environment. Because the decisions of human agents are more predictable, the software agents can
be programmed with simple rules to mimic human agent behavior. As opposed to other simulation
environments that are static, software agents can be millions in number with programmed with
adapting rules to make the market a living, breathing entity.
Test in
Synthetic
Environment
Confront
Business
Challenge
Generate
Alternatives
Pick the
“Winner”
Test in
Synthetic
Environment
Confront
Business
Challenge
Generate
Alternatives
Pick the
“Winner”
Synthetic Environment
Results
Human
decision
makers
Software
agents
Reactions
Decisions
Alternatives
Test in
Synthetic
Environment
Confront
Business
Challenge
Generate
Alternatives
Pick the
“Winner”
Test in
Synthetic
Environment
Confront
Business
Challenge
Generate
Alternatives
Pick the
“Winner”
Synthetic Environment
Results
Human
decision
makers
Software
agents
Reactions
Decisions
Alternatives
Features of SEAS
(not sure where this fits in the paper)
SEAS is so powerful and accurate because of the breadth and depth of it’s environment. The
technology is able to accurately simulate any environment because it takes into account all the
critical features that is face by every organization.
Technical features
• It is a web-based distributed computing environment that is robust and fault tolerant.
• It employs a state-of-the-art networking, collaboration, data-warehousing and knowledge
management technologies.
• It employs genetic algorithms that allow for re-configurable systems. One can customize
its framework and the rules of interaction (such as organizational behavior rules, trading
rules, regulatory constraints, and foreign policy) to the users exact needs using a highlevel interface, and dynamically alter them during a simulation exercise.
Economic features
• It can model the global economy as a collection of inter-linked national economies, and
each national economy can be governed independently.
• It can model a large number of configurable and inter-linked goods and services, labor,
asset and foreign exchange markets.
• Its production and demand processes can be extremely complex and can be plugged in
seamlessly.
• It can incorporate all the essential features of the government, including the legislative,
executive and judicial branches.
• It can incorporate external and environmental variables pertaining to technical change,
growth or societal shifts.
Management features
• It supports a full complement of management functionalities such as strategy, production,
marketing, finance, and human resources. In addition, one can configure SEAS to model
any firm, in any industry, in any economy at any level of detail.
• It can incorporate quantitative relationships as well as qualitative relationships, which are
calibrated using actual data and can be updated in real time as new data emerges either in
the real world or in the simulation.
Organizational features
• It records participants' every action and communication.
• It can accommodate large numbers of human and artificial agents playing in the same
setting.
• It provides high level decision making and analytical tools to every participant
• It allows teams to collaborate internally by sharing the various decision-making functions
across several different entities.
• It has a highly evolved visualization and decision support system that allows the human
players to rapidly assimilate and use the large quantity of real time information generated
during the actual simulation. |