Setting the Stage: Complex Systems, Emergence and Evolution
11
• Biological models: Of tissues, neurons or cellular models to study effects
of chemicals and drugs on cell behavior.
• Economic models: Of various markets such as stock markets, labor markets or economic systems to study the introduction of migration, taxes
and money on the overall market behavior.
• Social science models: To study effects of various population dynamics
on areas and resources.
• Evaluating systems: Hardware or software performance for a computer
system or new military weapons system or tactics on enemy forces.
• Designing communications systems and message protocols for communicating entities.
There are various steps involved in constructing M&S mapping from real
world situations and simulating them in a virtual world. The steps involved
are as follows:
Step 1. Identify problem being investigated in real world: This
is
very specific to hypothesis being tested, which can usually not be tested
in real or natural settings due to costs or impacts. This justifies it being
tried out as a virtual experiment first.
Step 2. Formulate model problem: Formulate a model for a system in a
manner by which it is created as a virtual representation. This involves
determining assumptions of the model, hypotheses being tested, kind of
data being collected from real world to test it and, finally determining
which tools to used to create the model. This usually involves talking to domain experts and collecting relevant data to construct most
accurate system representations. Computer simulations involve multidisciplinary approaches, where computer scientists work with biologists
or economists to construct computational models for systems from their
disciplines. A computer scientist has to ensure the model has been correctly represented and all necessary behaviors are captured by it.
Step 3. Simulate model using relevant software toolkits: Use software
tools to simulate a model.
Step 4. Analyze data collected: The simulation results are collected and
analyzed. The results can be used to find discrepancies and test theory
predictions, allowing modelers to verify their models.
Step 5. Data mining techniques: Data analysis techniques such as machine learning, pattern findings and data visualizations help determine
the simulation conclusions, in terms of testing the hypothesis.
11
• Biological models: Of tissues, neurons or cellular models to study effects
of chemicals and drugs on cell behavior.
• Economic models: Of various markets such as stock markets, labor markets or economic systems to study the introduction of migration, taxes
and money on the overall market behavior.
• Social science models: To study effects of various population dynamics
on areas and resources.
• Evaluating systems: Hardware or software performance for a computer
system or new military weapons system or tactics on enemy forces.
• Designing communications systems and message protocols for communicating entities.
There are various steps involved in constructing M&S mapping from real
world situations and simulating them in a virtual world. The steps involved
are as follows:
Step 1. Identify problem being investigated in real world: This
is
very specific to hypothesis being tested, which can usually not be tested
in real or natural settings due to costs or impacts. This justifies it being
tried out as a virtual experiment first.
Step 2. Formulate model problem: Formulate a model for a system in a
manner by which it is created as a virtual representation. This involves
determining assumptions of the model, hypotheses being tested, kind of
data being collected from real world to test it and, finally determining
which tools to used to create the model. This usually involves talking to domain experts and collecting relevant data to construct most
accurate system representations. Computer simulations involve multidisciplinary approaches, where computer scientists work with biologists
or economists to construct computational models for systems from their
disciplines. A computer scientist has to ensure the model has been correctly represented and all necessary behaviors are captured by it.
Step 3. Simulate model using relevant software toolkits: Use software
tools to simulate a model.
Step 4. Analyze data collected: The simulation results are collected and
analyzed. The results can be used to find discrepancies and test theory
predictions, allowing modelers to verify their models.
Step 5. Data mining techniques: Data analysis techniques such as machine learning, pattern findings and data visualizations help determine
the simulation conclusions, in terms of testing the hypothesis.