# data[].attributes.body
1. Relatively small numbers of parameters

2. Highly repetitive

3. Enormous database

4. Short period predictions

# data[].attributes.body
- uses mathematical language and concepts

# data[].attributes.body
Environmental sysytems are highly complex and not containable
therefore there's always uncertainty in how accurate models are e.g. ozone not included until ozone hole discovered

# data[].attributes.body
- uses concepts and ideas to form a representation

# data[].attributes.body
1.Conceptualization (how we frame the problem)

2.Empirical adequacy ( how well our mathemathical representations map onto natural processes.

3. Input parameterization (how well numerical varianles represent elements of the system)

# data[].attributes.headline
Failures of equipment and failures to understand the limitations of our equipment

# data[].attributes.subject
Failures of equipment and failures to understand the limitations of our equipment

# data[].attributes.body
model complexity and (number of variables) and level of detail limited by computer power

# data[].attributes.body
may have to omit certain parts of the real system in order to understand it
Thefore will be "wrong" to some extent

# data[].attributes.body
- made abstract ideas concrete
- visualize complex systems
-provide analogies
-serve as heuristic devices
-used as arguments for plausibility of causal agents

# data[].attributes.body
- A representation of a process, system, idea or object
- Used to explain a phenomena that cannot be experienced directly
- A simplification of the natural world

# data[].attributes.body
1. Physical
2. Mathematical
3. Conceptual

# data[].attributes.body
more variables increases how close the model is to the real system
However, more variables increases uncertainty
(Complexity Paradox, Oreskes)

# data[].attributes.body
Gain an understanding of the natural world by:
- organising data
- synthesising information
- making predictions