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inputs along with theoretical constraints and predictions. If we could afford and
obtain high-quality observations of all aspects of the FEW system (including the
future of the system…), we would not have any practical use for models. However,
models are useful measures to fill in the gaps in our observations, predict the future
(which is currently impossible to observe!), and economize when our theory provides an adequate shortcut to avoid expensive empirical work. Models are also useful for the development and testing of hypothesis and theory using the scientific
method, in which case empirical data are compared with modeled data. Assimilation
is a modeling technique that blends theoretical model estimates with observations to
produce an optimally accurate dataset. Data provide information which is the
answer to a question; to the extent that data answer interesting questions, data and
information are one and the same. Information theory concerns the measurement of
the quality of these questions, answers, and hypotheses.
As reviewed in other chapters, there are many different types of questions we
want to answer about FEW systems for purposes of science, management, and modeling. Each of these questions requires data, either modeled or observed. The most
common types of data we need to answer questions about a FEW system at any
scale include:
• Flows and supply chains of commodities, goods, services, and finances.
• Routes of transportation including Origin and Destination.
• Physical and Legal availability.
• Financial constraints, Price, and Cost.
• Infrastructure Capacity and Utilization.
• Supply and Demand.
• Environmental impacts or dependencies.
• Peak vs. average rates and totals.
• Mode of transportation.
• Storage, available and utilized.
• Inputs and Outputs.
• Environmental and Ecological quality.
• Natural resource availability and stocks.
• Boundaries and Governance.
• Regulatory and Legal constraints.
• Social objectives and performance metrics.
• Product performance metrics.
• Performance benchmarks.
• Life cycle effects and footprints.
• Teleconnections.
• Dynamics of Perturbation and Response, Shock and Stress.
• Risk, vulnerability, sustainability, resilience, and exposure.
These FEW systems data may originate from models—or from empirical methods such as sensors, surveys, or inventories. Sensors are used to make large volumes of observations and measurements automatically. Examples of sensors for
FEW systems include smart meters for water, gas, or electric service, RFID chips
B. L. Ruddell
inputs along with theoretical constraints and predictions. If we could afford and
obtain high-quality observations of all aspects of the FEW system (including the
future of the system…), we would not have any practical use for models. However,
models are useful measures to fill in the gaps in our observations, predict the future
(which is currently impossible to observe!), and economize when our theory provides an adequate shortcut to avoid expensive empirical work. Models are also useful for the development and testing of hypothesis and theory using the scientific
method, in which case empirical data are compared with modeled data. Assimilation
is a modeling technique that blends theoretical model estimates with observations to
produce an optimally accurate dataset. Data provide information which is the
answer to a question; to the extent that data answer interesting questions, data and
information are one and the same. Information theory concerns the measurement of
the quality of these questions, answers, and hypotheses.
As reviewed in other chapters, there are many different types of questions we
want to answer about FEW systems for purposes of science, management, and modeling. Each of these questions requires data, either modeled or observed. The most
common types of data we need to answer questions about a FEW system at any
scale include:
• Flows and supply chains of commodities, goods, services, and finances.
• Routes of transportation including Origin and Destination.
• Physical and Legal availability.
• Financial constraints, Price, and Cost.
• Infrastructure Capacity and Utilization.
• Supply and Demand.
• Environmental impacts or dependencies.
• Peak vs. average rates and totals.
• Mode of transportation.
• Storage, available and utilized.
• Inputs and Outputs.
• Environmental and Ecological quality.
• Natural resource availability and stocks.
• Boundaries and Governance.
• Regulatory and Legal constraints.
• Social objectives and performance metrics.
• Product performance metrics.
• Performance benchmarks.
• Life cycle effects and footprints.
• Teleconnections.
• Dynamics of Perturbation and Response, Shock and Stress.
• Risk, vulnerability, sustainability, resilience, and exposure.
These FEW systems data may originate from models—or from empirical methods such as sensors, surveys, or inventories. Sensors are used to make large volumes of observations and measurements automatically. Examples of sensors for
FEW systems include smart meters for water, gas, or electric service, RFID chips
B. L. Ruddell