28
plants. Another example is where the actions of some people cause significant externalities that impact the welfare of others via FEW system connections. For example,
the largest emissions of greenhouse gases come from affluent, high energy-use societies, while the adverse externality (climate change effects) fall disproportionately
on less wealthy, low-energy-use societies.
A more positive example of where there are greater impetus and opportunity for
integrated FEW science and governance is where potential benefits to many communities exist as a result of coordinated actions. For example, the international trade
of FEW commodities creates systemic benefits when certain products can be produced and transported with a smaller footprint and at a lower cost in one area compared to another. In such cases, both parties to the exchange benefit.
We will return to these examples of greater impetus and opportunity for integrated FEW science and governance in the final chapter.
1.5.3 Projections, Predictions, Assumptions,
and “Well- Known” Solutions
The behavior of complex systems is, by their nature, difficult to understand and
model accurately. When systems are studied in the present to help understand the
future, models of complex systems make projections, not predictions. Projections
are estimates of future outcomes, based on specified assumptions.
A set of assumptions is a scenario; that is, the assumptions constitute a stated
version of what the key inputs, conditions, and functioning will be for the system
under study. For example, assumptions about resource availability, economics, technologies, policies, as well as about the relationships between those and other factors
and outcomes like consumption, technology adoption, and changes in behavior,
collectively constitute a scenario for the future of a system.
While projections are statements about what “would” happen under certain
assumptions, predictions are forecasts of what “will” happen. Human actors in systems introduce uncertainty and make predictions are, at their core, informed guesses.
Good forecasts include clearly stated margins of uncertainty.
Models, as explored in this book, are based on assumptions and therefore produce projections best summarized as: “If such and such happen, this will be the
outcome. However, if such and such does not happen, the outcome will be
different.”
The simplest and most common assumption, but rarely the most accurate, is a
“Business as Usual” (BAU) assumption where trends in resource availability, economic change, and technological advancement occur as close to the same rate and
direction as they have in the recent past while policies remain unchanged. Given
that policies change, sometimes significantly, in accordance with political actions
that are very hard, if not impossible to know in advance, policy assumptions are, by
their very nature, highly uncertain. The future availability of natural resources is
P. Saundry and B. L. Ruddell
plants. Another example is where the actions of some people cause significant externalities that impact the welfare of others via FEW system connections. For example,
the largest emissions of greenhouse gases come from affluent, high energy-use societies, while the adverse externality (climate change effects) fall disproportionately
on less wealthy, low-energy-use societies.
A more positive example of where there are greater impetus and opportunity for
integrated FEW science and governance is where potential benefits to many communities exist as a result of coordinated actions. For example, the international trade
of FEW commodities creates systemic benefits when certain products can be produced and transported with a smaller footprint and at a lower cost in one area compared to another. In such cases, both parties to the exchange benefit.
We will return to these examples of greater impetus and opportunity for integrated FEW science and governance in the final chapter.
1.5.3 Projections, Predictions, Assumptions,
and “Well- Known” Solutions
The behavior of complex systems is, by their nature, difficult to understand and
model accurately. When systems are studied in the present to help understand the
future, models of complex systems make projections, not predictions. Projections
are estimates of future outcomes, based on specified assumptions.
A set of assumptions is a scenario; that is, the assumptions constitute a stated
version of what the key inputs, conditions, and functioning will be for the system
under study. For example, assumptions about resource availability, economics, technologies, policies, as well as about the relationships between those and other factors
and outcomes like consumption, technology adoption, and changes in behavior,
collectively constitute a scenario for the future of a system.
While projections are statements about what “would” happen under certain
assumptions, predictions are forecasts of what “will” happen. Human actors in systems introduce uncertainty and make predictions are, at their core, informed guesses.
Good forecasts include clearly stated margins of uncertainty.
Models, as explored in this book, are based on assumptions and therefore produce projections best summarized as: “If such and such happen, this will be the
outcome. However, if such and such does not happen, the outcome will be
different.”
The simplest and most common assumption, but rarely the most accurate, is a
“Business as Usual” (BAU) assumption where trends in resource availability, economic change, and technological advancement occur as close to the same rate and
direction as they have in the recent past while policies remain unchanged. Given
that policies change, sometimes significantly, in accordance with political actions
that are very hard, if not impossible to know in advance, policy assumptions are, by
their very nature, highly uncertain. The future availability of natural resources is
P. Saundry and B. L. Ruddell