- Linked to objectives, are performance measures.
These are often times also called,
indicators, criteria, attributes, metrics.
There's a lot of different names for them,
but they serve the same purpose.
Performance measures are created,
to measure the achievement of particular objectives.
So they're directly linked to the objectives.
They're also linked to how we wanna think,
about designing our model output.
Because performance measures are used,
within a decision modeling context.
And they're the pieces of information that we're assessing
for our different options.
We've need to know and need information that predicts,
those particular performance measures.
Relevant to how we think about performance measures is,
for a given objective,
you can have one performance measure,
or you can have multiple performance measures.
For example, to assess water quality.
You could look at one particular pollutant,
that you think is key to understand,
eutrophication problems,
such as total phosphorus.
What you might need to know for certain systems,
is you might need total phosphorus, total nitrogen,
a measure of water clarity,
and potentially measure of algal growth.
And so having more variables allows you to more fully assess
one of the key objectives that you care about.
Additionally, when you think about the performance measures,
one of the biggest design challenges
and choices that one can make,
is how you represent those performance measures.
So looking at a water quality example,
you can think about something like total nitrogen,
in milligrams per liter.
You can also think about it in terms of exceedances.
So if you have a policy threshold,
how many days
did a,
particular water body,
exceed a total nitrogen threshold.
Same objective, performance measure
that's trying to achieve similar things,
but different ways of measuring it.
That means that one of the key ways of,
thinking about performance measures,
is you really want to choose something,
that is most relevant, to understanding and measuring
the objective that you care about.
And that means there's three different ways,
that you can design a performance measure,
or choose a performance measure.
The first way, is a Natural Measure.
These are,
approaches or indicators,
that really fully capture the objective.
So for example, if we wanted to maximize,
the population of a species,
a natural indicator is abundance,
or the number of species that's in a particular ecosystem.
The second approach that you can use to identify
or design in a performance measure, is by using a Proxy.
Now, a Proxy is a performance measure,
that's highly correlated or reasonable substitute
for the objective that we care about.
So for example, using the,
maximizing the population of a species,
a proxy indicator for maximizing population of a species
could be area of habitat.
So it doesn't directly measure, the species,
but it does give us a sense of what we would anticipate
the number of species that could live in that place,
could be.
The third approach we can use to design
a performance measure, is using a Constructed Scale.
A constructed scale is oftentimes,
performance measures specific.
It can be an aggregate of lots of different ideas.
And it's used to translate,
concepts that might be more latent,
or more abstract into something that's more concrete
that we can incorporate into our decision process.
So for example, in a number of cases,
who are interested in the public support,
for a particular management action.
That can be mapped onto a five point scale,
where what we do is, we describe,
what level of public support,
is at each of those levels.
So for example, a level of,
two might indicate,
you have a tremendous amount of public support.
Most of the stakeholders have been fully engaged,
and are supportive of the particular management actions,
one is taking.
Zero might indicate, "Folks are indifferent."
Whereas a negative two might indicate that you have,
people who are actively working against action,
for that particular side.
So one could construct a scale where there's no,
natural measure or proxy,
that can serve, as a reasonable,
approach to developing a performance measure.
To really make sure that you're capturing that objective,
and measuring in a way that is meaningful.