Projections and Decision Support

Jasper Slingsby

Decision Support


Probably the hardest part of the whole ecological forecasting business… people!


It is also a huge topic. Here I just touch on a few hints and difficulties.

Decision Support


First and foremost, the decision at hand may not be amenable to a quantitative approach.

  • Ecological forecasting requires a clearly defined information need with measurable (and modelable) state variables, framed within one or multiple decision alternatives (scenarios).

Decision Support


Secondly, there’s also the risk of external factors making the forecasts unreliable, especially if they are not controlled by the decision maker and/or their probability is unknown (e.g. fire, pandemics, etc).

Decision Support

One way to try to deal with external factors is by developing scenarios with different boundary conditions.


  • e.g. scenarios with and without a fire, or different future climate states under alternative development pathways, etc.
  • Scenarios are often “what if” statements designed to address major sources of uncertainty that make it near-impossible to make accurate predictions with a single forecast.

IPCC AR6 illustrative mitigation pathways (IMPs).

Decision Support


A reminder of the distinction between predictions versus projections:

  • predictions are statements about the probability of the occurrence of events or the state of variables in the future based on what we currently know
  • projections are statements about the probability of the occurrence of events or the state of variables in the future given specific scenarios with clear boundary conditions

Decision Support


In an ideal world…

You’ll be working with an organized team that is a well-oiled machine at implementing Adaptive Management and Structured Decision Making and you can naturally slot into their workflow.


The advantages of Adaptive Management and Structured Decision Making are that they are founded on the concept of iterative learning cycles, which they have in common with the ecological forecasting cycle and the scientific method.

Decision Support

Conceptual relationships between iterative ecological forecasting, adaptive decision-making, adaptive monitoring, and the scientific method cycles (Dietze et al. 2018).


The iterative ecological forecast cycle integrates nicely with Adaptive Management…

Structured Decision Making


The Structured Decision Making Cycle sensu Gregory et al. (2012).


Focused on the process of coming to a decision, not the process of management, but very useful in the first iteration of the Adaptive Management Cycle.


Could easily be the topic of a whole course in itself, e.g. this online course by the US Fish and Wildlife Service.

Structured Decision Making


The Structured Decision Making Cycle sensu Gregory et al. (2012).


It is valuable when there are many stakeholders with disparate interests.

  • decisions are ultimately about values and often require evaluating trade-offs among properties with incomparable units - e.g. people housed/fed/watered vs species saved from extinction…
  • this can be a highly emotive space, and greatly benefits from a structured facilitation process

Structured Decision Making


The Structured Decision Making Cycle sensu Gregory et al. (2012).


It tries to bring all issues and values to light to be considered in a transparent framework where trade-offs can be identified and considered.

  • You can’t make the right choice if it isn’t on the table…
  • Step 0 is just identifying the necessary stakeholders (and getting them to the table).

Structured Decision Making


The Structured Decision Making Cycle sensu Gregory et al. (2012).


It directly addresses the social, political or cognitive biases that marginalise some values or alternatives.

  • Many decisions pit people’s immediate needs (water, housing, etc) against the environment. We’d rather ignore that choosing one is choosing against the other, but if we’re not transparent about this we’re not going to learn from our decisions and improve them in the next iteration.

Structured Decision Making


The Structured Decision Making Cycle sensu Gregory et al. (2012).


But…

  • Very tricky to do well and easy to do badly…
  • Requires a good, well-trained facilitator who understands stakeholder and researcher needs
  • Needs trust and buy-in from participants
  • Can take a lot of time to get right…
  • Often simplifies a problem so that it is feasible to analyse

Ideal world? (see slide 7)


The beauty for the forecaster in this scenario is that a lot of the work is already done.

  • The decision alternatives (scenarios) have been well framed.
  • The performance measures, state variables of interest and associated covariates mostly identified.
  • Iterations of the learning cycle may even have already begun (through the Adaptive Management Cycle) and all you need do is develop the existing qualitative model into something more quantitative as more data and understanding are accumulated.

Ideal world?


The Structured Decision Making Cycle sensu Gregory et al. (2012).


Can focus on estimating (forecasting) consequences and evaluating trade-offs among alternatives (steps 4 and 5), rather than having to do the whole process from scratch.

Estimating consequences…


Often one has to forecast multiple state variables, which may or may not be related to each other.


Decision-makers may also have to consider trade-offs among qualitative as well as quantitative consequences under different decision scenarios.


What’s missing?


An example consequence table adapted from Gregory et al. (2012) by Environmental Science Associates.

Uncertainty?

Model

Quantify and propagate uncertainty!

“It is better to be uncertain and right than confidently wrong.”

Sensitivity analysis:

  • How robust is the decision to uncertainty in the model and assumptions?

  • How wrong does your model have to be before the decision changes?

Communication

  • Communicate uncertainty in the model and forecasts clearly to decision-makers.

  • Be transparent about the limitations of the model and the assumptions made.

  • Use uncertainty visualizations to help decision-makers understand the range of possible outcomes.

  • Frame uncertainty in multiple ways - e.g. 5% vs 1 in 20

References

Dietze, Michael C, Andrew Fox, Lindsay M Beck-Johnson, Julio L Betancourt, Mevin B Hooten, Catherine S Jarnevich, Timothy H Keitt, et al. 2018. “Iterative near-term ecological forecasting: Needs, opportunities, and challenges.” Proceedings of the National Academy of Sciences of the United States of America 115 (7): 1424–32. https://doi.org/10.1073/pnas.1710231115.
Gregory, Robin, Lee Failing, Michael Harstone, Graham Long, Tim McDaniels, and Dan Ohlson. 2012. Structured Decision Making: A Practical Guide to Environmental Management Choices. John Wiley & Sons.