The Value of Information: Does More Data Mean Better Decisions?

Post provided by Dr Stefano Canessa

Applied ecology can be defined as scientific knowledge that helps in making good management decisions. Scientists have a natural desire to collect information, managers want that information so that they know they are doing the right thing, and both generally act under the assumption that more information equals better decisions. This is generally correct, since information helps us make, well, informed decisions. Therefore, when our ecological knowledge is uncertain (which is practically always the case) we usually advocate further research.

On the other hand, however, information comes at a cost. It may cost money to collect it and take time to set up studies: both are usually in short supply. We can’t learn everything and often the information we can actually collect is still imperfect. So how do we determine if that additional piece of information we’d like to have is really valuable for our management?

In ‘When do we need more data? A primer on calculating the value of information for applied ecologists’ , Stefano Canessa and colleagues provide a tutorial to the calculation of value of information (VOI) for applied ecologists and managers who would like to know more about it, but are not familiar with decision-theoretic principles and notation.

What is ‘Value of Information’?

In decision analysis, the value of information is the improvement in the outcomes of our actions that we would expect if we could reduce or eliminate uncertainty before making a decision. Previously applied in engineering, economics and healthcare planning, VOI is also intuitively appealing for environmental management, where decisions must be made in the face of ubiquitous uncertainty.  Knowing the value of information can assist in designing monitoring and experimental programs, implementing adaptive management and prioritising sources of uncertainty. In other words, it can help applied ecologists and conservation managers find a focused, transparent way to address the inevitable need for “more data”.

An increasing number of studies are applying VOI to conservation management; however, in spite of its potential the technique is still underused in real-world applications, particularly beyond the small community of applied ecologists trained in decision-analytic methods.

Click Image to begin a Prezi Presentation on Value of Information

Click Image to begin a Prezi Presentation on Value of Information

In summary, three things determine the value of information:

  1. How much we already know (the more we know, the less beneficial it is to collect more information)
  2. Whether and how we would react to that extra information by changing actions, and how much better would the updated action be
  3. How good is the information we can actually get (think about sample sizes, imperfect detection, time lags, etc)

What is Needed to Calculate VOI?

Since it is a tool of decision analysis, to calculate the value of information we need to have the right process in place. First, we need to clearly state what the decision is, and specify our objectives; then define a set of available management actions; third, we need to clearly formulate uncertainty as a series of hypotheses (competing models or states of the system of interest); fourth, we need to predict the expected outcomes of those actions, with particular attention to how they are expected to differ under different hypotheses.

Perfect Information Vs Sample Information

There are several formulations of VOI: the Canessa et al. article focuses on explaining how to calculate two of them. The first is the value of perfect information, representing the improvement that would result from eliminating uncertainty. While perfect knowledge is often unrealistic in real-world conservation, this provides a useful upper benchmark to assess how much better we could ever become. Second, the paper shows how to calculate the value of sample information, the improvement that results from reducing uncertainty by collecting a sample of information (such as some more data). This is a more realistic assessment of the learning process itself; the paper illustrates step-by-step how to use Bayesian updating and do a pre-posterior analysis to calculate the value of sample information.

Since it is aimed at applied ecologists and managers, the paper explains the key concepts and calculations of VOI using two practical examples from threatened species management. Both examples can be implemented in simple spreadsheets, which are provided as Supplementary Information. The following videos further guide readers through the calculation steps, using the first example presented in the paper. Here, decisions about the translocation of a frog species are complicated by uncertainty about disease risks.

Value of Information Video Tutorials

The first video shows how to calculate the value of perfect information, the expected improvement in management outcomes that could be achieved by eliminating uncertainty completely.

The second video guides you through the calculation of the value of sample information, the expected improvement in management outcomes provided by reducing uncertainty through the collection of additional data.

If you are an applied ecologist or environmental manager grappling with uncertainty, ‘When do we need more data? A primer on calculating the value of information for applied ecologists’ and its supplementary information could make a difference in the way you see experimental and monitoring programs designed to inform management. VOI is also a key concept if you are interested in exploring adaptive management. Overall, as any tool that helps structure decisions, VOI can assist decision-making in the face of those two ubiquitous problems of environmental management: uncertainty and limited resources.

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4 thoughts on “The Value of Information: Does More Data Mean Better Decisions?

  1. Pingback: Recommended Reading | August 2015 | Cindy E Hauser

  2. Pingback: Does More Data Mean Better Decisions? | The Quantitative & Applied Ecology Group

  3. Pingback: Issue 6.10 | methods.blog

  4. Pingback: When do we need more data? | Stefano's research

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