I am fascinated by networks, what a great way of graphically representing anything you can think about that interacts somehow! In decision-making, networks can represent complex decision trees (or strategy), i.e. they graphically tell you what to do under specific conditions. The issue is, these networks can become too large to make sense of! I’ve investigated interactive ways of representing decision trees, and I was fortunate to stumble upon Gephi. Few days of programming led me to some interesting results, that I happily share with you. Here the nodes represent springs, that can be occupied or empty. There are about 10 springs, so if you follow a path from the root (bottom node) to a top node, you will find the optimal action to perform. It is a work in progress, and more can be done. I believe this is the future of publication by the way, interactive figures/graph/networks, they make communication so effective.
In summary, don’t be shy, use network! I have noticed that network theory communities can be protective of their tools. With a tendency of making simple things such as networks and their metrics seem overly complex. But really networks are quite simple and fun to play with and they can be whatever you wish, you just have to define them.
Adaptive management or learning by doing, is praised as the best practice method to manage natural systems under uncertainty (see ESA’14 talk). Limited for a long time by our ability to solve adaptive management problems, our research now allows us to find the best adaptive management strategies when networks change over time. This was made possible thanks to our research in Artificial Intelligence (AI) and Conservation science.
What have we discovered in 2 steps?
1) Unlocking the beast. Being strategic about adaptive management means finding the best management strategy when we don’t know exactly what will happen in the future (structural uncertainty). Until very recently, finding the optimal strategy to such decision problems was possible for very small size problems, limiting the application of adaptive management principles. In 2012, we published a fundamental paper that demonstrates that adaptive management problems can be solved using a simplified POMDP (Partially Observable Markov Decision Process, see tiger paper). This is an important finding because modelling an adaptive management problem as a POMDP means we can use very fast algorithms from AI and solve very large adaptive management problems. On a side note, this paper was published at the top AI Conference (AAAI) and received “best paper award” (Computational Sustainability track, thanks for the support!).
2) Boldly go where no one has gone before. Our second step was to demonstrate the power of our findings on the most complex problem we could imagine. Thinking about it, the most difficult problems to solve in ecology are spatial problems (migratory networks) with changing dynamics over time (non stationarity, climate change) for which the consequences on species management are unknown (structural uncertainty, population dynamics). Well, we did it! Check our splendid paper in Proceedings B led by Sam Nicol that brings it all together. This work is amazing for so many good reasons: the shorebird application, the fundamental AI research, the writing, the figures, the authors, the journal and the 20-page supplementary information!
I hope you will enjoy it as much as we did.
Nicol, S., R.A. Fuller, T. Iwamura and I. Chadès (2015). Adapting environmental management to uncertain but inevitable change. Proceedings of the Royal Society B, 282(1808).
Sam also wrote a fantastic conversation article on the topic that explains the impact of our Proc B paper: We need to get smarter to save shorebirds from rising seas.
Stefano Canessa and colleagues have recently published a much needed paper in Methods in Ecology and Evolution to help managers answer: when do we need more data?
Stefano provides an excel spreadsheet and also wrote a blog in MEE! Well done Stefano.
Canessa, S., Guillera-Arroita, G., Lahoz-Monfort, J. J., Southwell, D. M., Armstrong, D. P., Chadès, I., Lacy, R. C., Converse, S. J. (2015), When do we need more data? A primer on calculating the value of information for applied ecologists. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12423
I have found that thinking through what uncertainty really matters a rewarding process. For example, my last talk at ICCB 2015 also showed that critical uncertainty is often limited to a small number of unknown in Conservation. Go ahead fellow scientists, embrace uncertainty. If you were an economist you would say that uncertainty and associated decisions bring opportunities and perhaps flexibility!
On a side note, I’ve been recently thinking about a dynamic version of the Expected Value of Perfect Information – For example you can check out this paper.