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Value of information and monitoring in conservation biology

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Abstract

In this paper we consider uses of value of information studies in conservation biology. It is a common assumption that more and better quality data will lead to better conservation management decisions. Indeed, this assumption lies behind, and motivates, a great deal of current work in conservation biology. Of course, more data can lead to better decisions in some cases but decision-theoretic models of the value of information show that this need not always be the case: sometimes the cost of data collection is too high. While such value of information studies are well known in economics and decision theory circles, their applications in conservation biology are relatively new. These studies are a valuable tool for conservation management, and we outline some of the potential applications. We also offer some advice about, and problems with, implementing value of information studies in conservation settings.

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Notes

  1. Let p be the probability of heads and \((1-p)\) the probability of tails. The expected monetary value of accepting the bet is: \(20p - 10(1-p) = 30p -10\). This is greater than zero (the expected monetary value of not accepting the bet) whenever p is greater than 1/3. So if the probability of heads is greater than 1/3, it is rational to accept the bet. In particular, in the case in question, the lowest p can be is 0.4 so it is rational to accept the bet—irrespective of the exact value of p.

  2. It might be tempting to argue that we should not accept the bet because there is more of the interval with unfavourable values of p. That is, the length of the interval [0.2, 1/3] is longer than the length of [1/3, 0.4]. But this is to make the further unwarranted assumption that there is a uniform distribution over the interval [0.2, 0.4]. This was not part of the set-up. We know nothing about the distribution in question and should not confuse such ignorance with knowing that p is distributed uniformly across the interval in question.

  3. See, for example, Colyvan et al. (2011), Keisler et al. (2014), Moore and McCarthy (2010), Rhodes et al. (2011), Runge et al. (2011) and Runting et al. (2013) and articles in the 2014 special issue of Environmental Systems and Decisions devoted to value of information.)

  4. It also helps with related debates about long-term effects of flipper tags on penguins (McCarthy and Parris 2008; Gauthier-Clerc et al. 2004).

  5. What follows should be thought of as a few useful tips and words of caution about applications of value of information studies in conservation biology. It is not intended as a serious challenge to actual or potential uses of the value of information framework.

  6. A means of affecting a currency conversion will do just as well.

  7. For example, vagueness in language (i.e. categories, such as “acceptable risk” that permit border-line cases and are not black and white) gives rise to such linguistic and arguably non-probabilistic uncertainty (Regan et al. 2002).

  8. To take an example from another science: the value of pretty much any cosmological research is zero—the knowledge gained of the structure of the big bang, for example, simply makes no difference to any of the decisions we make in our everyday lives. Yet there is no denying that such cosmological research is worthwhile. It’s just that standard value of information studies are not well equipped to demonstrate the value of such research.

  9. It is possible to include such less-tangible values into the set-up but the decision problem typically becomes less tractable, in part because of the disagreement over, and difficulty in, quantifying the values in question.

  10. Although we still need to identify, and give priority to, more interesting pure science over run-of-the-mill and mundane research.

  11. Thanks to an anonymous referee for this way of putting the issue. See also the related and very interesting literature on targeted versus surveillance monitoring (e.g. Nichols and Williams 2006).

  12. A game, in the intended technical sense, is a kind of decision situation where more than one agent is involved, and the agents do not necessarily share common goals. Each agent is thus making decisions to further their own agenda. Classic examples of such games are chess and the cold-war arms race (Hanley and Folmer 1999; Osborne 2003; Poundstone 1992).

  13. This game is named after a car game where two drivers drive at high speed down a road towards one another. If one driver swerves to avoid the impending collision (“cooperates”) that driver loses (represented by a payoff of 1 in the matrix) while the driver who does not swerve (“defects”) is the winner (represented by the payoff of 2 in the matrix). If they both swerve (i.e. both “cooperate”), they both lose (represented by the (1,1) outcome), and if neither swerves (i.e. they both “defect”), they collide and both are much worse off than in any other scenario (represented by the (0,0) cell in the matrix).

  14. This might seem like an odd way to proceed, since the regulator has the power to transform the structure of the game from chicken or prisoner’s dilemma into something else. But this is just to say that the regulator has moves at their disposal that change the payoffs of the other players in the game. This is just what it is to be a player in a game. Once the regulator is added to the games in question, the games are no longer simple games of chicken, prisoner’s dilemma and the like. But that is neither here nor there. We can still model the resulting scenario with game theory.

  15. This novel application of value of information studies was first suggested by Colyvan et al. (2011) but without presenting the details.

  16. In some cases we also need to factor in the cost of the value of information study itself. Sometimes these studies require considerable resources (additional scenario modelling and the like) and this cost should not be ignored.

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Acknowledgments

I am indebted to Jack Justus, Mick McCarthy, Maureen O’Malley, Kirsten Parris, Hugh Possingham, Helen Regan, and Luke Russell for valuable discussions on the topic of this paper and to two anonymous referees for this journal for several very constructive suggestions. I am also indebted to audiences at the University of Tromsø, Tromsø, Norway, the 2013 Munich-Sydney-Tilburg Models and Decisions conference at the Ludwig-Maximilians University in Munich, Germany and the 2013 Society for Risk Analysis (Australia and New Zealand) conference at the Australian National University, Canberra, Australia. Work on this paper was supported by an Australian Research Council Future Fellowship Grant (Grant number: FT110100909).

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Colyvan, M. Value of information and monitoring in conservation biology. Environ Syst Decis 36, 302–309 (2016). https://doi.org/10.1007/s10669-016-9603-8

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