Mark Colyvan

A TAXONOMY AND TREATMENT OF UNCERTAINTY FOR ECOLOGY AND CONSERVATION BIOLOGY

Uncertainty is pervasive in ecology where the difficulties of dealing with sources of uncertainty are exacerbated by variation in the system itself. Attempts at classifying uncertainty in ecology have, for the most part, focused exclusively on epistemic uncertainty. In this paper we classify uncertainty into two main categories: epistemic uncertainty (uncertainty in determinate facts) and linguistic uncertainty (uncertainty in language). We provide a classification of sources of uncertainty under the two main categories and demonstrate how each impacts on applications in ecology and conservation biology. In particular, we demonstrate the importance of recognizing the effect of linguistic uncertainty, in addition to epistemic uncertainty, in ecological applications. The significance to ecology and conservation biology of developing a clear understanding of the various types of uncertainty, how they arise and how they might best be dealt with is highlighted. Finally, we discuss the various general strategies for dealing with each type of uncertainty and offer suggestions for treating compounding uncertainty from a range of sources.

Buying into conservation: intrinsic versus instrumental value.

Many conservation biologists believe the best ethical basis for conserving natural entities is their claimed intrinsic value, not their instrumental value for humans. But there is significant confusion about what intrinsic value is and how it could govern conservation decision making. After examining what intrinsic value is supposed to be, we argue that it cannot guide the decision making conservation requires. An adequate ethical basis for conservation must do this, and instrumental value does it best.

A proposal for fuzzy International Union for the Conservation of Nature (IUCN) categories and criteria

Abstract The classification of endangered species uses categories “extinct in the wild”, “endangered” and so on that are intrinsically vague. This vagueness presents various problems for those trying to classify species. The usual way of dealing with this vagueness is to eliminate it by providing precise definitions of the categories in question. In this paper we propose a fuzzy set-theoretic alternative that respects the inherent vagueness of the crucial categories without compromising the utility of the classification scheme. Moreover, we argue that it leads to intuitively more appropriate classifications in many cases.

Philosophical Issues in Ecology: Recent Trends and Future Directions

Philosophy of ecology has been slow to become established as an area of philosophical interest, but it is now receiving considerable attention. This area holds great promise for the advancement of both ecology and the philosophy of science. Insights from the philosophy of science can advance ecology in a number of ways. For example, philosophy can assist with the development of improved models of ecological hypothesis testing and theory choice. Philosophy can also help ecologists understand the role and limitations of mathematical models in ecology. On the other side, philosophy of science will be advanced by having ecological case studies as part of the stock of examples. Ecological case studies can shed light on old philosophical topics as well as raise novel issues for the philosophy of science. For example, understanding theoretical terms such as “biodiversity” is important for scientific reasons, but such terms also carry political importance. Formulating appropriate definitions for such terms is thus not a purely scientific matter, and this may prompt a reevaluation of philosophical accounts of defining theoretical terms. We consider some of the topics currently receiving attention in the philosophy of ecology and other topics in need of attention. Our aim is to prompt further exchange between ecology and philosophy of science and to help set the agenda for future work in the philosophy of ecology. The topics covered include: the role of mathematical models, environmental problem formulation, biodiversity, and environmental ethics.

Is Probability the Only Coherent Approach to Uncertainty

In this article, I discuss an argument that purports to prove that probability theory is the only sensible means of dealing with uncertainty. I show that this argument can succeed only if some rather controversial assumptions about the nature of uncertainty are accepted. I discuss these assumptions and provide reasons for rejecting them. I also present examples of what I take to be non-probabilistic uncertainty.

The Miracle of Applied Mathematics

Mathematics has a great variety ofapplications in the physical sciences.This simple, undeniable fact, however,gives rise to an interestingphilosophical problem:why should physical scientistsfind that they are unable to evenstate their theories without theresources of abstract mathematicaltheories? Moreover, theformulation of physical theories inthe language of mathematicsoften leads to new physical predictionswhich were quite unexpected onpurely physical grounds. It is thought by somethat the puzzles the applications of mathematicspresent are artefacts of out-dated philosophical theories about thenature of mathematics. In this paper I argue that this is not so.I outline two contemporary philosophical accounts of mathematics thatpay a great deal of attention to the applicability of mathematics and showthat even these leave a large part of the puzzles in question unexplained.

The treatment of uncertainty and the structure of the IUCN threatened species categories

The classification of species with respect to their conservation status using the IUCN criteria is an important process in many countries, providing a guide for setting conservation priorities. Recent advances have resulted in several approaches to dealing with uncertainty in data used to classify species. These methods demand an unambiguous and transparent logical structure for the criteria. We suggest some changes to the ways in which the criteria are represented that correct an unnecessary inconsistency and which may serve to avoid important errors when uncertainty in the data is considered explicitly.

Right Decisions or Happy Decision‐makers?

Group decisions raise a number of substantial philosophical and methodological issues. We focus on the goal of the group decision exercise itself. We ask: What should be counted as a good group decision‐making result? The right decision might not be accessible to, or please, any of the group members. Conversely, a popular decision can fail to be the correct decision. In this paper we discuss what it means for a decision to be “right” and what components are required in a decision process to produce happy decision‐makers. Importantly, we discuss how “right” decisions can produce happy decision‐makers, or rather, the conditions under which happy decision‐makers and right decisions coincide. In a large range of contexts, we argue for the adoption of formal consensus models to assist in the group decision‐making process. In particular, we advocate the formal consensus convergence model of Lehrer and Wagner (1981), because a strong case can be made as to why the underlying algorithm produces a result that should make each of the experts in a group happy. Arguably, this model facilitates true consensus, where the group choice is effectively each person’s individual choice. We analyse Lehrer and Wagner’s algorithm for reaching consensus on group probabilities/utilities in the context of complex decision‐making for conservation biology. While many conservation decisions are driven by a search for objective utility/probability distributions (regarding extinction risks of species and the like), other components of conservation management primarily concern the interests of stakeholders. We conclude with cautionary notes on mandating consensus in decision scenarios for which no fact of the matter exists. For such decision settings alternative types of social choice methods are more appropriate.

Looking for contradictions

There has been considerable debate recently on whether there are true contradictions. A very natural and interesting question arises in the context of this debate: Why don’t we observe contradictions? The obvious answer is that there aren’t any true contradictions— observable or otherwise—so little wonder we don’t see any. Many philosophers would be happy to leave the matter there, but for those like Graham Priest who believe that there are true contradictions, the question of why we don’t observe contradictions is potentially embarrassing. Priest, however, does have an answer ready to hand [11]; he argues that the observable world is consistent. We suggest that Priest is a bit quick in drawing this conclusion. There are, we believe, a couple of reasons to doubt it. First, it is not clear that we would recognise a contradiction if we saw one; so, it does not follow from the lack of evidence for observable true contradictions that there are no observable contradictions. Second, we make a tentative case for the stronger claim that we do in fact see contradictions. If this latter claim is right, the observable world is inconsistent.

ANALOGICAL THINKING IN ECOLOGY: LOOKING BEYOND DISCIPLINARY BOUNDARIES

We consider several ways in which a good understanding of modern techniques and principles in physics can elucidate ecology, and we focus on analogical reasoning between these two branches of science. Analogical reasoning requires an understanding of both sciences and an appreciation of the similarities and points of contact between the two. In the current ecological literature on the relationship between ecology and physics, there has been some misunderstanding about the nature of modern physics and its methods. Physics is seen as being much cleaner and tidier than ecology. When compared to this idealized, fictional version of physics, ecology looks very different, and the prospect of ecology and physics learning from one another is questionable. We argue that physics, once properly understood, is more like ecology than ecologists have thus far appreciated. Physicists and ecologists can and do learn from each other, and, in this paper, we outline how analogical reasoning can facilitate such exchanges.