Jay Odenbaugh

Emotion, and its expression, is present in almost every facet of life: in our personal relationships, morality, politics, and art. Over time, emotions have evolved and have become intimately connected to our bodies, as matters of existential and scientific importance. In a Sentimental Mood is a philosophical examination of what emotion is, the kinds of emotions there are, and the ways in which they are expressed in our behavior and other social and moral expressions. Jay Odenbaugh's goal is to integrate the diverse work in psychology, neuroscience, evolutionary biology, and behavioural ecology with that from the philosophy of biology and psychology. This integration provides a unified, naturalistic understanding of this key component of human experience. Odenbaugh argues that emotion is a multimodal perceptual experience integrating exteroception and interoception. They are seamlessly about our environment, ourselves, and more specifically how we are faring in that environment. Many are natural kinds in virtue of being causally structured clusters of elicitors, behaviours, and autonomic nervous activities. Emotions are culturally widespread even with local inflections of place. Finally, Odenbaugh fruitfully integrates the nature of moral judgments with the resources of teleosemantics and hybrid expressivism. These judgments both describe and direct us to reciprocate, aid, and punish. Covering a wide range of research on human evolution and culture and bringing them into conversation, In a Sentimental Mood provides a bold new account of human emotional experience while also providing a useful synthesis of the state of the art on the topic.

This chapter provides an account of the basic emotions and their expression. Emotions are experiences that have the function of indicating how we are faring in our environment. Emotions are also objects of experience: our perceptual systems are sensitive to the expression of emotion in our environment by features that have the function of indicating emotions. Thus, we come to have to knowledge of emotions by perceptually representing properties that function to indicate them. The chapter applies this account to expression in art. What does it mean to say that an artwork expresses sadness? Is perceiving joy in an artwork the same kind of experience as perceiving joy in a friend’s face? How may artworks express emotions without having emotions? The chapter offers a representationalist account of the basic emotions on which exteroceptive and interoceptive systems combine to constitute a system whose states—emotions—indicate how we are faring. Building on the work of Dominic Lopes (2005) and Mitchell Green (2007), the chapter offers a teleosemantic account of emotional expression in art that is impersonal and continuous with a representationalist account of the basic emotions. Features in the environment express emotions even in conditions in which there is no person to whom the emotion is attributable. We experience emotions in two ways: we may have emotions, and we experience emotions as represented properties of the environment. In both cases, experiencing emotions is a matter of experiencing how things are in the world and thus provides perceptual knowledge.

There are choices about the number of ways to approach and understand a problem. Sometimes finding the one right analytical approach is sufficient. Other times, such as with the Manhattan Project, the use of many approaches is desirable. Increasing independence among multiple analytical approaches, i.e. using a pluralistic approach, can be a good strategy to get knowledge to make decisions and understand a system. We considered two frameworks that have attempted to provide advice to engineering and scientific practitioners on when and how to use multiple analytical approaches. The RAND Corporation’s parallel path strategy, as described by R.R. Nelson, is a way of using independent engineering efforts to explore what parts of the design space are feasible, as well as what the cost and schedule would be for different designs. Richard Levins and William Wimsatt’s focus on model independence provides motivation and insights for using multiple models to assess the same system. While these approaches may appear different, both rely on using a group of analytical approaches where the individual members are independent – or different from—one another. Comparing these two approaches provides suggestions about how to utilize independence to address uncertainties in design and model-systems. We argue that the deliberate creation of independence among engineering developments and models should be tied to key uncertainties in the model or system. With relatively low uncertainty, choosing one approach may be acceptable. Both suggest that there can be (but are not always) benefits from using multiple approaches, which can increase accuracy and reduce cost. Using a few independent approaches – as opposed to many – may be more desirable when there are only a few bounded uncertainties about the system.

Philosophical interest in ecology is relatively new. Standard texts in the philosophy of biology pay little or no attention to ecology (though Sterelny and Griffiths 1999 is an exception). This is in part because the science of ecology itself is relatively new, but whatever the reasons for the neglect in the past, the situation must change. A good philosophical understanding of ecology is important for a number of reasons. First, ecology is an important and fascinating branch of biology with distinctive philosophical issues that arise from its study. Second, ecology is only one small step away from urgent political, ethical, and management decisions about how best to live in an apparently increasingly-fragile environment. Third, philosophy of ecology, properly conceived, can contribute directly to both our understanding of ecology and help with its advancement. Philosophy of ecology can thus be seen as part of the emerging discipline of “biohumanities”, where biology and humanities disciplines together advance our understanding and knowledge of biology (Stotz and Griffiths forthcoming). In this paper, we focus primarily on this third role of the philosophy of ecology and consider a number of places where philosophy can play an important role in ecology. In the process, we..

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.

In this essay, I consider three philosophical issues that arise in the environmental sciences. First, these sciences depend on mathematical models and simulations which are highly idealized and are coupled with very uncertain data. Why should we trust these models and simulations? Second, in standard hypothesis testing, the burden of proof is in favor of the null hypothesis which claims some causal factor has no effect. The alternative hypothesis is accepted only when the likelihood of the null hypothesis is very low. Recently, some have argued that we should minimize Type II errors (not rejecting a false null) rather than Type I errors (rejecting a true null) given the environmental risks involved. I consider arguments for shifting this burden of proof when possible environmental harms are significant. Finally, in debates over global climate change, much is made of the apparent consensus concerning the effects of human induced greenhouse gas emissions on average surface temperatures. However, scientific methods are structured around dissent and criticism. Is consensus-based science orthogonal are even harmful to science?

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation

In the United States, the northern spotted owl has declined throughout the Pacific Northwest even though its habitat has been protected under the Endangered Species Act. The main culprit for this decline is the likely human-facilitated invasion of the barred owl. The United States Fish and Wildlife Service conducted an experiment in which they lethally removed the barred owls from selected areas in Washington, Oregon, and California. In those locations, the northern spotted owl populations have stabilized and increased. Some have argued that we should kill the barred owl to protect the northern spotted owl. In this essay, I argue that the competitive displacement of northern spotted owls by the barred owl should not be addressed by killing the later to save the former. The most powerful objection to this conclusion is that we will lose old-growth temperate rainforest without an indicator species like the spotted owl protected under the Endangered Species Act. In response, I argue that we should directly conserve old-growth temperate rainforest independent of the northern spotted owl. In effect, we need legislation and policies that protects endangered ecosystems.

In this chapter, Odenbaugh first provides a conceptual framework for thinking about climate modeling, specifically focused on general circulation models. Second, he considers what makes models independent of one another. Third, he shows robustness analysis, which depends on models being independent of one another, can be used to remove doubts about idealizations in general climate models. Finally, he considers a dilemma for robustness analysis; namely, it leads to either an infinite regress of idealizations or a complete removal of idealizations. A response to the dilemma is given defending a form of epistemic contextualism and by drawing a distinction between relative and absolute robustness.

Jonathan Newman, Gary Varner, and Stefan Linquist’s Defending Biodiversity: Environmental Science and Ethics is a critical examination of a panoply of arguments for conserving biodiversity. Their discussion is extremely impressive though I think one can push back on some of their criticisms. In this essay, I consider their criticisms of the argument for conserving biodiversity based on ecosystem services; specifically, ecosystem functioning. In the end, I try to clarify and defend this argument against their criticisms.

Functional diversity holds the promise of understanding ecosystems in ways unattainable by taxonomic diversity studies. Underlying this promise is the intuition that investigating the diversity of what organisms actually do—i.e. their functional traits—within ecosystems will generate more reliable insights into the ways these ecosystems behave, compared to considering only species diversity. But this promise also rests on several conceptual and methodological—i.e. epistemic—assumptions that cut across various theories and domains of ecology. These assumptions should be clearly addressed, notably for the sake of an effective comparison and integration across domains, and for assessing whether or not to use functional diversity approaches for developing ecological management strategies. The objective of this contribution is to identify and critically analyze the most salient of these assumptions. To this aim, we provide an “epistemic roadmap” that pinpoints these assumptions along a set of historical, conceptual, empirical, theoretical, and normative dimensions.

According to population biologist Richard Levins, every discipline has a “strategy of model building,” which involves implicit assumptions about epistemic goals and the types of abstractions and modeling approaches used. We will offer suggestions about how to model complex systems based upon a strategy focusing on independence in modeling. While there are many possible and desirable modeling strategies, we will contrast a model-independence-focused strategy with the more common modeling strategy of adding increasing levels of detail to a model. Levins calls the latter approach a ‘brute force’ strategy of modeling, which can encounter problems as it attempts to add increasing details and predictive precision. In contrast, a model-independence-focused strategy, which we call a ‘pluralistic strategy,’ draws off of Levins’s use of an assemblage of multiple, simple and—critically—independent models of ecological systems in order to do predictive and explanatory analysis. We use the example of model analysis of levee failure during Hurricane Katrina to show what a pluralistic strategy looks like in engineering. Depending on one’s strategy, one can deliberately engineer the set of available models in order to have more independent and complementary models that will be more likely to be accurate. We offer advice on ways of making models independent as well as a set of epistemic goals for model development that different models can emphasize.

In this essay, I first consider a popular view of models and modeling, the similarity view. Second, I contend that arguments for it fail and it suffers from what I call “Hughes’ worry.” Third, I offer a deflationary approach to models and modeling that avoids Hughes’ worry and shows how scientific representations are of apiece with other types of representations. Finally, I consider an objection that the similarity view can deal with approximations better than the deflationary view and show that this is not so.