Jay Odenbaugh

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.

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.

Dr. Jay Odenbaugh discusses psychological issues concerning American opinion on the topic of climate control, the relevance or irrelevance of scientific literacy to climate skepticism, and the role of affect and cognitive biases in environmental decision-making. He considers climate communication and how we might most effectively motivate pro-environmental behavior and beliefs. The discussion ends with a case study for persuading individuals on both sides of the political aisle for taking global climate change seriously.

Becoming Human by Jennifer Greenwood is one of the most thought-provoking books on emotion and its expression I have read. At its core, it attempts to provide an account of the development of full human emotionality and in so doing argues the emotions are “transcranial.” Emotions are radically realized outside our nervous systems and beyond our skin. As children, we are functionally integrated affectively with our mothers; so much so that in a sense our emotions are not ours alone. Regardless of whether one agrees with her radical claims, it is a must-read for those interested in emotion and expression. In order to appreciate the significance of this book, let me sketch its contents and raise a few criticisms.Many...

Ecological populations and communities are highly complex systems and our ability to understand these systems are limited. Thus, the mathematical models used to represent these systems are often highly idealized. In this dissertation. I examine in the context of theoretical ecology what mathematical models are, how these models are evaluated, and how models can explain the dynamics of these systems given their complexity and the idealizations introduced. ;In the first section, I argue that the semantic view of theories and models in its conservative and liberal forms is unacceptable. Models are neither related to the world through isomorphisms nor similarity as the semantic view requires. I argue that models are abstract, idealized structures which are related to systems by accurately representing them. ;In the second section, I consider how models are evaluated. First, I consider how different desiderata of model building can come into conflict. Richard Levins has argued that there is a necessary trade-off between the generality, realism, and precision of biological models. I argue that Levins is correct and respond to worries raised by Elliott Sober and Steven Orzack . Second, I examine Elisabeth Lloyd's account of confirmation of models in biology. I show that her account neglects how idealizations are discharged and I supply some of the details needed. Third, I argue that models are evaluated relative to various goals and can successfully achieve those goals even when they are representationally inaccurate or difficult to test. I provided an account of what the goals of model building in theoretical ecology are and show how they are achieved. ;In Section Three, I consider how idealized models can be explanatory. Many philosophers claim that a theory or model explains an event or regularity only if it is true . Yet, idealized models sometimes explain events and regularities. I argue that the notion of accurately representing phenomena is what matters and not truth. Hence, models are explanatory only if they accurately represent ecological systems in certain respects and in certain degrees

Some ecologists have argued that theoretical model building in population and community ecology has gone evidentially unconstrained. In the essay, I argue that "bottle experiments" offer ecological model building evidential constraints and illustrate this by considering work on chaotic models tested by the dynamics of flour beetles. Critics reply that these experiments are importantly unlike nonmanipulated natural systems and thus do not constitute genuine tests of the models. I conclude by considering two responses to this worry and a suggestion on how to move forward on this debate.

Foot , Hursthouse , and Thompson , along with other philosophers, have argued for a metaethical position, the natural goodness approach, that claims moral judgments are, or are on a par with, teleological claims made in the biological sciences. Specifically, an organism’s flourishing is characterized by how well they function as specified by the species to which they belong. In this essay, I first sketch the Neo-Aristotelian natural goodness approach. Second, I argue that critics who claim that this sort of approach is inconsistent with evolutionary biology due to its species essentialism are incorrect. Third, I contend that combining the natural goodness account of natural-historical judgments with our best account of natural normativity, the selected effects theory of function, leads to implausible moral judgments. This is so if selected effects function are understood in terms of evolution by natural selection, but also if they are characterized in terms of cultural evolution. Thus, I conclude that proponents of the natural goodness approach must either embrace non-naturalistic vitalism or troubling moral revisionism

In this essay, I examine the controversy concerning the advocacy of ethical values in conservation biology. First, I argue, as others have, that conservation biology is a science laden with values both ethical and non-ethical. Second, after clarifying the notion of advocacy at work, I contend that conservation biologists should advocate the preservation of biological diversity. Third, I explore what ethical grounds should be used for advocating the preservation of ecological systems by conservation biologists. I argue that conservation biologists should defend their preservationist positions on instrumentalist grounds alone if the context of discussion and debate is a scientific one

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?