Jay Odenbaugh

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.

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 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.

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.

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.

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.

Ecologist Richard Levins argues population biologists must trade-off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade-off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail since Levins's thesis concerns the pragmatic features of model building not just the formal properties of models.

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.

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