Michael Weisberg

Who is a Modeler

Many standard philosophical accounts of scientific practice fail to distinguish between modeling and other types of theory construction. This failure is unfortunate because there are important contrasts among the goals, procedures, and representations employed by modelers and other kinds of theorists. We can see some of these differences intuitively when we reflect on the methods of theorists such as Vito Volterra and Linus Pauling on the one hand, and Charles Darwin and Dimitri Mendeleev on the other. Much of Volterras and Paulings work involved modeling; much of Darwins and Mendeleevs did not. In order to capture this distinction, I consider two examples of theory construction in detail: Volterras treatment of post-WWI fishery dynamics and Mendeleevs construction of the periodic system. I argue that modeling can be distinguished from other forms of theorizing by the procedures modelers use to represent and to study real-world phenomena: indirect representation and analysis. This differentiation between modelers and non-modelers is one component of the larger project of understanding the practice of modeling, its distinctive features, and the strategies of abstraction and idealization it employs. 1. Introduction2. The essential contrast2.1. Modeling2.2. Abstract direct representation3. Scientific models4. Distinguishing modeling from ADR4.1. The first and second stages of modeling4.2. Third stage of modeling4.3. ADR5. Who is not a modeler?6. Conclusion: who is a modeler? Introduction The essential contrast2.1. Modeling2.2. Abstract direct representation Modeling Abstract direct representation Scientific models Distinguishing modeling from ADR4.1. The first and second stages of modeling4.2. Third stage of modeling4.3. ADR The first and second stages of modeling Third stage of modeling ADR Who is not a modeler? Conclusion: who is a modeler?

The Importance of Understanding the Nature of Science for Accepting Evolution

Many students reject evolutionary theory, whether or not they adequately understand basic evolutionary concepts. We explore the hypothesis that accepting evolution is related to understanding the nature of science. In particular, students may be more likely to accept evolution if they understand that a scientific theory is provisional but reliable, that scientists employ diverse methods for testing scientific claims, and that relating data to theory can require inference and interpretation. In a study with university undergraduates, we find that accepting evolution is significantly correlated with understanding the nature of science, even when controlling for the effects of general interest in science and past science education. These results highlight the importance of understanding the nature of science for accepting evolution. We conclude with a discussion of key characteristics of science that challenge a simple portrayal of the scientific method and that we believe should be emphasized in classrooms.

Epistemic Landscapes and the Division of Cognitive Labor

Because contemporary scientific research is conducted by groups of scientists, understanding scientific progress requires understanding this division of cognitive labor. We present a novel agent‐based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to find the most epistemically significant research approaches. We consider three different search strategies that scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community influence their actions. This is compared with two social research strategies: Followers are biased toward what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform the other strategies. Finally, we show that, in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains.

The Robust Volterra Principle

Theorizing in ecology and evolution often proceeds via the construction of multiple idealized models. To determine whether a theoretical result actually depends on core features of the models and is not an artifact of simplifying assumptions, theorists have developed the technique of robustness analysis, the examination of multiple models looking for common predictions. A striking example of robustness analysis in ecology is the discovery of the Volterra Principle, which describes the effect of general biocides in predator‐prey systems. This paper details the discovery of the Volterra Principle and the demonstration of its robustness. It considers the classical ecology literature on robustness and introduces two individual‐based models of predation, which are used to further analyze the Volterra Principle. The paper also introduces a distinction between parameter robustness, structural robustness, and representational robustness, and demonstrates that the Volterra Principle exhibits all three kinds of robustness.

The structure of tradeoffs in model building

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss what consequences those tradeoffs have for theoretical practice.

The Intelligent Design controversy: lessons from psychology and education

The current debate over whether to teach Intelligent Design creationism in American public schools provides the rare opportunity to watch the interaction between scientific knowledge and intuitive beliefs play out in courts rather than cortex. Although it is tempting to think the controversy stems only from ignorance about evolution, a closer look reinforces what decades of research in cognitive and social psychology have already taught us: that the relationship between understanding a claim and believing a claim is far from simple.

Water is Not H2O

In this essay I have discussed an assumption of semantic externalist theories which I called the coordination principle. This is the idea that natural language kinds and scientific kinds line up or can be mapped onto one another one-to-one. A closer look at water shows that there is not this type of simple one-to-one match between chemical and ordinary language kinds. In fact, the use of kind terms in chemistry is often context sensitive and in cases where chemists want to ensure no ambiguity, they use a very complex and nuanced set of kind terms, none of which could be reasonably associated with the ordinary language kind term “water” alone. Since we cannot just turn to chemistry to find a single chemical kind that can be used to determine the extension of “water,” there is not any strict sense in which water is H2O, because exactly what water is depends on the context in which “water” is uttered.

Qualitative Theory and Chemical Explanation

Roald Hoffmann and other theorists claim that we ought to use highly idealized chemical models (“qualitative models”) in order to increase our understanding of chemical phenomena, even though other models are available which make more highly accurate predictions. I assess this norm by examining one of the tradeoffs faced by model builders and model users—the tradeoff between precision and generality. After arguing that this tradeoff obtains in many cases, I discuss how the existence of this tradeoff can help us defend Hoffmann’s norm for modelling.

Robustness and idealization in models of cognitive labor

Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models’ robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes.

Challenges to the Structural Conception of Chemical Bonding

The covalent bond, a difficult concept to define precisely, plays a central role in chemical predictions, interventions, and explanations. I investigate the structural conception of the covalent bond, which says that bonding is a directional, submolecular region of electron density, located between individual atomic centers and responsible for holding the atoms together. Several approaches to constructing molecular models are considered in order to determine which features of the structural conception of bonding, if any, are robust across these models. Key components of the structural conception are absent in all but the simplest quantum mechanical models of molecular structure, seriously challenging the conception’s viability.