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The concept of levels of organization is prominent in science and central to a variety of debates in philosophy of science. Yet many difficulties plague the concept of universal and discrete hierarchical levels, and these undermine implications commonly ascribed to hierarchical organization. We suggest the concept of scale as a promising alternative. Investigating causal processes at different scales allows for a notion of quasi levels that avoids the difficulties inherent in the classic concept of levels. Our primary focus is ecology, but we suggest how the results generalize to other invocations of hierarchy in science and philosophy of science.

A common argument against explanatory reductionism is that higher-level explanations are sometimes or always preferable because they are more general than reductive explanations. Here I challenge two basic assumptions that are needed for that argument to succeed. It cannot be assumed that higher-level explanations are more general than their lower-level alternatives or that higher-level explanations are general in the right way to be explanatory. I suggest a novel form of pluralism regarding levels of explanation, according to which explanations at different levels are preferable in different circumstances because they offer different types of generality, which are appropriate in different circumstances of explanation.

The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, optimality and game-theoretic models best explain some central types of evolutionary phenomena. The Optimality ApproachThe optimality approach offers a way to model natural selection purely phenotypically, without directly representing the system of genetic transmission. This approach includes both optimality models and game-theoretic models, which are used when trait fitnesses are frequency-dependent. One determines the range of possible values for some phenotype and the fitness function relating these phenotypes to the environment. Based on this information, the model predicts which phenotypic value(s) will predominate in the population, given enough time in that environment. Many instances of long term evolutionary change can be modeled in this manner, resulting in information regarding the effect of the selection pressure(s) at work and any constraints arising from, e.g., the process of genetic transmission or of development.

There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. I suggest that continuing, widespread idealization calls into question the idea that science aims for truth. If instead science aims to produce understanding, this would enable idealizations to directly contribute to science's epistemic success. I also use the fact of widespread idealization to motivate the idea that science's wide variety aims, epistemic and non-epistemic, are best served by different kinds of scientific products. Finally, I show how these diverse aims—most rather distant from truth—result in the expanded influence of social values on science.