Understanding Patterns In Nature

Mingjun Zhang, University of Pennsylvania


Nature is full of interesting patterns. One of the most important tasks across the natural sciences, especially the biological sciences, is to identify and explain real patterns in nature. This dissertation provides a philosophical investigation of the nature and roles of real patterns in biological inquiry and the various strategies that biologists employ to explain and understand real patterns in nature. In Chapter 1, I advocate for a pragmatic approach to the reality of patterns in data. I argue that patterns in data are expected to play different scientific roles in different research contexts and that a pattern in data is real if and only if it fulfills the scientific role it is expected to play in a specific research context. In Chapter 2, I give a critical evaluation of the use and limitations of null-model-based hypothesis testing as a research strategy to explain patterns in the biological sciences. I argue that null-model-based hypothesis testing fails to work as a proper analog to traditional statistical null-hypothesis testing as used in well-controlled experimental research, and that the random process hypothesis should not be privileged as a null hypothesis. Instead, the possible use of the null model resides in its role of providing a way to challenge scientists’ commonsense judgments about how a seemingly unusual pattern could have come to be. In Chapter 3, I clarify the definition of a baseline model and apply it to the niche-neutral debate about how to understand biodiversity patterns. I argue that from a process-based perspective, a neutral model in ecology should not be regarded as a baseline model relative to classical niche-based models. In Chapter 4, I investigate the testability and the scientific value of the notion of overall relative causal importance by carefully examining the controversy over empirical adaptationism in evolutionary biology. My analysis of the case of empirical adaptationism provides reasons for scientists to reconsider the value and necessity of engaging in scientific debates involving the notion of overall relative causal importance.