About

Alan C. Love is a professor whose work explores the intersection of philosophy and biology. His research emphasizes the importance of framing questions carefully when investigating nature, as the answers are inherently linked to the way questions are posed. This perspective underscores his focus on conceptual clarity and the philosophical foundations underlying biological sciences. His academic activities include teaching, service, and engaging with projects and awards that reflect his commitment to advancing understanding in these fields.

Research topics

• Computer Science
• Evolutionary biology
• Physics
• Thermodynamics
• Mathematics
• Biology
• Epistemology
• Engineering
• Mechanical engineering
• Genetics
• Psychology
• Geography
• Meteorology
• Cognitive science
• Quantum mechanics
• Computational biology
• Neuroscience
• Environmental science
• Social psychology
• Philosophy

Selected publications

• Author response for "Improving scientific mentoring with history and philosophy of science"

  2026-01-23

  peer-review1st authorCorresponding
  PublisherDOI

• Author response for "Improving scientific mentoring with history and philosophy of science"

  2026-01-10

  peer-review1st authorCorresponding
  PublisherDOI

• The freedom to believe in free will: evidence from an adoption study against the first law of behavioral genetics

  Philosophical Psychology · 2025-07-17 · 1 citations

  articleOpen access

  Philosophers and psychologists alike have long debated the etiology of beliefs about human agency. Recently, empirical investigations have shown that lay beliefs about free will and determinism represent stable and important individual differences. Despite a perennial interest in the sources of agentic belief, genetic and environmental influences on such beliefs have never been studied. We administered a battery of items assessing these beliefs to a unique sample of 394 adoptive and biological families with adult offspring to investigate the origins of agentic beliefs and their relationships. We found significant differences between adopted and biological offspring and between the parents of such children, particularly in beliefs about determinism. Biometric modeling revealed especially surprising results: unlike the vast majority of traits studied in family designs, agentic beliefs appear to be weakly or not at all heritable. Since genetic factors might be regarded as typical of the "initial conditions" in philosophical thought experiments about free will and determinism, it is especially ironic that beliefs about free will and determinism may be among the traits least influenced by genetic differences.

  PublisherOA PDFDOI

• The epistemic strength of proxies in scientific practice

  European Journal for Philosophy of Science · 2025-07-10 · 1 citations

  articleOpen accessSenior author

  Abstract Scientists often rely on proxies when they identify or measure new or complex phenomena. However, these tools are frequently seen as epistemologically inferior because they are indirect and make it difficult to properly control for confounding factors. This view implies two methodological norms. First, if possible, proxies should be replaced with more direct and better-controlled tools. Second, if proxies cannot be replaced, they should be improved by increasing control over confounding factors. We evaluate this view by examining functional genomics, a field in which proxies are abundant. Analysing work from the largest contemporary initiative in functional genomics (ENCODE), we observe that researchers do not follow these methodological norms. Instead, they continue using the same proxies and even combine them to produce novel insights or measurements. This potentially paradoxical finding can be explained if we recognise that proxies have a “generativity” that is appreciated and leveraged by the researchers working with them. Proxies in functional genomics form a dynamic toolkit that equips researchers to discover unexpected insights and ask new questions. Our analysis thereby contributes to a better understanding of the epistemic roles that proxies play in scientific practice.

  PublisherOA PDFDOI

• Lost in Translation? LLMs, Education, and Linguistic Fairness

  2025-03-15

  article

  Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities, particularly in English prompts. However, lingering questions persist regarding these models’ ability to grasp the subtleties of language beyond mere rule recognition.As these models are increasingly used across various settings, particularly by students and trainees worldwide, a key concern is their ability to understand and accurately translate nuanced English, especially in tasks like translation and academic assistance, where English often dominates as the primary language. In this study, we collaborated as a group of researchers native to diverse linguistic backgrounds to assess LLMs’ aptitude and challenges in translating English idioms and context-rich phrases into 25 languages in a cross-cultural context. Our focus included idioms, gendered sentences, and contextually ambiguous cases. We identified significant patterns in LLMs’ performance on idiomatic expressions across languages. These patterns show substantial disparities and gender bias in translations, particularly disadvantaging low-resource languages. These findings raise concerns about these models worsening educational inequities and call for targeted improvements to ensure fairness and inclusivity. By revealing both limitations and strengths, our work emphasizes the need for a deeper understanding of language nuances to support multiculturalism, expand access to quality models for non-English speakers, and drive equitable progress in education and beyond.

  PublisherDOI

• <i>The Philosophy of Evolutionary Theory: Concepts, Inferences, and Probabilities</i>

  The Philosophical Review · 2025-10-01

  article1st authorCorresponding

  Many of us are familiar with the phenomenon of a box set. Successful musical acts collect their greatest hits into a single package that often includes previously unreleased or hard to find tracks. The box set serves as a nice analogy for Elliott Sober’s latest book on philosophical issues in evolutionary biology. Sober has been blazing a philosophical trail for decades through penetrating analyses of fitness, natural selection, species, systematics, and more. It is not an exaggeration to say that his work set the agenda in many ways for philosophy of biology. In this latest installment, we get the best of Sober’s work on these topics, with classic distinctions like “selection of” versus “selection for” (including the “selection toy” that celebrated its fortieth birthday last year), alongside material of more recent vintage, such as explorations of the likelihood approach to inferences about common ancestry. After summarizing the book’s well-executed themes, I offer a critical perspective, both in terms of methodology and themes, to situate its contributions.In evolutionary theory, it sometimes seems like all roads lead from Darwin. Sober adopts this conceit explicitly by introducing concepts from Darwin’s theory of evolution and then segueing to their more modern instantiations. His first chapter provides a short and helpful tour of these basic concepts. Subsequent chapters dive more deeply into specific topics: fitness and natural selection, units of selection, common ancestry, drift, mutation, taxa and genealogy (including a discussion of race), adaptationism, and “big-picture questions.” This last chapter touches on issues such as whether there are evolutionary laws, the status of determinism and indeterminism, and the contingency of the evolutionary process. Along the way, Sober introduces the reader to important terminology and key distinctions, giving simple but illuminating examples with line drawings and tables to unpack the ideas. The writing is clear and direct throughout. Sprinkled in the footnotes are questions for the reader to ponder on their own: “How can a bird that puts its eggs in two nests be reducing variance, as compared to a bird that puts all its eggs in one, since the first bird has more nests than the second?” (26). Sober graciously highlights places where others disagree with him (often in footnotes), but score settling is not a part of this book’s plan. I noticed a few places where others might demur on how their position was interpreted, but that is to be expected.A core contribution of this book—and of Sober’s scholarship more generally—is an explicit treatment of how general issues in philosophy of science manifest in evolutionary biology. These include instrumentalism, reductionism, underdetermination, and interpretations of probability. A good example is found in chapter 3 (“Units of Selection”), where Sober introduces Simpson’s paradox in the context of building an argument for why altruism can evolve because of group structure. This structure is invisible when viewing the fitness of the associated individuals in the entire metapopulation (i.e., aggregated together). Others include his analyses of conventionalism and realism with respect to genic selection and the difficulties of objective interpretations of probability in evolutionary models.Sober handles the complex models of population biology and systematics with grace and aplomb. He is especially perspicuous in discussing how the law of likelihood is applicable in the context of comparative hypothesis evaluation. However, he often undertakes these discussions without considering how these models are used in research practice. In the preface, Sober says, “My focus is on how these concepts ought to be understood; my project is normative, not purely descriptive” (xv). Still, one might be concerned that Sober’s analysis is not descriptive enough, relying largely on rational reconstructions abstracted from the wilds of scientific reasoning. His motivating questions take the form “What is X?” (e.g., What is fitness? What is a species?) but do not grapple with how evolutionary biologists conceptualize fitness or species in the practice of inquiry. He indicates that it is his “goal is to sharpen concepts so that they better serve the goals of theorizing” (xvi). What isn’t considered—but actual practices would suggest—is that concepts serve more than theorizing goals. It is an open question whether concepts sharpened for theorizing are also automatically sharpened for experimentation or data collection. One might also worry that there are different theorizing goals that require their own conceptual edges.From the outside, it might look like Sober is getting at most if not all the major conceptual issues at stake. However, there is a sense in which the book is quite narrow in what it covers. An interesting hint of this is in Sober’s acknowledgments: “I started work on the book you see before you with the idea of revising my book Philosophy of Biology” (xviii). What changed in recent decades for “biology” to become “evolutionary theory” in the title? In short, a lot. However, we do not need to document the tremendous growth of different strands of analysis in philosophy of biology beyond evolution (e.g., ecology or cell biology and development) to indicate the narrowness of Sober’s treatment, which is often reflected in citation practices (e.g., little mention of the now diverse literature on natural kinds and evolution). Even within evolutionary theory, much territory is not covered, perhaps most noticeably the question: What is evolutionary theory? This has been tackled by many other philosophers in part because the structure of evolutionary theory is not obvious and there is not a consensus on the individuation of its content.Regarding the content of evolutionary theory, consider the following question: What is a biological trait? Given that the models discussed throughout the book typically involve using traits as evidence for common ancestry or understanding how traits evolve under different conditions, you might think evolutionary theory should have an account of what counts as a trait. Sober talks as if traits are already known objects in the ontology of evolutionary theory. Although an ontology of traits may not be perceived as the responsibility of evolutionary theory, many have thought it was a task for evolutionary biology. Almost fifty years ago, in the context of one of Sober’s central topics (adaptationism), evolutionary biologists Stephen Jay Gould and Richard Lewontin worried about this openly: “We must omit an extended discussion of the vital issue: ‘what is a trait?’ Some evolutionists may regard this as a trivial, or merely a semantic problem. It is not” (Gould and Lewontin 1978: 585). More recently, Lewontin (2001: xvii) claimed that “No issue is of greater importance in the study of biology.” If evolutionary theory is foundational for all of biology (as many philosophers hold), it might seem odd to say that what counts as a trait doesn’t fall under its aegis. Since Sober ignores large regions of evolutionary biology, including biogeography, coevolution, evo-devo, quantitative genetics, and speciation, it would be helpful to have an account of how we know what is or is not part of the content of evolutionary theory, especially as the book advertises comprehensiveness (The Philosophy of Evolutionary Theory).In addition to these overlooked issues, another concern about Sober’s presentation is its occasional use of anachronistic labeling. Although Sober is not doing history of science, his rational reconstructions are not as benign as he envisions. He says in the preface, I also do not blush when I supply Darwin’s various theses with clarification and arguments that he never considered. When I do so … I am evaluating the propositions that Darwin endorsed; these propositions are separable from the thought processes that let him embrace those propositions. This is not anachronistic. What would be anachronistic is attributing those clarifications and arguments to the man himself. (xvi)But then Sober goes on to say in chapter 6, “I quoted the analogy that Darwin drew between beneficial mutations and the stones that fall from a cliff… Darwin’s thesis is that mutations also have their causes, but they do not occur because they would be useful to the organism in which they occur” (141). How could Darwin endorse or even articulate a proposition about mutation when he did not have the concept (Sober refers to Darwin’s view or idea about mutation throughout)? The original analogy was between beneficial variations and stones. To substitute “mutation” for “variation” (silently) in these claims changes the meaning and theoretical implications of the propositions in view. The claim that mutation does not occur because it is useful to immediate organismal needs is not identical to the claim that variation does not occur because it is useful to immediate organismal needs. For example, organisms exhibit adaptive plasticity, such as not growing as large under conditions of food scarcity, and this variation manifests in response to immediate needs (though not due to mutation). It is especially problematic to ascribe a concept of mutation to other nineteenth-century thinkers with whom Darwin was in conversation (i.e., to the community), such as Asa Gray. In fact, later in life, Gray offered a similar viewpoint to Darwin on variation (not mutation): “Incipient variations are wholly vague and irrespective of ends—are as likely to occur in the direction of unfitness as of eventual fitness to the environment” (Gray 1883: 78). Claiming that “Darwin’s idea remains the received view in modern biology” (142) runs roughshod over important differences between these two concepts, some of which are relevant to active controversies in evolutionary biology, such as the compatibility of random genetic mutation with nonrandom phenotypic variation.One final concern is that Sober has not engaged with recent work on the significance of polysemy in the terms used to represent a concept (“gene” or “species”). In chapter 7, a thought experiment about whether a particular species concept is suitable for phylogenetics leads Sober to conclude, “Maybe we need to get used to the fact that the word ‘species’ is ambiguous. The term in evolutionary biology would then be like the term ‘bank’ in ordinary English” (182). However, philosophers like Philip Haueis and Rose Novick (among others) have given positive arguments for concepts having patchwork architectures that yield an account of the interacting relationships between multiple meanings associated with a concept, thereby moving beyond a bare acknowledgment of polysemy and toward a better understanding of how and why scientific concepts behave the way they do in the context of inquiry. Multiple meanings for scientific concepts are the beginning rather than end of philosophical investigation.Criticisms aside, you should buy the box set. Sober is a great philosophical musician, and there is good reason to be familiar with his melodies and virtuoso performance. At the same time, I would encourage philosophers to expand their range and sample from a wider diversity of styles and genres available in the philosophy of (evolutionary) biology. Altogether, they are a testimony to one of the most exciting fields in contemporary philosophy, one that owes a lot to Sober’s pioneering efforts. Keep an eye out for a live concert playing near you soon.

  PublisherDOI

• D’Arcy Thompson’s Conceptual Legacy

  Biological Theory · 2025-03-26

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Comparing Building Thermal Dynamics Models and Estimation Methods for Grid-Edge Applications

  ArXiv.org · 2025-08-12

  preprintOpen access

  We need computationally efficient and accurate building thermal dynamics models for use in grid-edge applications. This work evaluates two grey-box approaches for modeling building thermal dynamics: RC-network models and structured regression models. For RC-network models, we compare parameter estimation methods including Nonlinear Least Squares, Batch Estimation, and Maximum Likelihood Estimation. We use the Almon Lag Structure with Linear Least Squares for estimating the structured regression models. The performance of these models and methods is evaluated on simulated house and commercial building data across three different simulation types.

  PublisherOA PDFDOI

• Comparing Building Thermal Dynamics Models and Estimation Methods for Grid-Edge Applications

  2025-07-27

  article

  We need efficient and accurate building thermal models for grid-edge applications. This work evaluates two grey-box approaches: Resistance-Capacitance (RC) network models and structured regression models. For RC-Networks, we compare three nonlinear optimization-based formulations. For regression models, we introduce the Almon Lag Structure (ALS) as an alternative to Linear Least Squares (LLS). We propose three different testing simulation types to accurately assess model generalization under arbitrary control policy. Results show moderately complex RC-Network models learned via Batch Estimation (BE) and Maximum Likelihood Estimation (MLE) formulations generalize the best under arbitrary control policy.

  PublisherDOI

• Measuring cell movement: Concepts and quantification

  Developmental Biology · 2025-06-03 · 1 citations

  article1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Michela Massimi

  369 shared

• John Dupré

  University of Exeter

  369 shared

• Craig Callender

  Case Western Reserve University

  365 shared

• Robert A. Wilson

  University of Western Australia

  365 shared

• David Papineau

  King's College London

  365 shared

• Thomas W. Polger

  364 shared

• Stephan Hartmann

  Ludwig-Maximilians-Universität München

  364 shared

• Brian Skyrms

  364 shared

Awards & honors

• Distinguished McKnight University Professor, Philosophy