It is often thought that the vindication of experimental work lies in its capacity to be revelatory of natural systems. I challenge this idea by examining laboratory experiments in ecology. A central task of community ecology involves combining mathematical models and observational data to identify trophic interactions in natural systems. But many ecologists are also lab scientists: constructing microcosm or ‘bottle’ experiments, physically realizing the idealized circumstances described in mathematical models. What vindicates such ecological experiments? I argue that ‘extrapolationism’, the (...) view that ecological lab work is valuable because it generates truths about natural systems, does not exhaust the epistemic value of such practices. Instead, bottle experiments also generate ‘understanding’ of both ecological dynamics and empirical tools. Some lab work, then, aids theoretical understanding, as well as targeting hypotheses about nature. 1Introduction2Trophic Interactions and Observational Techniques3Cryptic Dynamics in Bottle Experiments4Extrapolationism 4.1Ecological possibility and actuality4.2Ecological heterogeneity5Understanding 5.1The epistemic good of understanding5.2Bottle experiments as understanding-generators5.3How understanding travels6Conclusion. (shrink)

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that (...) our account could be fruitfully used in appraising other models in philosophy and science. (shrink)

The practice of model-building is very common in analytic philosophical theology. Yet many other theologians worry that any attempt to model God must be hubristic and idolatrous. A better understanding of scientific modeling can set the stage for a more fruitful engagement between analytic theologians and their critics. I first present an account of scientific modeling that draws on recent work in the philosophy of science. I then apply that account to a prominent analytic model of the trinity, Michael Rea (...) and Jeffrey Brower’s “material constitution model.” I argue that modeling – whether scientific or theological – need not be understood as a hubristic enterprise. A model does not always try to grasp its target at all, let alone grasp it fully and completely. Even theologians who are committed to a strong doctrine of divine mystery can therefore find value in analytic modeling. (shrink)

Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare the positions of these (...) two views with respect to four points regarding ontological assumptions: (1) the origin of ontological assumptions, (2) the relation of such assumptions to the formal models of the same theory, (3) their use in integrating and negotiating different formal models of distinct theories, and (4) their employment in explanatory activity. Dialectical is here used in both its Hegelian–Marxist sense of opposition and tension between alternative positions and in its Platonic sense of dialogue between advocates of distinct theories. I investigate three case studies, from Levins and Lewontin as well as from a recent paper of mine, that show the relevance and power of the dialectical understanding of theorizing and modeling. (shrink)

It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..

In this paper, I consider the recent resurgence of “evolutionary economics”—the idea that evolutionary theory can be very useful to push forward key debates in economics—and assess the extent to which it rests on a plausible foundation. To do this, I first distinguish two ways in which evolutionary theory can, in principle, be brought to bear on an economic problem—namely, evidentially and heuristically—and then apply this distinction to the three major hypotheses that evolutionary economists have come to defend: the implausibility (...) of rational choice theory as an account of economic rationality, the idea that firms are autonomous economic agents, and the need for a more dynamic, less equilibrium-focused economic methodology. In each of these cases, I conclude negatively: the relevant evolutionary considerations neither suggest interesting and novel hypotheses to investigate further nor are backed up by the needed data to constitute genuine evidence. I end by distinguishing this criticism of evolutionary economics from others that have been put forward in the literature: in particular, I make clear that, unlike those of other critics, the arguments of this paper are based on epistemic—not structural—considerations and therefore leave more room for a plausible form of evolutionary economics to come about in the future. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system. Furthermore, in many contexts, it is in virtue of (...) their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach. (shrink)

Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...) on the nature of models and modeling, the unique issues surrounding this kind of interdisciplinary model building have yet to be independently investigated. In this paper, we utilize evolutionary economics as a case study in the investigation of more general issues concerning interdisciplinary modeling. We begin by critiquing the distinctions currently used within the evolutionary economics literature and propose an alternative carving of the conceptual terrain. We then argue that the three types of evolutionary economics we distinguish capture distinctions that will be important whenever resources of model-based theorizing are borrowed across distinct scientific domains. Our analysis of these model-building strategies identifies several of the unique methodological and philosophical issues that confront interdisciplinary modeling. (shrink)

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.

The aim of this paper is to develop ideas about robustness analyses. I introduce a form of robustness analysis that I call sufficient parameter robustness, which has been neglected in the literature. I claim that sufficient parameter robustness is different from derivational robustness, the focus of previous research. My purpose is not only to suggest a new taxonomy of robustness, but also to argue that previous authors have concentrated on a narrow sense of robustness analysis, which they have inadequately distinguished (...) from other investigations of models such as sensitivity analysis. (shrink)

In addition to their core explanatory and predictive assumptions, scientific models include simplifying assumptions, which function as idealizations, approximations, and abstractions. There are methods to investigate whether simplifying assumptions bias the results of models, such as robustness analyses. However, the equally important issue – the focus of this paper – has received less attention, namely, what are the methodological and epistemic strengths and limitations associated with different simplifying assumptions. I concentrate on one type of simplifying assumption, the use of mega (...) parameters as abstractions in ecological models. First, I argue that there are two kinds of mega parameters qua abstractions, sufficient parameters and aggregative parameters, which have gone unnoticed in the literature. The two are associated with different heuristics, holism and reductionism, which many view as incompatible. Second, I will provide a different analysis of abstractions and the associated heuristics than previous authors. Reductionism and holism and the accompanying abstractions have different methodological and epistemic functions, strengths, and limitations, and the heuristics should be viewed as providing complementary research perspectives of cognitively limited beings. This is then, third, used as a premise to argue for epistemic and methodological pluralism in theoretical ecology. Finally, the presented taxonomy of abstractions is used to comment on the current debate whether mechanistic accounts of explanation are compatible with the use of abstractions. This debate has suffered from an abstract discussion of abstractions. With a better taxonomy of abstractions the debate can be resolved. (shrink)

Mathematical models of biological patterns are central to contemporary biology. This paper aims to consider what these models contribute to biology through the detailed consideration of an important case: Hamilton’s selfish herd. While highly abstract and idealized, Hamilton’s models have generated an extensive amount of research and have arguably led to an accurate understanding of an important factor in the evolution of gregarious behaviors like herding and flocking. I propose an account of what these models are able to achieve and (...) how they can support a successful scientific research program. I argue that the way these models are interpreted is central to the success of such programs. (shrink)

Frigg and Reiss (2009) argue that philosophical problems in simulation bear enough resemblance to recognized issues in the philosophy of modeling that they only pose challenges analogous to those found in standard analytic models used to represent natural systems. They suggest that there are no new philosophical problems in computer simulation modeling beyond those found in traditional mathematical modeling. Winsberg (2009) has countered that there appear to be genuinely new epistemological problems in simulation modeling because the knowledge obtained from them (...) is ‘downward, motley, and autonomous.’ Here I draw out some specific ways that these epistemological problems are manifest in complex ecological simulation, especially in agent-based models. These models contain novel features that were impossible to anticipate prior to the computer revolution, and continue to present difficulties and challenges of both a practical and philosophical nature (Humphreys 2002). (shrink)

Anti-exceptionalists about logic maintain that it is continuous with the empirical sciences. Taking anti-exceptionalism for granted, we argue that traditional approaches to explanation are inadequate in the case of logic. We argue that Andrea Woody's functional analysis of explanation is a better fit with logical practice and accounts better for the explanatory role of logical theories.

In this essay, I critically evaluate components of Michael Weisberg’s approach to models and modeling in his book Simulation and Similarity. First, I criticize his account of the ontology of models and mathematics. Second, I respond to his objections to fictionalism regarding models arguing that they fail. Third, I sketch a deflationary approach to models that retains many elements of his account but avoids the inflationary commitments.

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. (shrink)

Science and Engineering Ethics, to appear.

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has (...) been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation. (shrink)

Ecological neutral theory has been controversial as an alternative to niche theory for explaining community structure. Neutral theory, which explains community structure in terms of ecological drift, is frequently charged with being unrealistic, but commentators have usually not provided an account of theory or model realism. In this paper, I propose a framework for comparing the “realism” or accuracy of alternative theories within a domain with respect to the extent to which the theories abstract and idealize. Using this framework I (...) argue, contrary to most previous commentators, that neutral and niche theories are similarly realistic. Realism cannot provide a basis for accepting or rejecting either type of theory; instead, community ecologists should continue working with a plurality of models. While theoretical unification may become possible, we should treat a plurality of complementary, partial models as the expected situation within community ecology. (shrink)

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...) which idealisations are truly harmful. (shrink)

The world’s leading environmental advisory institutions look to ecological theory and research as an objective guide for policy and resource management decision-making. In addition to various theoretical merits of doing so, it is therefore crucially important to clear up confusions about ecology’s conceptual foundations and to make plain the basic workings of inferential methods used in the science. Through discussion of key moments in the genesis of the theoretical branch of ecology, this essay elucidates a general heuristic role of teleological (...) metaphor in ecological research and defuses certain enduring confusions and misguided criticisms of current work in ecology. (shrink)

Methodological individualism has a long, successful, and controversial track record in the social sciences. Its record in ecology is much shorter but proving as successful and controversial with so-called individual-based models. Distinctions and debates about methodological individualism in social sciences clarify the commitments of this general, individualistic approach to modeling ecological phenomena and show that there is a lot recommending it. In particular, a representational priority on individual organisms yields a cogent albeit deflationary account of ecological emergence and helps reveal (...) how quite disparate models and theories in ecology might be unified. (shrink)

In a (2016) paper in this journal, I defuse allegations that theoretical ecological research is problematic because it relies on teleological metaphysical assumptions. Mark Sagoff offers a formal reply. In it, he concedes that I succeeded in establishing that ecologists abandoned robust teleological views long ago and that they use teleological characterizations as metaphors that aid in developing mechanistic explanations of ecological phenomena. Yet, he contends that I did not give enduring criticisms of theoretical ecology a fair shake in my (...) paper. He says this is because enduring criticisms center on concerns about the nature of ecological networks and forces, the instrumentality of ecological laws and theoretical models, and the relation between theoretical and empirical methods in ecology that that paper does not broach. Below I set apart the distinct criticisms Sagoff presents in his commentary and respond to each in turn. (shrink)

Some ecologists have argued that theoretical model building in population and community ecology has gone evidentially unconstrained. In the essay, I argue that "bottle experiments" offer ecological model building evidential constraints and illustrate this by considering work on chaotic models tested by the dynamics of flour beetles. Critics reply that these experiments are importantly unlike nonmanipulated natural systems and thus do not constitute genuine tests of the models. I conclude by considering two responses to this worry and a suggestion on (...) how to move forward on this debate. (shrink)

The cis-regulatory hypothesis is one of the most important claims of evolutionary developmental biology. In this paper I examine the theoretical argument for cis-regulatory evolution and its role within evolutionary theorizing. I show that, although the argument has some weaknesses, it acts as a useful example for the importance of current scientific debates for science education.

The inclusion of the practice of “developing and using models” in the Framework for K-12 Science Education and in the Next Generation Science Standards provides an opportunity for educators to examine the role this practice plays in science and how it can be leveraged in a science classroom. Drawing on conceptions of models in the philosophy of science, we bring forward an agent-based account of models and discuss the implications of this view for enacting modeling in science classrooms. Models, according (...) to this account, can only be understood with respect to the aims and intentions of a cognitive agent, not solely in terms of how they represent phenomena in the world. We present this contrast as a heuristic—models of versus models for—that can be used to help educators notice and interpret how models are positioned in standards, curriculum, and classrooms. (shrink)

Evolutionary biology, indeed any science that attempts to reconstruct prehistory, faces practical limitations on available data. These limitations create the problem of contrast failure: specific observations may fail to discriminate between rival evolutionary hypotheses. Assessing the risk of contrast failure provides a way to evaluate testing protocols in evolutionary science. Here I will argue that part of the methodological critique in the Spandrels paper involves diagnosing contrast failure problems. I then distinguish the problem of contrast failure from the more familiar (...) philosophical problem of underdetermination, and demonstrate how contrast failure arises in scientific practice with an investigation into Lewontin and White’s estimation of an adaptive landscape. (shrink)

Several prominent philosophers of science, most notably Ron Giere, propose that scientific theories are collections of models and that models represent the objects of scientific study. Some, including Giere, argue that models represent in the same way that pictures represent. Aestheticians have brought the picturing relation under intense scrutiny and presented important arguments against the tenability of particular accounts of picturing. Many of these arguments from aesthetics can be used against accounts of representation in philosophy of science. I rely on (...) Dominic Lopes' recent summary of arguments against various views of picturing and reformulate some of them to fit the philosophy of science context. My specific targets here are Giere and Steven French. I go on to argue that assuming all scientific models and images represent in the same way is not the best guide to understanding scientific practice. (shrink)

Value claims about ecological entities, their functionality, and properties take center stage in so-called “ecological” ethical and aesthetic theories. For example, the claim that the biodiversity in an old-growth forest imbues it with “value in and for itself” is an explicit value claim about an ecological property. And the claim that one can study “the aesthetics of nature, including natural objects...such as ecosystems” presupposes that natural instances of a type of ecological entity exist and can be regarded as more or (...) less aesthetically valuable. My discussion below will bear wide implications for how claims like... (shrink)

Sagoff (2016) criticizes widely used “theoretical” methods in ecology; arguing that those methods employ models that rely on problematic metaphysical assumptions and are therefore uninformative and useless for practical decision-making. In this paper, I show that Sagoff misconstrues how such model-based methods work in practice, that the main threads of his argument are problematic, and that his substantive conclusions are consequently unfounded. Along the way, I illuminate several ways the model-based inferential methods he criticizes can be, and have been, usefully (...) informative. (shrink)

This inaugural handbook documents the distinctive research field that utilizes history and philosophy in investigation of theoretical, curricular and pedagogical issues in the teaching of science and mathematics. It is contributed to by 130 researchers from 30 countries; it provides a logically structured, fully referenced guide to the ways in which science and mathematics education is, informed by the history and philosophy of these disciplines, as well as by the philosophy of education more generally. The first handbook to cover the (...) field, it lays down a much-needed marker of progress to date and provides a platform for informed and coherent future analysis and research of the subject. -/- The publication comes at a time of heightened worldwide concern over the standard of science and mathematics education, attended by fierce debate over how best to reform curricula and enliven student engagement in the subjects There is a growing recognition among educators and policy makers that the learning of science must dovetail with learning about science; this handbook is uniquely positioned as a locus for the discussion. -/- The handbook features sections on pedagogical, theoretical, national, and biographical research, setting the literature of each tradition in its historical context. Each chapter engages in an assessment of the strengths and weakness of the research addressed, and suggests potentially fruitful avenues of future research. A key element of the handbook’s broader analytical framework is its identification and examination of unnoticed philosophical assumptions in science and mathematics research. It reminds readers at a crucial juncture that there has been a long and rich tradition of historical and philosophical engagements with science and mathematics teaching, and that lessons can be learnt from these engagements for the resolution of current theoretical, curricular and pedagogical questions that face teachers and administrators. (shrink)

Conservation biology emerged as an organized academic discipline in the United States in the 1980s though much of its theoretical framework was originally developed in Australia. Significant differences of approach in the two traditions were resolved in the late 1990s through the formulation of a consensus framework for the design and adaptive management of conservation area networks. This entry presents an outline of that framework along with a critical analysis of conceptual issues concerning the four theoretical problems that emerge from (...) it: (i) place prioritization for conservation action; (ii) the selection of surrogates for biodiversity in conservation planning; (iii) the assessment of vulnerability of conservation areas; and (iv) the synchronization of incommensurable criteria including socio-economic constraints on conservation planning. (shrink)

The central aim of science is to make sense of the world. To move forward as a community endeavor, sense-making must be systematic and focused. The question then is how do scientists actually experience the sense-making process? In this chapter we examine the “practice turn” in science studies and in particular how as a result of this turn scholars have come to realize that models are the “functional unit” of scientific thought and form the center of the reasoning/sense-making process. This (...) chapter will explore a context-dependent view of models and modeling in science. From this analysis we present a framework for delineating the different aspects of model-based reasoning and describe how this view can be useful in educational settings. This framework highlights how modeling supports and focuses scientific practice on sense-making. (shrink)

This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an innovative research program which combines findings and modelling tools from economics, psychology and neuroscience to account for human choice behaviour. The proponents of Neuroeconomics often manifest the ambition to foster radical modifications in the accounts of choice behaviour developed by its parent disciplines. This enquiry provides a philosophically informed appraisal of the potential for success and the relevance of neuroeconomic research for economics. My central claim is that neuroeconomists can (...) help other economists to build more predictive and explanatory models, yet are unlikely to foster revolutionary modifications in the economic theory of choice. The contents are organized as follows. In chapters 1-2, I present neuroeconomists’ investigative tools, distinguish the most influential approaches to neuroeconomic research and reconstruct the case in favour of a neural enrichment of economic theory. In chapters 3-7, I combine insights from neuro-psychology, economic methodology and philosophy of science to develop a systematic critique of Neuroeconomics. In particular, I articulate four lines of argument to demonstrate that economists are provisionally justified in retaining a methodologically distinctive approach to the modelling of decision making. My first argument points to several evidential and epistemological concerns which complicate the interpretation of neural data and cast doubt on the inferences neuroeconomists often make in their studies. My second argument aims to show that the trade-offs between the modelling desiderata that neuroeconomists and other economists respectively value severely constrain the incorporation of neural insights into economic models. My third argument questions neuroeconomists’ attempts to develop a unified theory of choice behaviour by identifying some central issues on which they hold contrasting positions. My fourth argument differentiates various senses of the term ‘revolution’ and illustrates that neuroeconomists are unlikely to provide revolutionary contributions to economic theory in any of these senses. (shrink)

Ecology has gradually gained salience during the last few decades and ecological issues, including land use changes, global warming, biodiversity loss, food shortage, and so forth, seem to be gaining public attention. Though philosophers of science had given little attention to ecology, there is a lot of interesting work being currently pursued in philosophy of ecology and environmental philosophy. As Colyvan and colleagues put it, “ecology is an important and fascinating branch of biology, with distinctive philosophical issues” (Colyvan et al. (...) 2009, p. 21). Given its conceptual and methodological familiarity with the social sciences, ecology occupies a unique position among other disciplines (Cooper 2003). (shrink)

All economic models involve abstractions and idealisations. Economic theory itself does not tell which idealizations are truly fatal or harmful for the result and which are not. This is why much of what is seen as theoretical contribution in economics is constituted by deriving familiar results from different modelling assumptions. If a modelling result is robust with respect to particular modelling assumptions, the empirical falsity of these particular assumptions does not provide grounds for criticizing the result. In this paper we (...) demonstrate how derivational robustness analysis does carry epistemic weight and answer criticism concerning its non-empirical nature and the problematic form of the required independence of the ways of derivation. The epistemic rationale and importance of robustness analysis also challenge some common conceptions of the role of theory in economics. (shrink)

In Economics Rules, Dani Rodrik (2015) argues that what makes economics powerful despite the limitations of each and every model is its diversity of models. Rodrik suggests that the diversity of models in economics improves its explanatory capacities, but he does not fully explain how. I offer a clearer picture of how models relate to explanations of particular economic facts or events, and suggest that the diversity of models is a means to better economic explanations.