The current view of the relationship between areas of the mind-brain sciences is one where cross-disciplinary collaboration is required to advance claims about the mind-brain that stand on firm epistemic footing. My goal in this dissertation is to analyze what it means for information from different areas of science to fit together to produce strong epistemic claims by addressing how and to what extent claims about the mind-brain are corroborated in scientific practice. Philosophers of science have advanced various concepts of (...) the notion of fitting together information from different areas of science and its relation to scientific progress (e.g., Bickle, 1998, 2003, 2006; Darden and Maull, 1977; Mitchell, 2002, 2003; Mitchell and Dietrich, 2006; Nagel 1949, 1970, 1979; Piccinini and Craver, 2011; and Roskies, 2010). However, each concept of fitting together is vague and subject to multiple clarificatory questions. To get a handle on the notion of fitting together, I introduce the term ‘interdisciplinary corroboration’ as a placeholder for the various accounts of fitting together to facilitate my investigation of how claims about the mind-brain are corroborated in scientific practice. I argue review papers are a good place to begin analyzing interdisciplinary corroboration; accordingly, I conduct a two-part analysis of a review paper by Eichenbaum (2013) entitled ‘Memory on Time’. I use the lessons from my analysis to develop and advance a methodology for philosophers of science interested in knowledge production for evaluating review papers for corroboration in the mind-brain sciences. (shrink)

We highlight the limitations of formal methods by exhibiting two results in recursive function theory: that there is no effective means of finding a program that satisfies a given formal specification; or checking that a program meets a specification. We also exhibit a ‘simple’ MAS which has all the power of a Turing machine. We then argue that any ‘pure design’ methodology will face insurmountable difficulties in today’s open and complex MAS. Rather we suggest a methodology based on the classic (...) experimental method – that is ‘scientific foundations’ for the construction and control of complex MAS. (shrink)

When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference ­ a simple model of (...) the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there are (at least) two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actually applies (the `internal' approach); or (if this is feasible) one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down (or `foundationalist') approach where one tries to lay down general, a priori principles and a bottom-up (or `scientific') approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment. It ends with a plea for the cooperation of the AI and Machine Learning communities as both learning and inference is needed if context is to make complete sense. (shrink)

Although the role of morphology in evolutionary theory remains a subject of debate, assessing the contributions of morphological investigation to evolutionary developmental biology (Evo-devo) is a more circumscribed issue of direct relevance to ongoing research. Historical studies of morphologically oriented researchers and the formation of the Modern Synthesis in the Anglo-American context identify a recurring theme: the synthetic theory of evolution did not capture multiple levels of biological organization. When this feature is incorporated into a philosophical framework for explaining the (...) origin of evolutionary innovations and novelties (a core domain of inquiry in Evo-devo) two specific roles for morphology can be described: (1) the conceptualization and operational identification of the targets of explanation; and (2) the elucidation of causal interactions at higher levels of organization during ontogeny and through evolutionary time. These roles are critical components of any adequate explanation of innovation and novelty though not exhaustive of the parts played by morphology in evolutionary investigation. They also invite reflection on what counts as an evolutionary cause in contemporary evolutionary biology. (shrink)

This chapter provides an introduction to the study of the philosophical notions of mechanisms and causality in biology and economics. This chapter sets the stage for this volume, Mechanism and Causality in Biology and Economics, in three ways. First, it gives a broad review of the recent changes and current state of the study of mechanisms and causality in the philosophy of science. Second, consistent with a recent trend in the philosophy of science to focus on scientific practices, it in (...) turn implies the importance of studying the scientific methods employed by researchers. Finally, by way of providing an overview of each chapter in the volume, this chapter demonstrates that biology and economics are two fertile fields for the philosophy of science and shows how biological and economic mechanisms and causality can be synthesized. (shrink)

This is a thesis about interdisciplinarity, scientific integration, and problem solving in sustainability science. Sustainability science is an emerging and highly interdisciplinary field that seeks to integrate vastly differentiated bodies of knowledge in addressing the challenge of transitioning contemporary societies towards sustainability. Interdisciplinarity is paramount. Interdisciplinarity in general, and in the context of sustainability science in particular, has often been associated with solving particular problems and problem solving is one important theme in this thesis. A central idea that is developed (...) is that of problem-feeding. Sometimes problems arise within one discipline that can only be solved with the help of another. This concept, that has predecessors in e.g. the work of Lindley Darden and Nancy Maull, is explored considerably. It is argued that in interdisciplinary contexts—such as sustainability science—where collaboration is sought it is important to maintain cross-boundary problem stability. That is to say, as the problem is transferred from one discipline to another transformations will often be necessary. These transformations then, need to be acceptable to all involved parties to maintain an active interdisciplinary connection. Another topic that is discussed both in the introductory essay and some of the papers included is that of scientific imperialism. Scientific imperialism—the infringement of one discipline upon the domain of another—is here suggested to be primarily a threat to in-terdisciplinary collaborations. A distinction is introduced between imperialist failures of expansionism and failures of replacement. These are labelled type-I and type-II imperialism respectively. Particular attention is devoted to the latter form. Type-II imperialism concerns cases where imperialist infringements fail as the imperial- izing framework replaces viable, or compatible alternatives. Such an error of replacement does, importantly, not imply that the framework or theory should be disregarded completely. This type of imperialist error can both be quite subtle, and damaging. For one, if one directs the attention to specific contexts knowledge is actually lost in the process. This is particularly serious in fields such as sustainability science that are, to such an large extent, aimed at influencing concrete policy. Finally, interdisciplinarity is difficult to achieve and in many cases represents a grand challenge in itself. There are however many different ways in which interdisciplinarity may be accomplished and different forms are suitable in different contexts. In a field such as sustainability science where complexity is such a prevalent feature, an inclusive, pluralist, approach is likely to be appropriate. (shrink)

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting a putative cause (...) and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences. (shrink)

Two different reasons for using agents are distinguished: the `engineering' perspective and the `social simulation' perspective. It is argued that this entails some differences in approach. In particular the former will want to prevent unpredictable emergent features of their agent populations whilst the later will want to use simulation to study precisely this phenomena. A concept of `social embeddedness' is explicated which neatly distinguishes the two approaches. It is argued that such embedding in a society is an essential feature of (...) being a truly social agent. This has the consequence that such agents will not sit well within an `engineering' methodology. (shrink)

Biochemists often adopt what may be called the “Strategy of Decomposition” for the causal discovery of biochemical pathway dynamic behaviours. This involves decomposing a pathway into a set of isolated parts, which are then analysed separately. It is assumed that knowledge gained of the isolated parts can then be used to explain the dynamic behaviours of the whole pathway. My thesis addresses the extent to which use of the Strategy of Decomposition is warranted. I evaluate two challenges contained in Bechtel (...) and Richardson’s Discovering Complexity. The first challenge is that pathways lack the ‘modular’ structure assumed in the Strategy of Decomposition. Bechtel and Richardson take biochemists to use a concept of modularity called ‘near decomposability’. The second challenge is that pathways have ‘Pathway Emergent’ behaviours. I reject both challenges. I show that near decomposability is the wrong type of modularity to apply to pathways, and that the occurrence of Pathway Emergence has not been established. I argue that an underlying problem with Bechtel and Richardson’s analyses is that they overstate the consequences of feedback and nonlinearity for the Strategy of Decomposition. Instead, the analysis of pathway modularity and emergence needs to be centered on the context-sensitivity of pathways’ ‘local causal laws’. I identify that the type of modularity assumed in the Strategy of Decomposition is ‘causal law modularity’, which requires the invariance of local causal laws. I also identify a necessary condition for Pathway Emergence: a pathway must manifest at least one local causal law that is not manifested by its isolated parts. I argue that the use of the Strategy of Decomposition may often be unwarranted. This is because the local causal laws of pathways are highly context-sensitive, and pathways might often not be causal law modular. This context-sensitivity also leaves open the possibility of Pathway Emergence. (shrink)

Most philosophical debate over mental causation has been concerned with reconciling commonsense intuitions that there are causal interactions between the mental and the physical with philosophical theories of the nature of the mental that seem to suggest otherwise. My concern is with a different and more practical problem. We often confront some cognitive, affective, or bodily phenomenon, and wonder about its source – its etiology or its underlying causal basis. For instance, you might wonder whether your queasiness due to something (...) you ate, or whether it is just nervousness, or whether your aunt’s memory loss is a neurological problem or a psychological response to trauma. Such questions attempt to localize the causes of a phenomenon at some level in the complex multi-level systems that we human animals are. In this paper I will attempt to tease out the sense of level implicit in such questions, and to show how it is related to current mechanistic accounts of levels. I will argue that the explanation of our practices of level attribution is deeply pragmatic. Such attributions are often attempts to locate the causes of problems, and to identify interventions that could solve those problems. (shrink)

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)