Dissertation Research: Methods and Causes in Social Science

这篇科学与技术研究论文改进补助金探讨了社会科学中最突出的两种因果推理方法--归纳法和演绎法。它遵循约翰·斯图尔特·密尔对因果趋势的关注，因为对这种趋势的了解对于社会政策和规划是必要的。在归纳方面，将对最近在社会学中使用的一种定性比较方法进行批判性分析。这种方法借用了布尔代数的某些基本原理，以确定一种社会现象发生的充分必要条件。由于伪相关性问题是这一方法成功应用的主要障碍，我们将从最近有关因果关系的哲学文献中探索对这一问题的几种解决方案。在演绎方面，该项目将研究在经济学和政治学中使用理性选择模型来推断原因。这种方法经常被认为是为社会科学家提供了关于社会世界中最普遍和最重要的因果趋势的知识，即那些由理性战略行动产生的趋势。然而，这需要解释具有高度限制性假设的模型如何实际描述现实世界的原因。该项目认为，这样的解释在实践中往往是不存在的。理性选择理论最成功的应用之一被普遍认为是最近在频谱拍卖设计中使用博弈论。因此，该项目将通过访问用于测试模型的实验实验室并采访拍卖，详细研究在这种情况下，如何从博弈论模型中获得稳健的因果趋势。设计师。作者将从中得出结论，以期对理性选择模型在社会科学中的普遍前景做出展望。本项目所涉及的问题具有重要的实践和理论意义。在社会科学中制定因果推理的规范性解释的现实意义是毋庸置疑的。社会科学家提出的因果关系论断经常进入一些最重要的政策决策，并在关于其适当性的辩论中广泛出现。明确不同归纳和演绎方法的方法论前提，并对其进行批判性分析，有助于揭开预测社会趋势和捍卫某些政策措施的现实面纱，从而为我们提供更严格的工具来评估社会科学及其应用。例如，所谓的理性选择革命据说使社会科学更具科学性。然而，其特有的形式严谨性和数学表达本身并不能提高我们研究原因的能力。因此，了解这一模式的潜力和局限性在社会和政治上都很重要。研究社会科学如何、能够而且应该从经验数据和理论模型中推断因果趋势的理论重要性，无论怎么强调都不为过。在物理学等自然科学中，因果推理的任务与实验实践密切相关。由于后者在社会研究的背景下并不普遍和可行，社会科学中对因果规律和机制的研究提出了科学方法统一的问题。这个项目将直接有助于解决这样一个问题，即社会科学是否应该努力开发研究社会世界的独特方法，还是应该试图模仿自然科学的受控实验。尽管它们在社会科学中很突出，但在这个项目中探索的因果推理的演绎或归纳方法都没有得到科学哲学家的足够重视。通过纠正这一疏忽，该项目将使科学哲学的智力议程更接近社会科学家的实际关切。

This Science and Technology Studies Dissertation Improvement Grant explores the two most prominent methods of causal inference in social science, the inductive and deductive approaches. It follows John Stuart Mill in focusing on causal tendencies, as knowledge of such tendencies is necessary for social policy and planning. On the inductive side, a recently proposed qualitative comparative method used in sociology will be critically analyzed. This method borrows certain basic principles of Boolean algebra in order to identify the necessary and sufficient conditions for the occurrence of a social phenomenon. Since the problem of spurious correlation is the main obstacle to the successful application of this method, several solutions to this problem drawn from the recent philosophical literature on causality will be explored. On the deductive side, the project will study the use of rational choice modeling for inferring causes in economics and political science. This method is often claimed to provide social scientists with knowledge of the most general and important causal tendencies in the social world, namely those that result from rational strategic action. Yet this requires an explanation of how models with highly restrictive assumptions can actually describe real-world causes. Such an explanation, the project argues, is in practice often absent. One of the most successful applications of rational choice theory is generally agreed to be the recent use of game theory in the design of spectrum auctions. So the project will study in detail how robust causal tendencies are obtained from game theoretical models in this case, by visiting the experimental laboratories used to test the models and interviewing the auctions. designers. The authors will draw conclusions from this for the general prospects of rational choice modeling in social science. The issues addressed in this project carry both practical and theoretical importance. The practical significance of formulating a normative account of causal inference in social science is indubitable. Causal claims made by social scientists often enter into some of the most important policy decisions and figure widely in debates about their appropriateness. Making explicit the methodological presuppositions of different inductive and deductive approaches and critically analyzing them, will help to lift the curtain on the realities of predicting social trends and defending certain policy measures, and hence give us more rigorous tools with which to evaluate social science and its applications. For example, the so-called rational choice revolution is said to have made social sciences more scientific. Yet its characteristic formal rigor and mathematical expression cannot by themselves improve our ability to study causes. It is thus important socially and politically to understand both the potential and the limitations of this paradigm. The theoretical importance of studying how social science does, can and should infer causal tendencies from empirical data and theoretical models also cannot be overemphasized. In natural sciences such as physics the task of causal inference has been closely connected with the practice of experimentation. Since the latter is not nearly as pervasive and feasible in the context of social research, the study of causal laws and mechanisms in social sciences raises the question of the unity of method in science. This project will directly contribute to the question of whether the social sciences should strive to develop distinct methods to study the social world, or instead attempt to imitate the controlled experiments of the natural sciences. Despite their prominence in social science, neither the deductive nor inductive methods of causal inference explored in this project have received sufficient attention from philosophers of science. By correcting that oversight, this project will move the intellectual agenda of philosophy of science closer to the actual concerns of social scientists.