This article is part of a symposium (in Society) on a target article by Amitai Etzioni. Using that article as a point of departure, I take the opportunity to elaborate a reading of Adam Smith’s moral philosophy that sees it as quite non-foundationalist. Whereas foundationalism’s metaphor is a block or pillar, as non-foundationalism’s metaphor I suggest a spiral. I claim that non-foundationalism and Smithian liberalism dovetail.
I develop an interpretation of reason using the thought of David Hume and Adam Smith. I contend that reason in Hume and Smith can plausibly be interpreted as a kind of sensation. Reason is a sensation in that it is a sentimental conception of the relationship between two objects that impels certain interpretations. Reason is developed sympathetically in experiential contexts that not only guide but constitute reason’s operation. I comment on Hume’s talk of reason in his Treatise of Human Nature to build my interpretation. I use Smith’s work in The Theory of Moral Sentiments to develop an understanding of the sympathetic formation of reason. I briefly integrate my interpretation with talk of confirmatory bias in psychology and behavioral economics. I conclude by considering implications for scientific conversation.
This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion.