The Logic of Law

Like other early Christian writers, Nemesius condemns any theory which denies that humans are by nature free. Though he believes that the human body is an instrument, he passionately rejects the idea that ‘humankind is a mere instrument’. He cannot tolerate any reduction of humans to the status of a tool, whether by ‘pagans’ (in theories of fate), or by Christians (in theories of providence). In this chapter, we reconstruct Nemesius’ theories of human freedom and divine providence. The bishop believes that human laws—and, hence, crime and punishment—are inconceivable in the absence of human choice. Since all cities have laws, he reasons, humans must have a natural power of choice. From this cosmopolitan line of reasoning (which has roots in Greek antiquity), Nemesius derives a subtle theory of divine world-governance in the final pages of his (unfinished) treatise.

The Social Sustainability of Infrastructure: Constructing for Justice

Socially sustainable infrastructure eliminates unfreedoms that reduce human choice and agency. These unfreedoms include the lack of clean energy, clean water, clean air, sanitation, mobility, information, or safe shelter, which collectively impact billions of people today, and the lack of a stable climate, which impacts everyone on earth and everyone who will be born in the coming decades. The built environment can be collaboratively built and collaboratively used to solve collective problems like these; in this sense, construction is a feminist project of creation. In this paper, I argue that the goal of all engineering projects and organizations must be a built environment that provides every person on the planet a greater ability to lead a life they value, recognizing that the diversity of those chosen lives is both the enabler and the outcome of what we pursue.

Imprecise Oracles Impose Limits to Predictability in Supervised Learning (Extended Abstract)

Supervised learning operates on the premise that labels unambiguously represent ground truth. This premise is reasonable in domains wherein a high degree of consensus is easily possible for any given data record, e.g. in agreeing on whether an image contains an elephant or not. However, there are several domains wherein people disagree with each other on the appropriate label to assign to a record, e.g. whether a tweet is toxic. We argue that data labeling must be understood as a process with some degree of domain-dependent noise and that any claims of predictive prowess must be sensitive to the degree of this noise. We present a method for quantifying labeling noise in a particular domain wherein people are seen to disagree with their own past selves on the appropriate label to assign to a record: choices under prospect uncertainty. Our results indicate that `state-of-the-art' choice models of decisions from description, by failing to consider the intrinsic variability of human choice behavior, find themselves in the odd position of predicting humans' choices better than the same humans' own previous choices for the same problem. We conclude with observations on how the predicament we empirically demonstrate in our work could be handled in the practice of supervised learning.

How Neurobiology Elucidates the Role of Emotions in Financial Decision-Making

Over the last 15 years, a revolution has been taking place in neuroscience, whereby models and methods of economics have led to deeper insights into the neurobiological foundations of human decision-making. These have revealed a number of widespread mis-conceptions, among others, about the role of emotions. Furthermore, the findings suggest that a purely behavior-based approach to studying decisions may miss crucial features of human choice long appreciated in biology, such as Pavlovian approach. The findings could help economists formalize elusive concepts such as intuition, as I show here for financial “trading intuition.”

To predict human choice, consider the context

Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitive processes.