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Individual Moral Judgment and Normative Evaluation

The research question of Iwata’s project is what kind of cognitive mechanism people’s moral judgments follow and how to evaluate people’s actions and public policies based on their moral judgments. To achieve this goal, his project adopts a behavioral welfare economics approach in which normative implications are derived from people’s actual actions and judgments. His project does not aim to propose a moral principle that resolves real-world moral dilemmas. Rather, it aims to provide a prescription for people’s actions and public policies under the dilemmas. Iwata will write a book on welfare economics and moral psychology and complete three papers, one of which is on evaluating choice architecture under a policymaker’s dilemma.

Public Reason and Values in Science

Public reason, which requires adopting rules justifiable to all under their authority, is essential for justice in liberal democracies. Given the role of science in rule formation, there is growing interest in connecting the idea of public reason to ongoing discussions about values in science. This project aims to tackle two foundational questions related to public reason and values in science: Should public reason regulate scientific research, and if so, in what ways? Does public reason impose duties on members of the scientific community? If so, what are the duties, and to whom do they apply?

Groups That Advise: On the Epistemology of Immunisation Advisory Groups

Expert groups are increasingly involved in policy making, this became especially clear during the COVID-19 pandemic. Although philosophers of science have done extensive work on how individual scientists should (and should not be) involved in policy making, little work has focused on groups as groups, and how they should ideally provide policy advice. The main aim of this project is to combine insights from collective epistemology with insights from the discussions on scientific policy advice to draw conclusions about how scientific advisory groups can provide effective and good scientific advice.

Idealization in Scientific Models: Controversy and a Potential Defense

Within the study of scientific models, representationalism posits that a model’s explanatory power stems from its ability to represent a target system to a certain degree. This view has gained considerable traction among scholars. However, Wang Shuyu, through concrete modeling examples, found that representationalism faces certain theoretical dilemmas when explaining idealization. He analyzes the root causes of these difficulties and draws on a non-representational perspective to interpret the role of idealization. This, however, does not imply that representationalism is entirely flawed. The two viewpoints provide a complementary framework for understanding model explanations.

Belief, Acceptance, and Pragmatic Equivalence

In his 2008 book, Scientific Representation, van Fraassen provides his ‘argument from pragmatic equivalence’ for how mathematical abstractions can represent the world: from the perspective of the scientist involved in the representation of a phenomenon using a theoretical model, there is a pragmatic equivalence between asserting that the model fits the data and that the model fits the phenomena. I defend this argument from Nguyen’s (2016) criticisms by offering my own view of the cognitive attitudes and norms involved in acceptance of scientific representations that clarifies van Fraassen’s position and retains the spirit of his empiricist structuralism.

Ethics, Neuro-aesthetics, and Evolutionary Modelling

Neuroaesthetics seeks neural correlates of beauty, surprise, and desire, including moral goodness and beauty judgments. Despite intriguing findings like the orbitofrontal cortex's involvement in moral judgment, beauty judgment, and decision-making, the neural apparatus's complexity hinders many potentially illuminating analyses. Computational evolutionary modelling offers an alternative approach. Time-evolving models using empirical data and generative heuristics create novel representations of systems or datasets. Evolutionary techniques measure the asymmetry in model agreement within an ensemble, providing automated hypothesis generation and inherent epistemic uncertainty. Propositions with certain goodness of fit interact in consensus with measurable aesthetic symmetrical qualities, while dissensus serves as an explanatory artifact for model ensemble uncertainty. The central question is: can aesthetics assist a modeler in gaining understanding?