How to Avoid Both the Repugnant and Sadistic Conclusions without Dropping Standard Axioms in Population Economics
This study investigates possibility and impossibility results of the repugnant and sadistic conclusions in population ethics and economics. The repugnant conclusion says that an enormous population with very low well-being is socially better than any smaller population with sufficiently high well-being. The sadistic conclusion says that adding individuals with negative well-being to a society is socially better than adding individuals with positive well-being to it. Previous studies have often found it challenging to avoid both undesirable conclusions. However, I demonstrate that a class of acceptable social welfare orderings can easily prevent these conclusions while adhering to standard axioms, such as anonymity, strong Pareto, Pigou-Dalton transfer, and extended continuity. Nevertheless, if the avoidance requirements for the repugnant and sadistic conclusions are strengthened, it is possible to encounter new impossibility results. These results reveal essential conflicts between the independence axiom and the avoidance of the weak repugnant conclusion when evaluating well-being profiles with different populations.
Code (0)
등록된 구현이 없습니다.
Tasks
EthicsSimilar Papers 제목 키워드 기반
Normative Conditional Reasoning as a Fragment of HOL
We report on the mechanization of (preference-based) conditional normative reasoning. Our focus is on Aqvist's system E for conditional obligation, and its extensions. Our mechanization is achieved via a shallow semantic…
EthicsPersuasivenessPhilosophyAdopting Robustness and Optimality in Fitting and Learning
We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index $\lambda$ to $-\infty$ for achieving robustness to outliers by optimizing a quasi-Minimin function. The robustness is realized …
How To Solve Moral Conundrums with Computability Theory
Various moral conundrums plague population ethics: the Non-Identity Problem, the Procreation Asymmetry, the Repugnant Conclusion, and more. I argue that the aforementioned moral conundrums have a structure neatly account…
EthicsPhilosophyA Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences
The reasoning abilities of Large Language Models (LLMs) are becoming a central focus of study in NLP. In this paper, we consider the case of syllogistic reasoning, an area of deductive reasoning studied extensively in lo…
In-Context LearningvalidWorld KnowledgeHow Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
The phenomenon of model collapse, introduced in (Shumailov et al., 2023), refers to the deterioration in performance that occurs when new models are trained on synthetic data generated from previously trained models. Thi…
Language ModelingLanguage Modelling