Carnapian Inductive Logic for Exponential Smoothing
Bibliographic Data
| ID | 19216014 |
|---|---|
| Authors | Simon M Huttegger (University of California, Irvine, corresponding author) |
| Year | 2026 |
| Volume | 93 |
| Issue | 3 |
| Pages | 1-24 |
| Publication date | 2026-04-13 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0031-8248 • E-ISSN: 1539-767X |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psa.2026.10197 |
| OpenAlex | W7154014207 |
| Language | EN |
| References cited | 29 |
This paper explores the inductive logic associated with exponential smoothing, the most widely used predictive rule that manifests the idea that more recent observations have a stronger influence on predictive probabilities than more remote ones. The main result shows that exponential smoothing can be derived from a set of plausible qualitative invariance assumptions about conditional probabilities. I discuss various aspects of the resulting inductive logic, including its connections to exchangeable processes, to Bayesian predictive inference and kernel methods in machine learning, as well as the philosophy of probabilistic invariance conditions and symmetries
Axiom · Bayesian probability · Exponential function · Exponential smoothing · Inductive reasoning · Inference · Probabilistic logic · Set (abstract data type) · Bayesian Modeling and Causal Inference · Explainable Artificial Intelligence (XAI · Philosophy and History of Science
| Citation velocity | historical |
|---|---|
| Highly cited | No |