Ontology, neural networks, and the social sciences
Bibliographic Data
| ID | 10812367 |
|---|---|
| Authors | David Strohmaier (0000-0002-1430-8212, University of Cambridge, corresponding author) |
| Year | 2021 |
| Volume | 199 |
| Issue | 1-2 |
| Pages | 4775-4794 |
| Publication date | 2021-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Synthese (JOURNAL) |
| Journal identifiers | ISSN: 0039-7857 • E-ISSN: 1573-0964 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11229-020-03002-6 |
| OpenAlex | W3118001131 |
| Language | EN |
| References cited | 63 |
The ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks (ANNs) have made their way into the social sciences, raising the question whether they can avoid controversial ontological assumptions. ANNs are largely distinguished from other statistical and machine learning techniques by being a representation-learning technique. That is, researchers can let the neural networks select which features of the data to use for internal representation instead of imposing their preconceptions. On this basis, I argue that neural networks can avoid ontological assumptions to a greater degree than common statistical models in the social sciences. I then go on, however, to establish that ANNs are not ontologically innocent either. The use of ANNs in the social sciences introduces ontological assumptions typically in at least two ways, via the input and via the architecture
Artificial neural network · Cognitive science · Data science · Epistemology · Field (mathematics) · Machine learning · Ontology · Philosophy of science · Political science · Representation (politics) · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Philosophy · Psychology · Qualitative Comparative Analysis Research
The Ant Trap
Learning representations by back-propagating errors
Multilayer feedforward networks are universal approximators
Representation Learning
A logical calculus of the ideas immanent in nervous activity
The perceptron
Thumbs up?
Fragile Families and Child Wellbeing
Mastering the game of Go with deep neural networks and tree search
Prediction and explanation in social systems
Validity Issues in the Use of Social Network Analysis with Digital Trace Data
Algorithmic Decision Making and the Cost of Fairness
Measuring the predictability of life outcomes with a scientific mass collaboration
Combining satellite imagery and machine learning to predict poverty
Approximation by superpositions of a sigmoidal function
Long Short-Term Memory
Group Agency
Macrocognition
Integrating Survey Data and Digital Trace Data
Neural Networks. An Introductory Guide for Social Scientists
The ontology of social groups
What are social groups? Their metaphysics and how to classify them
Error statistical modeling and inference
Social Structures and the Ontology of Social Groups
Presidential Popularity and Presidential Vote
The Metaphysics of Social Groups
Deep learning
Improving Quantitative Studies of International Conflict
Untangling Neural Nets
Group Membership and Parthood
Neural Network Models of Religious Belief
Black-Box Models and Sociological Explanations
Translating Answers to Open-ended Survey Questions in Cross-cultural Research
Prediction and Classification with Neural Network Models
Predicting Public Corruption with Neural Networks
| Citation velocity | historical |
|---|---|
| Highly cited | No |