PhilSci Archive

Setting the demons loose: computational irreducibility does not guarantee unpredictability or emergence

Tabatabaei Ghomi, Hamed (2022) Setting the demons loose: computational irreducibility does not guarantee unpredictability or emergence. Philosophy of Science, 89. pp. 761-783.

This is the latest version of this item.

[img]
Preview
Text
setting-the-demons-loose-computational-irreducibility-does-not-guarantee-unpredictability-or-emergence (1).pdf

Download (521kB) | Preview

Abstract

A phenomenon resulting from a computationally irreducible (or computationally incompressible) process is supposedly unpredictable except via simulation. This notion of unpredictability has been deployed to formulate recent accounts of computational emergence. Via a technical analysis, I show that computational irreducibility can establish the impossibility of prediction only with respect to maximum standards of precision. By articulating the graded nature of prediction, I show that unpredictability to maximum standards is not equivalent to being unpredictable in general. I conclude that computational irreducibility fails to fulfill its assigned philosophical roles in theories of computational emergence.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Tabatabaei Ghomi, Hamed
Keywords: emergence, weak emergence, computational irreducibility, prediction, unpredictability, simulation
Subjects: Specific Sciences > Complex Systems
Specific Sciences > Computation/Information
Specific Sciences > Computer Science
Depositing User: Hamed Tabatabaei Ghomi
Date Deposited: 20 Nov 2022 16:42
Last Modified: 20 Nov 2022 16:42
Item ID: 21450
Journal or Publication Title: Philosophy of Science
Official URL: https://www.cambridge.org/core/journals/philosophy...
DOI or Unique Handle: https://doi.org/10.1017/psa.2022.5
Subjects: Specific Sciences > Complex Systems
Specific Sciences > Computation/Information
Specific Sciences > Computer Science
Date: 2022
Page Range: pp. 761-783
Volume: 89
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/21450

Available Versions of this Item

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Altmetric.com

Actions (login required)

View Item View Item