Sterkenburg, Tom F. (2018) Universal Prediction.

Preview

Text proeffin.pdf Download (1MB) | Preview

Abstract

In this thesis I investigate the theoretical possibility of a universal method of prediction. A prediction method is universal if it is always able to learn from data: if it is always able to extrapolate given data about past observations to maximally successful predictions about future observations. The context of this investigation is the broader philosophical question into the possibility of a formal specification of inductive or scientific reasoning, a question that also relates to modern-day speculation about a fully automatized data-driven science.

I investigate, in particular, a proposed definition of a universal prediction method that goes back to Solomonoff (1964) and Levin (1970). This definition marks the birth of the theory of Kolmogorov complexity, and has a direct line to the information-theoretic approach in modern machine learning. Solomonoff's work was inspired by Carnap's program of inductive logic, and the more precise definition due to Levin can be seen as an explicit attempt to escape the diagonal argument that Putnam (1963) famously launched against the feasibility of Carnap's program.

The Solomonoff-Levin definition essentially aims at a mixture of all possible prediction algorithms. An alternative interpretation is that the definition formalizes the idea that learning from data is equivalent to compressing data. In this guise, the definition is often presented as an implementation and even as a justification of Occam's razor, the principle that we should look for simple explanations.

The conclusions of my investigation are negative. I show that the Solomonoff-Levin definition fails to unite two necessary conditions to count as a universal prediction method, as turns out be entailed by Putnam's original argument after all; and I argue that this indeed shows that no definition can. Moreover, I show that the suggested justification of Occam's razor does not work, and I argue that the relevant notion of simplicity as compressibility is already problematic itself.

---

Export/Citation:

EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL

Social Networking:

Share |

---

Item Type:	Published Article or Volume
Creators:	Creators Email ORCID Sterkenburg, Tom F. tom.sterkenburg@lmu.de 0000-0002-4860-727X
Additional Information:	PhD dissertation, University of Groningen
Subjects:	Specific Sciences > Computation/Information > Classical Specific Sciences > Artificial Intelligence General Issues > Confirmation/Induction Specific Sciences > Probability/Statistics
Depositing User:	Mr Tom Sterkenburg
Date Deposited:	14 Jan 2018 21:47
Last Modified:	14 Jan 2018 21:47
Item ID:	14186
Subjects:	Specific Sciences > Computation/Information > Classical Specific Sciences > Artificial Intelligence General Issues > Confirmation/Induction Specific Sciences > Probability/Statistics
Date:	January 2018
URI:	https://philsci-archive.pitt.edu/id/eprint/14186

Available Versions of this Item

• Universal Prediction. (deposited 14 Jan 2018 21:47) [Currently Displayed]
  • Universal Prediction. (deposited 17 Sep 2018 16:04)

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item