Mark Burgess (born 19 February 1966) is an independent researcher and writer, formerly professor at Oslo University College in Norway and creator of the CFEngine software and company,[1] who is known for work in computer science in the field of policy-based configuration management.

[4] In the early 1990s, Burgess asserted that programmatic models of computer programs could not describe observed behaviour at the macroscopic scale, and that statistical physics could be used instead, thus likening artificial systems to a quasi-natural phenomenon.

In 1993, Burgess introduced the software CFEngine based in intuitions and practice, focusing on the idea of repeatable desired end-state 'convergence', to manage system configuration.

The empirical studies were published in various formats between 1999 and 2003, culminating in a journal summary review,[7] and a more practical method for automated machine learning of system behavioural characters.

[8] This incorporated the idea of so-called exponential smoothing (which was called a geometric average) for fast learning, along with a two-dimensional, cylindrical time model[9] which was based on the result that network client-server traffic would be expected to behave like a quasi-periodic stochastic function (a characteristic of a system driven close to equilibrium).

He later developed these further and made connection with Claude Shannon's work on error correction in a paper discussing how separation of timescales plays an important role in computer science, by analogy with physics.

[citation needed] Promise theory was introduced as a model of voluntary co-operation between agents, in 2004,[18] for understanding human-computer systems with complex interactions, and was later developed with Dutch computer scientist and friend Jan Bergstra into a book.

Semantic Spacetime was introduced by Mark Burgess, in a series of papers,[25][26][27] as an alternative to describing space and time, initially for Computer Science, after finding earlier models by Milner and others to be wanting.

Working with search engine researchers Geoffrey Canright and Knut Engø Monsen, Burgess developed a page ranking algorithm similar to PageRank eigenvalue sink remedies in directed graphs.

[32] With PhD Student Kyrre Begnum, he explored the related technique of Principal Component Analysis for analysing correlations in the machine-learned anomalies described above.

[39] During the 2020 pandemic, Burgess produced a “zero budget” series of three documentary films called Bigger, Faster, Smarter in which he interviewed a number of industry luminaries about the nature of processes in space and time, networks, and the future of technology.