One of our most visible developments is the OACC, the Online Algorithmic Complexity Calculator. The OACC is an advanced tool based on several years of research devoted to a new method for evaluating and approximating the information content and algorithmic complexity of strings, particularly short ones. The OACC Complexity Calculator is constantly evolving to incorporate new and sounder ways to deal with all kinds of data (e.g. non-binary and n-dimensional, such as images).

The Online Algorithmic Complexity Calculator has been the result of our research lines mostly focused on:

  • The challenge of the calculation and evaluation of program-size complexity of finite strings, particularly short strings.
  • Devising methods and applications to areas such as cognitive sciences, psychometrics, molecular biology, physics and economics.
  • The study and evaluation of algorithmic information-theoretic measures such as Solmonoff’s algorithmic probability and Bennett’s logical depth.
  • Tradeoffs among complexity measures, notably time computational complexity, descriptive Kolmogorov-Chaitin complexity and fractal dimension.
  • The investigation of all forms of computational irreducibility and unpredictability.
  • The challenge of dealing with uncomputable frequency distributions (in connection to Levin’s Universal distribution).
  • The comparison among as many different models of computation as possible–such as deterministic Turing machines, n-dimensional cellular automata, Post tag systems, rewriting systems, SK combinatorics, register machines, finite automata, etc.–and physical empirical datasets of information such as images, sounds, DNA sequences and so on.
  • The study of the distribution of runtimes and applications (for example, in mathematical logic and theorem proving).
  • The qualitative behaviour of small simple computer programs in relation to information content and time-space complexity.