Literature

Foundations of Complexity Science

  • Waldrop, M. M. (1993). Complexity: The emerging science at the edge of order and chaos. Simon and Schuster.

  • Feldman, D. P., & Crutchfield, J. P. (1998). Measures of statistical complexity: Why?. Physics Letters A, 238(4-5), 244-252.

  • Thurner, S., Hanel, R., & Klimek, P. (2018). Introduction to the theory of complex systems. Oxford University Press.

Information Theory on Time Series

  • Crutchfield, J. P., & Feldman, D. P. (2003). Regularities unseen, randomness observed: Levels of entropy convergence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 13(1), 25-54.

  • Crutchfield, J. P., Ellison, C. J., & Mahoney, J. R. (2009). Time’s barbed arrow: Irreversibility, crypticity, and stored information. Physical Review Letters, 103(9), 094101.

  • James, R. G., Ellison, C. J., & Crutchfield, J. P. (2011). Anatomy of a bit: Information in a time series observation. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21(3), 037109.

Transfer Entropy

  • James, R. G., Barnett, N., & Crutchfield, J. P. (2016). Information flows? A critique of transfer entropies. Physical Review Letters, 116(23), 238701.

  • Barnett, L., & Bossomaier, T. (2012). Transfer entropy as a log-likelihood ratio. Physical Review Letters, 109(13), 138105.

Partial Information Decomposition

  • Williams, P. L., & Beer, R. D. (2010). Nonnegative decomposition of multivariate information. arXiv preprint arXiv:1004.2515.

  • Griffith, V., & Koch, C. (2014). Quantifying synergistic mutual information. In Guided Self-Organization: Inception (pp. 159-190). Springer, Berlin, Heidelberg.

  • Timme, N., Alford, W., Flecker, B., & Beggs, J. M. (2014). Synergy, redundancy, and multivariate information measures: An experimentalist’s perspective. Journal of Computational Neuroscience, 36(2), 119-140.

Integrated Information Theory

  • Tononi, G., Sporns, O., & Edelman, G. M. (1994). A measure for brain complexity: Relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences, 91(11), 5033-5037.

  • Tononi, G., Edelman, G. M., & Sporns, O. (1998). Complexity and coherency: Integrating information in the brain. Trends in Cognitive Sciences, 2(12), 474-484.

  • Balduzzi, D., & Tononi, G. (2008). Integrated information in discrete dynamical systems: Motivation and theoretical framework. PLoS Computational Biology, 4(6), e1000091.

  • Barrett, A. B., & Seth, A. K. (2011). Practical measures of integrated information for time-series data. PLoS Computational Biology, 7(1), e1001052.

  • Mediano, P., Seth, A., & Barrett, A. (2019). Measuring integrated information: Comparison of candidate measures in theory and simulation. Entropy, 21(1), 17.

Computational Mechanics

  • Shalizi, C. R., & Crutchfield, J. P. (2001). Computational mechanics: Pattern and prediction, structure and simplicity. Journal of Statistical Physics, 104(3-4), 817-879.

  • Crutchfield, J. P. (1994). The calculi of emergence: Computation, dynamics and induction. Physica D: Nonlinear Phenomena, 75(1-3), 11-54.