IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002236.html
   My bibliography  Save this article

Integrated Information Increases with Fitness in the Evolution of Animats

Author

Listed:
  • Jeffrey A Edlund
  • Nicolas Chaumont
  • Arend Hintze
  • Christof Koch
  • Giulio Tononi
  • Christoph Adami

Abstract

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent (“animat”) evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its “fit” to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data. Author Summary: Intelligent behavior encompasses appropriate navigation in complex environments that is achieved through the integration of sensorial information and memory of past events to create purposeful movement. This behavior is often described as “complex”, but universal ways to quantify such a notion do not exist. Promising candidates for measures of functional complexity are based on information theory, but fail to take into account the important role that memory plays in complex navigation. Here, we study a different information-theoretic measure called “integrated information”, and investigate its ability to reflect the complexity of navigation that uses both sensory data and memory. We suggest that measures based on the integrated-information concept correlate better with fitness than other standard measures when memory evolves as a key element in navigation strategy, but perform as well as more standard information processing measures if the robots navigate using a purely reactive sensor-motor loop. We conclude that the integration of information that emanates from the sensorial data stream with some (short-term) memory of past events is crucial to complex and intelligent behavior and speculate that integrated information–to the extent that it can be measured and computed–might best reflect the complexity of animal behavior, including that of humans.

Suggested Citation

  • Jeffrey A Edlund & Nicolas Chaumont & Arend Hintze & Christof Koch & Giulio Tononi & Christoph Adami, 2011. "Integrated Information Increases with Fitness in the Evolution of Animats," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-13, October.
    • Handle: RePEc:plo:pcbi00:1002236
    DOI: 10.1371/journal.pcbi.1002236
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002236
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002236&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1002236?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. William McGill, 1954. "Multivariate information transmission," Psychometrika, Springer;The Psychometric Society, vol. 19(2), pages 97-116, June.
    2. N. Ay & N. Bertschinger & R. Der & F. Güttler & E. Olbrich, 2008. "Predictive information and explorative behavior of autonomous robots," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(3), pages 329-339, June.
    3. Adam B Barrett & Anil K Seth, 2011. "Practical Measures of Integrated Information for Time-Series Data," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-18, January.
    4. Jack W. Szostak, 2003. "Functional information: Molecular messages," Nature, Nature, vol. 423(6941), pages 689-689, June.
    5. Richard E. Lenski & Charles Ofria & Robert T. Pennock & Christoph Adami, 2003. "The evolutionary origin of complex features," Nature, Nature, vol. 423(6936), pages 139-144, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Takayuki Niizato & Kotaro Sakamoto & Yoh-ichi Mototake & Hisashi Murakami & Takenori Tomaru & Tomotaro Hoshika & Toshiki Fukushima, 2020. "Finding continuity and discontinuity in fish schools via integrated information theory," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    2. Max Tegmark, 2016. "Improved Measures of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-34, November.
    3. Masafumi Oizumi & Larissa Albantakis & Giulio Tononi, 2014. "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-25, May.
    4. Masafumi Oizumi & Shun-ichi Amari & Toru Yanagawa & Naotaka Fujii & Naotsugu Tsuchiya, 2016. "Measuring Integrated Information from the Decoding Perspective," PLOS Computational Biology, Public Library of Science, vol. 12(1), pages 1-18, January.
    5. David Engel & Thomas W Malone, 2018. "Integrated information as a metric for group interaction," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:hig:wpaper:98sti2019 is not listed on IDEAS
    2. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    3. Park, Han Woo & Leydesdorff, Loet, 2010. "Longitudinal trends in networks of university-industry-government relations in South Korea: The role of programmatic incentives," Research Policy, Elsevier, vol. 39(5), pages 640-649, June.
    4. Songyot Nakariyakul, 2019. "A hybrid gene selection algorithm based on interaction information for microarray-based cancer classification," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-17, February.
    5. Louis Verny & Nadir Sella & Séverine Affeldt & Param Priya Singh & Hervé Isambert, 2017. "Learning causal networks with latent variables from multivariate information in genomic data," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-25, October.
    6. Xiaojun Hu & Xian Li & Ronald Rousseau, 2021. "Mathematical reflections on Triple Helix calculations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8581-8587, October.
    7. Inga A. Ivanova & Loet Leydesdorff, 2014. "A simulation model of the Triple Helix of university–industry–government relations and the decomposition of the redundancy," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 927-948, June.
    8. Loet Leydesdorff & Han Woo Park & Balazs Lengyel, 2014. "A routine for measuring synergy in university–industry–government relations: mutual information as a Triple-Helix and Quadruple-Helix indicator," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(1), pages 27-35, April.
    9. Mariusz Kubkowski & Jan Mielniczuk, 2021. "Asymptotic Distributions of Empirical Interaction Information," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 291-315, March.
    10. Masafumi Oizumi & Larissa Albantakis & Giulio Tononi, 2014. "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-25, May.
    11. Loet Leydesdorff, 2011. "“Structuration” by intellectual organization: the configuration of knowledge in relations among structural components in networks of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 499-520, August.
    12. Dimitris Iliopoulos & Arend Hintze & Christoph Adami, 2010. "Critical Dynamics in the Evolution of Stochastic Strategies for the Iterated Prisoner's Dilemma," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-8, October.
    13. Lengyel, Balázs & Leydesdorff, Loet, 2015. "The Effects of FDI on Innovation Systems in Hungarian Regions: Where is the Synergy Generated?," MPRA Paper 73945, University Library of Munich, Germany.
    14. Christoph Salge & Daniel Polani, 2011. "Digested Information as an Information Theoretic Motivation for Social Interaction," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(1), pages 1-5.
    15. Irad Ben-Gal & Marcelo Bacher & Morris Amara & Erez Shmueli, 2023. "A Nonparametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 99-115, October.
    16. Han Woo Park, 2014. "Mapping election campaigns through negative entropy: Triple and Quadruple Helix approach to South Korea’s 2012 presidential election," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(1), pages 187-197, April.
    17. David Engel & Thomas W Malone, 2018. "Integrated information as a metric for group interaction," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    18. Adam B Barrett & Michael Murphy & Marie-Aurélie Bruno & Quentin Noirhomme & Mélanie Boly & Steven Laureys & Anil K Seth, 2012. "Granger Causality Analysis of Steady-State Electroencephalographic Signals during Propofol-Induced Anaesthesia," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-12, January.
    19. Antonio J. Ibáñez-Molina & Sergio Iglesias-Parro, 2018. "A Comparison between Theoretical and Experimental Measures of Consciousness as Integrated Information in an Anatomically Based Network of Coupled Oscillators," Complexity, Hindawi, vol. 2018, pages 1-8, April.
    20. Loet Leydesdorff & Igone Porto-Gomez, 2019. "Measuring the expected synergy in Spanish regional and national systems of innovation," The Journal of Technology Transfer, Springer, vol. 44(1), pages 189-209, February.
    21. Strand, Øivind & Leydesdorff, Loet, 2013. "Where is synergy indicated in the Norwegian innovation system? Triple-Helix relations among technology, organization, and geography," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 471-484.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1002236. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.