IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003111.html
   My bibliography  Save this article

The Minimal Complexity of Adapting Agents Increases with Fitness

Author

Listed:
  • Nikhil J Joshi
  • Giulio Tononi
  • Christof Koch

Abstract

What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness.Author Summary: It has often been asserted that as organisms adapt to natural environments with many independent forces and actors acting over a variety of different time scales, they become more complex. We investigate this question from the point of view of information theory as applied to the nervous systems of simple creatures evolving in a stereotyped environment. We performed a controlled in silico evolution experiment to study the relationship between complexity, as measured using different information-theoretic measures, and fitness, by evolving animats with brains of twelve binary variables over 60,000 generations. We compute the complexity of these evolved networks using three measures based on mutual information and one measure based on the extent to which their brain contain states that are both differentiated and integrated. All measures show the same trend - the minimal complexity at any one fitness level increases as the organisms become more adapted to their environment, that is, as they become fitter. Above this minimum, there exists a large degree of degeneracy in evidence.

Suggested Citation

  • Nikhil J Joshi & Giulio Tononi & Christof Koch, 2013. "The Minimal Complexity of Adapting Agents Increases with Fitness," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-10, July.
  • Handle: RePEc:plo:pcbi00:1003111
    DOI: 10.1371/journal.pcbi.1003111
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003111
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003111&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003111?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. Touchette, Hugo & Lloyd, Seth, 2004. "Information-theoretic approach to the study of control systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 331(1), pages 140-172.
    2. Sean B. Carroll, 2001. "Chance and necessity: the evolution of morphological complexity and diversity," Nature, Nature, vol. 409(6823), pages 1102-1109, February.
    3. Daniel W. McShea, 1996. "Metazoan Complexity and Evolution: Is There a Trend?," Working Papers 96-01-002, Santa Fe Institute.
    4. 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.
    5. George E. Monahan, 1982. "State of the Art---A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms," Management Science, INFORMS, vol. 28(1), pages 1-16, January.
    6. 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.
    7. 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)

    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. 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.
    2. Wooseung Jang & J. George Shanthikumar, 2002. "Stochastic allocation of inspection capacity to competitive processes," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(1), pages 78-94, February.
    3. Xiao, Baichun & Yang, Wei, 2021. "A Bayesian learning model for estimating unknown demand parameter in revenue management," European Journal of Operational Research, Elsevier, vol. 293(1), pages 248-262.
    4. Dinah Rosenberg & Eilon Solan & Nicolas Vieille, 2009. "Protocols with No Acknowledgment," Operations Research, INFORMS, vol. 57(4), pages 905-915, August.
    5. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    6. Williams, Byron K., 2009. "Markov decision processes in natural resources management: Observability and uncertainty," Ecological Modelling, Elsevier, vol. 220(6), pages 830-840.
    7. Xin Jin, 2021. "Can we imitate the principal investor's behavior to learn option price?," Papers 2105.11376, arXiv.org, revised Jan 2022.
    8. Yanling Chang & Alan Erera & Chelsea White, 2015. "Value of information for a leader–follower partially observed Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 129-153, December.
    9. Churlzu Lim & J. Neil Bearden & J. Cole Smith, 2006. "Sequential Search with Multiattribute Options," Decision Analysis, INFORMS, vol. 3(1), pages 3-15, March.
    10. Anyan Qi & Hyun-Soo Ahn & Amitabh Sinha, 2017. "Capacity Investment with Demand Learning," Operations Research, INFORMS, vol. 65(1), pages 145-164, February.
    11. Chiel van Oosterom & Lisa M. Maillart & Jeffrey P. Kharoufeh, 2017. "Optimal maintenance policies for a safety‐critical system and its deteriorating sensor," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(5), pages 399-417, August.
    12. Hodgson, Geoffrey M. & Knudsen, Thorbjørn, 2010. "Generative replication and the evolution of complexity," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 12-24, July.
    13. Ciriaco Valdez‐Flores & Richard M. Feldman, 1989. "A survey of preventive maintenance models for stochastically deteriorating single‐unit systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(4), pages 419-446, August.
    14. Fabien Lafuma & Ian J. Corfe & Julien Clavel & Nicolas Di-Poï, 2021. "Multiple evolutionary origins and losses of tooth complexity in squamates," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    15. Grosfeld-Nir, Abraham, 2007. "Control limits for two-state partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 182(1), pages 300-304, October.
    16. E. M. S. Ribeiro & G. A. Prataviera, 2014. "Information theoretic approach for accounting classification," Papers 1401.2954, arXiv.org, revised Sep 2014.
    17. 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.
    18. Paul L Fackler & Krishna Pacifici & Julien Martin & Carol McIntyre, 2014. "Efficient Use of Information in Adaptive Management with an Application to Managing Recreation near Golden Eagle Nesting Sites," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-14, August.
    19. 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.
    20. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.

    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:pcbi00:1003111. 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.