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A Tale of Two Animats: What Does It Take to Have Goals?

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What does it take for a system, biological or not, to have goals? Here, this question is approached in the context of in silico artificial evolution. By examining the informational and causal properties of artificial organisms (“animats”) controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for intrinsic information, autonomy, and meaning.


  • Intrinsic Information
  • Artificial Organisms
  • Adaptive Neural Network
  • Cause-effect Structure
  • Intrinsic Perspective

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Fig. 2.1

Adapted from [7] with permission

Fig. 2.2

Adapted from [5] with permission


  1. 1.

    Furthermore, representations of individual environmental features are typically distributed across many elements [6], and thus do no coincide with the Markov Brain’s elementary (micro) logic components.

  2. 2.

    Note that this holds, even if we could evaluate the correlation between internal and external variables in an observer-independent manner, except then the correlations might not even be meaningful for the investigator.

  3. 3.

    If M would not constrain its inputs, its state would just be a source of noise entering the system, not causal information.

  4. 4.

    Sets of elements can constrain their joint inputs and outputs in a way that is irreducible to the constraints of their constituent elements taken individually [13]. The irreducible cause-effect information of a set of elements can be quantified similarly to Eqs. 2.22.3, by partitioning the set and measuring the distance between \(p\left( {\left. {z_{t \pm 1} } \right|m_{t} } \right)\) and the distributions of the partitioned set.

  5. 5.

    By contrast to the uniform, perturbed distribution, the stationary, observed distribution of system Z entails correlations due to the system’s network structure which may occlude or exaggerate the causal constraints of the mechanism itself.

  6. 6.

    Take a neuron that activates, for example, every time a picture of the actress Jennifer Aniston is shown [22]. All it receives as inputs is quasi-binary electrical signals from other neurons. The meaning “Jennifer Aniston” is not in the message to this neuron, or any other neuron.

  7. 7.

    For example, an AND logic gate receiving 2 inputs is what it is, because it switches ON if and only if both inputs were ON. An AND gate in state ON thus constrains the past states of its input to be ON.

  8. 8.

    This notion of causal autonomy applies to deterministic and probabilistic systems, to the extent that their elements constrain each other, above other background inputs, e.g. from the sensors.


  1. Schrödinger, E.: What is Life? With Mind and Matter and Autobiographical Sketches. Cambridge University Press (1992)

    Google Scholar 

  2. Still, S., Sivak, D.A., Bell, A.J., Crooks, G.E.: Thermodynamics of Prediction. Phys. Rev. Lett. 109, 120604 (2012)

    ADS  CrossRef  Google Scholar 

  3. England, J.L.: Statistical physics of self-replication. J. Chem. Phys. 139, 121923 (2013)

    ADS  CrossRef  Google Scholar 

  4. Walker, S.I., Davies, P.C.W.: The algorithmic origins of life. J. R. Soc. Interface 10, 20120869 (2013)

    CrossRef  Google Scholar 

  5. Albantakis, L., Hintze, A., Koch, C., Adami, C., Tononi, G.: Evolution of integrated causal structures in animats exposed to environments of increasing complexity. PLoS Comput. Biol. 10, e1003966 (2014)

    ADS  CrossRef  Google Scholar 

  6. Marstaller, L., Hintze, A., Adami, C.: The evolution of representation in simple cognitive networks. Neural Comput. 25, 2079–2107 (2013)

    MathSciNet  CrossRef  Google Scholar 

  7. Albantakis, L., Tononi, G.: The intrinsic cause-effect power of discrete dynamical systems—from elementary cellular automata to adapting animats. Entropy 17, 5472–5502 (2015)

    ADS  CrossRef  Google Scholar 

  8. Online Animat animation.

  9. Quiroga, R.Q., Panzeri, S.: Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 10, 173–185 (2009)

    CrossRef  Google Scholar 

  10. King, J.-R., Dehaene, S.: Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210 (2014)

    CrossRef  Google Scholar 

  11. Haynes, J.-D.: Decoding visual consciousness from human brain signals. Trends Cogn. Sci. 13, 194–202 (2009)

    CrossRef  Google Scholar 

  12. Bateson, G.: Steps to an Ecology of Mind. University of Chicago Press (1972)

    Google Scholar 

  13. Oizumi, M., Albantakis, L., Tononi, G.: From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0. PLoS Comput. Biol. 10, e1003588 (2014)

    ADS  CrossRef  Google Scholar 

  14. Pearl, J.: Causality: models, reasoning and inference. Cambridge University Press (2000)

    Google Scholar 

  15. Ay, N., Polani, D.: Information Flows in Causal Networks. Adv. Complex Syst. 11, 17–41 (2008)

    MathSciNet  CrossRef  Google Scholar 

  16. Krakauer, D., Bertschinger, N., Olbrich, E., Ay, N., Flack, J.C.: The Information Theory of Individuality. The architecture of individuality (2014)

    Google Scholar 

  17. Marshall, W., Albantakis, L., Tononi, G.: Black-boxing and cause-effect power (2016). arXiv: 1608.03461

    Google Scholar 

  18. Marshall, W., Kim, H., Walker, S.I., Tononi, G., Albantakis, L.: How causal analysis can reveal autonomy in biological systems (2017). arXiv: 1708.07880

    Google Scholar 

  19. Tononi, G., Boly, M., Massimini, M., Koch, C.: Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci. 17, 450–461 (2016)

    CrossRef  Google Scholar 

  20. Albantakis, L., Tononi, G.: Fitness and neural complexity of animats exposed to environmental change. BMC Neurosci. 16, P262 (2015)

    CrossRef  Google Scholar 

  21. Tononi, G.: Integrated information theory. Scholarpedia 10, 4164 (2015)

    ADS  CrossRef  Google Scholar 

  22. Quiroga, R.Q., Reddy, L., Kreiman, G., Koch, C., Fried, I.: Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005)

    ADS  CrossRef  Google Scholar 

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I thank Giulio Tononi for his continuing support and comments on this essay, and William Marshall, Graham Findlay, and Gabriel Heck for reading this essay and providing helpful comments. L.A. receives funding from the Templeton World Charities Foundation (Grant#TWCF0196).

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Correspondence to Larissa Albantakis .

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Albantakis, L. (2018). A Tale of Two Animats: What Does It Take to Have Goals?. In: Aguirre, A., Foster, B., Merali, Z. (eds) Wandering Towards a Goal. The Frontiers Collection. Springer, Cham.

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