Skip to main content

Markov Blankets for Sustainability

  • Conference paper
  • First Online:
Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops (SEFM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13765))

Included in the following conference series:

  • 522 Accesses

Abstract

This paper’s aim is twofold: on the one hand, to provide an overview of the state of the art of some kind of Bayesian networks, i.e. Markov blankets (MB), focusing on their relationship with the cognitive theories of the free energy principle (FEP) and active inference. On the other hand, to sketch how these concepts can be practically applied to artificial intelligence (AI), with special regard to their use in the field of sustainable development. The proposal of this work, indeed, is that understanding exactly to what extent MBs may be framed in the context of FEP and active inference, could be useful to implement tools to support decision-making processes for addressing sustainability. Conversely, looking at these tools considering how they could be related to those theoretical frameworks, may help to shed some light on the debate about FEP, active inference and its linkages with MBs, which still seems to be clarified. For the above purposes, the paper is organized as follows: after a general introduction, Sect. 2 explains what a MB is, and how it is related to the concepts of FEP and active inference. Thus, Sect. 3 focuses on how MBs, joint with FEP and active inference, are employed in the field of AI. On these grounds, Sect. 4 explores whether MBs, FEP, and active inference can be useful to face the issues related to sustainability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. United Nations General Assembly: Transforming our World: The Sustainable Development Agenda to 2030 (2015)

    Google Scholar 

  2. Pedemonte, V.: AI for Sustainability: an overview of AI and the SGDS to contribute to the European policy making (2020)

    Google Scholar 

  3. Alsharkawi, A., Al-Fetyani, M., Dawas, M.: Poverty classification using machine learning: the case of Jordan. Sustainability 13, 1412 (2021). https://doi.org/10.3390/su13031412

    Article  Google Scholar 

  4. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kauffman, Burlington (1988)

    MATH  Google Scholar 

  5. Koski, T., Noble, J.M.: Bayesian Networks: An Introduction. Wiley and Sons, Chichester (2009)

    Book  MATH  Google Scholar 

  6. Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. Chapman and Hall/CRC, Florida (2004)

    MATH  Google Scholar 

  7. Facchin, M.: Extended predictive minds: do Markov blankets matter? Rev. Philos. Psychol. (2021). https://doi.org/10.1007/S13164-021-00607-9

    Article  Google Scholar 

  8. Friston, K.: Learning and inference in the brain. Neural Netw. 16, 1325–1352 (2003)

    Article  Google Scholar 

  9. Friston, K.: The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13, 293–301 (2009)

    Article  Google Scholar 

  10. Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–38 (2010)

    Article  Google Scholar 

  11. Friston, K.: Life as we know it. J. Roy. Soc. Interface 10, 20130475 (2013). https://doi.org/10.1098/rsif.2013.0475

    Article  Google Scholar 

  12. Friston, K., Stephan, K.: Free-energy and the brain. Synthese 159, 417–458 (2007). https://doi.org/10.1007/s11229-007-9237-y

    Article  Google Scholar 

  13. Friston, K., FitzGerald, T., Rigoli, F.: A free energy principle for the brain. J. Physiol. Paris 100, 70–87 (2006)

    Article  Google Scholar 

  14. Colombo, M., Wright, C.: First principles in the life sciences: the free-energy principle, organicism, and mechanism. Synthese 198(14), 3463–3488 (2018). https://doi.org/10.1007/s11229-018-01932-w

    Article  MathSciNet  Google Scholar 

  15. Chen, J.: Understanding social systems: a free energy perspective. J. Hum. Thermodyn. 5 (2009). https://doi.org/10.2139/ssrn.1269035

  16. Friston, K., Mattout, J., Kilner, J.: Action understanding and active inference. Biol. Cybern. 104, 137–160 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kirchhoff, M., Parr, T., Palacios, E.: The Markov blankets of life: autonomy, active inference and the free energy principle. J. Roy. Soc. Interface 15(138), 20170792 (2018). https://doi.org/10.1098/rsif.2017.0792

    Article  Google Scholar 

  18. Rubin, S., Parr, T., Da Costa, L.: Future climates: Markov blankets and active inference in the biosphere. J. Roy. Soc. Interface 17, 20200503 (2020). https://doi.org/10.1098/rsif.2020.0503

    Article  Google Scholar 

  19. Hohwy, J.: Quick’n’Lean or slow and rich? Andy Clark on predictive processing and embodied cognition. In: Colombo, M., Irvine, E., Stapleton, M. (Eds.): Andy Clark and His Critics, pp. 191–205. Oxford University Press, New York (2019)

    Google Scholar 

  20. Palacios, E.R., Razi, A., Parr, T.: On Markov blanket and hierarchical self-organization. J. Theor. Biol. 486, 110089 (2020)

    Article  MATH  Google Scholar 

  21. Bruineberg, J., Dolega, K., Dewhurst, J.: The emperor’s new Markov blankets. Behav. Brain Sci. 1–63 (2021). https://doi.org/10.1017/S0140525X21002351

  22. Ramstead, M.: The empire strikes back: Some responses to Bruineberg and colleagues. Behav. Brain Sci. 45 (2022). https://doi.org/10.1017/s0140525x22000139

  23. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. MIT Press, Cambridge (2000)

    MATH  Google Scholar 

  24. LeCun, Y., Bengio, Y., Hinton, G.: Deep Learning. Nature 52(7553), 436–444 (2015)

    Article  Google Scholar 

  25. Derks, I.P., de Waal, A.: A taxonomy of explainable Bayesian networks. In: Gerber, A. (ed.) Artificial Intelligence Research. CCIS, Springer, Cham (2020). https://doi.org/10.48550/arXiv.2101.11844

    Chapter  Google Scholar 

  26. Seth, A.: The brain as a prediction machine. In: Mendonça, D., Curado, M., Gouveia, S. S. (Eds.). The Philosophy and Science of Predictive Processing, pp. XIV-XVII. Bloomsbury, London (2020)

    Google Scholar 

  27. Lieto, A.: Cognitive Design for Artificial Minds. Routledge, New York (2021)

    Book  Google Scholar 

  28. Friedman, D., Isaac, R.M., James, D.: Risky Curves: On the Empirical Failure of Expected Utility. Routledge, New York (2014)

    Book  Google Scholar 

  29. Gigerenzer, G.: How to explain behavior? Top. Cogn. Sci. 12(4), 1363–1381 (2019)

    Article  Google Scholar 

  30. Mazzaglia, P., Verbelen, T., Çatal, O.: The free energy principle for perception and action: a deep learning perspective. Entropy 24(2), 301 (2022)

    Article  Google Scholar 

  31. Mirza, M.B., Adams, R.A., Mathys, C.D.: Scene construction, visual foraging, and active inference. Front. Comput. Neurosci. 10, 56 (2016). https://doi.org/10.3389/fncom.2016.00056

    Article  Google Scholar 

  32. Daucé, E.: Active fovea-based vision through computationally-effective model-based prediction. Front. Neurorobotics 12, 76 (2018). https://doi.org/10.3389/fnbot.2018.00076

    Article  Google Scholar 

  33. Lanillos, P., Meo, C., Pezzato, C.: Active inference in robotics and artificial agents: survey and challenges. ArXiv (2021). arXiv:2112.01871

  34. Van De Maele, T., Verbelen, T.: Çatal, O: Active vision for robot manipulators using the free energy principle. Front. Neurorobotics 15, 642780 (2021). https://doi.org/10.3389/fnbot.2021.642780

    Article  Google Scholar 

  35. Friston, K., FitzGerald, T., Rigoli, F.: Active inference and learning. Neurosci. Biobehav. Rev. 68, 862–879 (2016). https://doi.org/10.1016/j.neubiorev.2016.06.022

    Article  Google Scholar 

  36. Çatal, O., Wauthier, S., De Boom, C.: Learning generative state space models for active inference. Front. Comput. Neurosci. 14(103), 574372 (2020). https://doi.org/10.3389/fncom.2020.574372

    Article  Google Scholar 

  37. Cruz, J.: Deep Learning vs Markov Model in Music Generation. Honors College Theses [graduate thesis] (2019)

    Google Scholar 

  38. Ramstead, M.J.D., Badcock, P.B., Friston, K.: Answering schrödinger’s question: a free-energy formulation. Phys. Life Rev. 24, 1–16 (2018)

    Article  Google Scholar 

  39. Veissière, S.P.L., Constant, A., Ramstead, M.J.D.: Thinking through other minds: a variational approach to cognition and culture. Behav. Brain Sci. 43, e90 (2020)

    Article  Google Scholar 

  40. Kim, J., Jun, S., Jang, D.: Sustainable technology analysis of artificial intelligence using bayesian and social network models. Sustainability 10(1), 115 (2018)

    Article  Google Scholar 

  41. Bromley, J.: Guidelines for the use of Bayesian Networks as a Participatory Tool for Water Resource. Wallingford, United Kingdom (2005)

    Google Scholar 

  42. Phan, T.D., Smart, J.C., Capon, S.J.: Applications of Bayesian belief networks in water resource management: a systematic review. Environ. Model. Softw. 85, 98–111 (2016). https://doi.org/10.1016/j.envsoft.2016.08.006

    Article  Google Scholar 

  43. Requejo Castro, D.: Data driven Bayesian networks modelling to support decision-making: application to the context of sustainable development goal 6 on water and sanitation. Universitat Polìtecnica de Catalunya. Ph.D. thesis (2021)

    Google Scholar 

Download references

Acknowledgements

I would like to thank the three anonymous reviewers for their comments and very useful feedback on the first version of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Raffa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Raffa, M. (2023). Markov Blankets for Sustainability. In: Masci, P., Bernardeschi, C., Graziani, P., Koddenbrock, M., Palmieri, M. (eds) Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops. SEFM 2022. Lecture Notes in Computer Science, vol 13765. Springer, Cham. https://doi.org/10.1007/978-3-031-26236-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26236-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26235-7

  • Online ISBN: 978-3-031-26236-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics