Synergic kinds


According to the homeostatic property cluster family of accounts, one of the main conditions for groups of properties to count as natural is that these properties be frequently co-instantiated. I argue that this condition is, in fact, not necessary for natural-kindness. Furthermore, even when it is present, the focus on co-occurrence distorts the role natural kinds play in science. Co-occurrence corresponds to what information theorists call redundancy: observing the presence of some of the properties in a frequently co-occurrent cluster makes observations of other members of the cluster comparatively uninformative. Yet, scientific practice often, and increasingly often, singles out as natural groups of properties that are not redundant, but synergic: instantiations of properties in synergic clusters are not necessarily informative about instantiations of other properties in the cluster; rather, it is their joint instantiation that plays the explanatory role for which the natural kind is recruited.

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

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    Not always. See Bird (2015) for discussion.

  2. 2.

    Inductive inference takes widely different forms, of course, and go far beyond this somewhat caricaturesque example. It is possible that some of the shortcomings of the homeostatic property cluster account I identify in this paper could be traced back to relying on this kind of simple induction as a guiding example. As we will see, much of the inductive inference science engages in is significantly more complex.

  3. 3.

    In current philosophy of biology, essentialism about species has taken a historical turn (Godman 2015; Griffiths 1999; Millikan 1999; Okasha 2002) whereby essences are no longer taken to be intrinsic, but rather historical properties of lineages of populations. In what follows I will be defending a conservative modification of the homeostatic property cluster theory of natural kinds, and in particular one that is compatible with the postulation of historical essences for species.

  4. 4.

    The original formulation is by Richard Boyd, in his (1989); see also Boyd (1999), Chakravartty (2007), Kornblith (1993), Magnus (2011), among many others.

  5. 5.

    While throughout this paper I often talk of causal structure, the models I will describe in subsequent sections are causally agnostic, and focus on probabilistic (informational) connections. In any case, whenever these connections are grounded on causal facts my discussion applies to them as well. See also footnote 6.

  6. 6.

    Note that I have not given a causal gloss on the role of essences. This is because causal facts cannot be read off probability distributions. This causal agnosticism is, I submit, an advantage of the model: it allows it to apply to the HPC account proper, with its reliance on causal connections, and to other, related accounts such as Slater (2015)’s stable property clusters, that substitute causal connections with a modal stability constraint. This kind of constraints, arguably, are precisely what probability distributions inform us of. I discuss Slater’s account in the following subsection.

  7. 7.

    More formally, two properties \(P_i\) and \(P_j\) provide redundant information about the world iff the mutual information between the joint random variable, \(W\), of all random variables in the world, and the joint random variable (\(P_i, P_j\)) is less than the sum of the mutual informations of W and each individual random variable: \(I(W; P_1) + I(W; P_2) > I(W; P_1, P_2)\). The definition of redundancy for more than two random variables is an open theoretical problem.

  8. 8.

    More formally, two properties \(P_i\) and \(P_j\) provide synergic information about the world iff the mutual information between the joint random variable, \(W\), of all random variables in the world, and the joint random variable (\(P_i, P_j\)) is more than the sum of the mutual informations of W and each individual random variable: \(I(W; P_1) + I(W; P_2) < I(W; P_1, P_2)\). The definition of synergy for more than two random variables is also an open theoretical problem.

  9. 9.

    The answer to this question might have to do, for example, with whether Ken has an individual essence, and whether such essence is independent of his sex. Adherents to Kripkean essentiality of origin theses (Kripke 1980) will answer this question affirmatively if triplewart seadevil sex is fixed after conception, but there might be other reasonable positions. The worry I am sketching here, and is developed more fully by Magnus in his (2011), is that Boyd’s approach makes polymorphism hostage to these comparatively arcane metaphsical considerations.

  10. 10.

    This discussion is indebted to an anonymous referee.

  11. 11.

    I would like to thank an anonymous referee for helping me distinguish these two sorts of examples.

  12. 12.

    That is, graphs in which individual nodes have a small average number of connections to other nodes, yet the average distance between two arbitrary nodes in the graph is also small. See Watts and Strogatz (1998).

  13. 13.

    Suppose, that is, that cortical minicolumns are the atomic functional units in a mammalian cerebral cortex. See Sporns et al. (2005), p. 247, for discussion.

  14. 14.

    Although the precise way in which information should be separated into its synergic and redundant components is an open theoretical problem, and one which is the focus of much recent research. See Williams and Beer (2010), Griffith et al. (2014), Bertschinger et al. (2013), Griffith and Koch (2014).

  15. 15.

    No analogue of the Homeostasis condition is needed: the presence of informational dependence already presupposes that probabilistic connections are not a matter of chancy coinstantiations.


  1. Alivisatos, A. P., Chun, M., Church, G. M., Greenspan, R. J., Roukes, M. L., & Yuste, R. (2012). The brain activity map project and the challenge of functional connectomics. Neuron, 74(6), 970–974.

    Article  Google Scholar 

  2. Anastassiou, D. (2007). Computational analysis of the synergy among multiple interacting genes. Molecular Systems Biology, 3(1), 83.

    Article  Google Scholar 

  3. Bertschinger, N., Rauh, J., Olbrich, E., & Jost, J. (2013). Shared information—New insights and problems in decomposing information in complex systems. In Proceedings of the European conference on complex systems 2012 (pp. 251–269). Berlin: Springer.

  4. Bird, A. (2015). The metaphysics of natural kinds. Synthese. doi:10.1007/s11229-015-0833-y.

  5. Boyd, R. (1989). What realism implies and what it does not. Dialectica, 43(1–2), 5–29.

    Article  Google Scholar 

  6. Boyd, R. (1999). Homeostasis, species, and higher taxa. In R. A. Wilson (Ed.), Species: New interdisciplinary essays (pp. 141–185). Cambridge: MIT Press.

    Google Scholar 

  7. Chakravartty, A. (2007). A metaphysics for scientific realism: Knowing the unobservable. Cambridge: Cambridge University Press.

    Google Scholar 

  8. Cordell, H. J. (2002). Epistasis: What it means, what it doesn’t mean, and statistical methods to detect it in humans. Human Molecular Genetics, 11(20), 2463–2468.

    Article  Google Scholar 

  9. Edelman, G. M., & Tononi, G. (2013). Consciousness: How matter becomes imagination. London: Penguin.

    Google Scholar 

  10. Ereshefsky, M. (2010). What’s wrong with the new biological essentialism. Philosophy of Science, 77(5), 674–685.

    Article  Google Scholar 

  11. Ereshefsky, M., & Matthen, M. (2005). Taxonomy, polymorphism, and history: An introduction to population structure theory. Philosophy of Science, 72, 1–21.

    Article  Google Scholar 

  12. Ereshefsky, M., & Reydon, T. A. C. (2015). Scientific kinds. Philosophical Studies, 172(4), 969–986.

    Article  Google Scholar 

  13. Godman, M. (2015). The special science dilemma and how culture solves it. Australasian Journal of Philosophy, 93(3), 491–508.

    Article  Google Scholar 

  14. Griffiths, P. E. (1999). Squaring the circle: Natural kinds with historical essences. In R. A. Wilson (Ed.), Species: New interdisciplinary essays (pp. 209–28). Cambridge: MIT Press.

    Google Scholar 

  15. Griffith, V., & Koch, C. (2014). Quantifying synergistic mutual information. In M. Prokopenko (Ed.), Guided self-organization: Inception. Berlin: Springer.

  16. Griffith, V., Chong, E. K., James, R. G., Ellison, C. J., & Crutchfield, J. P. (2014). Intersection information based on common randomness. Entropy, 16(4), 1985–2000.

    Article  Google Scholar 

  17. Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Grant, P. E., Wedeen, V., et al. (2010). MR connectomics: Principles and challenges. Journal of Neuroscience Methods, 194(1), 34–45.

    Article  Google Scholar 

  18. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge: MIT Press.

    Google Scholar 

  19. Kornblith, H. (1993). Inductive inference and its natural ground: An essay in naturalistic epistemology. Cambridge: The MIT Press.

    Google Scholar 

  20. Kripke, S. (1980). Naming and necessity. Oxford: Blackwell.

    Google Scholar 

  21. Mackay, T. F. (2014). Epistasis and quantitative traits: Using model organisms to study gene-gene interactions. Nature Reviews Genetics, 15(1), 22–33.

    Article  Google Scholar 

  22. Mackay, T. F., Stone, E. A., & Ayroles, J. F. (2009). The genetics of quantitative traits: Challenges and prospects. Nature Reviews Genetics, 10(8), 565–577.

    Article  Google Scholar 

  23. Magnus, P. D. (2011). Drakes, seadevils, and similarity fetishism. Biology and Philosophy, 26(6), 857–870.

    Article  Google Scholar 

  24. Martínez, M. (2015). Informationally-connected property clusters, and polymorphism. Biology and Philosophy, 30(1), 99–117.

    Article  Google Scholar 

  25. Millikan, R. G. (1999). Historical kinds and the “special sciences”. Philosophical Studies, 95(1), 45–65.

    Article  Google Scholar 

  26. Moore, J. H. (2003). The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity, 56(1–3), 73–82.

    Article  Google Scholar 

  27. Okasha, S. (2002). Darwinian metaphysics: Species and the question of essentialism. Synthese, 131(2), 191–213.

    Article  Google Scholar 

  28. Schaffner, K. F. (2016). Behaving. New York: Oxford University Press.

    Google Scholar 

  29. Slater, M. H. (2015). Natural kindness. British Journal for the Philosophy of Science, 66, 374–411.

    Google Scholar 

  30. Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1(4), e42.

    Article  Google Scholar 

  31. Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(1), 42.

    Article  Google Scholar 

  32. Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282(5395), 1846–1851.

    Article  Google Scholar 

  33. van den Heuvel, M. P., Bullmore, E. T., & Sporns, O. (2016). Comparative connectomics. Trends in Cognitive Sciences, 20(5), 345–361.

    Article  Google Scholar 

  34. Van Dijk, K. R., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103(1), 297–321.

    Article  Google Scholar 

  35. Watkinson, J., Wang, X., Zheng, T., & Anastassiou, D. (2008). Identification of gene interactions associated with disease from gene expression data using synergy networks. BMC Systems Biology, 2(1), 10.

    Article  Google Scholar 

  36. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  37. Weber, K., Eisman, R., Higgins, S., Morey, L., Patty, A., Tausek, M., et al. (2001). An analysis of polygenes affecting wing shape on chromosome 2 in Drosophila melanogaster. Genetics, 159(3), 1045–1057.

    Google Scholar 

  38. Weber, K., Eisman, R., Morey, L., Patty, A., Sparks, J., Tausek, M., et al. (1999). An analysis of polygenes affecting wing shape on chromosome 3 in Drosophila melanogaster. Genetics, 153(2), 773–786.

    Google Scholar 

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

Download references


Financial support for this work was provided by the Research Foundation—Flanders, Research Grant FWO G0C7416N. I would like to thank two anonymous referees, audiences in Barcelona and Paris, and my colleagues at the Centre for Philosophical Psychology, University of Antwerp, for comments and suggestions on earlier drafts.

Author information



Corresponding author

Correspondence to Manolo Martínez.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Martínez, M. Synergic kinds. Synthese 197, 1931–1946 (2020).

Download citation


  • Homeostatic property clusters
  • Information theory
  • Synergy
  • Redundancy
  • Natural kinds
  • Richard Boyd
  • Epistasis
  • Connectomics