Mayer, J., Mutchler, P. and Mitchell, J. (2016) Evaluating the privacy properties of telephone metadata. Proceedings of the National Academy of Sciences USA, 113(20), pp. 5536–5541.CrossRefGoogle Scholar

Gantz, J. and Reinsel, D. (2011) Extracting value from chaos. EMC Corporation. Available from https://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf Google Scholar

This mainstream definition is one of many– see Press, G. (2014) 12 Big Data definitions: what’s yours? Forbes Magazine. Available from http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/#25517fdf21a9. However, nearly all emphasize size (without specifying size quantitatively) and the difficulty of managing it.Google Scholar

Stahl, F., Gaber, M., Bramer, M. and Yu, P. (2010) Pocket data mining: towards collaborate data mining in mobile computing environments. Proceedings of the Twenty-Second IEEE International Conference on Tools and Artificial Intelligence 2010. Available from http://eprints.port.ac.uk/3248/1/pdm1.pdf CrossRefGoogle Scholar

Bentley, R.A., O’Brien, M. and Brock, W. (2014) Mapping collective behavior in the Big-Data era. Behavioral and Brain Sciences, 37, pp. 63–119.CrossRefGoogle ScholarPubMed

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.L., Brewer, D., et al. (2009) Computational social science. Science, 323, pp. 721–723, p. 722.CrossRefGoogle ScholarPubMed

Aral, S. and Walker, D. (2012) Identifying influential and susceptible members of social networks. Science, 337, pp. 337–341.CrossRefGoogle ScholarPubMed

Salganik, M. Dodds, P. and Watts, D. (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), pp. 854–856.CrossRefGoogle Scholar

Onnela, J.P. and, Reed-Tsochas, F. (2010) Spontaneous emergence of social influence in online systems. Proceedings of the National Academy of Sciences USA, 107, pp. 18375–18380.CrossRefGoogle Scholar

Wu, F. and Huberman, B. (2007) Novelty and collective attention. Proceedings of the National Academy of Sciences USA, 104, pp. 17599–17601.CrossRefGoogle Scholar

Kossinets, G. and Watts, D. (2009) Origins of homophily in an evolving social network. American Journal of Sociology, 115(2), pp. 405–500.CrossRefGoogle Scholar

Roesch, E., Stahl, F. and Gaber, M. (2014) Bigger data for Big Data: from Twitter to brain-computer interfaces. Behavioral and Brain Sciences, 37(1), pp. 97–98.CrossRefGoogle ScholarPubMed

Baronchelli, A., Ferrer-i-Cancho, R., Pastor-Satorras, R., Chater, N. and Christiansen, M. (2013) Networks in cognitive science. Trends in Cognitive Sciences, 17(7), pp. 348–360.CrossRefGoogle ScholarPubMed

Bullmore, E. and Sporns, O. (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, pp. 186–198.CrossRefGoogle ScholarPubMed

Barabasi, A.L. and Albert, R. (1999) Emergence of scaling in random networks. Science, 286, pp. 509–512.Google ScholarPubMed

Mason, W., Jones, A. and Goldstone, R. (2008) Propagation of innovation in networked groups. Journal of Experimental Psychology: General, 173(3), 422–433.CrossRefGoogle Scholar

Guimerà, R., Uzzi, B., Spiro, J. and Nunes Amaral, L. (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science (New Series), 308(5722), 697–702.CrossRefGoogle ScholarPubMed

Froese, T., Gershenson, C. and Manzanilla, L. (2014) Can government be self-organized? A mathematical model of the collective social organization of ancient Teotihuacan, Central Mexico. PLoS One, 9(10), e109966.CrossRefGoogle ScholarPubMed

Katz, N., Lazer, D., Arrow, H. and Contractor, N. (2004) Network theory and small groups. Small Group Research, 35(3), 307–322.CrossRefGoogle Scholar

Watts, D. (2007) A twenty-first century science. Nature, 445, p. 489.CrossRefGoogle ScholarPubMed

Social connectionism involves the use of connectionist models for social psychology– for example, connectionist models of how individuals form prejudices– see Van Rooy, D., Van Overwalle, F., Vanoomissen, T., Labiouse, C. and French, R. (2003) A recurrent connectionist model of group biases. Psychological Review, 110(3), pp. 536–563. The focus here is on social structures (e.g. a racist society) rather than individual cognition (e.g. a racist individual). That said, the morals of this paper also apply to psychology– see Figdor, C. (2018). Pieces of Mind: The Proper Domain of Psychological Predicates (Oxford and New York: Oxford University Press).CrossRefGoogle ScholarPubMed

That said, the Santa Fe Institute (https://www.santafe.edu/research/projects/hidden-laws-in-biological-and-social-systems) comprises one group of researchers dedicated to this sort of cross-domain research. Regardless, the field is in its infancy.Google Scholar

Mateos, P., Longley, P. and O’sullivan, D. (2011) Ethnicity and population structure in naming networks. PLoS ONE, 6(9), e22493.CrossRefGoogle ScholarPubMed

I adopt the convention of using all capital letters to denote concepts (e.g. FISH) and quotes to denote predicates (e.g., ‘fish’, ‘pesce’). The differences between concepts (FISH), predicates (‘fish’), and categories (which are denoted by either) won’t matter here. Also, a standard distinction in philosophy of language is drawn between sense and reference– the meaning of a term and what it refers to. For example, ‘the morning star’ and ‘the evening star’ are names with the same reference– the planet Venus– but different meanings. For predicates, a corresponding distinction holds between extension (a set of entities) and intension (a property shared by the members of the set). For example, the extension of ‘fish’ is the set of all individual fish, and its intention is the property shared by fish (call it Fishness). As before, predicates with different intensions (meanings) can have the same extension (reference). My focus here is on the reference (or extension) of predicates or concepts. For some theorists, reference exhausts meaning anyway.Google Scholar

Sparrow, R. and Sparrow, L. (2006) In the hands of machines? The future of aged care. Minds and Machines, 16, pp. 141–161.CrossRefGoogle Scholar

The Nonhuman Rights Project. http://www.nonhumanrightsproject.org Google Scholar

Not all models are mathematical (e.g. scale models, animal models). However, I am construing ‘mathematical’ models as all those that can be described using equations or other mathematical tools (e.g. connectionist networks or computational models). In philosophical terms, mathematical equations are descriptions of models, although scientists often call the equations themselves models. Mathematical models can also be depicted in various ways– i.e. by standard graphs with x- and y-axes, and by network graphs with nodes and edges.Google Scholar

Weisberg, M. (2013) Simulation and Similarity (New York and Oxford: Oxford University Press).CrossRefGoogle Scholar

Equation (1) equates the change in prey population over time to the difference between its intrinsic growth rate and the rate at which prey are captured by predators. Equation (2) equates the change of predator population over time to the difference between the birth rate of predators and the predator mortality rate. The equations are linked in that the conversion of dead prey into baby predators in equation (2) is a function of the capture of prey by predators in equation (1).Google Scholar

Arora, V. and Boer, G. (2006) Simulating competition and coexistence between plant functional types in a dynamic vegetation model. Earth Interactions, 10, Paper No. 10.CrossRefGoogle Scholar

Goodwin, R. (1967) A growth cycle. In: Feinstein, C. (Ed.), Socialism, Capitalism, and Economic Growth (Cambridge, UK: Cambridge University Press).Google Scholar

Pettet, G., McElwain, D. and Norbury, J. (2000) Lotka-Volterra equations with chemotaxis: walls, barriers and travelling waves. IMA Journal of Mathematics Applied in Medicine and Biology, 27, pp. 395–413.CrossRefGoogle Scholar

Ratcliff, R. (1978) A theory of memory retrieval. Psychological Review, 85(2), pp. 59–108.CrossRefGoogle Scholar

Dasgupta, S., Ferreira, C. and Miesenböck, G. (2014) FoxP influences the speed and accuracy of a perceptual decision in Drosophila. Science, 344, pp. 901–904.CrossRefGoogle Scholar

Elsewhere I argue that they really do make decisions (Figdor, C. (2018) Pieces of Mind: The Proper Domain of Psychological Predicates (Oxford and New York: Oxford University Press). In this paper, my goal is not to defend an interpretation of social concepts but rather to show what the overlap of Big Data and network science implies for the likelihood that we will be facing a lot more of these sorts of conceptual issues in very short order.CrossRefGoogle Scholar

Bruno, J., Stachowicz, J. and Bertness, M. (2003) Inclusion of facilitation into ecological theory. Trends in Ecology and Evolution, 18(3), pp. 119–125.CrossRefGoogle Scholar

Note that some bacteria colonies are already considered predatory while others are described as making collective decisions, see G. Velicer, L. Kroos and R. Lenski (2000) Developmental cheating in the social bacterium Myxococcus xanthus. Nature, 404, pp. 598–601; Ben-Jacob, E., Becker, I., Shapira, Y. and Levine, H. (2004) Bacterial linguistic communication and social intelligence. Trends in Microbiology, 12(8), pp. 366–372.CrossRefGoogle ScholarPubMed

I am not endorsing these theories, and they are not the only ones in which social relationships play a critical role. I use them to illustrate the general point.Google Scholar

Baker, L. (2015) Human persons as social entities. Journal of Social Ontology, 1(1), pp. 77–87.CrossRefGoogle Scholar

Mehler, A. (2007) Large text networks as an object of corpus linguistic studies. In: Lüdeling, A., and Kytö, M. (Eds), Corpus Linguistics: An International Handbook of the Science of Language and Society (Berlin and New York: De Gruyter), pp. 328–382.Google Scholar

Baronchelli, A., Felici, M., Loreto, V., Caglioti, E. and Steels, L. (2006) Sharp transition towards shared vocabulary in multi-agent systems. arXiv:physics/0509075v2.CrossRefGoogle Scholar

Ben-Jacob, E., Becker, I., Shapira, Y. and Levine, H. (2004) Bacterial linguistic communication and social intelligence. Trends in Microbiology, 12(8), pp. 366–372.CrossRefGoogle ScholarPubMed

Watson, S., Townsend, S., Schel, A., Wilke, C., Wallace, E., Cheng, L., et al. (2015) Vocal learning in the functionally referential food grunts of chimpanzees. Current Biology, 25, pp. 495–499.CrossRefGoogle ScholarPubMed

Harms, W. (2004) Primitive content, translation, and the emergence of meaning in animal communication. In: Kimbrough, D. Oller, U. Griebel, (Eds), Evolution of Communication Systems: A Comparative Approach (Cambridge, MA: MIT Press), pp. 31–48.Google Scholar

Warren, M. Moral Status: Obligations to Persons and Other Living Things (New York: Oxford University Press).Google Scholar

Jaworska, A. and Tannenbaum, J. (2014) Person-rearing relationships as a key to higher moral status. Ethics, 124(2), pp. 242–271.CrossRefGoogle Scholar

In their view, ‘a form of life is given by the practice or activity manifested in that life; the end informs (and forms) what the activity is, and so constitutes that kind of life’ (A. Jaworska and J. Tannenbaum (2014) Person-rearing relationships as a key to higher moral status. Ethics, 124(2), p. 254 fn. 23).Google Scholar