Skip to main content
SpringerLink
Log in

Evolutionary Explanations of Simple Communication: Signalling Games and Their Models

  • Article
  • Published:
Journal for General Philosophy of Science Aims and scope Submit manuscript

Abstract

This paper applies the theoretical criteria laid out by D’Arms et al. (1998) to various aspects of evolutionary models of signalling. The question that D’Arms et al. seek to answer can be formulated as follows: Are the models that we use to explain the phenomena in question conceptually adequate? The conceptual adequacy question relates the formal aspects of the model to those aspects of the natural world that the model is supposed to capture. Moreover, this paper extends the analysis of D’Arms et al. by asking the following additional question: Are the models that we use sufficient to explain the phenomena in question? The sufficiency question asks what formal resources are minimally required for the model to get the right results most of the time.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. There is, of course, a broader concern about modelling and simulations, in general, and their explanatory significance; however, as a first step toward a critical analysis of the ever-increasing literature on signalling games, I will here take a more narrow focus. However, for more general discussion of modelling see, e.g., Sugden (2000) and Humphreys and Imbert (2012).

  2. Cooperation in this sense requires a notion of joint action—the idea that individuals have shared intentions and, perhaps, awareness of their roles. See Gilbert (1989), Cohen and Levesque (1991), Searle (1995), Clark (1996) and Bratman (1999).

  3. Though the symmetric case is not stated explicitly, Lewis (2002) proves that, in an asymmetric signalling game with m states and n signals (\(n \ge m)\), there are \(\frac{n!}{(n-m)!}\) possible signalling systems. As such, when \(m=n\), as we have here, it follows immediately that there are n! possible signalling systems.

  4. See Lewis’s formal definition of conventions (Lewis 2002, 78–79).

  5. This ‘rational baggage’ is exemplified by Lewis’s discussion of higher-order expectations in the coordination game: “In order to figure out what you will do by replicating your practical reasoning, I need to figure out what you expect me to do. I know that, just as I am trying to figure out what you will do by replicating your reasoning, so you may be trying to figure out what I will do by replicating my reasoning. [...] So I may expect you to try to replicate my attempt to replicate your attempt to replicate my reasoning. So my own reasoning may have to include an attempt to replicate your attempt to replicate my attempt to replicate your attempt to replicate my reasoning. And so on” (Lewis 2002, 27–28). Without an excessive requirement on the rationality of the players, or a prior language already in place, it is not clear how Lewis’s notion of signalling can arise spontaneously.

  6. See Skyrms (1990, 2014) and Vanderschraaf (1995).

  7. In particular, Kullback–Leibler divergence (Kullback and Leibler 1951). See Skyrms (2010a, b).

  8. See Skyrms (2014) for details of this model.

  9. Note, however, that this is also true of fairness as a target phenomenon to be explained by an evolutionary model.

  10. Sterelny (2012) highlights that the underlying problem that the vervet alarm-call system solves is decidedly not a coordination problem since a receiver vervet’s payoff for, e.g., running up a tree (upon hearing bark) does not in any way depend upon the sender vervet’s running up a tree. Thus, the vervets do not have mutually dependent rewards (Sterelny 2012, 76).

  11. Proximate explanations are contrasted with (and complementary to) ultimate explanations. The latter would require detailing the evolutionary trajectory by which signalling arises in addition to the selective forces driving dynamic changes in the population, whereas the former would require describing the developmental and physiological mechanisms by which signalling is implemented. See Mayr (1961).

  12. See also Brusse and Bruner (2017) for an explicit response to several of the worries brought up in Sterelny (2012).

  13. Here, Skyrms is referring to a definition in Jacob Bernoulli’s Art of Conjecture where he says that something is morally certain if its probability comes so close to complete certainty that the difference cannot be perceived. However, it should be noted that in light of subsequent work in this area, Skyrms weakened this claim in a later edition of the book. The claim itself is still true in a particular case; it is just not true generally.

  14. The replicator equations capture the idea that an individual with higher-than-average fitness is more likely to reproduce; the composition of the population changes over time, resulting in corresponding changes to the fitness of a particular strategy relative to the average fitness of the population. See Taylor and Jonker (1978) for further details.

  15. In this case, an individual in the (finite) population is selected proportional to its fitness; that individual produces an identical offspring which then replaces a randomly chosen individual in the population. Crucially, it is possible for a single mutant strategy that has a disadvantage with respect to relative fitness to generate a lineage that eventually takes over the entire population. See Nowak et al. (2004) and Nowak (2006) for further details.

  16. Herrnstein reinforcement learning, based on the matching law (which, in turn, is a formalisation of the law of effect), supposes that the probability of selecting a particular action is proportional to the accumulated rewards for that action. See Thorndike (1905, 1911, 1927) and Herrnstein (1970) for further details.

  17. More complex cases will be examined in Sect. 3.2.

  18. To borrow a turn of phrase from John Woods.

  19. See also Zeeman (1980), Hofbauer and Sigmund (1998, 2003), and Skyrms (2009, 2010a).

  20. That is, each player gets exactly the same payoff. The spectrum that Lewis (2002) refers to is due to Schelling (1980), with games of pure coordination at one extreme, and games of pure conflict (i.e., zero-sum games) at the other extreme.

  21. Note that this question is also taken up in Crawford and Sobel (1982); their results suggest that “perfect communication is not to be expected in general unless agents’ interests completely coincide” (Crawford and Sobel 1982, 1450); however, Ahern and Clark show that when misalignment of preferences is not too strong, a ‘cyclic’ signalling system can evolve—they note a “range of behavior, from separating, to cycling, to collapse” as conflict increases (Ahern and Clark 2014, 31–32). See also Godfrey-Smith and Martínez (2013) and Martínez and Godfrey-Smith (2016).

  22. Small-world network is a technical term characterised by a graph with a specific set of properties—e.g., high clustering coefficient (of nodes), short average path length (between nodes), etc. For example, many forms of the underlying architecture of the internet are small-world networks.

  23. Mühlenbernd and Franke (2014) give a nice overview of how different network topologies shift the basin of attraction for signalling systems and pooling equilibria (by assuming non-equiprobable states).

  24. See also the discussion of correlation in, e.g., Skyrms (1994), D’Arms (1996, 2000), Kitcher (1999), Gintis (2000), Harms (2000) and Alexander (2007).

  25. The results of the Roth–Erev model (Roth and Erev 1995; Erev and Roth 1998) are quite similar to the results of the Bush–Mosteller reinforcement learning model (Bush and Mosteller 1955), where learning parameters play a role analogous to initial weights. See the discussion in Skyrms (2010a, 97–98).

  26. Though, see Huttegger (2007c) for an analysis of robustness across different dynamics.

  27. One such justification is got by analysing the payoffs or success rates for suboptimal pooling strategies. For example, in the \(4\times 4\) signalling game, the most efficient pooling strategy has an expected payoff of 0.75, and a success rate of 0.75. A cutoff of 0.80, in this case, is justified, since a suboptimal random walk may spend some time above the 0.75 success rate before settling into a partial-pooling equilibrium. However, the most efficient pooling strategy in an \(8 \times 8\) signalling game has an expected payoff (and success rate) of 0.875. As such, a cutoff of 0.80 is not warranted here, since this will include many runs that ended up with suboptimal conventions.

  28. See also Skyrms (2010a).

  29. Both Sterelny (2012) and Santana (2014) highlight that signalling in nature is often one-to-many or many-to-many, rather than the neat and tidy bijection that obtains in a signalling system. Thus, in targeting more complex signalling phenomena than merely the emergence of meaning, pooling should be expected.

  30. For an extensive and systematic discussion of the problem of determining whether the right simplifications have been chosen, see Wimsatt (2007).

  31. Orzack and Sober (1993) provide a critical analysis of the views in Levins (1966). See also the response to this in Levins (1993), and the general overview given in Weisberg (2006).

  32. See also Schreiber (2001) for an analysis of the connection between the replicator dynamics and Pólya urns more generally.

  33. For more details on this, see Sandholm (2010).

  34. See also Skyrms (2009, 2010a, b) for other cases of signalling networks.

  35. Note that a lot is being assumed here, so this may seem question-begging. However, Steinert-Threlkeld (2014) has shown that function words, such as ‘not’, may arise in signalling contexts.

  36. See also Barrett et al. (2018) and LaCroix (2019).

  37. See also the iterated learning model (Kirby and Hurford 2002; Smith et al. 2003), which is connected to the signalling game framework by Spike et al. (2013).

  38. Most researchers hold that the key distinction between language and animal communication systems is that the former utilises compositional syntax. See Progovac (2019) for an overview.

References

  • Ahern, C., & Clark, R. (2014). Diachronic processes in language as signaling under conflicting interests. In E. Cartmill, S. Roberts, H. Lyn, & H. Cornish (Eds.), The evolution of language: Proceedings of the 10th international conference on the evolution of language, Vienna, Austria, 14–17 April 2014 (pp. 25–32). Singapore: World Scientific.

  • Alexander, J. M. (2007). The structural evolution of morality. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Alexander, J. M., Skyrms, B., & Zabell, S. L. (2012). Inventing new signals. Dynamic Games and Applications, 2(1), 129–145.

    Article  Google Scholar 

  • Argiento, R., Pemantle, R., Skyrms, B., & Volkov, S. (2009). Learning to signal: Analysis of a micro-level reinforcement model. Stochastic Processes and Their Applications, 119(2), 373–390.

    Article  Google Scholar 

  • Barrett, J. A. (2006). Numerical simulations of the lewis signaling game: Learning strategies, pooling equilibria, and evolution of grammar. Technical Report, Institute for Mathematical Behavioral Science, University of California.

  • Barrett, J. A. (2007). Dynamic partitioning and the conventionality of kinds. Philosophy of Science, 74(4), 527–546.

    Article  Google Scholar 

  • Barrett, J. A. (2009). The evolution of coding in signaling games. Theory and Decision, 67(2), 223–237.

    Article  Google Scholar 

  • Barrett, J. A. (2016). On the evolution of truth. Erkenntnis, 81(6), 1323–1332.

    Article  Google Scholar 

  • Barrett, J. A. (2017). Truth and probability in evolutionary games. Journal of Experimental and Theoretical Artificial Intelligence, 29(1), 219–225.

    Article  Google Scholar 

  • Barrett, J. A., & Skyrms, B. (2017). Self-assembling games. The British Journal for the Philosophy of Science, 68(2), 329–353.

    Article  Google Scholar 

  • Barrett, J. A., Skyrms, B., & Cochran, C. (2018). Hierarchical models for the evolution of compositional language. http://philsci-archive.pitt.edu/14725/. Accessed 30 May 2018.

  • Barrett, J. A., & Zollman, K. (2009). The role of forgetting in the evolution and learning of language. Journal of Experimental and Theoretical Artificial Intelligence, 21(4), 293–309.

    Article  Google Scholar 

  • Batterman, R. W. (2009). Idealization and modeling. Synthese, 169(3), 427–446.

    Article  Google Scholar 

  • Beggs, A. W. (2005). On the convergence of reinforcement learning. Journal of Economic Theory, 122(1), 1–36.

    Article  Google Scholar 

  • Bereby-Meyer, Y., & Erev, I. (1998). On learning to become a successful loser: A comparison of alternative abstractions of learning processes in the loss domain. Journal of Mathematical Psychology, 42(2), 266–286.

    Article  Google Scholar 

  • Berleman, J. E., Scott, J., Chumley, T., & Kirby, J. R. (2008). Predataxis behaviour in Myxococcus xanthus. In Proceedings of the National Academy of Sciences, 105(44), 17127–17132.

  • Bratman, M. E. (1999). Faces of intention. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Bruner, J., O’Connor, C., Rubin, H., & Huttegger, S. M. (2018). David lewis in the lab: Experimental results on the emergence of meaning. Synthese, 195(2), 603–621.

    Article  Google Scholar 

  • Brusse, C., & Bruner, J. (2017). Responsiveness and robustness in the David Lewis signaling game. Philosophy of Science, 84(5), 1068–1079.

    Article  Google Scholar 

  • Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning. Oxford: Wiley.

    Book  Google Scholar 

  • Cheney, D. L., & Seyfarth, R. (1990). How monkeys see the world: Inside the mind of another species. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Clark, H. H. (1996). Using language. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Cohen, P. R., & Levesque, H. J. (1991). Teamwork. Nôus, 25(4), 487–512.

    Google Scholar 

  • Crawford, V. P., & Sobel, J. (1982). Strategic information transmission. Econometrica, 50(6), 1431–1451.

    Article  Google Scholar 

  • D’Arms, J. (1996). Sex, fairness, and the theory of games. Journal of Philosophy, 93(12), 615–627.

    Article  Google Scholar 

  • D’Arms, J. (2000). When evolutionary game theory explains morality, what does it explain? Journal of Consciousness Studies, 7(1–2), 296–299.

    Google Scholar 

  • D’Arms, J., Batterman, R., & Gorny, K. (1998). Game theoretic explanations and the evolution of justice. Philosophy of Science, 65(1), 76–102.

    Article  Google Scholar 

  • Donaldson, M. C., Lachmann, M., & Bergstrom, C. T. (2007). The evolution of functionally referential meaning in a structured world. Journal of Theoretical Biology, 246(2), 225–233.

    Article  Google Scholar 

  • Erev, I., & Roth, A. E. (1998). Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 88(4), 848–881.

    Google Scholar 

  • Fitch, W. T. (2010). The evolution of language. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Fitch, W. T. (2017). Empirical approaches to the study of language evolution. Psychonomic Bulletin & Review, 24(1), 3–33.

    Article  Google Scholar 

  • Franke, M. (2016). The evolution of compositionality in signaling games. Journal of Logic, Language and Information, 25(3), 355–377.

    Article  Google Scholar 

  • Franke, M., de Jager, T., & van Rooij, R. (2012). Relevance in cooperation and conflict. Journal of Logic and Computation, 22(1), 23–54.

    Article  Google Scholar 

  • Franke, M., & Wagner, E. O. (2014). Game theory and the evolution of meaning. Language and Linguistics Compass, 8(9), 359–372.

    Article  Google Scholar 

  • Gilbert, M. (1989). On social facts. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Gintis, H. (2000). Classical versus evolutionary game theory. Journal of Consciousness Studies, 7(1–2), 300–304.

    Google Scholar 

  • Godfrey-Smith, P., & Martínez, M. (2013). Communication and common interest. PLoS Computational Biology, 9(11), 1–6.

    Article  Google Scholar 

  • Greene, E., & Maegher, T. (1998). Red squirrels, Tamiasciurus hudsonicus, produce predator-class specific alarm calls. Animal Behaviour, 55(3), 511–518.

    Article  Google Scholar 

  • Hailman, J., Ficken, M., & Ficken, R. (1985). The ‘chick-a-dee’ calls of Parus atricapillus. Semiotica, 56(3–4), 191–224.

    Google Scholar 

  • Harms, W. F. (2000). The evolution of cooperation in hostile environments. Journal of Consciousness Studies, 7(1–2), 308–313.

    Google Scholar 

  • Harms, W. F. (2004a). Information and meaning in evolutionary processes. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Harms, W. F. (2004b). Primitive content, translation, and the emergence of meaning in animal communication. In D. K. Oller & U. Griebel (Eds.), Evolution of communication systems: A comparative approach (pp. 31–48). Cambridge, MA: The MIT Press.

    Google Scholar 

  • Herrnstein, R. J. (1970). On the law of effect. Journal of Experimental Analysis of Behavior, 13(2), 243–266.

    Article  Google Scholar 

  • Hofbauer, J., & Huttegger, S. (2008). The feasibility of communication in binary signaling games. Journal of Theoretical Biology, 254(4), 843–849.

    Article  Google Scholar 

  • Hofbauer, J., & Sigmund, K. (1998). Evolutionary games and population dynamics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Hofbauer, J., & Sigmund, K. (2003). Evolutionary game dynamics. Bulletin of the American Mathematical Society, 40(4), 479–519.

    Article  Google Scholar 

  • Hopkins, E., & Posch, M. (2005). Attainability of boundary points under reinforcement learning. Games and Economic Behavior, 53(1), 110–125.

    Article  Google Scholar 

  • Humphreys, P., & Imbert, C. (2012). Models, simulations, and representations, routledge studies in the philosophy of science (Vol. 9). New York: Routledge.

    Google Scholar 

  • Huttegger, S. M. (2007a). Evolution and the explanation of meaning. Philosophy of Science, 74(1), 1–27.

    Article  Google Scholar 

  • Huttegger, S. M. (2007b). Evolutionary explanations of indicatives and imperatives. Erkenntnis, 66(3), 409–436.

    Article  Google Scholar 

  • Huttegger, S. M. (2007c). Robustness in signaling games. Philosophy of Science, 74(5), 839–847.

    Article  Google Scholar 

  • Huttegger, S. M., Skyrms, B., Smead, R., & Zollman, K. J. S. (2010). Evolutionary dynamics of Lewis signaling games: Signaling systems vs. partial pooling. Synthese, 172(1), 177–191.

    Article  Google Scholar 

  • Jackendoff, R. S. (1999). Possible stages in the evolution of the language capacity. Trends in Cognitive Sciences, 3(7), 272–279.

    Article  Google Scholar 

  • Jackendoff, R. S. (2002). Foundations of language: Brain, meaning, grammar, evolution. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Kirby, S., & Hurford, J. R. (2002). The emergence of linguistic structure: An overview of the iterated learning model. In A. Cangelosi & D. Parisi (Eds.), Simulating the evolution of language (pp. 121–147). London: Springer.

    Chapter  Google Scholar 

  • Kirchhof, J., & Hammerschmidt, K. (2006). Functionally referential alarm calls in tamarins (Saguinus fuscicollis and Saguinus mystax)—Evidence from playback experiments. Ethology, 112(4), 346–354.

    Article  Google Scholar 

  • Kitcher, P. (1999). Games social animals play: Commentary on Brian Skyrms’ evolution of the social contract. Philosophy and Phenomenological Research, 59(1), 221–228.

    Article  Google Scholar 

  • Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86.

    Article  Google Scholar 

  • LaCroix, T. (2018). On salience and signaling in sender receiver games: Partial pooling, learning, and focal points. Synthese. https://doi.org/10.1007/s11229-018-1766-z.

    Article  Google Scholar 

  • LaCroix, T. (2019). Using logic to evolve more logic: Composing logical operators via self-assembly (unpublished manuscript). http://philsci-archive.pitt.edu/16555/.

  • Levins, R. (1966). The strategy of model building in population biology. In E. Sober (Ed.), Conceptual issues in evolutionary biology (pp. 18–27). Cambridge, MA: The MIT Press.

    Google Scholar 

  • Levins, R. (1993). A response to Orzack and Sober: formal analysis and the fluidity of science. Quarterly Review of Biology, 68(4), 547–555.

    Article  Google Scholar 

  • Lewis, D. (2002). Convention. Oxford: Blackwell. (First published 1969 by Harvard University Press.)

  • Lieberman, P. (2000). Human language and our reptilian brain: The subcortical bases of speech, syntax and thought. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Macedonia, J. M. (1990). What is communicated in the antipredator calls of lemurs: Evidence from antipredator call playbacks to ring-tailed and ruffed lemurs. Ethology, 86(3), 177–190.

    Article  Google Scholar 

  • Martínez, M., & Godfrey-Smith, P. (2016). Common interest and signaling games: A dynamic analysis. Philosophy of Science, 83(3), 371–392.

    Article  Google Scholar 

  • Maynard Smith, J. (1991). Honest signalling: The Philip Sidney game. Animal Behaviour, 42(6), 1034–1035.

    Article  Google Scholar 

  • Mayr, E. (1961). Cause and effect in biology. Science, 134(3489), 1501–1506.

    Article  Google Scholar 

  • Millikan, R. G. (1995). Pushmi-pullyu representations. Philosophical Perspectives, 9, 185–200.

    Article  Google Scholar 

  • Mühlenbernd, R. (2011). Learning with neighbours: Emergence of convention in a society of learning agents. Synthese, 183(S1), 87–109.

    Article  Google Scholar 

  • Mühlenbernd, R., & Franke, M. (2014). Meaning, evolution, and the structure of society. CEUR Workshop Proceedings, 1283, 28–39.

  • Mühlenbernd, R., & Nick, J. D. (2014). Language change and the force of innovation. In M. Colinet, S. Katrenko, & R. K. Rendsvig (Eds.), Pristine perspectives on logic, language, and computation. ESSLLI 2013, ESSLLI 2012. Lecture notes in computer science, Vol. 8607 (pp. 194–213). Berlin/Heidelberg: Springer.

  • Nash, J. (1951). Non-cooperative games. The Annals of Mathematics, 54(2), 286–295.

    Article  Google Scholar 

  • Neumann, J. V., & Morgenstern, O. (2007). Theory of games and economic behavior. Princeton, NJ: Princeton University Press. (First published 1944 by Princeton University Press.)

  • Nowak, M. A. (2006). Evolutionary dynamics: Exploring the equations of life. Cambridge, MA: The Belknap Press of Harvard University Press.

    Book  Google Scholar 

  • Nowak, M. A., & Krakauer, D. C. (1999). The evolution of language. Proceedings of the National Academy of Sciences, 96(14), 8028–8033.

    Article  Google Scholar 

  • Nowak, M. A., Sasaki, A., Taylor, C., & Fudenberg, D. (2004). Emergence of cooperation and evolutionary stability in finite populations. Nature, 428(6983), 646–650.

    Article  Google Scholar 

  • Orzack, S. H., & Sober, E. (1993). A critical assessment of Levins’s ‘The strategy of model building in population biology’ (1966). Biology and Philosophy, 68(4), 533–546.

    Google Scholar 

  • Pawlowitsch, C. (2007). Finite populations choose an optimal language. Journal of Theoretical Biology, 249(3), 606–616.

    Article  Google Scholar 

  • Pawlowitsch, C. (2008). Why evolution does not always lead to an optimal signaling system. Games and Economic Behavior, 63(1), 203–226.

    Article  Google Scholar 

  • Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13(4), 707–727.

    Article  Google Scholar 

  • Pinker, S., & Jackendoff, R. (2005). The faculty of language: What’s special about it? Cognition, 95(2), 201–236.

    Article  Google Scholar 

  • Progovac, L. (2015). Evolutionary syntax. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Progovac, L. (2019). A critical introduction to language evolution: Current controversies and future prospects. Berlin: Springer.

    Book  Google Scholar 

  • Quine, W. V. O. (1967). Truth by covention. In O. H. Lee (Ed.), Philosophical essays for Alfred North Whitehead (pp. 90–124). New York: Russell & Russell.

    Google Scholar 

  • Roth, A. E., & Erev, I. (1995). Learning in extensive form games: Experimental data and simple dynamical models in the intermediate term. Games and Economic Behavior, 8, 164–212.

    Article  Google Scholar 

  • Russell, B. (1922). Analysis of mind. New York: The MacMillan Company.

    Google Scholar 

  • Sandholm, W. H. (2010). Population games and evolutionary dynamics. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Santana, C. (2014). Ambiguity in cooperative signaling. Philosophy of Science, 81(3), 398–422.

    Article  Google Scholar 

  • Schelling, T. C. (1980). The strategy of conflict. Cambridge, MA: Harvard University Press.

  • Schreiber, S. J. (2001). Urn models, replicator processes, and random genetic drift. SIAM Journal on Applied Mathematics, 61(6), 2148–2167.

    Article  Google Scholar 

  • Searle, J. (1995). The construction of social reality. New York: Free Press.

    Google Scholar 

  • Skyrms, B. (1990). The dynamics of rational deliberation. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Skyrms, B. (1994). Darwin meets the logic of decision: Correlation in evolutionary game theory. Philosophy of Science, 61(4), 503–528.

    Article  Google Scholar 

  • Skyrms, B. (2000). Stability and explanatory significance of some simple evolutionary models. Philosophy of Science, 67(1), 94–113.

    Article  Google Scholar 

  • Skyrms, B. (2004). The stag hunt and the evolution of social structure. Cambridge: Cambridge University Press.

    Google Scholar 

  • Skyrms, B. (2009). Evolution of signaling systems with multiple senders and receivers. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1518), 771–779.

    Article  Google Scholar 

  • Skyrms, B. (2010a). Signals: Evolution, learning, & information. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Skyrms, B. (2010b). The flow of information in signaling games. Philosophical Studies, 147(1), 155–165.

    Article  Google Scholar 

  • Skyrms, B. (2014). Evolution of the social contract (2nd ed.). Cambridge: Cambridge University Press.

  • Slobodchikoff, C. N., Kiriazis, J., Fischer, C., & Creef, E. (1991). Semantic information distinguishing individual predators in the alarm calls of Gunnison’s prairie dogs. Animal Behaviour, 42(5), 713–719.

    Article  Google Scholar 

  • Smith, K., Kirby, S., & Brigthon, H. (2003). Iterated learning: A framework for the emergence of language. Artificial Life, 9(4), 371–386.

    Article  Google Scholar 

  • Spelke, E. (2003). What makes us smart? Core knowledge and natural language. In D. Gentner & S. Goldin-Meadow (Eds.), Language in mind: Advances in the investigation of language and thought (pp. 277–311). Cambridge, MA: The MIT Press.

    Google Scholar 

  • Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374.

    Article  Google Scholar 

  • Spike, M., Stadler, K., Kirby, S., & Smith, K. (2013). Learning, feedback and information in self-organizing communication systems. Proceedings of Cogsci, 87(3), 3442–3447.

    Google Scholar 

  • Steinert-Threlkeld, S. (2014). Learning to use function words in signaling games. In E. Lorini & L. Perrussel (Eds.), Proceedings of information dynamics in artificial societies (IDAS-14).

  • Steinert-Threlkeld, S. (2016). Compositional signaling in a complex world. Journal of Logic, Language, and Information, 25(3–4), 379–397.

    Article  Google Scholar 

  • Sterelny, K. (2012). A glass half-full: Brian Skyrms’s signals. Economics and Philosophy, 28(1), 73–86.

    Article  Google Scholar 

  • Sugden, R. (2000). Credible worlds: The status of theoretical models in economics. Journal of Economic Methodology, 7(1), 1–31.

    Article  Google Scholar 

  • Taylor, P., & Jonker, L. (1978). Evolutionarily stable strategies and game dynamics. Mathematical Biosciences, 40(1–2), 145–156.

    Article  Google Scholar 

  • Thorndike, E. L. (1905). The elements of psychology. Syracuse: The Mason Press.

    Book  Google Scholar 

  • Thorndike, E. L. (1911). Animal intelligence: Experimental studies. New York: The Macmillan Company.

    Book  Google Scholar 

  • Thorndike, E. L. (1927). The law of effect. American Journal of Psychology, 39(1/4), 212–222.

    Article  Google Scholar 

  • Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Vanderschraaf, P. (1995). Convention as correlated equilibrium. Erkenntnis, 42(1), 65–87.

    Article  Google Scholar 

  • von Frisch, K. (1967). The dance language and orientation of the bees. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Wagner, E. O. (2009). Communication and structured correlation. Erkenntnis, 71(3), 377–393.

    Article  Google Scholar 

  • Wagner, E. O. (2012). Deterministic chaos and the evolution of meaning. British Journal for the Philosophy of Science, 63(3), 547–575.

    Article  Google Scholar 

  • Weisberg, M. (2006). Robustness analysis. Philosophy of Science, 73(5), 730–742.

    Article  Google Scholar 

  • Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Zeeman, E. C. (1980). Population dynamics from game theory. In Z. Nitecki & C. Robinson (Eds.), Global theory of dynamical systems. Lecture notes in mathematics, Vol. 819 (pp. 471–497). Berlin/Heidelberg: Springer.

  • Zollman, K. J. S. (2005). Talking to neighbors: The evolution of regional meaning. Philosophy of Science, 72(1), 69–85.

    Article  Google Scholar 

  • Zollman, K. J. S. (2011). Separating directives and assertions using simple signaling games. The Journal of Philosophy, 108(3), 158–169.

    Article  Google Scholar 

  • Zuberbühler, K. (2000). Referential labeling in Diana monkeys. Animal Behaviour, 59(5), 917–927.

    Article  Google Scholar 

  • Zuberbühler, K. (2001). Predator-specific alarm calls in Campbell’s monkeys, Cercopithecus campbelli. Behavioural Ecology and Sociobiology, 50(5), 414–422.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Social Sciences and Humanities Research Council of Canada, as well as a fellowship from the Department of Philosophy at Simon Fraser University. I would like to acknowledge the following people for their helpful comments and support during the writing of this paper: Nic Fillion, Matt DeVos, Holly Anderson, Jeffrey Barrett, Brian Skyrms, Louis Narens, Simon Huttegger, Cailin O’Connor, Aydin Mohseni, John Woods, Endre Begby, Gabriel Larivière, Nikolas Hamm, Sarah LaCroix. I would like to thank audiences at the IV Philogica Conference for Logic, Epistemology, and Philosophy of Science in Bogotà, Colombia and the Fall (2016) Social Dynamics Seminar at the Department of Logic and Philosophy of Science at UC Irvine. I would additionally like to thank the anonymous reviewers for extremely helpful comments and criticisms. Finally, thanks to Yoshua Bengio and the Québec AI Institute for providing generous resources.

Author information

Authors and Affiliations

  1. Department of Logic and Philosophy of Science, University of California, Irvine, USA

    Travis LaCroix

  2. Mila (Québec Artificial Intelligence Institute/Institut Québécois d’Intelligence Artificielle), Montréal, Canada

    Travis LaCroix

Authors
  1. Travis LaCroix
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Travis LaCroix.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

LaCroix, T. Evolutionary Explanations of Simple Communication: Signalling Games and Their Models. J Gen Philos Sci 51, 19–43 (2020). https://doi.org/10.1007/s10838-019-09481-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10838-019-09481-7

Keywords

Search

Navigation