Abstract
Empirical inquiry involves the coevolution of predictive theory and descriptive language. Here we consider how one might model this coevolution using the tools of evolutionary game theory. We will see how subsequently evolved languages might exhibit semantic drift, invention, and discard. These evolutionary models also illustrate how subsequently evolved languages might be incommensurable yet nevertheless provide faithful descriptions of nature. Finally, we will consider how a model for the coevolution of predictive theory and descriptive language accounts for endogenous epistemic norms.
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Notes
- 1.
All of the quotations from Quine here are from the last section of his 1951 paper “Two Dogmas of Empiricism.” It is here where he gives his positive view of nature of empirical inquiry in terms of a pragmatic belief-revision model of knowledge.
- 2.
The first edition of Kuhn’s Structure of Scientific Revolutions was published in 1962, a few years following Quine’s “Two Dogmas of Empiricism.”
- 3.
For just a bit more detail on the three examples, epicycles are no longer required to explain the retrograde motion of planets, the entangled states of particles at difference locations explain the empirical violation of Bell-type inequalities, and we needed some biological theory to properly classify whales.
- 4.
And we will consider the evolution of the notion of truth itself in the companion paper (Barrett 2021) in this volume.
- 5.
- 6.
Lewis (1969) first presented this sort of game in the context of classical game theory as a way of studying how conventions might be established. Skyrms (2006, 2010) translated Lewis’ signaling games into the context of evolutionary game theory. Here one need not suppose that the agents are in any way rationally sophisticated. Rather, in an evolutionary game, the agents may start with no knowledge whatsoever and gradually learn as they interact with each other and the world. Further, there is no need for some sort of natural salience to break the symmetry between equally good conventions. In an evolutionary game, such symmetries may be broken by random fluctuations in the dispositions of the agents as they evolve.
- 7.
We will consider other learning dynamics in other models. Different learning dynamics exhibit different properties. Indeed, as we will see in the companion paper, the agents in the present game do not do nearly as well evolving an optimal descriptive language and predictive dispositions if they learn by simple reinforcement without bounds or punishment.
- 8.
These are the mean cumulative success rates on 103 runs with 106 plays per run.
- 9.
As we will see in the companion paper, the level of success in this game is a function of the particular learning dynamics.
- 10.
- 11.
This is again on 103 runs with 106 plays per run.
- 12.
See Barrett (2007a) for a discussion of the evolution of natural kind language.
- 13.
This is a variation of the two-sender game above. This type of game was suggested in conversation by Michael Dickson. It is first discussed in Barrett (2009).
- 14.
The second stage of the game is run with the same parameters as the first stage.
- 15.
To get a sense of how the behavior of this present model scales, one might try an eight-state, three-sender (each with only two terms) game with changing reinforcements. One would predict on the present considerations that the receiver would revise his predictive behavior yet more often in the context of the more complicated evolved language.
- 16.
See Barrett and Zollman (2009) for a discussion of the role of forgetfulness in learning.
- 17.
We will discuss simple reinforcement learning in more detail in the companion paper in this volume (Barrett 2021).
- 18.
- 19.
- 20.
See Skyrms (2010) for a discussion of information transfer in signaling games. The partition that one uses to measure the information context of the signals might be stipulated from the god-eye-view. But the inquirer’s might also measure the information content of their signals relative to the partition of states induced by their evolved descriptions. See the section on indifference in the other Barrett paper (2021) in this volume for more details regarding the properties of such induced partitions.
- 21.
Barrett (2008) discusses the sense in which one can know that one’s descriptions of approximately true without know how they are approximately true.
- 22.
In this regard, the present model shares much with the sort of belief-revision model of knowledge championed by C. S. Peirce. See, for example, Peirce’s papers “The Fixation of Belief” and “How We Make our Ideas Clear” in Houser and Kloesel (1992).
- 23.
- 24.
I would like to thank Brian Skyrms for many conversations on the topics of the paper and Travis LaCroix for helpful comments on an earlier draft.
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Barrett, J. (2021). Scientific Inquiry and the Evolution of Language. In: Gonzalez, W.J. (eds) Language and Scientific Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-60537-7_4
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