Insofar as empirical inquiry involves the coevolution of descriptive language and theoretical commitments, a satisfactory model of empirical knowledge should describe the coordinated evolution of both language and theory. But since we do not know what conceptual resources we might need to express our future theories or to provide our best future faithful descriptions of the world, we do not now know even what the space of future descriptive options might be. One strategy for addressing this shifting-resource problem is to (...) track the predictive and linguistic dispositions of inquirers rather than to track their theories and conceptual resources directly. Sender-predictor games, a variant of Skyrms–Lewis sender-receiver games, provide very simple models for the coordinated coevolution of predictive and linguistic dispositions. Such models explain how it is possible for (1) predictive and descriptive dispositions of inquirers to coevolve, (2) term-wise incommensurable, but nevertheless descriptively faithful languages, to sequentially evolve, and (3) a sort of underdetermination to occur where inquirers might satisfy their descriptive and predictive aims by revising their linguistic dispositions, their theoretical dispositions, or both. Such models also provide an elementary characterization of what it might mean for descriptions of the world to be faithful and hence for empirical inquiry to be successful. In doing so they provide a relatively weak, but perfectly clear, endogenous account of epistemic norms. (shrink)

In his new foreword to this edition, Hilary Putnam forcefully rejects these nativist claims.

This article concerns how rule-following behavior might evolve, how an old evolved rule might come to be appropriated to a new context, and how simple concepts might coevolve with rule-following behavior. In particular, we consider how the transitive inferential rule-following behavior exhibited by pinyon and scrub jays might evolve in the context of a variety of the Skyrms-Lewis signaling game, then how such a rule might come to be appropriated to carry out inferences regarding stimuli different from those involved in (...) the original evolution of the rule, and how this appropriation involves a step toward the evolution of basic ordinal concepts. (shrink)

Sender–receiver games, first introduced by David Lewis ([1969]), have received increased attention in recent years as a formal model for the emergence of communication. Skyrms ([2010]) showed that simple models of reinforcement learning often succeed in forming efficient, albeit not necessarily minimal, signalling systems for a large family of games. Later, Alexander et al. ([2012]) showed that reinforcement learning, combined with forgetting, frequently produced both efficient and minimal signalling systems. In this article, I define a ‘dynamic’ sender–receiver game in which (...) the state–action pairs are not held constant over time and show that neither of these two models of learning learn to signal in this environment. However, a model of reinforcement learning with discounting of the past does learn to signal; it also gives rise to the phenomenon of linguistic drift. 1 Introduction2 Dynamic Signalling Games with Reinforcement Learning2.1 Introducing new states2.2 Swapping state–action pairs3 Discounting the Past3.1 Learning to signal in a dynamic world3.2 An unexpected outcome: linguistic drift4 ConclusionAppendix: A Markov Chain Analysis. (shrink)

To coordinate action, information must be transmitted, processed, and utilized to make decisions. Transmission of information requires the existence of a signaling system in which the signals that are exchanged are coordinated with the appropriate content. Signaling systems in nature range from quorum signaling in bacteria [Schauder and Bassler, Kaiser ], through the dance of the bees [Dyer and Seeley ], birdcalls [Hailman, Ficken, and Ficken, Gyger, Marler and Pickert, Evans, Evans, and Marler, Charrier and Sturdy ], and alarm calls (...) in many species [Seyfarth and Cheney, Green and Maegner,Manser, Seyfarth and Cheney ], up to human language. Information processing includes filtering – that is discarding irrelevant information and passing along what is important – and integration of multiple pieces of information. Integration includes logical inference and voting. Finally, the information must be used to make decisions with consequences for payoffs that drive evolution or learning. (shrink)

Lewis sender‐receiver games illustrate how a meaningful term language might evolve from initially meaningless random signals (Lewis 1969; Skyrms 2006). Here we consider how a meaningful language with a primitive grammar might evolve in a somewhat more subtle sort of game. The evolution of such a language involves the co‐evolution of partitions of the physical world into what may seem, at least from the perspective of someone using the language, to correspond to canonical natural kinds. While the evolved language may (...) allow for the sort of precise representation that is required for successful coordinated action and prediction, the apparent natural kinds reflected in its structure may be purely conventional. This has both positive and negative implications for the limits of naturalized metaphysics. (shrink)

Signaling games provide basic insights into some fundamental questions concerning the explanation of meaning. They can be analyzed in terms of rational choice theory and in terms of evolutionary game theory. It is argued that an evolutionary approach provides better explanations for the emergence of simple communication systems. To substantiate these arguments, I will look at models similar to those of Skyrms (2000) and Komarova and Niyogi (2004) and study their dynamical properties. My results will lend partial support to the (...) thesis that evolution leads to communication. In general, states of partial communication may evolve with positive probability under standard evolutionary dynamics. However, unlike states of perfect communication, they are unstable relative to neutral drift. (shrink)

Transfer of information between senders and receivers, of one kind or another, is essential to all life. David Lewis introduced a game theoretic model of the simplest case, where one sender and one receiver have pure common interest. How hard or easy is it for evolution to achieve information transfer in Lewis signaling?. The answers involve surprising subtleties. We discuss some if these in terms of evolutionary dynamics in both finite and infinite populations, with and without mutation.

Recently there has been some interest in studying the explanation of meaning by using signaling games. I shall argue that the meaning of signals in signaling games remains sufficiently unclear to motivate further investigation. In particular, the possibility of distinguishing imperatives and indicatives at a fundamental level will be explored. Thereby I am trying to preserve the generality of the signaling games framework while bringing it closer to human languages. A number of convergence results for the evolutionary dynamics of our (...) models will be proved. (shrink)

In response to those who argue for ‘property cluster’ views of natural kinds, I use evolutionary models of sim-max games to assess the claim that linguistic terms will appropriately track sets of objects that cluster in property spaces. As I show, there are two sorts of ways this can fail to happen. First, evolved terms that do respect property structure in some senses can be conventional nonetheless. Second, and more crucially, because the function of linguistic terms is to facilitate successful (...) action in the world, when such success is based on something other than property clusters, we should not expect our terms to track those clusters. The models help make this second point salient by highlighting a dubious assumption underlying some versions of the cluster kinds view—that property clusters lead to successful generalization and induction in a straightforward way. As I point out, those who support property cluster kinds as natural can revert to a promiscuous realism in response to these observations. (shrink)

In response to those who argue for ‘property cluster’ views of natural kinds, I use evolutionary models of similarity-maximizing games to assess the claim that linguistic terms appropriately track sets of objects that cluster in property spaces. As I show, there are two sorts of ways this can fail to happen. First, evolved terms that do respect property structure in some senses can be conventional nonetheless. Second, and more crucially, because the function of linguistic terms is to facilitate successful action (...) in the world, when such success is based on something other than property clusters, we should not expect our terms to track those clusters. The models help make this second point salient by highlighting a dubious assumption underlying some versions of the cluster kinds view—that property clusters lead to successful generalization and induction in a straightforward way. As I point out, those who support property cluster kinds as natural can revert to a promiscuous realism in response to these observations. 1 Introduction2Cluster Kinds3Models and Results 3.1Similarity-maximizing games3.2Voronoi languages, clusters, and categories4Conventionality and Functionality in Kind Terms 4.1Conventional categories4.2Functionality and categories5Unnatural Perceptual Categories6The Payoff Relevance of Property Space7Conclusion. (shrink)

I explore an agent-based model of the development and dissemination of scientific theory that makes very little use of any pre-defined “social structure” (such as partnerships or collaborations). In these models, under a broad range of values of the parameters, widespread (but not universal) “agreement” about scientific theory emerges. Moreover, the residual disagreement turns out to be important to developing new theories in the face of new evidence.