This article uses sim-max games to model perceptual categorization with the goal of answering the following question: To what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar results. (...) This conclusion indicates that both strongly realist and strongly antirealist views about perceptual categories are too simple. (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)

Vague predicates, those that exhibit borderline cases, pose a persistent problem for philosophers and logicians. Although they are ubiquitous in natural language, when used in a logical context, vague predicates lead to contradiction. This paper will address a question that is intimately related to this problem. Given their inherent imprecision, why do vague predicates arise in the first place? I discuss a variation of the signaling game where the state space is treated as contiguous, i.e., endowed with a metric that (...) captures a similarity relation over states. This added structure is manifested in payoffs that reward approximate coordination between sender and receiver as well as perfect coordination. I evolve these games using a variation of Herrnstein reinforcement learning that better reflects the generalizing learning strategies real-world actors use in situations where states of the world are similar. In these simulations, signaling can develop very quickly, and the signals are vague in much the way ordinary language predicates are vague—they each exclusively apply to certain items, but for some transition period both signals apply to varying degrees. Moreover, I show that under certain parameter values, in particular when state spaces are large and time is limited, learning generalization of this sort yields strategies with higher payoffs than standard Herrnstein reinforcement learning. These models may then help explain why the phenomenon of vagueness arises in natural language: the learning strategies that allow actors to quickly and effectively develop signaling conventions in contiguous state spaces make it unavoidable. (shrink)

Vague predicates, those that exhibit borderline cases, pose a persistent problem for philosophers and logicians. Although they are ubiquitous in natural language, when used in a logical context, vague predicates lead to contradiction. This paper will address a question that is intimately related to this problem. Given their inherent imprecision, why do vague predicates arise in the first place? I discuss a variation of the signaling game where the state space is treated as contiguous, i.e., endowed with a metric that (...) captures a similarity relation over states. This added structure is manifested in payoffs that reward approximate coordination between sender and receiver as well as perfect coordination. I evolve these games using a variation of Herrnstein reinforcement learning that better reflects the generalizing learning strategies real-world actors use in situations where states of the world are similar. In these simulations, signaling can develop very quickly, and the signals are vague in much the way ordinary language predicates are vague—they each exclusively apply to certain items, but for some transition period both signals apply to varying degrees. Moreover, I show that under certain parameter values, in particular when state spaces are large and time is limited, learning generalization of this sort yields strategies with higher payoffs than standard Herrnstein reinforcement learning. These models may then help explain why the phenomenon of vagueness arises in natural language: the learning strategies that allow actors to quickly and effectively develop signaling conventions in contiguous state spaces make it unavoidable. (shrink)

Lewis signaling games illustrate how language might evolve from random behavior. The probability of evolving an optimal signaling language is, in part, a function of what learning strategy the agents use. Here we investigate three learning strategies, each of which allows agents to forget old experience. In each case, we find that forgetting increases the probability of evolving an optimal language. It does this by making it less likely that past partial success will continue to reinforce suboptimal practice. The learning (...) strategies considered here show how forgetting past experience can promote learning in the context of games with suboptimal equilibria. (shrink)

This paper uses sim-max games to model perceptual categorization with the goal of answering the following question: to what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I will argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar (...) results. This conclusion indicates that both strongly realist and strongly anti-realist views about perceptual categories are too simple. (shrink)

Signaling games with reinforcement learning have been used to model the evolution of term languages (Lewis 1969, Convention. Cambridge, MA: Harvard University Press; Skyrms 2006, “Signals” Presidential Address. Philosophy of Science Association for PSA). In this article, syntactic games, extensions of David Lewis’s original sender–receiver game, are used to illustrate how a language that exploits available syntactic structure might evolve to code for states of the world. The evolution of a language occurs in the context of available vocabulary and syntax—the (...) role played by each component is compared in the context of simple reinforcement learning. (shrink)

Gärdenfors (Conceptual spaces, 2000) argues that the semantic domains that natural language deals with have a geometrical structure. He gives evidence that simple natural language adjectives usually denote natural properties, where a natural property is a convex region of such a “conceptual space.” In this paper I will show that this feature of natural categories need not be stipulated as basic. In fact, it can be shown to be the result of evolutionary dynamics of communicative strategies under very general assumptions.

One problem faced in discussions of the evolution of intelligence is the need to get a precise fix on what is to be explained. Terms like "intelligence," "cognition" and "mind" do not have simple and agreed-upon meanings, and the differences between conceptions of intelligence have consequences for evolutionary explanation. I hope the papers in this volume will enable us to make progress on this problem. The present contribution is mostly focused on these basic and foundational issues, although the last section (...) of the paper will look at some specific models and programs of empirical work. (shrink)

In recent years, many scholars have suggested that the Baldwin effect may play an important role in the evolution of language. However, the Baldwin effect is a multifaceted and controversial process and the assessment of its connection with language is difficult without a formal model. This paper provides a first step in this direction. We examine a game-theoretic model of the interaction between plasticity and evolution in the context of a simple language game. Additionally, we describe three distinct aspects of (...) the Baldwin effect: the Simpson– Baldwin effect, the Baldwin expediting effect and the Baldwin optimizing effect. We find that a simple model of the evolution of language lends theoretical plausibility to the existence of the Simpson– Baldwin and the Baldwin optimizing effects in this arena, but not the Baldwin expediting effect. (shrink)

It is generally thought that cognition evolved to help us navigate complex environments. Social interactions make up one part of a complex environment, and some have argued that social settings are crucial to the evolution of cognition. This article uses the methods of evolutionary game theory to investigate the effect of social interaction on the evolution of cognition broadly construed as strategic learning or plasticity. I delineate the conditions under which social interaction alone, apart from any additional external environmental variation, (...) can provide the selective pressure necessary for the initial evolution of learning. Furthermore, it is argued that in the context of social interactions we should notexpect traditional learners that ‘best-respond’ to dominate the population. Consequently, it may be important to consider non-traditional learners when modelling social evolution. 1 Introduction2 The Model3 Adapting to the Population3.1 Learning an equlibrium4 Adapting to Individuals4.1 Learning a best response and non-traditional learners5 Conclusion. (shrink)

Recent research into the evolution of higher cognition has piqued an interest in the effect of natural selection on the ability of creatures to respond to their environment (behavioral plasticity). It is believed that environmental variation is required for plasticity to evolve in cases where the ability to be plastic is costly. We investigate one form of environmental variation: frequency dependent selection. Using tools in game theory, we investigate a few models of plasticity and outline the cases where selection would (...) be expected to maintain it. Ultimately we conclude that frequency dependent selection is likely insufficient to maintain plasticity given reasonable assumptions about its costs. This result is very similar to one aspect of the well-discussed Baldwin effect, where plasticity is first selected for and then later selected against. We show how in these models one would expect plasticity to grow in the population and then be later reduced. Ultimately we conclude that if one is to account for the evolution of behavioral plasticity in this way, one must appeal to a very particular sort of external environmental variation. (shrink)

Recent research into the evolution of higher cognition has piqued an interest in the effect of natural selection on the ability of creatures to respond to their environment. It is believed that environmental variation is required for plasticity to evolve in cases where the ability to be plastic is costly. We investigate one form of environmental variation: frequency dependent selection. Using tools in game theory, we investigate a few models of plasticity and outline the cases where selection would be expected (...) to maintain it. Ultimately we conclude that frequency dependent selection is likely insuffcient to maintain plasticity given reasonable assumptions about its costs. This result is very similar to one aspect of the well-discussed Baldwin effect, where plasticity is first selected for and then later selected against. We show how in these models one would expect plasticity to grow in the population and then be later reduced. Ultimately we conclude that if one is to account for the evolution of behavioral plasticity in this way, one must appeal to a very particular sort of external environmental variation. (shrink)

Belief-revision models of knowledge describe how to update one’s degrees of belief associated with hypotheses as one considers new evidence, but they typically do not say how probabilities become associated with meaningful hypotheses in the first place. Here we consider a variety of Skyrms–Lewis signaling game (Lewis in Convention. Harvard University Press, Cambridge, 1969; Skyrms in Signals evolution, learning, & information. Oxford University Press, New York, 2010) where simple descriptive language and predictive practice and associated basic expectations coevolve. Rather than (...) assigning prior probabilities to hypotheses in a fixed language then conditioning on new evidence, the agents begin with no meaningful language or expectations then evolve to have expectations conditional on their descriptions as they evolve to have meaningful descriptions for the purpose of successful prediction. The model, then, provides a simple but concrete example of how the process of evolving a descriptive language suitable for inquiry might also provide agents with conditional expectations that reflect the type and degree of predictive success in fact afforded by their evolved predictive practice. This illustrates one way in which the traditional problem of priors may simply fail to apply to one’s model of evolving inquiry. (shrink)