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I argue that the offense generation pattern of slurring terms parallels that of impoliteness behaviors, and is best explained by appeal to similar purely pragmatic mechanisms. In choosing to use a slurring term rather than its neutral counterpart, the speaker signals that she endorses the term. Such an endorsement warrants offense, and consequently slurs generate offense whenever a speaker's use demonstrates a contrastive preference for the slurring term. Since this explanation comes at low theoretical cost and imposes few constraints on (...) |
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Multiarm bandit problems have been used to model the selection of competing scientific theories by boundedly rational agents. In this paper, I define a variable-arm bandit problem, which allows the set of scientific theories to vary over time. I show that Roth-Erev reinforcement learning, which solves multiarm bandit problems in the limit, cannot solve this problem in a reasonable time. However, social learning via preferential attachment combined with individual reinforcement learning which discounts the past, does. |
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Cheap talk has often been thought incapable of supporting the emergence of cooperation because costless signals, easily faked, are unlikely to be reliable (Zahavi and Zahavi, 1997). I show how, in a social network model of cheap talk with reinforcement learning, cheap talk does enable the emergence of cooperation, provided that individuals also temporally discount the past. This establishes one mechanism that suffices for moving a population of initially uncooperative individuals to a state of mutually beneficial cooperation even in the (...) |
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This paper proposes a sender-receiver model to explain two large-scale patterns observed in natural languages: Zipf’s inverse power law relating the frequency of word use and word rank, and the negative correlation between the frequency of word use and rate of lexical change. Computer simulations show that the model recreates Zipf’s inverse power law and the negative correlation between signal frequency and rate of change, provided that agents balance the rates with which they invent new signals and forget old ones. (...) |
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This paper proposes a sender-receiver model to explain two large-scale patterns observed in natural languages: Zipf’s inverse power law relating the frequency of word use and word rank, and the negative correlation between the frequency of word use and rate of lexical change. Computer simulations show that the model recreates Zipf’s inverse power law and the negative correlation between signal frequency and rate of change, provided that agents balance the rates with which they invent new signals and forget old ones. (...) |
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Barrett and others have used Lewis-style evolutionary games to argue that we ought not to trust our scientific languages to inform us about ontology. More specifically, Barrett has shown that in some simple evolutionary contexts the best descriptive languages need not cut nature at its joints, that they may guide action as successfully as possible while simultaneously being deeply conventional. The present article expands upon Barrett’s argument, exploring the space for conventionalism in more metaphysically robust causal evolutionary models. By using (...) |
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