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. (...) Results are robust across a wide range of parameter values and structural assumptions, such as different forgetting rules and forgetting rates. Analysis of the model further suggests that Zipf’s law relating word frequency and rank arises because of language-external factors and that frequent signals change less because frequent signals are less subject to drift than rare ones. The paper concludes with some brief considerations on model-based and data-driven approaches in philosophy. (shrink)

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. (...) Results are robust across a wide range of parameter values and structural assumptions, such as different forgetting rules and forgetting rates. Analysis of the model further suggests that Zipf’s law relating word frequency and rank arises because of language-external factors and that frequent signals change less because frequent signals are less subject to drift than rare ones. The paper concludes with some brief considerations on model-based and data-driven approaches in philosophy. (shrink)

How can players reach a Nash equilibrium? I offer one possible explanation in terms of a low-rationality learning method called probe and adjust by proving that it converges to strict Nash equilibria in an important class of games. This demonstrates that decidedly limited learning methods can support Nash equilibrium play.

The communist norm requires that scientists widely share the results of their work. Where did this norm come from, and how does it persist? Michael Strevens provides a partial answer to these questions by showing that scientists should be willing to sign a social contract that mandates sharing. However, he also argues that it is not in an individual credit-maximizing scientist's interest to follow this norm. I argue against Strevens that individual scientists can rationally conform to the communist norm, even (...) in the absence of a social contract or other ways of socially enforcing the norm, by proving results to this effect in a game-theoretic model. This shows that the incentives provided to scientists through the priority rule are sufficient to explain both the origins and the persistence of the communist norm, adding to previous results emphasizing the benefits of the incentive structure created by the priority rule. (shrink)

Communication involves a great deal of uncertainty. Prima facie, it is therefore surprising that biological communication systems—from cellular to human—exhibit a high degree of ambiguity and often leave its resolution to contextual cues. This puzzle deepens once we consider that contextual information may diverge between individuals. In the following we lay out a model of ambiguous communication in iterated interactions between subjectively rational agents lacking a common contextual prior. We argue ambiguity’s justification to lie in endowing interlocutors with means to (...) flexibly adapt language use to each other and the context of their interaction to serve their communicative preferences. Linguistic alignment is shown to play an important role in this process; it foments convergence of contextual expectations and thereby leads to agreeing use and interpretation of ambiguous messages. We conclude that ambiguity is ecologically rational when interlocutors’ contextual expectations are generally in line or they interact multiple times in an informative context, enabling for the alignment of their expectations. In light of these results meaning multiplicity can be understood as an opportunistic outcome enabled and shaped by linguistic adaptation and contextual information. (shrink)

Communication involves a great deal of uncertainty. Prima facie, it is therefore surprising that biological communication systems—from cellular to human—exhibit a high degree of ambiguity and often leave its resolution to contextual cues. This puzzle deepens once we consider that contextual information may diverge between individuals. In the following we lay out a model of ambiguous communication in iterated interactions between subjectively rational agents lacking a common contextual prior. We argue ambiguity’s justification to lie in endowing interlocutors with means to (...) flexibly adapt language use to each other and the context of their interaction to serve their communicative preferences. Linguistic alignment is shown to play an important role in this process; it foments convergence of contextual expectations and thereby leads to agreeing use and interpretation of ambiguous messages. We conclude that ambiguity is ecologically rational when interlocutors’ contextual expectations are generally in line or they interact multiple times in an informative context, enabling for the alignment of their expectations. In light of these results meaning multiplicity can be understood as an opportunistic outcome enabled and shaped by linguistic adaptation and contextual information. 1Meaning Multiplicity in Communication 2Ambiguous Signalling through Pragmatic Inference 2.1Preliminaries 2.2Signalling behaviour 2.3Communicative success 2.4Iterated interactions 3Predictions for Single and Iterated Interactions 3.1Simulations 3.2Exploration and past experience 3.3Preemptive adaptation 4Discussion 5Conclusion Appendix. (shrink)

‘Noise’ in this study, in the sense of evolutionary game theory, refers to deviations from prevailing behavioral rules. Analyzing data from a laboratory experiment on coordination in networks, we tested ‘what kind of noise’ is supported by behavioral evidence. This empirical analysis complements a growing theoretical literature on ‘how noise matters’ for equilibrium selection. We find that the vast majority of decisions constitute myopic best responses, but deviations continue to occur with probabilities that are sensitive to their costs, that is, (...) less frequent when implying larger payoff losses relative to the myopic best response. In addition, deviation rates vary with patterns of realized payoffs that are related to trial-and-error behavior. While there is little evidence that deviations are clustered in time or space, there is evidence of individual heterogeneity. (shrink)

I discuss a game-theoretic model in which scientists compete to finish the intermediate stages of some research project. Banerjee et al. have previously shown that if the credit awarded for intermediate results is proportional to their difficulty, then the strategy profile in which scientists share each intermediate stage as soon as they complete it is a Nash equilibrium. I show that the equilibrium is both unique and strict. Thus rational credit-maximizing scientists have an incentive to share their intermediate results, as (...) long as this is sufficiently rewarded. (shrink)