Brian Skyrms offers a fascinating demonstration of how fundamental signals are to our world. He uses various scientific tools to investigate how meaning and communication develop. Signals operate in networks of senders and receivers at all levels of life, transmitting and processing information. That is how humans and animals think and interact.

When we speak, we mean more than we say. In this book Stephen C. Levinson explains some general processes that underlie presumptions in communication.

In this paper we study language use and language organisation by making use of Lewisean signalling games. Standard game theoretical approaches are contrasted with evolutionary ones to analyze conventional meaning and conversational interpretation strategies. It is argued that analyzing successful communication in terms of standard game theory requires agents to be very rational and fully informed. The main goal of the paper is to show that in terms of evolutionary game theory we can motivate the emergence and self-sustaining force of (...) (i) conventional meaning and (ii) some conversational interpretation strategies in terms of weaker and, perhaps, more plausible assumptions. (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)

In seeking to explain the evolution of social cooperation, many scholars are using increasingly complex game-theoretic models. These complexities often model readily observable features of human and animal populations. In the case of previous games analyzed in the literature, these modifications have had radical effects on the stability and efficiency properties of the models. We will analyze the effect of adding spatial structure to two communication games: the Lewis Sender-Receiver game and a modified Stag Hunt game. For the Stag Hunt, (...) we find that the results depart strikingly from previous models. In all cases, the departures increase the explanatory value of the models for social phenomena. (shrink)

In this paper I will discuss why (un) marked expressionstypically get an (un)marked interpretation: Horn''sdivision of pragmatic labor. It is argued that it is aconventional fact that we use language this way.This convention will be explained in terms ofthe equilibria of signalling games introduced byLewis (1969), but now in an evolutionary setting. Iwill also relate this signalling game analysis withParikh''s (1991, 2000, 2001) game-theoretical analysis ofsuccessful communication, which in turn is compared withBlutner''s: 2000) bi-directional optimality theory.

Philosophers and social scientists have recently turned to Lewis sender–receiver games to provide an account of how lexical terms can acquire meaning through an evolutionary process. However, the evolution of meaning is contingent on both the particular sender–receiver game played and the choice of evolutionary dynamic. In this paper I explore some differences between models that presume an infinitely large and randomly mixed population and models in which a finite number of agents communicate with their neighbors in a social network. (...) My results show that communication with neighbors is more conducive to the evolution of meaning than communication with strangers. Additionally, I show that the behavior of the system is highly dependent on the topological structure of the social network. I argue that a specific class of networks—small world graphs—is especially conducive to the evolution of meaning. This is because small world graphs have a short characteristic path length while still maintaining a high degree of correlation between neighbors. Since many actual social networks, such as friendship networks and nervous systems, are conjectured to be small world structures, these results indicate that these networks are quite hospitable to the efficient evolution of meaning. (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)