Language Evolution and Computation Bibliography

Iterated learning takes place when the input into a particular individual’s learning process is itself the output of another individual’s learning process. This is an important feature to capture when investigating human language change, or the dynamics of culturally learned behaviours in general. Over the last fifteen years, the Iterated Learning Model (ILM) has been used to shed light on how the population-level characteristics of learned communication arise. However, until now each iteration of the model has tended to feature a single immature language user learning from their interactions with a single mature language user. Here, the ILM is extended to include a population of immature and mature language users. We demonstrate that the structure and make-up of this population influences the dynamics of language change that occur over generational time. In particular, we show that, by increasing the number of trainers from which an agent learns, the agent in question learns a fully compositional language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds even if a learner’s trainers include other agents that do not yet posses full linguistic competence.

Observations of alarm calling behavior in putty-nosed monkeys are suggestive of a link with human language evolution. However, as is often the case in studies of animal behavior and cognition, competing theories are underdetermined by the available data. We argue that computational modeling, and in particular the use of individual-based simulations, is an effective way to reduce the size of the pool of candidate explanations. Simulation achieves this both through the classification of evolutionary trajectories as either plausible or implausible, and by putting lower bounds on the cognitive complexity required to perform particular behaviors. A case is made for using both of these strategies to understand the extent to which the alarm calls of putty-nosed monkeys are likely to be a good model for human language evolution.

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Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshop's eight speakers, plus a section designed to reflect the debates that took place during the workshop's discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor.

Simulated evolution is used as a tool for investigating the selective pressures that have influenced the design of animal signalling systems. The biological literature on communication is first reviewed: central concepts such as the handicap principle and the view of signalling as manipulation are discussed. The equation of ``biological function'' with ``adaptive value'' is then defended, along with a workable definition of communication. Evolutionary simulation models are advocated as a way of testing the coherence of a given theory. Contra some ALife enthusiasts, simulations are not alternate worlds worthy of independent study; in fact they fit naturally into a Quinean picture of scientific knowledge as a web of modifiable propositions. Existing simulation work on the evolution of communication is reviewed: much of it consists of simple proofs of concept that fail to make connections with existing theory. A particular model (MacLennan and Burghardt, 1994) of the evolution of referential communication in a co-operative context is replicated and critiqued in detail.

Evolutionary simulations are then presented that cover a range of ecological scenarios; the first is a general model of food- and alarm-calling. In such situations signallers and receivers can have common or conflicting interests; the model allows us to test the idea that a conflict of interests will lead to an arms race of ever more costly signals, whereas common interests will result in signals that are as cheap as possible. The second model is concerned with communication during aggressive interactions. Many animals use signals to settle contests, thus avoiding the costs associated with fighting. Conventional game-theoretic results suggest that the signalling of aggression or of strength will not be evolutionarily stable unless it is physically unfakeable, but some recent models imply that cost-free, arbitrary signals can be reliable indicators of both intent and ability. The simulation, which features continuous-time perception of the opponent's strategy, is an attempt to settle the question. The third model deals with sexual signalling, i.e., elaborate displays that are designed to persuade members of the opposite sex to mate. The results clarify the question of whether such displays are the pointless result of runaway sexual selection, or whether they function as honest and costly indicators of genetic quality.

The models predict the evolution of reliable communication in a surprisingly narrow range of circumstances; a serious gap remains between these predictions and the ethological data. Future directions for simulation work are discussed.