Cooperation and antagonism in information exchange in a growth scenario with two species.

We consider a simple information-theoretic model of communication, in which two species of bacteria have the option of exchanging information about their environment, thereby improving their chances of survival. For this purpose, we model a system consisting of two species whose dynamics in the world are modelled by a bet-hedging strategy. It is well known that such models lend themselves to elegant information-theoretical interpretations by relating their respective long-term growth rate to the information the individual species has about its environment. We are specifically interested in modelling how this dynamics are affected when the species interact cooperatively or in an antagonistic way in a scenario with limited resources. For this purpose, we consider the exchange of environmental information between the two species in the framework of a game. Our results show that a transition from a cooperative to an antagonistic behaviour in a species results as a response to a change in the availability of resources. Species cooperate in abundance of resources, while they behave antagonistically in scarcity.

An Informational Study of the Evolution of Codes and of Emerging Concepts in Populations of Agents.

We consider the problem of the evolution of a code within a structured population of agents. The agents try to maximize their information about their environment by acquiring information from the outputs of other agents in the population. A naive use of information-theoretic methods would assume that every agent knows how to interpret the information offered by other agents. However, this assumes that it knows which other agents it observes, and thus which code they use. In our model, however, we wish to preclude that: It is not clear which other agents an agent is observing, and the resulting usable information is therefore influenced by the universality of the code used and by which agents an agent is listening to. We further investigate whether an agent that does not directly perceive the environment can distinguish states by observing other agents' outputs. For this purpose, we consider a population of different types of agents talking about different concepts, and try to extract new ones by considering their outputs only.