Being able to measure time, whether directly or indirectly, is a significant advantage for an organism. It allows for timely reaction to regular or predicted events, reducing the pressure for fast processing of sensory input. Thus, clocks are ubiquitous in biology. In the present article, we consider minimal abstract pure clocks in different configurations and investigate their characteristic dynamics. We are especially interested in optimally time-resolving clocks. Among these, we find fundamentally diametral clock characteristics, such as oscillatory behavior for purely local time measurement or decay-based clocks measuring time periods on a scale global to the problem. We include also sets of independent clocks ( clock bags ), sequential cascades of clocks, and composite clocks with controlled dependence. Clock cascades show a condensation effect , and the composite clock shows various regimes of markedly different dynamics.

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.

In this article we study a model for the evolution of the spectral sensitivity of visual receptors for agents in a continuous virtual environment. The model uses a genetic algorithm (GA) to evolve the agent sensors along with the control of the agents by requiring the agents to solve certain tasks in the simulation environment. The properties of the evolved sensors are analyzed for different scenarios. In particular, it is shown that the GA is able to find a balance between sensor costs and agent performance in such a way that the spectral sensor sensitivity reflects the emission spectrum of the target objects and that the capability of the sensors to evolve can help the agents significantly in adapting to their task.