Claude Shannon formalized the notion of information transmission rate and capacity for pre-existing channels. Wittgenstein in his later work insisted that linguistic meaning be defined in terms of use in language games. C. S. Peirce, the father of semiotics, realized the importance of sign, signified, and interpretant in processes of semiosis. In particular, the connection between sign and signified does not take place in a platonic vacuum but is situated, embodied, embedded, and must be mediated by an interpretant. We introduce a rigorous mathematical notion of meaning, as (1) agent- and observer- perceptible information in interaction games between an agent and its environment or between an agent and other agents, that is (2) useful for satisfying homeostatic and other drives, needs, goals or intentions. With this framework it is possible to address issues of sensor- and actuator- design, origins, evolution, and maintenance for biological and artificial systems. Moreover, correspondences between channels of meaning are exploited by biological entities in predicting the behavior or reading the intent of others, as in predator-prey and social interaction. Social learning, imitation, communication of experience also develop and can be developed on this substrate of shared meaning.

Building on the work of Von Neumann, Langton, and Sayama among others, we introduce the rst examples of evolution in populations of self-reproducing con gurations in asynchronous cellular automata. Reliance on a global synchronous update signal has been a limitation of all solutions since the problem of achieving self-production in cellular automata was rst attacked by Von Neumann half a century ago. Results of the author obviate the need for this restriction.

Abstract. We consider temporal aspects of self-replication and evolvability – in particular, the massively asynchronous parallel and distributed nature of living systems. Formal views of self-reproduction and time are surveyed, and a general asynchronization construction for automata networks is presented. Evolution and evolvability are distinguished, and the evolvability characteristics of natural and artificial examples are overviewed. Minimal implemented evolvable systems achieving (1) asynchronous self-replication and evolution, as well as (2) protocultural transmission and evolution, are presented and analyzed for evolvability. Developmental genetic regulatory networks (DGRNs) are suggested as a novel paradigm for massive asynchronous computation and evolvability. An appendix classifies modes of life (with different degrees of aliveness) for natural and artificial living systems and possible transitions between them. 1 Models of Time: Logical vs. Physical Time We consider time in discrete dynamical systems. St. Augustine considered time as something intuitively graspable yet ineffable. Varshavsky distinguished two kinds of time: Time as a logical variable in a system defined by events vs. time as an independent physical variable [96], and studied self-timing and asynchrony theory for computing devices as the problem of reconciling the two types of time via design of system timing for the appropriate functioning asynchronous devices interacting with external environments. For a single observer or location, we can consider three main views of the (logical) time:

role of the macrocyst stage

The opportunistic character of adaptation through natural selection can lead to ‘evolutionary pathologies’—situations in which traits evolve that promote the extinction of the population. Such pathologies include imprudent predation and other forms of habitat over-exploitation or the ‘tragedy of the commons’, adaptation to temporally unreliable resources, cheating and other antisocial behavior, infectious pathogen carrier states, parthenogenesis, and cancer, an intra-organismal evolutionary pathology. It is known that hierarchical population dynamics can protect a population from invasion by pathological genes. Can it also alter the genotype so as to prevent the generation of such genes in the first place, i.e. suppress the evolvability of evolutionary pathologies? A model is constructed in which one locus controls the expression of the pathological trait, and a series of modifier loci exist which can prevent the expression of this trait. It is found that multiple ‘evolvability checkpoint ’ genes can evolve to prevent the generation of variants that cause evolutionary pathologies. The consequences of this finding are discussed. 1

A study is made to learn how multicellular organisms might have emerged from singlecelled organisms. An understanding of this phenomenon might provide a better understanding of natural and man-made multicellular systems. The experiment performed is an Artificial Life simulation that uses Cartesian Genetic Programming to evolve the behaviors of individual cells. Cells have the ability to sense chemicals around them, signal, divide, and move towards or away from chemicals. Interesting group behavior emerged from the simple instruction sets used by the cells.

Cooperation received much less attention 30 years ago than other forms of ecological interaction, such as competition and predation. Workers generally viewed cooperation as being of limited interest, of special relevance to certain species (e.g. social insects, birds, humans and our primate relatives) but not of general significance to life on earth. This view has changed, due in large part to the study of evolutionary transitions in individuality (ETIs). What began as the study of animal social behaviour some 40 years ago has now embraced the study of social interactions at all levels in the hierarchy of life. Instead of being seen as a special characteristic clustered in certain lineages of social animals, cooperation is now seen as the primary creative force behind ever greater levels of complexity through