Several evolutionary processes influence virulence, the amount of damage a parasite causes to its host. For example, parasites are favored to exploit their hosts prudently to prolong infection and avoid killing the host. Parasites also need to use some host resources to reproduce and transmit infections to new hosts. Thus parasites face a tradeoff between prudent exploitation and rapid reproduction -a life history tradeoff between logevity and fecundity. Other tradeoffs among components of parasite fitness also influence virulence. For example, competition among parasite genotypes favors rapid growth to achieve greater relative success within the host. Rapid growth may, however, lower the total productivity of the local group by overexploiting the host, which is a potentially renewable food suply. This is a problem of kin selection and group selection. I summarize models of parasite virulence with the theoretical tools of life history analysis, kin selection, and epidemiology. I then apply the theory to recent empirical studies and models of virulence. These applications, to nematodes, to the extreme virulence of hospital epidemics, and to bacterial meningitis, show the power of simple life history theory to highlight interesting questions and to provide a rich array of hypotheses. These examples also show the kinds of conceptual mistakes that commonly arise when only a few components of parasite fitness are analysed in isolation. The last part of the article connects standard models of parasite virulence to diverse topics, such as the virulence of bacterial plasmids, the evolution of genomes, and the processes that influence conflict and cooperation among the earliest replicators near the origin of life.

We typically observe large‐scale outcomes that arise from the interactions of many hidden, small‐scale processes. Examples include age of disease onset, rates of amino acid substitutions and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased or random stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show where the classic patterns come from, such as the Poisson pattern, the normal or Gaussian pattern and many others. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present a simple and consistent informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non‐neutral generative processes that attract to the same neutral pattern.