The Robust Volterra Principle

Theorizing in ecology and evolution often proceeds via the construction of multiple idealized models. To determine whether a theoretical result actually depends on core features of the models and is not an artifact of simplifying assumptions, theorists have developed the technique of robustness analysis, the examination of multiple models looking for common predictions. A striking example of robustness analysis in ecology is the discovery of the Volterra Principle, which describes the effect of general biocides in predator-prey systems. This paper details the discovery of the Volterra Principle and the demonstration of its robustness. It considers the classical ecology literature on robustness and introduces two individual-based models of predation, which are used to further analyze the Volterra Principle. The paper also introduces a distinction between \emph{parameter robustness}, \emph{structural robustness}, and \emph{representational robustness}, and demonstrates that the Volterra Principle exhibits all three kinds of robustness

Is culture inherited through social learning?

In this article I challenge the widely held assumption that human culture is inherited by means of social learning. First, I address the distinction between “social” learning and “individual” learning. I argue that most cultural ideas are not acquired by one form of learning or the other, but from a hybrid of both. Second, I discuss how individual learning can interact with niche construction. I argue that these processes collectively provide a non-social route for learned ideas to be inherited and cumulatively modified. I conclude that human culture is not inherited by social learning alone; the capacities to learn from and modify our environments also play a significant role

Manipulation and the Causes of Evolution

Evolutionary processes such as natural selection and random drift are commonly regarded as causes of population-level change. We respond to a recent challenge that drift and selection are best understood as statistical trends, not causes. Our reply appeals to manipulation as a strategy for uncovering causal relationships: if you can systematically manipulate variable A to bring about a change in variable B, then A is a cause of B. We argue that selection and drift can be systematically manipulated to produce different kinds of population-level change. They should therefore be regarded as causes

To be published in Cognition and Instruction, 2006

Thinking Like a Wolf 2 Biological phenomena can be investigated at multiple levels, from the molecular to the cellular to the organismic to the ecological. In typical biology instruction, these levels have been segregated. Yet, it is by examining the connections between such levels that many phenomena in biology, and complex systems in general, are best explained. We describe a computation-based approach that enables students to investigate the connections between different biological levels. Using agent-based, embodied modeling tools, students model the micro-rules underlying a biological phenomenon, and observe the resultant aggregate dynamics. We describe two cases in which this approach was employed. In both cases, students frame hypotheses, construct multi-agent models that incorporate these hypotheses, and test these by running their models and observing the outcomes. Contrasting these cases against traditionally employed, classical equations-based approaches, we argue that the embodied modeling approach connects more directly to students ’ experience, enables extended investigations as well as deeper understanding, and enables “advanced ” topics to be productively introduced into the high school curriculum. Thinking Like a Wolf, a Sheep, or a Firefly