This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. � 1999 John Wiley &

ABSTRACT This paper clarifies one of the puzzling results of the economic growth literature: the impact of military expenditure is frequently found to be non-significant or negative, yet most countries spend a large fraction of their GDP on defense and the military. We start by empirical evaluation of the non-linear interactions between military expenditure, external threats, corruption, and other relevant controls. While growth falls with higher levels of military spending, given the values of the other independent variables, we show that military expenditure in the presence of threats increases growth. We explain the presence of these non-linearities in an extended version of Barro and Sala-i-Martin (1995), allowing the dependence of growth on the severity of external threats, and on the effective military expenditure associated with these threats.

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me1 Mono-methylation me2 Di-methylation me3 Tri-methylation LC-MS/MS Tandem liquid chromatography mass spectrometry PIC Phenyl isocyanate HCD higher energy collisional dissociation C18 Carbon 18 column ac Acetylation

In a recent paper, Brodie and Brodie provide a very detailed description of advances and counter-measures among predator–prey communities with a poisonous prey that closely parallel an arms race in modern society. In this work, we provide a mathematical model and simulations that provide a theory as to how this might work. The model is built on a twodimensional classical predator–prey model that is then adapted to account for the genetics and random mating. The deterministic formulation for the genetics for the prey population has been developed and used in other contexts. Adapting the model to allow for genetic variation in the predator is much more complicated. The model allows for the evolution of the poisonous prey and for the evolution of the resistant predator. The biological paradigmis that of the poisonous newt and the garter snake which has been studied extensively although the models are broad enough to cover other examples.

Turing instability and traveling fronts for a nonlinear reaction-diffusion system with cross-diffusion

Cross-diffusion driven instability in a

Cross-diffusion, the phenomenon in which a gradient in the concentration of one species induces a flux of another chemical species, has generally been neglected in the study of reaction–diffusion systems. We summarize experiments that demonstrate that cross-diffusion coefficients can be quite significant, even exceeding ‘‘normal,’ ’ diagonal diffusion coefficients in magnitude in systems that involve ions, micelles, complex formation, excluded volume effects (e.g., surface or polymer reactions) and other phenomena commonly encountered in situations of interest to chemists. We then demonstrate with a series of model calculations that cross-diffusion can lead to spatial and spatiotemporal pattern formation, even in relatively simple systems. We also show that, in the absence of cross-diffusion among the reacting species, introduction of a nonreactive species that induces appropriate cross-diffusive fluxes with reactive species can lead to pattern formation. 1.

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