These notes correspond to a set of lectures given at the Univer sity of Alberta during the spring semester, 1973. The first four sec tions present a systematic development of a deterministic, threshold model for the spraad of an infection. Section 5 presents some compu tational results and attempts to tie the model with other mathematics. In each of the last three sections a separate, specialized topic is presented. The author wishes to thank Professor F. Hoppensteadt for making available preprints of two of his papers and for reading and comment ing on a preliminary version of these notes. He also wishes to thank Professor J. Mosevich for providing the graphs in Section 5. The visit at the University of Alberta was a very pleasant one and the author wishes to express his appreciation to Professors S. Ghurye and J. Macki for the invitation to visit there. Finally, thanks are due to the very competent secretarial staff at the University of Alberta for typing the original draft of the lecture notes and to Mrs. Ada Burns of the University of Iowa for her excellent typescript of the final version. TABLE OF CONTENTS 1. A Simple Epidemic Model with Permanent Removal . . . • . . . 1 2. A More General Model and the Determination of the Intensity of an Epidemic. 10 21 3. A Threshold Model. 4. A Threshold Model with Temporary Immunity. 34 5. Some Special Cases and Some Numerical Examples 48 A Two Population Threshold Model . 62 6.

First multi-year cumulation covers six years: 1965-70.

The laws and methodology of physics are starting to provide powerful insights into the nature and dynamics of computation. This book contains a number of articles that illustrate how fields ranging from quantum mechanics to statistical physics and nonlinear dynamics can help elucidate the nature of computation.

Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.

This book is based on a series of lectures on mathematical biology, the essential dynamics of complex and crucially important social systems, and the unifying power of mathematics and nonlinear dynamical systems theory.