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This research in the public interest was supported by a generous grant from Frederick S. Pardee to develop new methods for conducting longer term global policy and improving the future human condition. RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND ® is a registered trademark. RAND's(More)
Agent-based models (ABM) are examples of complex adaptive systems, which can be characterized as those systems for which no model less complex than the system itself can accurately predict in detail how the system will behave at future times. Consequently, the standard tools of policy analysis, based as they are on devising policies that perform well on(More)
Variability is one of the most salient features of the earth's climate, yet quantitative policy studies have generally ignored the impact of variability on society's best choice of climate-change policy. This omission is troubling because an adaptive emissions-reduction strategy, one that adjusts abatement rates over time based on observations of damages(More)
1. We describe an exploratory approach to the parametric modeling of dynamical (time-varying) neurophysiological data. The models use stimulus data from a window of time to predict the neuronal firing rate at the end of that window. The most successful models were feedforward three-layered networks of input, hidden, and output "nodes" connected by weights(More)
RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND ® is a registered trademark. RAND's publications do not necessarily reflect the opinions or policies of its research sponsors. All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means(More)
This product is part of the RAND Infrastructure, Safety, and Environment working paper series. RAND working papers are intended to share researchers' latest findings and to solicit additional peer review. This paper has been peer reviewed but not edited. Unless otherwise indicated, working papers can be quoted and cited without permission of the author,(More)
Agent-based modeling (ABM) is a powerful representational formalism that has wide utility for modeling nonlinear systems. For ABM to achieve its potential as a scientific tool, our ability to build models that embody our knowledge must be complemented by rigorous means for making inferences using such models. Due to nonlinearity, this rigor cannot in(More)
* The work reported here was done in collaboration with Robert Lempert and Steven Popper. The reader should assume the good ideas are theirs and the poor exposition is mine. Abstract Real institutions are " open " , which can result in unpredictable changes in both their internal resources and external environment. This implies that a broadly useful(More)
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