While a large body of work exists on presidents' public approval, no study identifies the conditions under which approval generates policy influence. This gap is particularly significant since empirical research has produced inconsistent findings on whether popularity affects a president's legislative success. In the following, we argue that public salience and issue complexity determine the extent to which a president can capitalize on approval, and we proceed to test this hypothesis on U.S. House of Representatives roll-call votes between 1989 and 2000. The empirical analysis provides strong support for our hypothesis, which holds across a variety of econometric specifications and estimates of approval.
Self-reported regulatory data are hard to verify. This article compares air emissions reported by plants in the Toxics Release Inventory with chemical concentration levels measured by EPA pollution monitors. We find that the large drops in air emissions reported by firms in the TRI are not always matched by similar reductions in measured concentrations from EPA monitors. When the first digits of the monitored chemical concentrations follow a monotonically decreasing distribution, we expect (via Benford's Law) a similar distribution of first digits for the TRI data. For lead and nitric acid the self-reported data do not follow the expected first digit pattern. This suggests that for these two heavily regulated chemicals plants are not reporting accurate estimates of their air emissions. Copyright Springer Science + Business Media, Inc. 2006Toxics release inventory, Benford's law, Reporting accuracy, Information provision,
Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world.
The road to science is paved with sacrifices. Sacrifices to learn and pursue rigor, under the strict constraints of any formalism. Sacrifices to collect empirical data and to search for empirical foundations of models. Sacrifices to fine-tune theories and empirical findings. Sacrifices to avoid the sirens' song of social science philosophers, theorists, and pamphletists, who never did analytical science and empirical research. This book can be seen as a kind of homage to real social scientists, as well as good bread for daily use. In fact, the author doesn't limit himself to argue in favour of an analytical approach to social sciences, but suggests a framework for a unified model-based approach to social sciences, trying to combine game theory, statistics and computational methods. The author rightly argues that, even if most of scientists would see these as different methods or even theories, formal models, statistics and computation should be viewed just as different tools of the same scientific enterprise. The author's purpose is both to defend the social sciences from the anti-positivistic and post-modernistic drifts, and to improve the capacity of science to tackle complex problems and challenges, increasing, at the same time, theoretical transparency and empirical foundations of models. In doing so, he strengthens the foundations of a computational approach to social science. It is good to see that he tried to do it with a clear-cut and well written book, with examples, intuitions, and personal anecdotes.
Bargaining, Gamson鈥檚 law, Game theory, Social choice theory, Coalition formation, Formateur,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright 漏 2024 scite LLC. All rights reserved.
Made with 馃挋 for researchers
Part of the Research Solutions Family.