Additive and Multiplicative Models and Multistage Carcinogenesis Theory

In light of the Armitage-Doll multistage carcinogenesis theory, this paper examines the assumption that an additive relative risk relationship is indicative of two carcinogens that affect the same stage in the cancer process. We present formulas to compute excess cancer risks for a variety of patterns for limited exposure durations to two carcinogens that affect the first and penultimate stages; and using an index of synergy proposed by Thomas (1982), we find a number of these patterns to produce additive, or nearly additive, relative risk relationships. The consistent feature of these patterns is that the two exposure periods are of short duration and occur close together.     

Modelling multibrand consumption

In this article a model for the multibrand consumption of fast moving non-durable consumer goods is presented. The model is derived by the mixing of a multinomial distribution (representing the brand consumption probabilities of an individual household) with the continuous multivariate Beta (Dirichlet) distribution (which allows for differences between households). The core of the analysis involves the use of complete bivariate tables. The model is shown to provide an adequate representation of a set of consumption data and to satisfactorily estimate the various brand market shares.     

Topological aggregation of preferences: the case of a continuum of agents

 This paper studies the topological approach to social choice theory initiated by G. Chichilnisky (1980), extending it to the case of a continuum of agents. The social choice rules are continuous anonymous maps defined on preference spaces which respect unanimity. We establish that a social choice rule exists for a continuum of agents if and only if the space of preferences is contractible. We provide also a topological characterization of such rules as generalized means or mathematical expectations of individual preferences. Received: 30 November 1994/Accepted: 22 April 1996     

Interest Organizations,Information, and Policy Innovation in the U.S. Congress1 2

Interest organizations are hypothesized to strongly affect public policy, but the evidence that they do so is mixed. This article argues that one reason for the disparity is a gap between theory and research: theory suggests that information provided by interest organizations should strongly influence elected officials, but there is no systematic research on its impact. We examine a potentially important source of information for members of the U.S. Congress—testimony at committee hearings—to ascertain if it affects the enactment of policy proposals. The data, based on content analysis of almost 1,000 testimonies on a stratified random sample of policy proposals, describe who testifies, their arguments, and the evidence they provide. Supporters of a proposal emphasize the importance of the problem being addressed, while opponents claim the proposed policy will be ineffective and try to reframe the debate. Information—particularly information regarding policy effectiveness—does affect the likelihood that a policy proposal will be enacted.     

Introduction of a child sexual abuse policy in a health district

SUMMARY. Three one day workshops for 40 senior clinical staff were held to introduce a new Operational Policy on Child Sexual Abuse (CSA) and to provide them with a teaching package to enable them to run ‘cascade’ teaching sessions for other staff and colleagues. Part I of this paper describes an evaluation of the workshop; Part II describes an evaluation of the cascade The workshop evaluation showed that knowledge about CSA and policy issues was already good, but although that knowledge did improve after the workshop, attitudes about sexual behaviour were not changed by the workshop. Evaluation of the cascade also showed an increase in knowledge after the dissemination exercise     

Risk,Media, and Stigma at Rocky Flats

Public responses to nuclear technologies are often strongly negative. Events, such as accidents or evidence of unsafe conditions at nuclear facilities, receive extensive and dramatic coverage by the news media. These news stories affect public perceptions of nuclear risks and the geographic areas near nuclear facilities. One result of these perceptions, avoidance behavior, is a form of "technological stigma" that leads to losses in property values near nuclear facilities. The social amplification of risk is a conceptual framework that attempts to explain how stigma is created through media transmission of information about hazardous places and public perceptions and decisions. This paper examines stigma associated with the U.S. Department of Energy's Rocky Flats facility, a major production plant in the nation's nuclear weapons complex, located near Denver, Colorado. This study, based upon newspaper analyses and a survey of Denver area residents, finds that the social amplification theory provides a reasonable framework for understanding the events and public responses that took place in regard to Rocky Flats during a 6-year period, beginning with an FBI raid of the facility in 1989.     

Statistical mechanical analysis of the dynamics of learning in perceptrons

We describe the application of tools from statistical mechanics to analyse the dynamics of various classes of supervised learning rules in perceptrons. The character of this paper is mostly that of a cross between a biased non-encyclopaedic review and lecture notes: we try to present a coherent and self-contained picture of the basics of this field, to explain the ideas and tricks, to show how the predictions of the theory compare with (simulation) experiments, and to bring together scattered results. Technical details are given explicitly in an appendix. In order to avoid distraction we concentrate the references in a final section. In addition this paper contains some new results: (i) explicit solutions of the macroscopic equations that describe the error evolution for on-line and batch learning rules; (ii) an analysis of the dynamics of arbitrary macroscopic observables (for complete and incomplete training sets), leading to a general Fokker–Planck equation; and (iii) the macroscopic laws describing batch learning with complete training sets. We close the paper with a preliminary expose´ of ongoing research on the dynamics of learning for the case where the training set is incomplete (i.e. where the number of examples scales linearly with the network size).