Application of aggregated indices randomization method for prognosing the consumer demand on features of mobile navigation applications

The issue of this paper is to implement aggregated indices randomization method for prognosing the consumer demand on features of mobile navigation applications. Modern consumers are eager for the applications to provide them with more vast and sophisticated set of options than just building a shortest rout from A to B. Our goal is to analyse and compare the market leading navigating products and to compile the number of necessary and useful features the future product ought to possess for it to be competitive and profitable. After we examined a set of competing products we distinguish the most popular properties they possess. Using the «NNN-information» from several groups of experts, we then range this properties according to their «value» to the predicted success of future application

Диагностика регрессионных уравнений в анализе интенсивности рискованного поведения по его последним эпизодам

We describe a technique that improves the beta-prime models fitted for the last episodes of risky behavior. Regression models show interconnections between rate parameters and respondents’ demographic and psychological trades. We examine these models using such criteria as jackknife and test of overdispersion. Also we develop a method for uncertainty processing in case of a special type of respondents’ answers (―today‖ answers) about the time of their last behavior episode.В статье описан один из возможных подходов к развитию модели, предложенной ранее для обработки сведений о последних эпизодах рискованного поведения. Построены модели, позволяющие определить взаимосвязи между параметрами, определяющими интенсивность поведения, и некоторыми демографическими и психологическими характеристиками респондента. Рассмотрен ряд критериев качества для таких моделей. Кроме того, описан один из методов обработки неопределенности, возникающей при исследовании ответов вида «сегодня» на вопрос о времени последнего эпизода

Подходы к представлению и обработке неопределенности данных и знаний о поведении индивидов

We provide a description of the methods for representation and processing uncertainty that may be implemented to the problem of respondents’ behavior rate estimate on the base of respondents’ self-reports about last behavior episodes. We consider probability approach, Bayesian approach, Dempster–Shafer evidence theory, fuzzy sets theory and their application to the described problem.Предложен обзор средств представления и обработки неопределенности, которые могут оказаться полезными для решения задачи оценки интенсивности и производных характеристик поведения респондентов по их самоотчетам об эпизодах поведения. Рассмотрен вероятностный подход, байесовский подход, теория Демпстера–Шефера, теория нечетких множеств и их приложения к решению указанной задачи

Обработка систематической ошибки, связанной с длиной временных интервалов между интервью и последним эпизодом в гамма-пауссоновской модели поведения

We develop a technique for quantitative estimates of respondents’ behavior that uses respondents’ answers about the time interval since the last episode. The paper provides the block of questions and formalized set of answers to be used in a questionnaire as well as the mathematical approach for data processing and making the estimates. The respondents’ behavior mathematical model under discussion belongs to the class of generalized Gamma-Poisson stochastic process and takes into account the length bias inherent to the data collected from the respondents’ answers about the last episodes of their behavior.Рассматривается подход к оцениванию интенсивности и производных параметров поведения респондентов по сведениям о последнем эпизоде их поведения. В качестве модели поведения предложен гамма-пуассоновский процесс, описаны его характеристики, а также различные варианты его параметризации. Разработан метод, позволяющий обработать систематическую ошибку, возникающую из-за неявного предположения, что момент интервью является эпизодом поведения. В работе также предложены способы обработки исходных данных, характеризующихся гранулярностью

Multicriteria estimation of probabilities on basis of expert non-numeric, non-exact and non-complete knowledge

A new method of alternatives' probabilities estimation under deficiency of expert numeric information (obtained from different sources) is proposed. The method is based on the Bayesian model of uncertainty randomization. Additional non-numeric, non-exact, and non-complete expert knowledge (NNN-knowledge, NNN-information) is used for final estimation of the alternatives' probabilities. An illustrative example demonstrates the proposed method application to forecasting of oil shares price with the use of NNN-information obtained from different experts (investment firms).Non-numeric information (knowledge) Multiple criteria analysis Randomization of uncertainty Random probabilities and weights

An analysis of reasonableness models for research assessments

Individuals who screen research grant applications often select candidates on the basis of a few key parameters; success or failure can be reduced to a series of peer-reviewed Likert scores on as little as four criteria: risk, relevance, return, and reasonableness. Despite the vital impact these assessments have upon the sponsors, researchers, and society in general as a benefactor of the research, there is little empirical research into the peer-review process. The purpose of this study was to investigate how reviewers evaluate reasonableness and how the process can be modeled in a decision support system. The research questions both address the relationship between an individual\u27s estimates of reasonableness and the indicators of scope, resources, cost, and schedule as well as evaluate the performance of several cognitive models as predictors of reasonableness. Building upon Brunswik\u27s theory of probabilistic functionalism, a survey methodology was used to implement a policy-capturing exercise that yielded a quantitative baseline of reasonableness estimates. The subsequent data analysis addressed the predictive performance of six cognitive models as measured by the mean-square-deviation between the models and the data. A novel mapping approach developed by von Helversen and Rieskamp, a fuzzy logic model, and an exemplar model were found to outperform classic linear regression. A neural network model and the QuickEst heuristic model did not perform as well as linear regression. This information can be used in a decision support system to improve the reliability and validity of future research assessments. The positive social impact of this work would be more efficient allocation and prioritization of increasingly scarce research funds in areas of science such as social, psychological, medical, pharmaceutical, and engineering