Abstracts - faqs.org

Abstracts

Economics

Search abstracts:
Abstracts » Economics

Optimal growth with unobservable resources and learning

Article Abstract:

The problem of selecting optimal resource consumption from an imperfectly observable aggregate capital, wealth or resource stock which the decisionmaker learns about across time is examined. In this learning scenario, the planner obtains information about an object which is a moving target. Assuming that the utility of zero consumption is negative infinity, the optimal policy observes a fully deterministic 'cautious' or 'minmax' pattern that considers the worst in each period and maximizes against it. The model is then compared to a model with wholly observable wealth levels.

Author: Olson, Lars J., Nyarko, Yaw
Publisher: Elsevier B.V.
Publication Name: Journal of Economic Behavior & Organization
Subject: Economics
ISSN: 0167-2681
Year: 1996
Analysis, Learning, Resource allocation

User Contributions:

Comment about this article or add new information about this topic:

CAPTCHA


Old dogs and new tricks: determinants of the adoption of productivity-enhancing work practices

Article Abstract:

Modelling the employment practices of various firms comprises a large part of theoretical research in economics. Other studies focus on work practices rather than compensation practices, while still others highlight the modelling of groups of work practices instead of individual ones. Such models explain the variations observed in work practices of firms by noting differences in several factors. An empirical investigation of the employment of new work practices in a unique data set is presented. A comment on and general discussion of the article are also included.

Author: Shaw, Kathryn, Ichniowski, Casey, Crandall, Robert W.
Publisher: Brookings Institution
Publication Name: Brookings Papers on Economic Activity
Subject: Economics
ISSN: 0007-2303
Year: 1995
Iron and Steel Mills, Blast furnaces and steel mills, Steel Mill Products, Economic aspects, Steel industry, Steel products, Labor productivity

User Contributions:

Comment about this article or add new information about this topic:

CAPTCHA


A Bayesian learning model fitted to a variety of empirical learning curves

Article Abstract:

A learning model based on the learning curve was presented. Productive efficiency increases through the process of learning by doing actual work. The learning curve was first studied in 1899 when two psychologists discovered that operators of the Morse code became more productive as they gained experience in their work. It was discovered that processes which are focused on learning will have longer learning times, will be complex and will result in inequality among learners. A comment on and general discussion of the article are included.

Author: Griliches, Zvi, Jovanovic, Boyan, Nyarko, Yaw
Publisher: Brookings Institution
Publication Name: Brookings Papers on Economic Activity
Subject: Economics
ISSN: 0007-2303
Year: 1995
Bayesian statistical decision theory, Bayesian analysis, Indifference curves

User Contributions:

Comment about this article or add new information about this topic:

CAPTCHA


Subjects list: Models, Research, Industrial productivity
Similar abstracts:
  • Abstracts: Optimal selling procedures with fixed costs. Rivalrous benefit taxation: the independent viability of separate agencies or firms
  • Abstracts: Public education and income distribution: a dynamic quantitative evaluation of education-finance reform. Michigan's recent school finance reforms: a preliminary report
  • Abstracts: Intraregional foreign investment in East Asia. The determinants of bilateral trade among Asia-Pacific countries
  • Abstracts: The information-integrated channel: a study of the U.S. apparel industry in transition. Market segmentation and the sources of rents from innovation: personal computers in the late 1980s
This website is not affiliated with document authors or copyright owners. This page is provided for informational purposes only. Unintentional errors are possible.
Some parts © 2025 Advameg, Inc.