The simple analytics of commodity futures markets: do they stabilize prices? Do they raise welfare?

This paper uses a simple, graphical approach to analyze what happens to commodity prices and economic welfare when futures markets are introduced into an economy. It concludes that these markets do not necessarily make prices more or less stable. It also concludes that, contrary to common belief, whatever happens to commodity prices is not necessarily related to what happens to the economic welfare of market participants: even when futures markets reduce the volatility of prices, some people can be made worse off. These conclusions come from a series of models that differ in their assumptions about the primary function of futures markets, the structure of the industries involved, and the tastes and technologies of the market participants.Futures ; Commercial products

The influence of DSS types, decision style, and environment on individual decision making

Cognitive style, measured by Myers-Briggs Type Indicator, was used to categorize decision makers. Information source in the form of different DSS types was provided to help the decision makers make more effective decisions. The research attempted to investigate systematically the effects of cognitive style and DSS usage on the decision maker\u27s perception of risk in the context of capital expansion projects. The research encompassed analysis of behavior under conditions of uncertainty for two values of the cognitive dimension, sensing-intuition (S-N), and use of two types of information sources, data-bases DSS (DBDSS) and model based DSS (MBDSS). The behavior was studied within the boundaries of four decision scenarios (2 information sources x 2 cognitive styles). The research attempted to establish the interaction of decision support systems and cognitive style on perceived risk, in a decision-making situation under uncertainty. The decision maker\u27s choice in a risky situation is influenced by the risk perceived by the decision maker. The perception of risk is a result of an interaction between a decision maker\u27s personal characteristics and the environment in which he/she faces the problem. Each type of individual needs the kind of information to which he/she is psychologically attuned in order to use it most effectively. The information needed by the decision maker can come from different types of DSS. DSS supports the decision-making activity and enhances the decision maker\u27s effectiveness. From the literature review, previous researchers have indicated that considering the human variable of cognitive style is very necessary for the successful design of decision support systems. The objective of this research was to study the level of risk perceived by people of different cognitive styles, using different types of decision support systems, when they face problems under uncertainty/. The following research hypothesis was supported in Experiment 2, when decision environment was introduced as a control variable. Perceived risk will be influenced by the compatibility of the information source and the cognitive style of the decision maker

A contingency framework—The influence of computerized information systems on organizational performance

Evaluation is a task most designers, builders, and supporters of information systems agree is significant. However, most information systems evaluations are performance evaluations focusing on the efficiency of the computer system. There is another dimension to the evaluation of the information system that must be considered if computerized information systems (CIS) are to be designed to fit an organization: impact evaluations. Impact evaluations are concerned with those effects on an organization which result from the development and use of an information system. The actual task of performing an impact evaluation is hindered by the complexity of the task and by the apparent lack of methods. The complexity is characterized by the difficulties in choosing measures, by the multiplicity and interactions of factors influencing impacts, by the inability to control some of those factors, and by the varying criteria for judging impacts. The lack of methods is characterized by inexperience and insufficient documentation

Jordan-Schwinger realizations of three-dimensional polynomial algebras

A three-dimensional polynomial algebra of order $m$ is defined by the commutation relations $[P_0, P_\pm]$ $=$ $\pm P_\pm$, $[P_+, P_-]$ $=$ $\phi^{(m)}(P_0)$ where $\phi^{(m)}(P_0)$ is an $m$-th order polynomial in $P_0$ with the coefficients being constants or central elements of the algebra. It is shown that two given mutually commuting polynomial algebras of orders $l$ and $m$ can be combined to give two distinct $(l+m+1)$-th order polynomial algebras. This procedure follows from a generalization of the well known Jordan-Schwinger method of construction of $su(2)$ and $su(1,1)$ algebras from two mutually commuting boson algebras.Comment: 10 pages, LaTeX2

Describing intelligent agent behaviors

The development of new intelligent agents requires an interdisciplinary approach to programming. The initial challenge is to describe the desired agent behaviors and abilities without necessarily committing the agent development project to one particular programming language. What are the appropriate linguistic and logical tools for creating a top level, unambiguous, program-independent, and consistent description of the functions and behaviors of the agent? And how can that description then be translated easily into one of a number of program languages? This article provides a case study of the application of a simple Belief, Desire, and Intention (EDI) first order logic to a complex set of agent functions of a theoretical community of intelligent nano-spacecraft. The basic research was conducted at NASA-GSFC (Greenbelt), Advanced Architecture Branch, during the summer of 2001. The simple examples of applied BDI logic presented here suggest broad application in agent software development

A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Bigdata

In this paper, a multi-step dimension-reduction approach is proposed for addressing nonlinear relationships within attributes. In this work, the attributes in the data are first organized into groups. In each group, the dimensions are reduced via a parametric mapping that takes into account nonlinear relationships. Mapping parameters are estimated using a low rank singular value decomposition (SVD) of distance covariance. Subsequently, the attributes are reorganized into groups based on the magnitude of their respective singular values. The group-wise organization and the subsequent reduction process is performed for multiple steps until a singular value-based user-defined criterion is satisfied. Simulation analysis is utilized to investigate the performance with five big data-sets

Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata

In this paper, generalization error for traditional learning regimes-based classification is demonstrated to increase in the presence of bigdata challenges such as noise and heterogeneity. To reduce this error while mitigating vanishing gradients, a deep neural network (NN)-based framework with a direct error-driven learning scheme is proposed. To reduce the impact of heterogeneity, an overall cost comprised of the learning error and approximate generalization error is defined where two NNs are utilized to estimate the costs respectively. To mitigate the issue of vanishing gradients, a direct error-driven learning regime is proposed where the error is directly utilized for learning. It is demonstrated that the proposed approach improves accuracy by 7 % over traditional learning regimes. The proposed approach mitigated the vanishing gradient problem and improved generalization by 6%