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

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

Military development and applications of simulation systems

Simulations used in training attempt to portray reality for the units or individuals participating in training. Some of the benefits of simulation training include; cost efficient training, training units in tasks that are too dangerous to perform otherwise, and training units in complex tasks in a repetitive fashion