An Artificial Laboratory Environment for Studying Distributed Decision Processes.

We examine the feasibility of using an adaptive systems approach for generating the non-linear and dynamic aspects of distributed decision processes. First, the issues that need to be considered in modeling agent interactions are discussed. We then present details of a computational prototype based on the interplay of agents and their actions. Our model represents agent decision making as an adaptive search activity. The agents in our model learn by using a system that rewards strategies that generate high payoffs and penalize strategies that do not

Data Mining for Decision Support

The amount of data collected by businesses today is phenomenal. The analysis of this data is critical as more and more businesses are using this data to analyze their competition, product or market. Data mining is the process of digging through this mass of data to discover information (patterns or new knowledge) that can be critical to decision making in organizations. Data mining has added importance as organizations begin to rely more heavily on this information to make critical decisions. The need for using the right data mining tools effectively to support decision making cannot be overemphasized

Risk Visualization: A Mechanism for Supporting Unstructured Decision Making Processes

The premise of this paper is that risk visualization has the potential to reduce the seemingly irrational behavior of decision makers. In this context, we present a model that enhances our understanding of visualization and how it can be used to support risk based decision making. The contribution of our research stems from the fact that decision making scenarios in business are characterized by uncertainty and a lack of structure. The complexity inherent in such scenarios is manifested in the form of unavailability of information, too many alternatives, inability to quantify alternatives, or lack of knowledge of the payoff matrix. This is particularly prevalent in domains such as investment decision making. Rational decision making in such domains requires a careful assessment of the risk reward payoff matrix. However, individuals cope with such uncertainty by resorting to a variety of heuristics. Prior decision support models have been unsuccessful in dealing with complexity and nuances that have come to typify such heuristic based decision making

Towards An Architecture for Acquiring, Representing, and Mining Informal Knowledge

Organizations generally make use of two kinds of knowledge: formal and informal. The two can be distinguished based on the extent of documentation. Formal knowledge is usually contained in books and manuals. On the other hand, informal knowledge could include assumptions, ideas, and viewpoints. From an organizational perspective, it also includes the culture, the shared beliefs, the core values, and very often past experiences or contexts in which decisions were made. Examples of informal knowledge can be seen in answering questions like: Why did we do it that way? ; What happened the last time we tried this approach? ; Who would I go to solve this problem? ; How are things done around here? ; and so on. Anand et al., (1998) refer to this as “soft knowledge” or knowledge that cannot be easily communicated. This includes tacit knowledge, belief structures, intuition, and judgmental abilities. The label of organizational memory collectively describes both formal and informal knowledge primitives. Stein and Zwass (1995) define organizational memory as the means by which knowledge from the past is brought to bear on present activities, thus resulting in higher or lower levels of organizational effectiveness