Enhancing decision analysis models with web agents

Decision analysis (DA) can be enhanced by taking advantage of vast, real-time data available from the World Wide Web (the Web). Human intensive DA models such as influence diagrams may be linked with electronic agents that actively utilize the Web to generate data. At the time of modeling, results of agent actions are treated as stochastic events. Probability distributions are assessed conditioned on the range of outcomes for these events. When the DA model is evaluated the agent performs actions defined by the model in terms of the state of nature. Structuring links for these models presents technical challenges including programming and assessment. Agents can operate in the Internet as probes, sensors, monitors, beacons or in other roles, sometimes making information acquisition decisions. Certain managerial decision classes are especially well-suited to this approach

Connecting big data with big decisions: Ideas for synthesizing analytics and decision analysis

This paper describes an approach to connect decision analysis models with outputs of analytic methods applied to various types of big data. Decision analysis models focus on issues of concern to a decision maker and incorporate use of a range of methods and axioms to develop insights about what the decision maker should do. In particular, decision analysis models typically use subjective judgments from the decision maker to describe beliefs about the likelihood of events and the desirability of outcomes. In order for human judgments to be improved by the availability of large amounts of data and processing power, it is necessary to define the right variables to interpolate between the data source and the decision model. Several applications are reviewed and suggest a more general approach