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big data - artificial intelligence decision models

Monty Python Had It Right: “I’m Not Dead!”

I had the opportunity to attend a conference last week on big data, machine learning, and artificial intelligence that brought back images from Monty Python and the Holy Grail. The Governor of Virginia, Terry McAuliffe, gave a rousing keynote speech on how well the Commonwealth of Virginia is doing in autonomous vehicles, cybersecurity, vineyards, and even microbreweries. Then, the panelists came out and clanged the proverbial bell
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Using Decision Models for Multi-Criteria Decision-Making

Buying a new home, relocating to a new city, making a healthcare decision for an aging family member – these are all multi-criteria decisions that can be complex and stressful when there are only one or two decision-makers. Presume for a moment that you and ten members of your extended family need to make a group decision. Better yet, not one of the decisions, but all of the decisions –
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group decision support, definitive business solutions

Group Decision Support for Agency Budget Formulation

  Over the last few years, the compliance-driven “slice of the budget pie” has grown considerably in government agencies. In the good ole days, we contended with the classic trade-off of how much should be spent on new initiatives and enhancements versus operations and maintenance (O&M). The more efficiency that we could achieve in O&M, the more financial and human resources would be available to do new things and address new mission needs. Read More
group decision support, analytic hierarchy process

Bringing Group Decision Support into the 21st Century

  If time travel were possible, I’d like to go back and facilitate the boardroom decisions when Blockbuster passed on Netflix, when Excite passed on Google, or when Mars passed on placing its M&M product in movie E.T. These are a few of the biggest blunders in the history of business. I can’t help but wonder what their decision criteria was, whether they were accurately weighted to reflect their relative importance,
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