Gartner’s Definition of “Open Systems”

Posted on August 17, 2024 by decisionmanagementcommunity

David Pidsley, The Decision Strategist at Gartner wrote: By “open” we’re referring to an open systems computing design pattern that allows for easy integration with other tools and platforms, often through well-documented APIs and standard protocols. Open systems provide some combination of interoperability, portability, and open software standards. This may but isn’t necessarily open source (with permission to use, copy and distribute it, either as is or with modifications, and that may be offered either free or with a charge – i.e. where the source code must be made available). Link

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Decision Model as Approximation for Business Problem

Posted on August 16, 2024 by decisionmanagementcommunity

Carlos Armando Zetina: “The model is an approximation for a business’s decision problem because it inevitably needs to make simplifying assumptions such as limited scope, finite horizons, deterministic parameters, and cost approximations to be amenable to solving. Since the optimality gap is a measure based on the model, using it to measure a solution’s success is misleading.

What matters to the business is its Key Performance Indicators (KPIs) e.g. out of stock, actual transportation costs, lead times, revenue, profit margins and other KPIs the solution can directly impact. These KPIs should be tracked continuously before and after the optimization solution is in production, guiding model adjustments to make decisions that align better with the ultimate business goals. Optimization solutions in industry are living entities that require continuous improvement and KPI monitoring. Solving the model is only the beginning of the decision science process.” Link

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Solutions for July-2024 Challenge “Smart Investment”

Posted on August 11, 2024 by decisionmanagementcommunity

We received 9 solutions for our July-2024 Challenge “Smart Investment”.
Used decision optimization tools:
– IBM CPLEX
– Llama3/CPLEX/watsonx
– Corticon
– Prolog
– ChatGPT/Zimpl
– IPython/ortools
– cDMN
– Excel Solver
– OpenRules Rule Solver.

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Problem-solve with your peers

Posted on August 6, 2024 by decisionmanagementcommunity

Gartner distributed a nicely formulated email: “Where is your next great idea going to come from? With an industry and role that is constantly evolving, some of the greatest insights you can get are from your peers.” It is true for several upcoming conferences such as:

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When will anyone start making actual money from AI?

Posted on August 5, 2024 by decisionmanagementcommunity

Wall Streeters have been asking this question for weeks and some for months: “When will anyone start making actual money from artificial intelligence?  “In the 18 months since ChatGPT kicked off an AI arms race, tech giants have promised that the technology is poised to revolutionize every industry and used it as justification for spending tens of billions of dollars on data centers and semiconductors needed to run large AI models. Compared to that vision, the products they’ve rolled out so far feel somewhat trivial — chatbots with no clear path to monetization, cost saving measures like AI coding and customer service, and AI-enabled search that sometimes makes things up.” Link

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About LLM Hallucinations

Posted on August 1, 2024 by decisionmanagementcommunity

Andriy Burkov: “I often hear that LLMs hallucinate because they weren’t trained on quality data. This is not what a hallucination is. A hallucination is a situation when the model generates information that was close to the fringe of its training domain. So, you cannot fix hallucinations by providing better data because the fringe of the training set will not go anywhere; it can only change the shape.”

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Dantzig’s Story

Posted on July 27, 2024 by decisionmanagementcommunity

“If I had known that the problems were not homework but were in fact two famous unsolved problems in statistics, I probably would not have thought positively, would have become discouraged, and would never have solved them.” George Dantzig

The movie “Good Will Hunting” has a scene that is inspired by the life of George Dantzig, the inventor of the simplex algorithm who introduced the world to the power of optimization. In the movie, Matt Damon plays an MIT janitor who anonymously solves a difficult math problem that a math professor posted on a hallway blackboard. In real life, George Dantzig was studying statistics under professor Jerzy Neyman at UC Berkeley. Here is how Dantzig described it: “I arrived late one day at one of Neyman’s classes. On the blackboard there were two problems that I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework — the problems seemed to be a little harder than usual.” Read more here.

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 AI Colors Past Videos

Posted on July 27, 2024 by decisionmanagementcommunity

“This is mind-blowing AI-restored footage (by HistoryColored) – from 1896 of the cities Paris and Lyon in France. You can see pedestrians and vehicles of the past in various locations across the two cities. This footage was primarily filmed and produced by pioneers of motion pictures, the Lumière brothers. Brothers Auguste and Louis Lumière created one of the first motion picture cameras, the Cinématographe Lumière. Locations featured in this video are: The Eiffel Tower, Place des Cordeliers, Place de la Concorde, and Place du Pont.” Link

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More about Decision Fairness

Posted on July 25, 2024 by decisionmanagementcommunity

The question “Could we achieve fairness in our automatic decision-making?” continues to be on minds of many decision intelligence partitioners. Jacob Feldman in his new post “How decision models deal with fairness” looks at 7 real-world decision-making applications in development of which he was involved. Majority of these applications dealt with complex business problems in which “fairness” was presented (or not) in clearly defined business objectives. Geoffrey De Smet considers “What is fair?” in the context of employee scheduling. From cashiers to nurses to police officers: everyone demands a fair shift schedule. He refers to the new article “Load balancing and fairness in constraints” from Timefold in which they define constraints that penalize possible solutions based on their unfairness. The overwhelming interest in the above publications shows that decision fairness will continue to be a hot topic.

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Making decisions within an uncertain environment

Posted on July 23, 2024 by decisionmanagementcommunity

Warren Powell provided interesting comments the the latest NY Times article “When it comes to math, AI is dumb” that states: “Early computers followed rules. AI follows probabilities. But in mathematics, there is no probable answer, only the right one”.

Probabilists will argue (correctly) that we have very elegant mathematics for describing probabilities. After all, the world is stochastic, and we need to model this uncertainty. Industries like airlines and hotels have mastered the art of modeling an uncertainty as part of their revenue management problems. @optimaldynamics models the uncertainty of shipper demands when planning fleets.

It is true that when making decisions, we ultimately have to pick a single decision despite having to consider its impact on an uncertain future. And in theory there is a single optimal decision, not just in terms of maximizing performance now, but which maximizes performance into an uncertain future. However, finding what we think (and hope) is the best decision, we absolutely have to model the uncertainties that affect how well this decision performs. Link

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