Don’t confuse modeling the system with modeling the decisions

Adam DeJans Jr. wrote: “Modeling the world isn’t the same as making decisions in it! A common trap is to spend months building a perfect simulator… a beautiful digital twin with clean architecture, smooth animations, and every physical nuance modeled. And yet… when it comes time to actually make decisions?

No policy. No framework. Just dashboards and “what-if” buttons.

Always separate the two:

🔹 Modeling the system (the physics, flows, stochastic processes) gives you a sandbox to play in.
🔹 Designing the policy (decision model) means deciding how you’ll act over time, based on what you observe in the system.”
Link

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The Future of the U.S. Insurance Industry

The U.S. insurance industry is at a critical juncture, grappling with a crisis in consumer trust, mounting climate-related risks, and the transformative power of advanced technologies. As insurers navigate these challenges, they must balance innovation with regulatory compliance, prioritize customer-centric approaches, and upskill their workforce to stay ahead.” Discover the key takeaways, trends, and solutions from the new report: The Future of Insurance: Trust, Tech Smart & Talent Focused

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Constraint Modeling Challenge

The annual MiniZinc Challenge celebrates the state of the art in constraint modelling and solving. Across the different problems, this year’s result once again highlighted the diversity of approaches, pushing the boundaries of what’s possible in modelling and solving complex problems. Here are this year’s results:

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Not everything needs reinvention

“Sometimes innovation is just panic dressed as strategy.
Sometimes marketing is just noise to distract from a lack of meaning.
Sometimes chasing growth, valuations, market share etc. can make you forget why you started in the first place.”

https://lnkd.in/eDXuiNvS

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If you want to build real-world decision intelligence…

Adam DeJans Jr. just posted on LinkedIn practical advice on how to build real-world decision intelligence: Most “decision intelligence” projects fail because they skip one thing: Engineering. A model is not a system. A dashboard is not a decision. If you want to build real-world decision intelligence, here are 5 practical principles I use when building production systems:

  1. Build around the decision, not the data
  2. Encode policies, not one-off answers
  3. Build the simulation before the pipeline
  4. Log everything
  5. Test for uncertainty, not just correctness

It’s not about perfect models. It’s about resilient systems that make smart decisions, log them, and learn. Test for uncertainty, not just correctness. Link

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Challenge Aug-2025 “Grid Covering”

This month, we offered our readers to ponder a problem that was among the most complex on the 2025 International Mathematical Olympiad. This problem did stump DeepMind and OpenAI’s models, but it wasn’t just problematic for AI. Of the 630 student contestants, 569 also received zero points. Only six received the full credit of seven points. Will our readers be able to solve it using their favorite tools or just pen and paper? Link

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Measuring AI Impact on Experienced Developers

“Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.” Link

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Interesting Kaddle Competition

Markets don’t always stick to the plan. Like a sudden shift in the weather, price swings can throw off even the best predictions. Investors and businesses need models that can keep up, but many struggle to adapt when past trends no longer apply.

In this Kaddle competition, you’ll develop an ML model to predict commodity prices using historical data from LME, JPX, US Stock, and Forex markets. The challenge is creating a model that stays accurate even as conditions change. Total Prizes: $100,000. Entry Deadline: Sep 29, 2025. Learn more

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Meta-Decision Support

Adam DeJans Jr.: “What if we treated the design of metrics, thresholds, and goals as decisions that need feedback loops and learning just like any other policy? That’s where I think the future lies. Meta-decision support: helping leaders choose what problems to define, what metrics to use, and how to revise those as the environment changes.Link

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Writing is thinking

This article discusses the value of human-generated scientific writing in the age of LLMs. “Writing compels us to think — not in the chaotic, non-linear way our minds typically wander, but in a structured, intentional manner. By writing it down, we can sort years of research, data and analysis into an actual story, thereby identifying our main message and the influence of our work.Link

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