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Responsibility, authority, and insanity

So this is pretty basic.  But it’s huge.

Problem: You are working at a bank and have been tasked with developing a new customer care program.  You’re making good progress, and reach out one day to a colleague for their ideas.  Word of the meeting gets around, and an executive walks into your office one day, assuming you’re floundering, and tells you what to do.

Problem: Your second grader is in trouble at school.  Teachers and other parents call you on the phone, asking you to fix the problem.  You are starting to develop some good ideas and making plans.  But one day, the school principal makes the decision to move your child to a special classroom.

Problem: You work on an automobile assembly line.  Your bonus depends on the quality of the cars you help to build.  You see a problem with a welding machine which would cost $100 to fix.  But management won’t approve the repair.

What’s the common pattern here?  It’s responsibility without authority: a good recipe for insanity.

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About Levers

I’m going to write a series of posts about the core elements of a decision model.  This one’s about Levers: simulations of things you can change as you make the decision.   We might also have called levers choices.

It can be confusing: you’d think that a decision model would produce the choices as output, not as input.  Because it’s supposed to tell us what decision to make, right?  But things are a little backwards: the right decision is the one for which the levers will set in motion a chain of events, that in the future will lead to a desired outcome.  So from this point of view, the action of a lever belongs at the beginning of a decision model, not the end. Continue reading

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Decision Intelligence in conflict and disaster recovery

In our years building decision intelligence models for domains like banking, telecom, and more, the project that I am most proud of is the work that we did for Liberia in collaboration with The Carter Center.

The basic idea: countries are complex systems. Understanding how to recover after a conflict or disaster can be a particular challenge. Decision makers often end up working accidentally at cross purposes, due to shared, but invisible, mental models of a situation. This often produces unintended negative consequences. Continue reading

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Predictive analytics is not enough

The idea of predictive analytics can seem like magic: how, really, can a computer predict the future? Yet we’ve seen a lot of success based on this advanced technology in recent years, from Netflix to Amazon, Google, and more. These companies mine a massive amount of data every day for patterns, and it drives massive revenues.

However, for a widespread class of situations, predictive analytics alone aren’t enough. Consider the decision model below, which I introduced in my last post. The blue graphs on the right-hand side are based on predictive analytics, but they are only building blocks in the full model.  They are not enough on their own.

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Announcing interactive web-based decision intelligence

At Quantellia, we’ve been delivering enterprise-scale, desktop- and PC-based decision intelligence models to our clients for a few years now, using our World Modeler™ software.  In the last few months, every single one of our clients has asked for our work to be delivered through a web interface, so we’ve been heads-down in delivery and development to meet their needs.  These are not available to be viewed by the general public, however, so I’ve spent the last few days building a demonstration to show you what we do, and as part of my answer to a recent Quora question on Agency theory as well as Mark’s upcoming talk on this topic at MLConf Seattle.

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Decision models are the requirements language for DI apps

The software engineering revolution is happening again.

I was a coder before software engineering, and it wasn’t pretty.  When we needed to build a new program, we’d get together with the end customer, and ask a lot of questions, then go back to the office to write code.  It didn’t go very well.  It was like construction without blueprints, manufacturing without CAD. Continue reading

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Meat and Bones: Machine learning and DI working together for evidence-based management

I had a great call with the CEO of a possible partner company for Quantellia this week, where I found myself saying that Decision Intelligence is the “bones” to the “meat” of machine learning.   The image above shows what I mean.  Each star shows an influence link, and each on of these is a possible place where the results of machine learning—whether it’s a decision tree, neural network, simple linear regression, or even a deep learner—contributes to the decision model. As you can see: lots of stars = lots of ways that machine learning can help a decision model to be as powerful as it can be.

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