Those of you who follow this blog (or know me) know that I am quite passionate about decision management, and have essentially articulated the past decade of my professional career around the many problems it requires us software and analytic people to solve.
One key issue that keeps bugging me is the lack of support in the academic world for decision management as a full blown discipline available to both business types (your MBA suits) and technical types (your typical CS or analytics specialist).
I wrote a post on this in Carole-Ann Matignon's TechDec - would love to read your comments.
Tuesday, January 26, 2010
Monday, January 25, 2010
Books: Adaptive systems - Dynamic Networks - Evolutionary Dynamics
I just finished reading "Complex Adaptive Systems: An Introduction to Computational Models of Social Life", by John H Miller and Scott E Page. Interesting book, dealing with problems that are dear to me, although from a fairly different perspective from the one I am used to.
I come to this from the angle of decision management. As Carole-Ann Matignon writes in her blog, a lot of developments are expected to make 2010 an interesting year for the discipline. Dealing with uncertainty is one of them, and one of the key sources of uncertainty is related to complexity.
One aspect that deserves attention is how we can manage decisions in light of the effects of the very high level of interconnectedness between multiple entities, with relations that are complex, non linear, etc. In the business world I have up to very recently been active in, this translates into the effects of customer psychology, mass effects, etc. All problems that frequently vex traditional approaches, and tend to create challenges to standard modeling approaches.
The book properly highlights the key difference between a complex system and a complicated system: the complex system's behavior cannot be explained by reducing it to sub-components and explaining the components and their interactions. Which, in a certain sense, creates a significant challenge to some of the traditional scientific or engineering approaches. That is what makes these complex systems so fascinating: they are composed of a multitude of agents inter-related and inter-acting in massive terms, yet they exhibit emerging behavior similar, a posteriori , to that of a single agent but that cannot be explained by decomposing it.
John H Holland, from the Santa Institute, has written a lot about this subject. His analysis of the characteristics of ComplexAdaptive Systems is often reduced to this: order is emergent (or behavior is emergent), history is irreversible, future behavior is often unpredictable. I am not sure about the last point - it's not as much a question of whether the future behavior is predictable, it's much more a question of whether traditional approaches allow the prediction of future behavior.
The key challenge is how to model that behavior. The typical techniques used in most decision management approaches today do not deal with complex systems - they focus only on simple agent behavior, modeling away through extreme simplification the network effects. When those intervene in real life, the models become totally irrelevant - and I would venture to say that a lot of the events we've seen in the past decade and in particular the last couple of years have shown the limits of the simplification. After all, it could be claimed that network effects are largely responsible for the propagation of the bad loan practice, as well as the reactions when the bubble collapsed.
It reminds me of the classical work "The Structure and Dynamics of Networks", another nice - though less accessible, collection of papers (Jean-Marie Chauvet refers to it in another post - in French but I am sure he will gladly translate should you ask for it). Read it, challenging by parts (some I ended up skipping) but good. And let me know what you got from it.
Not in the same space, but connected, I got "Evolutionary Dynamics: Exploring the Equations of Life" by Martin Nowak for my 14 yo old. Probably a little bit too ambitious for him (I read it first and am still waiting for him to pick it up), but the book is beautiful, and the theme is connected to Complex Adaptive Systems. Nowak is an expert, and his book covers a wide range of analytic approaches that should become part of the arsenal of those studying complex systems.
This whole thing may seem like a scientist's dream (remember, I am not a scientist), but the reality is that the large consumer-oriented companies are already dealing with this kind of problem. Just think for a minute about what happens in an EBay auction, or an Amazon recommendation.
I am looking forward to significant synthesis of the approaches, and, as a result, techniques that will enrich in the future the way we approach decision management.
What's your take?
I come to this from the angle of decision management. As Carole-Ann Matignon writes in her blog, a lot of developments are expected to make 2010 an interesting year for the discipline. Dealing with uncertainty is one of them, and one of the key sources of uncertainty is related to complexity.
One aspect that deserves attention is how we can manage decisions in light of the effects of the very high level of interconnectedness between multiple entities, with relations that are complex, non linear, etc. In the business world I have up to very recently been active in, this translates into the effects of customer psychology, mass effects, etc. All problems that frequently vex traditional approaches, and tend to create challenges to standard modeling approaches.
The book properly highlights the key difference between a complex system and a complicated system: the complex system's behavior cannot be explained by reducing it to sub-components and explaining the components and their interactions. Which, in a certain sense, creates a significant challenge to some of the traditional scientific or engineering approaches. That is what makes these complex systems so fascinating: they are composed of a multitude of agents inter-related and inter-acting in massive terms, yet they exhibit emerging behavior similar, a posteriori , to that of a single agent but that cannot be explained by decomposing it.
John H Holland, from the Santa Institute, has written a lot about this subject. His analysis of the characteristics of ComplexAdaptive Systems is often reduced to this: order is emergent (or behavior is emergent), history is irreversible, future behavior is often unpredictable. I am not sure about the last point - it's not as much a question of whether the future behavior is predictable, it's much more a question of whether traditional approaches allow the prediction of future behavior.
The key challenge is how to model that behavior. The typical techniques used in most decision management approaches today do not deal with complex systems - they focus only on simple agent behavior, modeling away through extreme simplification the network effects. When those intervene in real life, the models become totally irrelevant - and I would venture to say that a lot of the events we've seen in the past decade and in particular the last couple of years have shown the limits of the simplification. After all, it could be claimed that network effects are largely responsible for the propagation of the bad loan practice, as well as the reactions when the bubble collapsed.
It reminds me of the classical work "The Structure and Dynamics of Networks", another nice - though less accessible, collection of papers (Jean-Marie Chauvet refers to it in another post - in French but I am sure he will gladly translate should you ask for it). Read it, challenging by parts (some I ended up skipping) but good. And let me know what you got from it.
Not in the same space, but connected, I got "Evolutionary Dynamics: Exploring the Equations of Life" by Martin Nowak for my 14 yo old. Probably a little bit too ambitious for him (I read it first and am still waiting for him to pick it up), but the book is beautiful, and the theme is connected to Complex Adaptive Systems. Nowak is an expert, and his book covers a wide range of analytic approaches that should become part of the arsenal of those studying complex systems.
This whole thing may seem like a scientist's dream (remember, I am not a scientist), but the reality is that the large consumer-oriented companies are already dealing with this kind of problem. Just think for a minute about what happens in an EBay auction, or an Amazon recommendation.
I am looking forward to significant synthesis of the approaches, and, as a result, techniques that will enrich in the future the way we approach decision management.
What's your take?
Wednesday, January 6, 2010
A discovery: PLINQO
I have recently spent some time looking at LINQ-to-SQL to see whether there would be an opportunity to benefit from it and replace things like NHibernate which I am used to. The best result of that effort was the discovery of PLINQO provided by the CodeSmith team you may be familiar with.
Before reading the rest of this short post, check them out: www.plinqo.com (and take also a look at www.codesmithtools.com).
Initially, I was just looking for simple time saving template-based code generation to facilitate the usual fastidious work. I had the surprise to discover that PLINQO also removes a number of the most annoying limitations of LINQ-to-SQL, and that it does that in a safe (no loss of customization, yeah), fairly nice (a lot of developer friendliness in what gets generated) and extensible way.
Three key distinctive features of PLINQO:
- Many-to-Many relationships
- Entity detach
- Complete serialization and cloning
The availability of these three features essentially removes a significant amount of tedious, error prone code with problematic maintenance that LINQ-to-SQL required.
One key area I need to look into is support for the LINQ Dynamic Query Library.
There is much more to PLINQO. More to come.
Before reading the rest of this short post, check them out: www.plinqo.com (and take also a look at www.codesmithtools.com).
Initially, I was just looking for simple time saving template-based code generation to facilitate the usual fastidious work. I had the surprise to discover that PLINQO also removes a number of the most annoying limitations of LINQ-to-SQL, and that it does that in a safe (no loss of customization, yeah), fairly nice (a lot of developer friendliness in what gets generated) and extensible way.
Three key distinctive features of PLINQO:
- Many-to-Many relationships
- Entity detach
- Complete serialization and cloning
The availability of these three features essentially removes a significant amount of tedious, error prone code with problematic maintenance that LINQ-to-SQL required.
One key area I need to look into is support for the LINQ Dynamic Query Library.
There is much more to PLINQO. More to come.
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