Cause-alities

Making Sense with Systems Science

When I named Decisio (almost 10 years ago now!) I was casting about for a tag line that extended the "decision motif" to capture the essence of what we do.  I settled on "Making Sense of the Future."  My idea was (and is) that if clients are going to be able to make good decisions in complicated situations then they first had to understand that situation -- they had to "make sense" of what was happening.  Then, they could use that understanding to make good decisions.  The invocation of the "future" in this was intended, firstly, to suggest that comprehending the role of time is important to understand problems.  Secondly, that we make decisions today in order to reap rewards in the future.

This idea of using systems modeling to "make sense" and support decision making was not and still isn't very common.  There seem to be two prevailing ideas about the role of models and modeling.  One common view is that they are sophisticated black box tools that consume data and produce predictions of the future.  My observation is that while good models have predictive qualities the future is slippery. All models are wrong (but some are useful). Decision making based on a "forecast" mentality will not turn out well.  An alternative perspective is that, since forecasting is difficult or impossible, modeling should be used for individual and organizational learning.  Well, that's fine but sooner or later somebody has to make decisions!

I've recently become aware of the science and some of the research around the formal idea of "sensemaking."  Gary Klein, well known in the field, describes sensemaking as "a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively".  Well, that's exactly what I help clients accomplish using systems models.  In my projects the modeling activity guides an effective sensemaking process that results in high quality decisions.

Recently, I think I've been guilty of describing my work from the perspective of systems science and modeling to the detriment of the "making sense of the future" perspective.  In fact, successful projects always integrate modeling with the sensemaking perspective.

I think that the intersection of systems modeling and sensemaking is not as well explored as it needs to be so I'll be blogging more about it.   To read more about sensemaking in general try this wikipedia article and publications by Gary Klein and K. E. Weick.

What's System Science and Why Should We Care?

It is very common for people to use the idea of a "system" pretty freely when discussing their ideas, projects, and problems .  Alas, they often have a pretty fuzzy idea about what a system is and how that perspective can be put to work.   "Systems Science" offers a concise definition of a system that is easy to contrast with traditional analytics.  In this post I'd like to start at the beginning and try to create a clear mental image of what systems science is and how it provides useful insights.

Creating a Future

I recently spent some time speaking with the director of a well known consulting firm about the possibility of incorporating complexity science models into his practice.  I was struck by his choice of language as he described to me what he was trying to accomplish for his clients.

He said that he was trying to make the "cause-and-effect relationship between the clients decisions and the resulting outcome clear."  For example, he went on to say that sometimes his clients did not seem to realize that saving $2 here was going to cost $2000 somewhere else.  Or, alternatively, the client might have a correct intellectual understanding of a situation but due to momentum or other pressures found it impossible to make decisions consistent with the outcome they wanted.  He went on to say that this was symptomatic of a general difficulty in seeing business issues holistically.

I got really excited as I listened to his story.  Recently, I have been wondering if naming this blog "Cause-alities" was wise. Much writing about complexity science seems to emphasize the elusiveness of cause and effect relationships and seem to suggest that we cannot understand the consequences of our decisions.  Well, this is where the "attitude" part of my blog's tagline kicks in.

In matters of management I think that leaders need to invest in understanding as much of the cause and effect chain that drives their business as they can. They will find the real value of a "systems understanding" (to rename the cause-and-effect chain a little bit) in the realization that, to a large extent, the future they get is the one that they create through the decisions that they make. 

This is a vastly more powerful notion than the traditional "predict the future and I'll react" mentality.

What this boils down to is that in much of the business system the "cause-and-effect chain" is really a "cause-and-effect loop."  And our decisions are in fact part of that "cause-and-effect loop."

In practice, the only way to develop deep causal insight into business systems is through a complexity science simulation model.  As I've written before a spreadsheet, while sometimes useful, is inadequate to describe how a business works because business systems are feedback systems and spreadsheets simply don't have the expressive power to describe feedback. 

Finally, I have to acknowledge that not everything that happens in our business is something that we created through our actions.  The environment is important and external events are important.  Nevertheless, in most cases, the trajectory of the firm is a result of management leadership, not outside factors.

Modeling and Simulation

Industrial Science makes a good point about why we need simulation models and how, conceptually, the process of building a useful model works. 

I like to summarize the process of creating and using models into roughly three activities.  First, frame the problem by identifying the behaviors of interest, choosing system boundaries that will capture these behaviors and stating a structural hypothesis about how the system works to create the behaviors of interest.

Second, build a simulation model to test the hypothesis.

Finally, use the model to find management policies that improve system behavior..

Obviously, in practice this entire process is a bit iterative (you know: rinse, repeat).

In practice, applying complexity science means writing and using simulation models.

Extending "Predictive Analytics" Using Complexity Science Models -- Part 1

"Predictive Analytics" is the "next generation of data-mining" according to  Forrester Research. The promise of significant bottomline benefits is making it big business with most enterprise software vendors participating.  I believe that complexity sciences modeling has a generally unrecognized potential to play an important role in the development of this capability.

It's About Time

What's about time? Readers of this occasional blog are thinking "Yeah, its about time he posted something!" True, but not the answer I'm thinking of:)

The practice of management is about time. It is about making a decision in the present that will affect results at some time in the future. Peter Drucker used a somewhat unusual word for this in his famous tome "Management: Tasks, Responsibilities, Practices." He wrote about the "futurity" of decisions. If you look up futurity you will mostly read about horse racing. However, the definition that I think Drucker meant is "state of the future." According to Drucker, to manage well is, in part at least, to have a good grasp of what the future state of the business system will be as a result of a decision taken today.

Let's Get Statistical

When I begin working with a client that is unfamiliar with my applied view of complexity science one of the first questions they ask is "Why can't I do this with a spreadsheet?"  If you haven't read it already, here is my answer
to that one.

The second question in analytically sophisticated organizations is usually "How is this different than a multi-regression or econometric model?"  That's the subject of this post.