Cause-alities

Why Model?

2009-08-18T12:17:29-06:00

I recently ran onto a short paper (speakers notes, really) by Joshua M. Epstein titled "Why Model?" I spend a lot of my life answering that question and I am excited by Epstein's concise, reasoned explanation. He boils it right down to the basics:

We're all modelers, but most of our models are implicit, not explicit.
Sometimes we model to predict.
Sometimes we model to explain.
And there are at least 15 other good reasons to build explicit models . . .

In some sense Epstein's position on modeling is a presentation of the scientific worldview and its moral advantages. So, mixed in with some really concrete reasons (eg: #2 -- Guide data collection) are some seemingly more esoteric objectives (eg: #6 -- Promote a scientific habit of mind).

Alas, business, financial, and other organizational leaders are mostly not swayed by a "scientific approach". I find that business and organizational clients generally need an additional level of motivation to justify an investment in explicit modeling. Usually, for the business person it is not enough to believe that an investment in explicit modeling will accomplish any of Epstein's 16 reasons. The business person wants to know What Then? Often phrased as a somewhat derisive "So What?"

For most business leaders explicit modeling has to be linked to some decision making or problem solving process. And, unfortunately, this often boils down to a focus on prediction at the expense of the other 16 reasons that are also part of excellent decision making and problem solving.

If you like this paper by Epstein try his book Generative Social Science: Studies in Agent-Based Computational Modeling. It's one of my favorites.

Decisio Described Concisely

2009-01-22T15:06:31-07:00

I was having coffee with a colleague recently and he challenged me to be much more concise and concrete about what makes Decisio's approach to modeling, simulation, and analysis novel and valuable. In response I've boiled Decisio's proposition down to four dimensions. In this post I am going to try to summarize these as concisely as possible. In future posts I'll elaborate each one and present some concrete examples.

The first dimension is purpose. Modeling and simulation is used for a wide array of purposes. Decisio uses modeling, simulation, and visualization to support decision making and problem solving.

The second dimension is application. Models don't make decisions, people do. People do it by developing a belief about how the relevant part of the world works and then acting on this belief. I call this process sense-making. It can be very hard for people to accomplish it successfully when faced with difficult circumstances. Decisio uses modeling and simulation to accelerate and improve people's ability to make sense when faced with unique, unfamiliar, complex, and ambiguous circumstances.

The third dimension is modeling technology and approach. Dynamic systems models are uniquely capable of describing the time based interactions of relevant actors and processes, of which there may be many. The ability to describe all of the relevant structure and resulting behavior over time makes systems models the tool of choice to support the sensemaking process. Agent based modeling and system dynamics modeling techniques form the core technical approaches.

The fourth dimension is managing and accumulating knowledge. Explicit systems models are artifacts that become the means to share understanding between individuals and groups, support collaborative development and expansion of knowledge, and support the reuse of insight.

Well, I think that is about as direct as I can make it! Let me know what you think!

Making Sense and "Barabba's Law"

2008-11-28T11:13:44-07:00

Through the years I've had the good fortune to work on several complex projects for Vince Barabba (more about Vince here and here). Vince is a true leader -- visionary, creative, and effective. One of the management approaches he often applied was the use of modeling to help him and his team understand a complex situation and make good decisions. In fact, I consider Vince the ultimate "model consumer." That is, he did not write complex systems models -- he used them and guided others in their use and interpretation. This paper provides one detailed example of how he worked.

Vince had a guiding principle in the application of complex models that became known as "Barabba's Law". Here it is:

Never Say "The Model Says"
-- Vince Barabba

I've think I've sat in a hundred meetings where we were using a systems simulation model to understand some complex, uncertain, situation and at some point someone would say -- but the model says . . . . If you where working on a project for Vince (or even if you had EVER worked on a project for Vince) then you knew it was time to pause the action and reflect about what was happening. Because as soon as those words are uttered then somebody is about to depend on the model as a literal prediction of the future instead of a tool to "make sense" of the situation to support their decision making.

I started writing about sense-making in my last post but here it is again: Making Sense is the development of situational awareness including an understanding of the future trajectory of the system.

At the time, though, I didn't spend much energy thinking about the underlying philosophy of Barabba's Law. What I observed is that forcing a different choice of language inherently guided stakeholders towards a different and more effective application of the modeling. The nature of the team discussion changed from predictive thinking towards evaluating the correctness and completeness of the underlying causal hypothesis that the model represented.

Barabba's Law closes the, often disastrous, thinking shortcut that allows leaders to abdicate responsibility for understanding the relevant system and its behavior. ( "Gee, we thought we were doing the right thing because the model said we were. . ." )

I think that one of the reasons Vince was so effective in using sophisticated models is that he instinctively understood the difference between prediction and sense making (although I never heard him use exactly those words). Through his extensive experience he understood how leaders actually make decisions and he knew how to integrate sophisticated modeling into that process. And he distilled some of that into his law.

Making Sense with Systems Science

2008-11-07T13:16:39-07:00

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?

2008-09-25T13:26:41-06:00

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.

Let's start by recapping the paradigm that we're all familiar with -- traditional analytics. Then I'll introduce the Systemic perspective.

Traditional analytics, from before the time of the Greek philosophers, is based on a reductionist principle. This is the idea that to understand a thing we decompose it into its constituent parts and then further decompose those sub-parts and so forth until we reach something that cannot be further decomposed -- the "atoms" of the system. This produces the familiar hierarchical tree structure that appears so often in our world and is the result of this process. The idea is that once we have the atoms figured out we can assemble them to understand how each level of the hierarchy works. This perspective is so deeply ingrained into our thinking, institutions, and science that we aren't always aware of its influence. It leads directly to the saying that a "thing is the sum of its parts."

Traditional analysis is an undeniably useful way to organize, catalog, and index. But it does not describe how things work. Just try to understand how a firm works by examining an organizational chart, for example. Or how the body works by doing an autopsy and cataloguing the organs.

Systemic analysis employs a different approach. Instead of thinking about each component as a thing, we conceive it as a process. Accordingly, each component of the system has an input, an output, and a description of how the input is transformed into the output -- its function. Components are interconnected via their inputs and outputs to form the system. And, in the systems view, hierarchy exists such that each component can be broken down into a sub-system.

The specific pattern of interconnections is called the system structure. In most interesting real-life cases what the system does, its behavior, is more sensitive to the system structure than to the function of the individual components. As a result, in a systems analysis the focus is not so much on the details of the components but on the relationships between them. Because system behavior is dependent on the connections between components as well as the component themselves it's sometimes said that "a system is more than the sum of its parts."

By applying system science we can describe useful things about how a firm, market, or organism works and how it will behave over time. This is something traditional analysis can't do. This is why systems science is interesting. If we want analysis that provides insight into how the future will emerge from the present we need to apply a systemic approach.

Fish Tails and Teenagers

2008-08-22T12:39:07-06:00

I found this New York Times story about a couple of high school students foray into genetic fingerprinting fascinating on so many levels. Here it is in a nutshell:

. . . In a tale of teenagers, sushi and science, Kate Stoeckle and Louisa Strauss, who graduated this year from the Trinity School in Manhattan, took on a freelance science project in which they checked 60 samples of seafood using a simplified genetic fingerprinting technique to see whether the fish New Yorkers buy is what they think they are getting.

They found that one-fourth of the fish samples with identifiable DNA were mislabeled . . .

As the father of a teenaged woman I know how clever and motivated these young folk can be. There is nothing they cannot do if they set their minds to it. I certainly related to one girl's father who noted this about their field technique: “It involved shopping and eating, in which they were already fluent.”

At a different level, as a consumer of a fair bit of sushi, I'm totally appalled. If you can't trust your sushi-master, who CAN you trust!?!

Finally, the usefulness of the DNA Barcoding Technique, despite its apparent limitations, is pretty impressive. I think that supermarkets should go way beyond just labeling fresh food with the origin. I want a BAR CODE that I can read with a pocket scanner to determine EXACTLY what I'm getting. Those green beans, for instance, what variety are they really?

I'm going to setup a DNA Barcoding system in my garage . . . .

Creating a Future

2008-03-11T15:27:05-06:00

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

2008-03-05T09:59:53-07:00

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.

Harvard Business Review and Complexity Science

2008-03-02T11:03:25-07:00

If you are going to write a blog about complexity science sooner or later you have to define, or at least describe, what you mean by that. And, I'm afraid, my time is now. My reticence is due to the many who have tried and, basically, failed to write a useful definition. Saying that complexity science is the study of complex systems isn't any help -- what's a complex system? Wikipedia's discussion of complex systems is interesting but declines to offer a definition.

So I was excited to see this article in the Harvard Business Review. The authors

". . believe the time has come to broaden the traditional approach to leadership and decision making and form a new perspective based on complexity science."

I love that! However, while their description of complexity science has some things to like, it reads like a laundry list of buzz words. And then there is this assertion:

". . .in a complex system the agents and the system constrain one another, especially over time. This means that we cannot forecast or predict what will happen."

Seems to suggest that we should give up all hope! If you've read my post on Strategic Planning then you know that I believe that we can take effective management action in complex systems and create a surprising level of predictability.

From my perspective the defining characteristic of a "complex system" is that it produces complex dynamic behaviors from the interaction of its parts.

Let's focus on the "produces complex dynamic behaviors . . " part first. There is a pretty simple "know-it-when-you-see-it" type test for this. Draw a time series chart of the things that you are interested in (eg: revenue, sales, new customers, market share, whatever ) over the time period you are interested in. These are dynamic behaviors. Are there any turning points? How about exponential growth or decay? Is anything oscillating? Are there time lags evident from related time series? These things are hallmarks of complex behaviors.

Now let's focus on ". . .the interaction of its parts." The idea here is that the behaviors are endogenous and not pre-stored or pre-programmed into a component of the system. Sometimes this property is called "emergence." The specific way the components interact is the system structure.

This definition identifies "dynamic complexity" as the determining factor as opposed to "detail complex."

Therefore, it is not required that there be a large number of interacting components. Sometimes the system of interest has hundreds or thousands or more components interacting and sometimes it has ten.

There is a secret here and since you've read this far I'll tell you what it is. Most complex systems have a feedback structure that creates the interesting behaviors. And, very often, there is a human making a decision in that feedback loop. That's what makes this perspective so powerful and important.

Why Complexity Science?

2008-02-29T11:11:54-07:00

Why should complexity science models be part of your business analytics toolkit? Because, uniquely, they usefully describe how the future can emerge from the present.

Complexity science recognizes that the future is in part dictated by "the physics of the situation" and in part created by our decisions. And, crucially, it describes how and to what degree which is which. Because of this, not only can we get a grasp of the likely outcomes of our decisions but also the amount of risk we are assuming.