The lazy employee revisited

For some reason, In praise of the lazy employee was one of my more popular postings. Now Fast Company is saying much the same thing in Hard Work's Overrated, Maybe Detrimental.

"Just listen to your heart" supports the Fast Company article at least as far as decision making goes.

Causality

When we evaluate something, we typically are trying to understand and make claims about causal relationships. When we create a system dynamics model, we are mapping and modeling causal relationships. But how do we tell what relationships are causal and which are correlational?

Thanks to a recent pointer on the evaltalk mailing list, here's Sir Austin Bradford Hill's “The Environment and Disease: Association or Causation?” Hill gives nine considerations to ponder.

For a rather shorter read, see xkcd's take on causality. Be sure to see the alt tag.

Dealing with risk and uncertainty

Times are uncertain. Risks seem high. We may feel that the price of missteps is high; we know it's hard to decide what steps to take.

In situations such as this, how do you make decisions in and for your organization? How do you plan effective actions? How do you solve the inevitable problems that arise?

The German psychologist Dietrich Dörner, author of The Logic of Failure, has made a career of studying why people make mistakes and what we can do to improve. One of his key pieces of advice is to use computer simulation to get insight about the situations we face so that we can make better decisions in real life.

Perhaps today's uncertainties are your signal that the time is right to apply more systemic approaches in your work and to ground your planning, problem solving, and decision making with simulation that takes into account factors important to your business. Perhaps it's time to test and rehearse your plans before you implement them.

That's what we've been discussing here, and that's how I help others. If you're concerned that your standard approach to business may need augmentation in today's world, perhaps I can help you, too. Drop me an email or give me a call. There's no obligation—only opportunity.

Better decision making through lemonade??

I've written about good decision making a number of times, but I never included the effect of sweetened lemonade! Get the full research story.

While certainly interesting in its claims, this idea also has links to ideas about stocks and flows (your stock of glucose, etc.) and other systemic ideas. It also seems related to the notion that good nutrition can help, as Charlie Ayers claims in Food 2.0: Secrets from the Chef Who Fed Google (no, I haven't read this one yet, but it sounds delicious intriguing).

I sense the message that we need nutrition for at least three reasons: to survive (breathe, move blood through the body, etc.), to carry out physical activity, and to carry out mental activity. The process of doing those things (even mental activity) depletes nutritional stocks we've established, and thus we need to replenish them to be at our peak condition. (It may be that we need different types of nutrition for the different activities.) Of course, we also have to balance that with our overall accumulation of nutrition lest we find our weight increasing, and we have to watch the type of nutrition lest we find our teeth decaying or our bodies subject to various ills.

Thanks to Dan Goldstein of Decision Science News for the lead.

Decision making: a quiz

How's your German?

I've written multiple times about decision making, because it's important in business (it's important in life, too, but I'm writing about business here). Sometimes it's good to test one's insights against criteria set by others.

I just took the Entscheider-Test (Decision-maker Test) at manager-magazin. The result?

Sie sind ein Topentscheider!

Sie haben 23 von 24 möglichen Punkten erreicht.

Sie sind ein mit allen Wassern gewaschener Entscheider, der sich von niemandem mehr etwas vormachen lässt.

I feel good! Had I done worse, though, I wouldn't necessarily have felt bad; I would have used the time to review what I answered differently than was given in their solutions, and, whether I decided they were right or I was right, I would have learned.

This test, done by manager-magazin and Kai-Jürgen Lietz, author of Das Entscheider - Buch, seems based firmly in the rational decision-making model (I haven't read the book yet; I'm taking that from the questions and answers). That means it's from the same general philosophical thread as the Kepner-Tregoe approach. I took a week-long Kepner-Tregoe course years ago and have found it very helpful in my work, even as I try to incorporate other decision-making tools in my professional toolbox, so I'll keep an eye on Lietz' blog to see what I can learn.

As I was searching to find that test again, I found Die Deutschen und der Tunnelblick with the intriguing lead sentence, "Sind unsere Entscheider Fachidioten?" ("Are our decision-makers technical idiots?"). It's an interesting article for those who wish to compare attitudes in Germany, France, the U.K., and the USA on how managers get the education they need.

Scenario planning

Scenario planning is a business process we got largely from Royal Dutch Shell. If you just read The Art of the Long View: Planning for the Future in an Uncertain World, you should know things haven't stood still. Martin Börjesson has a very good reference site worth checking out.

If there's one thing I've learned about scenario planning, it's that it's a literary and creative process, not a mechanistic one. Those scenarios that offer the most value, that help me think the most, are also those that qualify as true stories, not as mechanical selections of various events.

Decision making: a bigger model

I've covered decision making at various times, but I just found a presentation by Andrew Gelman that puts another, very important light on the matter. See part 2 (starting on slide 16) of "Combining information and group decision making" by Andrew Gelman, Roger Clemen, Roger Cooke, Jim Hammitt, David Krantz, and Francis Tuerlinckx for three different types of decisions and what that might mean to you and me in group decision making.

Interestingly, Chris Argyris' action science seems to want to move everyone to a strategy for addressing inference problems. This presentation highlights a bit of why that can be challenging. What do you think? Is Argyris' approach uniformly good? What are the boundaries to its appropriate use?

A somewhat unified view of decision making: the table of contents

I recently completed a four-part series that tries to pull together some of my thinking over the past year about various decision-making models. In the process, it also highlights some of what I offer organizations with whom I work.

To make it easy to find those four articles, I've assembled a list of links and brief descriptions, much as I did with an earlier series on making sense of a tough problem:

1. A somewhat unified view of decision making: introduction
   
2. A somewhat unified view of decision making: part 1 (the basic model)
   
3. A somewhat unified view of decision making: part 2 (simulation)
   
4. A somewhat unified view of decision making: part 3 (learning from experience)
   
5. A somewhat unified view of decision making: part 4 (putting it all together)
   

A somewhat unified view of decision making: part 4

I promised to clean up some loose ends. What are they?

1. How do you do this stuff, really?
   
2. Where does Tom Peters fit in?
   

If you're like many companies, you're set up to make decisions and take action, but you're not necessarily set up to spend much time on explicitly increasing your Actionable Insight (or whatever you more informally call it). You may feel that some of the decisions you face tax the limits of your or anyone else's ability to make sense of them. You may feel you lack the time to get involved in something with the name "action research"; after all, you have a business to run.

That's where an outsider can often help. Such a person can often see things with fresh eyes and may have developed approaches you don't have the time to develop or maintain internally.

For example, I've helped others make sense of complex issues, gain insights, and test potential decisions using system dynamics simulations, and I've got other systems approaches available, in case the first ones we try aren't a great fit.

I've led project retrospectives as a light-weight way for organizations to learn more effectively from their own experiences, and I co-developed a popular set of learning logs to help guide personal as well as group improvement activities.

If we were to work together, you wouldn't lose control over the decisions you make. I'm just there to help you make sense of the situations you face, to help you test the alternatives you might want to try, and to learn more effectively from your experiences.

If you know someone facing complex issues or trying to figure out better ways of making progress, have them give me a call or send me an email. If it's you who is facing those challenges, I invite your call or email, too. Either way, there's no obligation and no charge for an initial conversation.

Now where does Tom Peters fit in? There's one more stock, Willingness to Act. We can have great, high-quality insights, but we don't do anything until we actually step up and do something. Tom is fond of saying we should just get on with it. I encourage you to take a look at some of his books and presentations to get more ideas and inspiration in building up your Willingness to Act. Don't accept everything he says at face value (I certainly don't), but do think seriously about it, and apply what works for you (it may be more than you think!).

A somewhat unified view of decision making: part 3

I last left you with two thoughts about decision making:

• You don't have to pick between the recognition-primed (intuitive) decision model and the more rational models. Both are valuable.
  
• Simulation can be involved in both decision-making models. That particular insight might help us integrate thinking about the two.
  

I'll stop talking about simulation for a bit and talk about Insight Quality.

We all make mistakes. If Insight Quality is measured on a (hypothetical) scale from 0 to 100, it's likely never at 0 and never at 100.

We learn by making mistakes and reacting appropriately to them. If you're a system dynamicist, you recognize that feedback systems work because of an error signal: that difference between what you want and what you've got. So don't worry about making a mistake (especially small ones), but do pay attention to what you do with mistakes.

Insight Quality, like Actionable Insight, declines if left alone. Pick a skill, even riding the proverbial bicycle: if you don't use it for a while, you'll get rusty at doing it, and you'll likely make a few mistakes you wouldn't have made earlier.

Before I go further, I'll admit that the distinctions between Actionable Insight and Insight Quality and how one increases either are a bit artificial. I'll keep them distinct to make my point, but please understand that I understand there's overlap.

How do we improve our organization's or our personal Insight Quality? We need a feedback loop, a means to observe when things aren't going right, so that we can learn from them. In this model, perhaps the easiest spot to observe those situations is when Latent Problems re-emerge (or, almost equivalently, when we see Problems we recognize as old friends).

There is a methodology called action research (or action learning—the distinction between the two is a bit fuzzy, but action research often carries the connotation of being written down and shared). Those describe the actions involved in that entire loop that encompasses both action and learning.

In a way, you can look on simulation and action learning as two different things. One, you might think, comes before the decision to inform you; the other comes after the decision to improve you.

You might also look on them as very similar. With simulation, you're looking at the analog (typically, a computer model) of the real-world situation to gain insights. With action learning, you're also looking at an analog of the situation you're facing, but this analog is the history of your and others' past attempts.

In either case, I submit that we need to blend both in our work. We need rational processes (and simulation is often a valuable approach) to build Actionable Insight, and we need action research or learning to correct lessons we mis-learned or forgot from our rational processes.

Based on these foundations, we need the ability and willingness to make good decisions in a timely fashion, often using a more intuitive (and yet still simulation-based, if we accept the recognition-primed decision model as a description of reality) approach, and we need the wisdom to know when our intuition won't suffice and we need to drop back to a rational model.

So what's left? I've got one more installment planned to tie a few loose ends together and to live up to my promise to fit Tom Peters into the equation.

A somewhat unified view of decision making: part 2

My last posting described the basic model, but it left lots of gaps. For one, Actionable Insight isn't static, as we all know. While some insights maintain their validity for years and some perhaps forever, others either lose validity as new knowledge is developed or lose relevance as their need goes away. I'll show that effect as a flow draining out of Actionable Insight into another cloud, for it doesn't matter what happens to it; we just don't have it anymore. Don't forget to click on the image to see a full-sized, pop-up version.

If Actionable Insight can drain away, how do we refresh it?

We no doubt have as many ways of gaining insight as there are people. Trying to make a comprehensive list might be futile, and it doesn't really contribute to my point. I want to highlight one approach that I think helps integrate the various ideas I've been exploring.

Someone once said, "Don't ever go where you have not been before." You could also say "Don't do anything for the first time"; it sounds similar and seems to make as much (or as little) sense.

With possible apologies to Tom Peters (I'll explain that note in a later column), I think there's a lot of merit to those statements. Actors and musicians practice it faithfully; symphony orchestras and theater companies always have rehearsals before they show their work to an audience. You might bring up improv or jazz as counter-examples, but, as learningimprov.com says, it takes hours of rehearsal to learn how to improvise.

On a more academic note, Dietrich Dörner has made a career of studying decisions and how people make decisions that go awry. He has advocated simulations as a way for us to learn more rapidly than we can in real life and with less risk.

An organizational simulation is, in many ways, nothing more than what musicians and actors do in rehearsals: an opportunity to go through the important parts in private, with the ability to try things different ways, and with little risk. We can learn what works so that things go more smoothly when we do it for real.

For example, I recently did a set of simulations to explore a marketing program for symphony orchestras. In a relatively short time, I was able to create a simulation model of the essence of the problem Drew McManus was trying to address and to apply multiple solutions to see how each might work. The model isn't good enough (nor, likely, should it be) to tell Drew exactly how many additional tickets he'll sell for Friday's concert, but it should give him better insight into what is key for making his program succeed.

Certainly there are other ways we also use to increase our insight; why am I writing about simulation? It's because of the link to Gary Klein's recognition-primed decision model. Gary's model, simply put, is that we tend to simulate situations quickly in our heads. If our plans of action work out, we put them into place in the real world. If they fail, we try a different approach until we find a mental simulation that works. We can do that really quickly, and that's key to our survival.

But there's a catch. As Barry Richmond once said, our mental models aren't always good enough, and our ability to simulate those mental models in our heads doesn't always suffice, thus leaving us with holes in our feet.

I've come to believe that simulations and rehearsals are important ways we prepare to make good, quick decisions later, when we need to. These, plus all the other analytic and rational tools we can bring to bear, are the means by which we enable ourselves to be good at the recognition-primed decision model. Certainly we can do those simulations in other, more manual ways, but, for many situations, the alternatives are tedious and error-prone. Certainly we can and do learn in other ways, and those are important—I'll even talk about some of them later. What simulation almost uniquely provides is the ability to specify a situation and see the logical ramifications of that situation.

All this has been to support two conclusions:

1. The recognition-primed model and more rational decision-making models aren't two choices from which you get to select only one. They can work together, the more rational model providing you the stock of insight you need to make faster, better recognition-primed decisions.
   
2. Simulations (rehearsals for musicians and actors) are great tools to build insight for those recognition-primed decisions, for both are in fact simulations; they just have different constraints. Thus the structure of the learning you get from the one should fit nicely into the structure of the insight you need for the other.
   

There's more to come. In future installments, I'll cover how Insight Quality, action learning, and Tom Peters figure in.

If you know someone who is facing tough organizational or business problems, problems that persist, problems that don't lend themselves to quick, intuitive solutions, introduce us. Perhaps I can help.

A somewhat unified view of decision making: part 1

Yesterday I promised a series trying to integrate my various thoughts on decision making as I've touched on in this blog before. I'll use this diagram of four stocks, denoted by rectangles, to guide the conversation (click on the image to see a pop-up, full-sized version). Let's start with the stock of Problems. As I'm using it here, those are situations that demand our attention or action. In a Kepner-Tregoe sense, they include both problems and decisions. Problems come at us from out of nowhere, it sometimes seems, and that's why I've represented them as coming out of a cloud. Incidentally, "pa" stands for the Latin "per annum" (per year).

We deal with problems by making decisions. If we make good decisions, problems go away. If we make poor decisions, they become Latent Problems, waiting to visit us again another day. Our capacity (and capability) for making decisions is controlled by a stock of Actionable Insights we or our organizations possess. It's also likely controlled by our willingness to act; I can make a heck of a lot of decisions in a hurry if I resolve them all by rolling a die. Whether we make good or bad decisions is controlled, to a large degree, by the quality of our insights (another stock); luck plays a role, too, as do other actors in the situation.

In this model, most decisions are made relatively rapidly, perhaps using the recognition-primed decision model without our even knowing of it. Increasing our insights and the quality of those insights is something we may do as part of OJT. Sometimes, though, we run up against problems we know we must study before we make our decisions, and so we do something explicit (commission a task force, conduct experiments, hire a consultant, etc.) to increase our Actionable Insights.

If any of you offer your observations, either as comments here or as email, I'll try to take them into account as I prepare the next installment, in which I'll begin to connect up some of the missing links.

A somewhat unified view of decision making: introduction

I've written several times about the various methodologies we make decisions: the rational model, as typified by the Kepner-Tregoe approach, Gary Klein's recognition-primed decision model, the Thomas Saaty Analytic Hierarchy Process, and others. Sometimes we get the impression from reading that there's one best approach, but it's often the case that diverse approaches (to most anything) are more robust.

Does that mean we just pick and choose the approach we like when we want?

While there will always be an element of personal choice in deciding which methods to use, I do think we can say more about the varied approaches that helps put them in context. The recognition-primed decision model seems to apply when we have to make a decision in a hurry—not surprising, since firefighters and other emergency response teams were the subjects Gary Klein studied. The rational model seems to apply when our intuitions lead us astray. As Jay Forrester has pointed out, that may happen more often than we'd like to admit (and his approach goes past the rational of Kepner-Tregoe to a more systemic view).

Over the next few days, I intend to walk through my developing views on how one might integrate these ideas, and I hope some of you will chime in with your own insights to help us all learn still more.

While you're waiting for this to start, review some of the ideas that led to this series.

There's another problem, too

In my last two postings, I've written about what I think is a very important issue we face as individuals, as businesses, and as a society. I think there's another issue that goes hand-in-hand with that issue: how we deal with tough, stressful problems, how we deal with conflict, and how we deal with perceived inequities. In almost any significant situation, we'll have differences of opinion, some small, some very large. (If we didn't, we should perhaps wonder if we're considering all the evidence.) How we deal with those differences can make all the difference as to what sort of outcomes we achieve.

No matter whether we're talking about differences between individuals, differences between groups in our companies or organizations, differences between factions involved in our local or national governments, or differences between nations, we have various approaches. Some approaches are violent; some are not. Some seem to lead to good resolutions; others do not. Some seem coercive, at least to some involved in those deliberations; others do not. Some seem to solve the problem today while creating new problems for the future; others don't.

If we can't figure out how to act effectively in such situations, I fear we'll have increasingly difficult times as stress mounts from climate change, from energy shortages, from perceived inequities (whether between individuals, groups, or nations), or simply from the challenges of doing business in competitive markets.

There are many ways to make communications in such situations more productive. As I've advocated for the use of multiple approaches (triangulation) in making sense of problems, I will say that I don't think any one approach has ability to save all deliberations about differences. Yet I have found the work of Chris Argyis, work he calls action science, to be impressively powerful in helping groups to hold productive discussions, to make breakthroughs in their organizations' abilities to get work done, and, as a nice side benefit, to help people feel good about working in their groups, not because they get their way, but because they get heard. It's based on three premises about productive decision-making in times of conflict and stress:

• Free and open decision making
  
• Testable and tested data
  
• Mutual commitment
  

The first, among other things, means I can't force you to use this approach. I can at best model the behavior I believe in and that I would like you to adopt; I have to give you the right to decide whether you want to follow suit.

The second means that we are willing to test data about all our important assumptions, not just the ones about quantitative data. Perhaps I (think I) know you'll never accept a certain proposal. If I'm following my principles, I'll figure out a effective way to test that assumption on my part (perhaps as simple as asking); otherwise, I'm unilaterally taking one possible solution off the table without us having the ability to talk about it.

While these are all hard in practice, while they require great attention to one's self, and while they sometimes require great courage, the third sometimes seems the hardest. Perhaps you and I disagree about a situation. Perhaps you've made your best case, and I see important issues I perceive as favorable to your position that you didn't bring up. If I'm committed to the first two premises of making free and open decisions with tested and testable data, then I'm committed to bringing up those issues, even if I perceive they weaken my case, for that's the path towards more productive, effective decisions. That may sound easy now; it isn't always so easy when I'm in a discussion involving a strongly-held belief.

I can introduce this approach to a group in perhaps 15 minutes, including some techniques for applying these premises in practice. It's hard work, though; it may take months of active help before a group begins to internalize these ideas in their routine interactions. This work is some of the hardest and yet some of the most rewarding I do. The first time I saw a group I had helped really begin to act this way, I was blown away by the progress they had made. Using these ideas along with others, they had shortened their process times by over 80%. They were highly effective at working together, cutting the time of making decisions from hours and days down to minutes, and the quality of those decisions had improved markedly. Deliberations were sometimes amazingly direct, and yet people felt really good about them. As one said (paraphrasing), "Now I know that people really hear me." If you're curious, you can read about that project in "Emphasis on Business, Technology, and People Cuts Turnaround Time at Hewlett-Packard's Lake Stevens Division." If you'd like to talk about this for your organization, give me a call.

And, when you're thinking about the topics of climate change and oil depletion (or business strategy and customer satisfaction), remember that how we talk about those issues may be as important as what we now think about those issues in achieving good results.

More on factorial designs and simulation

Factorial designs and simulation: apparently Professor Barlas is teaching it in Istanbul!

For the analytically-minded among you, I'd note that MCSim, coupled with a bit of infrastructure you can develop, can make running small factorial experiments (up to a few hundred runs) a fairly quick and painless task. That includes their development, their execution, and their analysis. If a system dynamics model is deterministic, as many are, there's no need for replication.

You can see a simple example in my upcoming TAFTO contribution. I was pleased at the way in which a factorial design approach enabled me to generate and see useful results from a relatively simple model.

"Scientific thinking" the modern way

Yesterday I wrote about Barry Richmond's notion of "operational thinking." I encourage you to read that essay; Barry had many good and important things to say. If you're not familiar with system dynamics but are curious, his essay offers a good introduction to pieces of the thinking approach that gives system dynamics its power.

Nevertheless, I disagree with his description of "scientific thinking," and I figure I should clarify that before others call me on it. On page 19, he writes, "People thinking scientifically modify only one thing at a time and hold all else constant." That used to be true, before Sir Ronald A. Fisher began describing a process for the design of experiments. Fisher and others gave us means to vary multiple factors at a time in a series of experiments and to learn more accurately, effectively, and productively that way.

One can do such designed experiments using any of the common system dynamics simulators, of course, but one of the reason that I like MCSim so much is that I found myself automatically doing factorial experiments using MCSim from the very beginning without thinking about it, thanks to the way it's designed. With other tools, I have tended to start with simpler approaches and then find myself having to make explicit decisions to design better experiments. Besides, MCSim can give results in a format that seems especially suited to this type of analysis.

Some of you might note that two of Fisher's attributes of designed experiments are randomization and replication. Those don't quite apply to many system dynamics models, those created without modeling any random effects. That's okay; it's still important to understand the effect of changes in various parameters, and, if the system is nonlinear (most are), it's important to understand interactions among those parameters, all of which is done effectively using methods pioneered by Fisher.

What does this all mean? It simply means that Fisher's designed experiments give us better and faster means to extract insight from tests on system dynamics models than the old one-factor-at-a-time approach.

I thank Deb Schenk, then (and perhaps now) statistician at Hewlett-Packard Company, for teaching me and others about the design of experiments using Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building back in 1981-82.

Now go read Barry's essay.

A systems language for business

Some time ago, I had an opportunity to teach system dynamics to a particular work team. It was a small team of perhaps 8-10 people, (only) one of whom had some system dynamics experience and one with some exposure to what system dynamics was all about.

On the first morning of the week-long class, I overheard a discussion between two or three of the participants during a break. They were talking about some issue they faced at work; one person said (let me paraphrase), "ABC," and the other said, "XYZ." The first repeated "ABC," and the second repeated, "XYZ."

You've probably heard these discussions before; you may even have participated in them. Both parties were stating their positions in what they saw as a clear and convincing manner. Neither either saw the need or had a way to try to link the two statements together so they could make progress deciding which was more useful to them, and so they kept talking past each other. It continued until the end of the break; there was no natural resolution. It was polite, it was friendly, and it wasn't very productive. I've heard those discussions many times in my career.

The system dynamics course was fun and intense: three full days of work learning system dynamics and working example problems, intermingled with two homework days in which they worked together as teams to solve some fairly difficult problems using simulation and without my help except by email. It must have been successful, too; while I had hoped to get the opportunity to come back and help their organization address specific problems using system dynamics, they felt comfortable enough afterwards to do it themselves.

I enjoyed working with that group for a number of reasons. One thing particularly caught my attention. In a break on the last day, two or three people, perhaps the same group, again started a discussion about a problem they faced at work. This time, instead of stating and restating their positions without a way to achieve resolution, the first person said, "DEF" (different problem this time) and then drew a stock and flow diagram that helped make that position clearer. The second person said, "UVW" and drew a stock and flow diagram to clarify that view. Then they started talking productively, using those models, about how their world really worked, about specific questions they had about each model, and about what they might need to change in one person's or the other's view to align more closely with reality and to be more useful for their work. While they didn't (yet) agree on answers, they gave every sign that they understood and appreciated each other's thinking and that they could converge on a common answer that was good for the organization, not necessarily one that agreed with their originial position.

Four and a half days of serious thinking on their part had taken them from being a normal workgroup to being one that could express ideas clearly, advocate for them effectively, and engage in serious dialog on ways to test their ideas and come to a resolution about how to proceed. While they didn't create a simulation model in that 15-minute break, they gave every sign they could likely have done so, given a bit more time.

Moreover, I think they had learned how to peer into each others' now rather explicit mental models to find the crux of problems or differences, which should let them focus their simulations on the essence, the core, of the situation and not the entire problem.

I was proud of them. In four and a half days, they had learned and decided to use what Barry Richmond called "operational thinking." I'm not sure they would have done that without the experience of learning to model and of seeing what their simulations could bring.

Do you see people saying "ABC" and "XYZ" in your organization? Do you wish your people had a better language for wrestling with tough problems? What techniques do you use?

While I like Barry's article quite a bit, I do have one point I'd like to take issue with. I'll save that for tomorrow.

Peer assists

Action learning has long had a process known as learning sets to help people develop the understanding they need to address their own problems. Now Bellanet and the University of Ottawa have described something quite close to this in a short Flash session on Peer Assists (also available in French). (Thanks to colleague Nancy White of Full Circle Associates for the lead.)

I've found that journaling can be a helpful adjunct to such a process. Perhaps the learning logs Bob Williams and I created may be of help to you. As a bit of practical advice, I've also found that it helps to inject new ideas into your thinking, either by reading or listening, while reflecting through journaling and working with a learning set.

If you're interested in studying action learning or its close cousin, action research, check out the excellent, free, online (asynchronous) AREOL course offered twice a year by Bob Dick. AREOL 25 begins February 2007; sign up soon!

Time horizons, step two

Yesterday, I posted about time horizons in problem solving. Step one was to expand your time horizon when looking at problems to ensure you see potential patterns of problems.

What should you do if you do identify that your problem is part of a pattern?

Step two is to figure out what might be causing that pattern. Often, it's something structural in the organization. In the example I gave, the organization inadvertently hid certain key financial information from managers, and that led to repeated bouts of overspending.

How do you figure out what structural issues might be causing the pattern you're seeing? Sometimes it's obvious; sometimes there are multiple, competing explanations, and you're not sure which is most likely.

One way to pick the right explanation is to do an experiment. Try making the structural changes that you think matter, and see what happens. If you observe no change (or if you make things worse), you probably picked the wrong explanation. If the problem disappears, you probably picked the right explanation.

Of course, patterns play out over (a long) time, and so it may take you a while to be sure whether you've seen a change or not. (If you do see an improvement in a bad situation, be sure you're not just seeing regression to the mean.)

Since you're making structural changes to the organization, you may find your experiments are costly in financial and material resources, in risk to the organization if you select poorly, and in human patience.

Is there a better way?

Perhaps.

What if you could create a mockup or simulation of your problem? What if that simulation could recreate the essence of your problem in a simpler form (a model), and you could conduct experiments on the model rather than on the real organization? What if you could take the lessons from that exercise and apply them to your real-world problem successfully?

There are lots of ways to model problems.

• You can create what is, in essence, a board game, and you can bring in people to help you experiment using the game. Of course, that may take time, and people's patience may run thin if you ask them to try too many different lengthy experiments using the board game.
  
  
• You can create a mathematical model (a set of equations) of your problem and solve them to give you your answer. Unfortunately, most organizational problems need rather complex mathematical models, and we can't solve most of those models as readily as we did homework assignments in school.
  
  
• You can create a computer model of your problem. The computer model may take time to create and test, but it simulates your problem and your proposed solutions much faster than the people playing the board game. The computer doesn't get bored if you ask it to run more experiments.
  

If done well, your model (of any sort) should exhibit the same problems you see in the real world, and its structure should match the key aspects of the real world.

Now you can try to fix the problem in the model by changing it. There are many approaches to help you do this effectively; the key is to determine which fix (or combination of fixes) in the model eliminates your problem. There's a good chance that your (successful) fix has identified the key factors causing your problem. There's a good chance that the same fixes, if applied in the real world, would fix the real problem. What you learned in making the model work might tell you what you need to monitor closely in the real world.

There's a bit more to it than that, of course, and you need to be skeptical of your model and your conclusions to avoid being misled too easily.

What might such models look like? You can see a small collection of one type of modeling in the archives of my At Any Rate^TM column published by Pegasus Communications.

Time horizons

Most of us spend much of our days solving (or attempting to solve) problems. We each probably have our own approaches, tailored by our life experiences, ideas we've read, and training we've received.

Here's a suggestion from the system dynamics tradition: pay attention to the time horizon of your problem.

Sometimes a problem is a one-time event. That's great, for those problems are often simpler to solve: fix it, and you're done.

Sometimes, though, a problem is part of a pattern. It's not just that you had a production or sales or financial (or other) problem at the end of last month; it's that you have such a problem at the end of every month (or quarter or year).

Such pattern-related problems usually arise because of something structural in your organization or work. If you fix the problem, you'll see it again next month (or quarter or year). If you fix the structure, you might just make it disappear.

The next time you're faced with a problem, take a few moments to look back, oh, at least twice as far as you think is reasonable. Do you see a pattern now?

For a real-life example, see "Pipeline Inventory: The Missing Factor in Organizational Expense Management" (National Productivity Review, Summer 1999) and "Applying System Dynamics to Business: An Expense Management Example".

Inaction in the face of uncertainty ...

Some time ago, I posted a note about John Sterman's thoughts on whether it was a good idea to wait for more data before doing something about climate change (he was against it).

Now my alma mater has published Inaction in face of uncertainty a risky proposition in global-warming conundrum, a news story about comments on the same topic by Texas A&M University atmospheric scientist Andrew Dessler. He shows how society changes its decision-making criteria based on the inherent risk in a decision.

The two seem to agree.

Decisions, decisions, decisions

I've thought and written about the decision-making process several times in this blog, starting when I discovered Gary Klein's recognition-primed decision model. Since then, I've seen opportunities to use both his approach and the more analytic Kepner-Tregoe approach I learned decades ago.

It would be simplistic to think these are the two choices, though. In Die Praxis des ganzheitlichen Problemlösens, Peter Gomez and Gilbert Probst make the point that there are three kinds of problems, each of which calls for its own approach. Simply put, I suspect they might use Klein's approach for what they call simple problems, Kepner-Tregoe for complicated ones, and forms of system dynamics for complex problems. (For the Kepner-Tregoe fans among you, Gomez and Probst define a problem such that it includes both problems and decisions in the Kepner-Tregoe sense.)

Even three are too few. I firmly believe in the importance of treating these approaches as tools, and we need sufficient tools to be able to address the problems we encounter. As with woodworking tools, it's not the quantity of tools that counts; it's how well we can use them to achieve the desired goals. A carpenter with 25 saws is probably not 25 times better at cutting wood than a carpenter with one, but that carpenter might find it useful to have more than one saw to manage different cutting tasks professionally and efficiently.

I found a new decision-making tool recently. While reading Geoff Coyle's A possible method for assessing the relative values of alternate system dynamic models, I discovered the Analytic Hierarchy Process, developed by Thomas Saaty. It appears to be a much more highly-developed, analytic approach to weighting inputs along multiple criteria than Kepner-Tregoe's simple approach.

While you can find professional software to lead you through the process and help you make the calculations, several of us on the J programming forum, including John Randall, Tarmo Veskioja, and Roger Hui, had fun developing a quick program to duplicate Coyle's results and possibly to use in future decision making. J fans might enjoy the dialog (look for most anything on LAPACK or principal eigenvectors) and the current version of the short script. Thanks to John, Tarmo, and Roger for helping make this work!


decisions
J language

Proof by authority: reflections on knowing something

One of my grown children once said something to the effect of, "I'm trying to be very careful about taking things on 'proof by authority'." I hadn't heard the phrase before, but I found it immediately compelling. I searched Google for the phrase and discovered there are a number of pages that include it along with 35 other proof methods we sometimes use.

There are a number of methods of proof. Many of you who took serious mathematics or logic courses along the way may have seen modus ponens, modus tollens, and the like. We also have other ways we can know things: we can use statistics to understand masses of data, we can use the scientific method to understand things about the natural world (some might suggest the principles of the scientific method underly much of the way we really know things), and we can use action research to understand fuzzy and often qualitative situations. We can have faith in things we may not have proven or be able to prove. And, yes, we often accept proof by authority when we read the morning newspaper or a good book, because we don't have the time to research the issues, and we trust the source based on past history.

I'm not suggesting that we distrust everything we read or hear unless we've proven it ourselves or read its primary source; for one thing, we don't have the time, and, for another, we may lack the skill in certain fields. I am suggesting we be honest with ourselves about how we know things, and we be clear in that when we tell others.

How does that relate to business? When someone tells you something, be inquisitive (or perhaps even skeptical). Ask questions. If people give you detailed numbers and graphs, be sure you know what they mean and where they came from, at least if you're going to use them to make decisions. That may require triangulation: assessing the same situation from various viewpoints and perspectives, as well as perhaps seeking out original data (the primary source), to see if you get the same answer multiple ways.

If, at the end of the day (or minute), you're going to make your decision based on intuition, that may be fine. Just be sure that you know something about the data you're using to inform your intuition.

And when you're providing information to others, be able to give them the same insights you'd ask of them, if they were giving you information.

decisions
knowledge
logic
research

Decisions update 2

I'm still thinking about what I'm learning about decision making, given my training in rational decision making from Kepner-Tregoe and my recent reading of some of Gary Klein's books on intuitive decision making.

I recently opened a direct mail ad from a reputable business publication, inviting me to subscribe at a special, limited time rate. I first unconsciously did a quick mental simulation, along the lines of what I understand the recognition-primed decision model to be. For a few seconds, I was thinking, "Yes, I can have the contents of that magazine at my fingertips for this relatively low rate, and that will give me even more, up-to-date topics to discuss with prospects and clients, leading directly to more and better business." (You see the mind of the independent consultant!) It was a quick mental simulation, directly suggested by the writer of the ad, and it was almost effective.

Then I stopped and almost unconsciously went back to the rational approach. The very attractive deal seemed significantly less attractive. While I didn't track the elapsed time, the whole thought process only seemed to take a few moments.

One lesson (re-)learned: if someone else suggests the model for the recognition-primed decision process and I have any reason at all to suspect they may want the decision to go that way, even if I think they're honorable people, it may well be time to switch to a more rational approach to find at least one alternative model and try at least one alternative simulation. That heuristic applies, in my mind, for business, social, and political issues. I'll even suggest it applies if I might be trying to sell you something (grin).

Decisions update 1

Recently I said I'd be focusing more on my decision-making processes to see what I could learn. Here's a quick update:

• I do more mental simulations of possible courses of action than I realized. I may be doing somewhat more due to the power of suggestion from Gary Klein's recognition-primed decision model, but I don't think I've changed my approach that much yet. Making those simulations more explicit helps me be more thorough in my decision-making activities.
  
  
• Perhaps because of training I've had or perhaps because of cautions I learned from Human Error, I generally throw in at least one alternative to reduce the chance that I get stuck on an inappropriate choice. I must admit that I rarely have to make the sort of split-second decisions Klein describes in his work with firefighters.
  
  
• The notion of simulating fits squarely with other parts of the work I do. Barry Richmond has noted that we often struggle both with creating useful mental models and then simulating them to understand their implications.
  
  
  
  
  • His notion that we often are better served, at least at the outset, by broader and shallower models rather than by deeper and narrower ones seems consistent with the idea of looking, no matter how briefly, at alternatives and with some of the ideas Reason suggests for not getting stuck on a bad answer.
    
  • His notion that we need to improve the fidelity of our simulation capabilities seems related both to Klein's ideas and to work done by Dietrich Dörner.
    
  
  

So, what am I doing next?

• I'll continue to observe and reflect on my decisions.
  
• I'll put more focus on creating simulation models as a test bed for the sorts of decisions I have make (as opposed to being more of a problem-solving tool), and I'll work with interested clients to help them do the same.
  

Stay tuned, and chime in!

An action research experiment: making better decisions

There's no point in reading a book if you don't try to learn from it. I'm putting two threads, TiddlyWikis and Gary Klein's recognition-primed decision model, together with action research to see if I can improve the way I make decisions. I'll give occasional updates on what I'm learning; I invite you to try it out, too, and let me know what you're learning.

I'm starting by creating a new TiddlyWiki that's customized for this experiment. I've saved it locally, set my name under "Options," and changed the Site Subtitle to reflect its purpose.

Then I edited the "Hello There" section to give a very concise entry point into the network of tiddlers I'll create. I've created tiddlers for each of the major aspects I saw in Klein's suggestions, and I've begun to customize them. For example, I created one as a template for a Decision Requirements Table, and then I've copied it into separate tiddlers for each decision I decide to address.

In the first day of trying this, I've made two observations that I think may be important:

• The decisions I noted as important or frequent in my work weren't always the areas I had previously focused on for improvement.
  
• Once I had selected a few decisions, it was relatively easy to identify potential ways to improve my skill.
  

That isn't terribly surprising, given my past experience with similar exercises.

If you're intrigued, join in! I'd be curious in the sort of lessons you're learning, too.

More on intuition

I've finished Gary Klein's Sources of Power: How People Make Decisions, and I'm glad I read both that and The Power of Intuition. I like his graphical model of recognition-primed decision making in The Power of Intuition better, and I like the breadth of this book.

Klein has apparently done much studying of how people really make decisions over a number of years, he's created what appear to be useful tools, and his ideas have apparently been picked up by the U.S. military and others. I applaud anyone doing that much to help us understand how we make decisions and how we can do a better job at making decisions.

Yet something is troubling me, too. Perhaps it's just that he's suggesting that something I was taught and used along with many at one of my former employer's may not be the best way to work, but I think it's more than that.

Here are a couple of reasons that my intuition (irony intended) is raising red flags. In chapter 16, he claims that scientists look for confirmation of their work, when they should try to disconfirm their theories. There is likely some truth to that: perhaps all of us wish our favorite theories are true, and we may indeed occasionally put our thumbs on the side of the scale favoring our pet theories. My introduction to the scientific method emphasized the need for a "null hypothesis": a statement that was the opposite of our conjecture. Instead of proving the conjecture, the scientist's task is to disprove the null hypothesis, and the scales are heavily biased in favor of accepting that null hypothesis unless the evidence is overwhelming. That seems like a serious attempt at disconfirmation.

When I became an engineer, I discovered that a key part of an engineer's job is trying to break what you've made. At one company I know, after circuits are designed, engineers test them over environmental extremes of temperature, humidity, and electromagnetic interference to see if you can break them in reasonable use. Then they're subjected to "abuse testing," in which people try potentially ridiculous things (shorting things together, plugging the circuit into the wrong power source, etc.) to see if they can withstand unreasonable use. That seems like another serious attempt at disconfirmation.

He also claims that few use the rational-decision-making approach of comparing alternatives in practice. To a large degree, he's correct: I'm sure I make many a decision in the course of a day using something close to his model. Yet I recall a decision I had to make for a client recently, and my process didn't seem all that unusual. They asked me to do a task, and they had always used approach A. I looked at the task, realized it would likely be challenging that way, and thought of approaches B and C. I first thought B would be the best, but I followed much the process mentioned above. With a bit more data and a bit more investigation, C turned out to be the likely best choice. I made my proposal to my client, they accepted it (I think at least partially because I had looked at alternatives, so I didn't appear biased), and we put it in place successfully.

What now? Perhaps it's time for a bit of action research, including its emphasis on attempts at disconfirmation, on how I make decisions. Perhaps we can each contribute to an improved understanding of this important process, and perhaps we can get better at what we do.

I've found I learn well when I combine the action and reflection cycle of action research with the exploration of new ideas, such as those I've gained from Klein's work. I'll be using many of the ideas I gained from reading his books.

What are your thoughts?

I'm glad I waited

I just finished Gary Klein's The Power of Intuition ("the way we translate our experience into action," he writes), and it made me think. I judge new ideas by their utility, their grounding in good research and thought, and their consistency with other ideas I've already found helpful.

That's what made this book initially so attractive. His idea of a Decision Making Exercise (DMX) to strengthen intuition aligned well with Dietrich Dörner's ideas in The Logic of Failure of using simulation for much the same purpose. His Figure 3.1 (page 23 in my copy) showing situations leading to cues, which leads to patterns, which leads to action scripts, which leads back to (changed) situations aligned well with my understanding of action research. I liked the concept of a Decision Requirements Table, a DMX, and a Decision-Making Critique. I liked the pre-mortem and the "recognition-primed decision" (RPD) model. His first and second sections seemed to offer many practical insights that I plan to use more intentionally.

Yet I'm glad I waited to write. The third section of the book bothered me a bit. While he still had good things to offer (including insights on effective coaching), I began to have a "doth protest too much, methinks" feeling with his comments on computer systems. Certainly we've made poor ones, and certainly he noted that computer systems and logical systems are good for some things. Klein's third section seemed to put more emphasis on the bad aspects, as if he were trying to bring too much about decision-making under the intuitive model to support his thesis rather than to support good decisions.

In the end, I'm back to the power of "and" (and so is Klein; his last subsection is called "Balancing Act"). We need multiple lenses through which to view and make sense of reality. Intuition (experience as translated into action) is one lens. Logic is another. Computations are another. If we (if I) let ourselves get too enamored of one approach, we risk making mistakes we don't need to. Perhaps it's related to James Reason's Swiss cheese model: we need a slice of logic, a slice of intuition, a slice of computation, and a slice of process (and perhaps more) to shut all the holes.

"Just listen to your heart"

On the surface, it's not a good week for rational thought, at least judging by my reading. First came Blink : The Power of Thinking Without Thinking, Malcolm Gladwell's new book, to talk about how our quick judgments often are better than our careful ones. Then I discovered Die Intelligenz der Gefühle in the Goethe-Institut's Markt online, which points out that our gut feelings and intuition may do a better job in many circumstances than our logic and reason. Often we're better off sleeping on major decisions to let our unconscious process things.

This article points out that, when reason and gut feelings diverge, it's a signal to try to understand the reason for the difference. It also points out that reason can keep our gut feelings in line, preventing us from doing really dumb things, and it can help us develop our gut feelings. So I guess I'm seeing a common thread, all unified around the action research cycle of acting and critical reflection, around the interplay of gut feeling, intuition, and reasoning.

I think I'll go sleep on it.

Business cycles (no, not those cycles)

blink is Malcolm Gladwell's recent book on how we think "without thinking," as he says.

The Logic of Failure is Dietrich Dörner's explanation of the logic behind bad decisions we make.

Gladwell writes about improv groups that can put together seemingly unrehearsed sketches or plays on the spur of the moment. In fact, they rehearse the process intensively so they can react well on the fly. That sounds a bit like business; no matter how much we plan, reality always seems to have little surprises for us.

Dörner shows how our unaided mental processes let us down in certain situations and how the use of computer simulations can help us improve our understanding and ability to make good decisions over a wide range of situations. Simulations should improve our ability to react well when we don't have time for extended thinking.

Two books, similar story. There's a time for action, often without much time for reflection. There's a time for critical reflection, that period when we review what has happened and plan for the future.

The ability to practice can help in the critical reflection phase. Sometimes we can practice with others; actors and speakers do that. Sometimes we may find simulations effective. How often do we in business do that?