Flying Logic: Just Another Outliner?

I am often asked to compare Flying Logic to other packages such as Austhink Rationale and MindMapper. I suppose the main thing that provokes this comparison is that all three are graphically-oriented programs for capturing knowledge. Traditional text-based outliner software is used for capturing knowledge too, but lacks the distinctive visual “boxes and lines” look that Flying Logic and the other packages share.

The main difference is that Flying Logic is not an outliner. What do I mean by this?

Outliners, whether they are traditional text-based ones or more visual ones like Rationale and MindMapper, are based on trees, also called strict hierarchies. If the sort of reasoning you want to do breaks down easily into this structure then outliners are fine, and of course Flying Logic does trees with no problem.

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A Tree

But Flying Logic is based on a more general structure called the Directed Acyclic Graph (or DAG). Unlike trees where every “child node” has exactly one “parent node,” in a DAG any child can have any number of parents. The only restriction is that a child not (directly or indirectly) be its own parent, a situation called a cycle or loop.

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A Directed Acyclic Graph (DAG)

In fact, FL allows cycles too, but specially treats the “back edges” that form them. This is useful when modeling so-called “virtuous cycles” or “vicious cycles.”

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A Vicious Cycle (back edge in blue)

So Flying Logic is based on DAGs. So what?

Outliners (whether text-based or graphical) are useful when you are simply breaking a thing down into its subparts. For instance, “A degree program consists of a number of courses, each of which consist of a number of assignments.” This is a strict hierarchy. But what if you want to say that a particular course is a prerequisite for several degree programs, and see at a glance what degrees require which courses, and what courses are required by what degrees? Then the “course” entity needs to have several parents, and trees (and outliner software) do not permit this.

When modeling real-life cause-and-effect (such as when using systems thinking techniques like the Theory of Constraints), the need to break away from strict trees becomes even more apparent. Causes can have several effects, and effects can have several causes, or require several conditions, or both. This makes DAGs the most natural choice. But unlike tree-based outlines, which can be easily represented as indented blocks of text, DAGs have no simple expression in pure text without having to redundantly replicate information wherever a child has more than one parent. In other words: for trees, a graphical layout is a nicety, but for DAGs it is a necessity.

Flying Logic also includes features that are specifically aimed at modeling cause-and-effect, including junctors, operators, edge weights, and confidence spinners. Together, these allow various logical and/or mathematical relationships to be expressed, tested, and demonstrated step-by-step, including belief networks and probabilistic networks. (And they stay neatly out of your way when you don’t need them.) Outliners simply don’t do any of that.

Finally, if you look at the screen shot galleries of many graphical outliners, it’s often hard to tell whether more time and effort went into the actual planning work, or into tweaking the plethora of graphics options available. Flying Logic upholds a philosophy of Let the Planner focus on Planning. Since graphic design is not part of the planning process, Flying Logic deliberately avoids adding any graphical options except those that can be justified on the basis of supporting clean, understandable reasoning.

Tip: Removing a Junctor

A user wrote and asked:

I’m evaluating Flying Logic Pro on a Mac. I found that an edge with a negate or complement operator in it can’t easily have the operator removed. Selecting the operator and hitting Delete removes the operator and the edges connected to it. It should just remove the operator and unify the input and output edges. This should happen for any operator with one input edge and one output edge. I also think it should happen when an entity with one input and one output is deleted.

Part of the problem here is that when an edge is deleted, large-scale rearrangement may occur on the graph, making the previous state obscure to the user. I expect that it would be common to remove unary operators and want the connection to remain.

The situation described is like this:

flying-logic-ng001.png

If the junctor or either of the two edges is selected and then deleted, the two entities will end up unconnected. This is because edges must always be connected at both ends, and junctors must always have at least one incoming edge and one outgoing edge.

It turns out there is a very easy way to remove the junctor and keep the entities connected. To do this, we use the “redirect” gesture, which is used to redirect the head or tail of an edge to a different entity or junctor. Redirect is initiated by clicking and dragging right at the end of an edge, and is signified by a special arrow with a circular head.

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By redirecting the head of the arrow before the junctor to the entity after the junctor…

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The junctor and the second arrow are removed, while the redirected arrow remains.

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You could just as easily redirect the tail of the second edge to the leftmost entity. Since edges can have different annotations or weights, this method gives you choice over which edge will remain after the junctor is removed.

Errata: Pricing Incorrect in Program

If you downloaded Flying Logic before today, you may notice that when you use the in-program “Purchase Now” features, the price you are given is incorrect (far too high!) although the prices have always been correct in the Sciral Store ($149/Pro, $79/Personal, $39/Student).

All copies of Flying Logic downloaded from today on do not have this problem.

We apologize for any confusion.

The Two Types of Thinking

Humans have two distinct systems for solving problems, both of which have their place. Psychologist Keith Stanovich termed these simply System 1 Thinking and System 2 Thinking.

System 1 Thinking System 2 Thinking
Automatic Deliberate
Effortless Effortful
Faster to act Slower to act
Slower to adapt Faster to adapt
Habitual Intellectual
Reactive Proactive
Specific-purpose General-purpose

When we instinctively reach out to catch a ball, or habitually snap at a loved one or co-worker, we are using System 1 thinking. When we carefully consider the positive and negative consequences of a set of possible actions, before deciding which to take, we are using System 2 thinking.

Creating a habit can be thought of as the process of training System 1 to behave in a way deemed beneficial as the result of System 2 thinking. If you have ever deliberately made a positive habit or broken a bad one, you know how much effort it takes. Nonetheless, we can do this when we are sufficiently motivated.

System 1 thinking is absolutely the best way to go when the situations you are facing are highly similar to previous situations you’ve faced over and over. Managers who have honed their System 1 thinking and are in a position to apply it are effective and powerful— they project a sense of mastery. In fact, System 1 thinking at its best can literally seem magical. For example, magicians such as Penn & Teller use painstaking System 2 thinking to develop their illusions, and then practice them to the point where they require no System 2 thinking at all to perform, no matter how spontaneous they may seem to onlookers. Every possibility in performance is handled by System 1 thinking.

Penn & Teller Demonstrate System 1 Thinking

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Unfortunately, when business conditions change many managers continue to apply the same System 1 thinking and cannot understand why the company’s situation continues to decline. Why is this? It is certainly not that companies move on such a short time scale that only System 1 thinking is applicable. And it is also not often a shortage of managers trained in System 2 thinking.

The answer is that applying System 2 thinking in an organizational context requires managers to shed their “cloak of invincibility” and demonstrate willingness to re-think the organization’s processes as a unified system rather than as a collection of parts. This usually requires the input of all stakeholders and the help of methodical analysts trained in systems thinking techniques such as the Theory of Constraints. The apparent loss of direct control this kind of program entails is what causes managers to resist. But the positive view (setting aside the potential for great improvement in the business itself) is that successful implementation of such comprehensive change is a true leadership challenge to which only the best managers rise.

Work to increase your awareness of the roles that System 1 and System 2 thinking play in your personal and work life, and let that awareness provide you additional choices— both for solving problems and for taking advantage of opportunities that come your way.

Flying Logic Writeup in TidBITS

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Today Matt Neuburg posted a nice writeup of Flying Logic in the well-regarded TidBITS newsletter.

Read the whole thing here. A short quote:

You could learn about the Theory of Constraints and develop a diagram on paper, on a blackboard, or using a diagramming tool such as OmniGraffle; but Flying Logic has some important advantages. It is dedicated to the appropriate kinds of diagram, so its tools and automatic layout and formatting facilitate rapid, accurate diagram construction without your having to worry about presentational details. A diagram is constructed, developed, and rearranged by a series of extremely simple mouse and keyboard gestures; for example, a simple drag connects two entities causally, and the diagram magically rearranges itself to reflect this. Thus you spend your time entirely on content, letting Flying Logic take care of form.

Edge.org: Danny Kahneman on Thinking About Thinking

Edge.org has some excellent excellent excerpts from a two-day talk given by psychologist and Nobel laureate Daniel Kahneman on Thinking About Thinking, including video with transcripts. I find cognitive biases fascinating, and this man has devoted a large part of his distinguished career to studying them. Some quotes I found interesting:

On the Two Views of a Problem

I’m deeply ashamed of the rest of the story, but there was something really instructive happening here, because there are two ways of looking at a problem; the inside view and the outside view. The inside view is looking at your problem and trying to estimate what will happen in your problem. The outside view involves making that an instance of something else—of a class. When you then look at the statistics of the class, it is a very different way of thinking about problems. And what’s interesting is that it is a very unnatural way to think about problems, because you have to forget things that you know—and you know everything about what you’re trying to do, your plan and so on—and to look at yourself as a point in the distribution is a very un-natural exercise; people actually hate doing this and resist it.

On Knowing What Makes You Happy

Just to give you a sense of how little people know, my first experiment with predictive utility asked whether people knew how their taste for ice cream would change. We ran an experiment at Berkeley when we arrived, and advertised that you would get paid to eat ice cream. We were not short of volunteers. People at the first session were asked to list their favorite ice cream and were asked to come back. In the first experimental session they were given a regular helping of their favorite ice cream, while listening to a piece of music—Canadian rock music—that I had actually chosen. That took about ten-fifteen minutes, and then they were asked to rate their experience.

Afterward, they were also told, because they had undertaken to do so, that they would be coming to the lab every day at the same hour for I think eight working days, and every day they would have the same ice cream, the same music, and rate it. And they were asked to predict their rating tomorrow and their rating on the last day.

It turns out that people can’t do this. Most people get tired of the ice cream, but some of them get kind of addicted to the ice cream, and people do not know in advance which category they will belong to. The correlation between what the change that actually happened in their tastes and the change that they predicted was absolutely zero.

On Remembering Happiness vs. Being Happy

Millions of people have been asked the question, how satisfied are you with your life? That is a question to the remembering self, and there is a fair amount that we know about the happiness or the well-being of the remembering self. But the distinction between the remembering self and the experiencing self suggests immediately that there is another way to ask about well-being, and that’s the happiness of the experiencing self.

But first I thought I’d show you the basic puzzles of well-being. There is a line on the Easterlin Paradox that goes almost straight up, which is GDP per capita. The line that isn’t going anywhere is the percentage of people who say they are very happy. And that’s a remembering self-type of question. It’s one big puzzle of the well-being research, and it has gotten worse in the last two weeks because there are now new data on international comparisons that makes the puzzle even more surprising.

So what is the puzzle here? The puzzle is related to the affective forecasting that most people believe that circumstances like becoming richer will make them happier. It turns out that people’s beliefs about what will make them happier are mostly wrong, and they are wrong in a directional way, and they are wrong very predictably. And there is a story here that I think is interesting.

When people did studies of various categories of people, like the rich and the poor, you find differences in life satisfaction. But everybody looks at those differences is surprised by how small they are relative to the variability within each of these categories. You address the healthy and the unhealthy: very small differences.

Age—people don’t like the idea of aging, but, at least in the United States, people do not become less happy or less satisfied with their life as they age. So a lot of the standard beliefs that people have about life satisfaction turn out to be false.

On the Way We Develop Answers

There seems to be a very general psychological principle at work here, which is that sometimes when you are asked a question that is difficult, the mind doesn’t stay silent if it doesn’t have the answer. The mind produces something, and what it produces very characteristically is the answer to an easier but related question. That’s one of the heuristics of good problem-solving, but it is a system one operation, which is an operation that takes place by itself.

You ask people, How many murders are there every year in Michigan, and the median answer is about a hundred. You ask people how many murders are there every year in Detroit, and the median estimate is about two hundred. And again, you can see what is happening. The people who notice that, “oh, Michigan: Detroit is there” will not make that mistake. Or if asked the two questions next to each other, many people will understand and will do it right.

The point is that life serves us problems one at a time; we’re not served with problems where the logic of the comparison is immediately evident so that we’ll be spared the mistake. We’re served with problems one at a time, and then as a result we answer in ways that do not correspond to logic.

When I was living in Canada, we asked people how much money they would be willing to pay to clean lakes from acid rain in the Halliburton region of Ontario, which is a small region of Ontario. We asked other people how much they would be willing to pay to clean lakes in all of Ontario.

People are willing to pay the same amount for the two quantities because they are paying to participate in the activity of cleaning a lake, or of cleaning lakes. How many lakes there are to clean is not their problem. This is a mechanism I think people should be familiar with. The idea that when you’re asked a question, you don’t answer that question, you answer another question that comes more readily to mind. That question is typically simpler; it’s associated, it’s not random; and then you map the answer to that other question onto whatever scale there is—it could be a scale of centimeters, or it could be a scale of pain, or it could be a scale of dollars, but you can recognize what is going on by looking at the variation in these variables. I could give you a lot of examples because one of the major tricks of the trade is understanding this attribute substitution business. How people answer questions.

COMMENT: So for example in the Save the Children—types of programs, they focus you on the individual.

KAHNEMAN: Absolutely. There is even research showing that when you show pictures of ten children, it is less effective than when you show the picture of a single child. When you describe their stories, the single instance is more emotional than the several instances and it translates into the size of contributions.

Sciral Introduces Flying Logic Planning Support Software

For Immediate Release

LOS ANGELES/EWORLDWIRE/Sep 24, 2007 — Today Sciral (“PSY-ruhl”) released the first version of its innovative planning support software, “Flying Logic.” Originally developed for a major defense contractor as part of its advanced concepts development program and targeted for use in military Course of Action Analysis (COA), Flying Logic uses a patented, highly visual interface to support techniques employed by strategists, planners, and consultants in the creation of plans at the earliest, most fluid stages.

“We set out to create a fresh, visual approach to the sort of critical thinking that military planners must apply to every mission, and realized that their methods had much in common with those used by professionals that specialize in business process reengineering and process improvement – they both have the goal of creating new, better situations out of existing, problematic situations,” said Robert McNally, Sciral’s president and the designer of Flying Logic.

Like spreadsheets do for financial planners, Flying Logic encourages strategic planners to play “what if” with cause-and-effect scenarios, giving them the ability to try many more possibilities in a shorter time than would be possible with any other kind of software. Planners focus on their planning, and Flying Logic takes care of the layout and formatting details, including using smooth, animated transitions as the diagram changes and grows. “Flying Logic is not a drawing program even though it is used to create diagrams,” said McNally. “It is a new kind of spreadsheet – a spreadsheet for general rational thinking.”

McNally continued, “When a system has problems, learning what really needs changing – and what to change to – encompasses a set of methodologies that are distinct from traditional project management. Answering these questions becomes even more complex in the world of organizational strategy: root causes must be identified, solutions created and tested, obstacles identifed and overcome, and negative outcomes mitigated or circumvented. There are proven practices for accomplishing these goals, but until now there has been no targeted software support for quickly and easily creating the cause-and-effect trees necessary to think them through.”

Flying Logic is available for Windows and Mac OS X in three editions at special introductory prices: Professional ($149), Personal ($79), and Student ($39). All editions are available for immediate download from FlyingLogic.com and include a fully-functional 30-day trial.

About Sciral

In business since 2000, Sciral develops innovative productivity tools for individuals and organizations.

CONTACT:
Robert McNally
Sciral
157 N. Glendora Ave.
Suite 209
Glendora, CA 91741
PHONE. 626-963-7760

KEYWORDS: software, productivity, strategy, business, improvement, consistency, flying logic, software, planning, project management

SOURCE: Sciral