Wednesday, June 26, 2013

Which Side Is Your Brain On, or, Once Again About Goal-free Living

In 2011 I found a book by Stephen Shapiro named “Goal-Free Living.” The book intrigued me a lot, and I even wrote a post, assuming that the author didn’t mean that all goals should be thrown into the trash. Such a thing would be too radical. I thought that the book would most likely talk about tweaks in working with goals. But I was finally able to read the book and was surprised to find that Stephen really did oppose the classical goal-setting approach. He instead used goal-free techniques, in which “goals” became “aspirations,” and “action plans” became an “intuitive compass.” On the surface it sounded pretty revolutionary. However, the more I read the book, the more I saw similarities with other ideas I’ve seen elsewhere.

I found the strongest association between Stephen’s book and another book by Dr. Beverly A. Potter with the long name, High Performance Goal Setting : Using Intuition to Conceive and Achieve Your Dreams.” The answer popped up almost immediately--it seemed that Stephen was really only talking about an intuitive approach to goal setting! The substitution of common terms with new ones confused me, but not too much. He was only trying to present his own ideas as something new. There were some ideas that I’d never seen before, but there was no true revolution. Stephen just looked at goal-setting from a different angle, complementing it with his approach instead of actually contradicting it.
Over the years, the dominating approach to goal-setting has always been based on the clear and elaborate definition of goals, action planning, and the critical analysis of results. This method can be heard from the majority of trainers and be read in numerous books. As more people have accumulated experience with goals, though, it has become apparent that these rational methods of goal-setting are not for everyone. In the beginning of his book, Stephen showed some interesting statistics. He demonstrated that more than half of all people who practice goal-setting do not achieve their desired results, but rather raise the amount of stress in their lives.

I have to admit that I was mostly focused on the rational approach. My main objective was to automate management, and automation, using modern computer technologies, is easier to accomplish with rational goal-setting than with intuitive. After reading Stephen’s book, I decided to figure out how the two approaches relate to each other.

A solution appeared right away. I just had to remember that people have different abilities in abstract thinking, analysis, and math. Those abilities, as modern research shows, depend on which of the two hemispheres of the human brain is the dominating side. People with a dominating left side use analytical thinking and are more inclined to operate with facts. People with a dominating right side, however, belong to a more synthetic type. They most often operate on emotions and are more inclined toward a holistic perception of reality, looking more at the big picture of life and less at the smaller details.

Here are some of the ways they differ:

Left Brain Functions
Right Brain Functions
Uses logic
Detail oriented
Facts rule
Words and language
Present and past
Math and science
Can comprehend
Knowing
Acknowledges
Order/pattern perception
Knows an object’s name
Reality based
Forms strategy
Rational
Practical
Safe
Uses feeling
“Big picture” oriented
Imagination rules
Symbols and images
Present and future
Philosophy & religion
Can “get it” (can understand the meaning)
Believes
Appreciates
Spatial perception
Knows an object’s function
Fantasy based
Presents possibilities
Intuitive
Impetuous
Risk taking


As you can see, for right-brained people the picture of modern goal-setting is not pretty. One way or another, our society imposes approaches on them that do not suit them well. By following the currently advertised methods, they set goals for themselves, spend their lives on trying to reach them, and then fail at the end. Individuals with a dominating right side get into a conflict with their own nature. Furthermore, because they are unable to accurately define and structure their feelings, they sometimes tend to copy goals from somebody else. They go after these goals, following a correct way that doesn’t match their actual situation, and receive generally negative results. Also, even when the adopted goals are achieved, since they are alien to the adopters, they don’t bring the satisfaction that was desired.

Because of the reasons above, it is nice to see more work being done on the intuitive approach. It minimizes the disproportion and helps people with a dominant right brain to reach success with more appropriate methods. The intuitive approach does not contradict to the rational one in its fundamental principles. It just sees them from a different perspective. In the table below I show how these goal-setting principles relate to each other in each approach:

Principal
Rational
Intuitive
Goal - desired state in the future, which is planned or intended to be achieved
Defined, measurable, limited in time, an endpoint of activities. It is achievable, but requires one to stretch and to leave his/her comfort zone. Results are described as a set of well-defined measures.
Attraction point in the future, which generates desire and inspires one to reach it. Often it is perceived ideally and cannot be completely achieved. Results are presented as mental pictures.
Action Plan - sequence of steps to reach a goal
Well structured, thought-out, analyzed plan. Actions are rational and planned a few steps ahead. Risks are evaluated and minimized.
There are no plans. All actions are intuitive, even impulsive. Decisions are made only for the next step depending on the situation. Risky behavior is welcome--everything that could happen is for the better.
Philosophy
Set your goal, plan your path, and follow it, overcoming all obstacles.
Become a flow, which goes into the direction of your aspirations.
Methods
Based on facts, analysis, and detailed and structured information. Obstacles are expected and overcome by rational efforts. New opportunities are found via a periodic reevaluation of the situation.
Based on the visualization of a mental picture, represented as images, phrases,  and sounds. Obstacles are overcome through strong desire. Opportunities are considered as an integral part of life, going “where the wind takes you.”
If a human being has only two hemispheres in his brain, does it mean that there are only two approaches to goal-setting? In reality, both approaches--rational and intuitive--belong to so-called “cognitive” (conscious) methods. There are other goal-setting methods, which are based on manipulations of the subconscious mind. Shamanism and witchcraft, meditation, religious practices, NLP (neuro-linguistic programming), and many other methods are able to predefine and direct human action at a deep level, without touching the cognitive mind. Their use is limited to typical patterns, though, and shifting away from those patterns can result in unpredictable circumstances and is practiced only by specialists. Those practices still require good research before they can ever be practical and efficient tools for regular people.

The last point I’d like to make in this post is related to goal-setting in groups and organizations. If an individual sets and reaches goals alone, one style would be more than enough. However, the practice of doing so shows that the one-sided approach in group goal-setting is ineffective. If goals are set using a rational approach, they are usually perceived as firm instructions to follow with no room for creativity and individuality. In this case, the involvement of group members is relatively low, and their potential, especially that of creative people with a dominant right side, remains underutilized. On the other hand, if goals are defined intuitively as a vivid but undefined picture, then every team member may understand the goals differently. As a result, their actions pull the group into different directions and the overall positive effect is minimal.

To make group goal-setting more effective, combined methods should be used. In doing so, goals should be presented colorfully to stimulate emotions, and at the same time be discussed, key points written down to synchronize the visions of all group members. Action plans should then be set up as clear steps with delegated responsibilities, but should not fall into micro-management. Giving more freedom to every player will allow them to do the work in their most convenient and efficient way. By incorporating both methods of goal-setting, the group will reach better results and higher satisfaction.

In literature you can find two typical leadership styles: charismatic and organizational. It is obvious that charismatic leaders rely on a right-sided, intuitive management style with vision and ideals. On the contrary, organizational leaders use a left-sided, rational approach with clear structure and deep elaboration. However, in many cases successful leaders cannot organize and lead a big group of people just by themselves. In order to do that, they need assistants to compensate for missing parts. Thus, in organizations, charismatic leaders need rational and logical assistants, who will transform their visions into clear action plans and execute them with all available energy. Organizational leaders then need the other type of assistants--idea generators. They can then take their ideas, structure them, and effectively implement them using available resources. In all things, balance is needed. Where there is a right brain, it is good to have a left brain. Therefore, in an organization, it is best to use both rational and intuitive goal-setting.

References:

Wednesday, June 19, 2013

GOMA Modeling

The first step in working with any system is to understand it by starting with its description, which can be expressed in a formal or informal manner. A model, which is an abstract formal definition of a system, can be very helpful. It describes a system’s structure and behavior and can be represented by several interrelated views. In this post, we will look at a general approach to modeling goal-oriented systems and the reasons behind doing so.

Below are two of the most typical methods of modeling and improving models: the reengineering of business processes, and comprehensive management automation.

In both cases the process starts when a business analyst looks at a real or planned system, and then creates a formal model of the system’s structure and its behavior. This initial model is called the domain or business model. After that, the next steps are significantly different:

  • Process Reengineering procedure:
    • The original model is improved to solve particular business problems--to optimize management processes, to minimize resource utilization, to maximize productivity, etc.
    • The improved model is deployed and then transforms the management system.
  • Management Automation procedure:
    • Similar to reengineering, the original model is improved since automation may significantly change various management processes (and there is no reason to drag the rudiments of manual management into the new world).
    • Next, the new automation system is added to the business model and linked to its functions, information flows, and other elements that will be automated--and their quantitative and qualitative characteristics are elaborated. The new model is called requirements as it defines what the automation system will require.
    • Based on these requirements an architecture is created, which is also a model. It describes the structure of new automated solutions, the system’s decomposition into subsystems and components, the component’s functions, and processed information.
      • The requirements can also be used to generate test cases to verify that the automated solutions do what they were intended to.
    • Based on the architecture model, automated solutions are then implemented and finally deployed throughout the organization.

The modeling of Management Automation leads into GOMA Models. In order to understand GOMA Models, it is good to know about the modeling elements that may be contained in them:

  • First, there are Systems (S) and Environments (E) that are broken down into Subsystems (SU) and Subenvironments (EU), which can all be called Contexts (C). Contexts allow designers to split a large model into smaller logical parts, which can be defined separately without losing connections with the whole.
  • Then there are Elements (E), which compose the system and environment, their relations (ER), and the parameters (EP) that define their state.
  • Next, there are the Roles (R) that elements play in a management process. There are 4 key roles plus one group role:
    • Active element (RA) or Decision Maker (DM) - An element role that is actively involved in management. It is able to perform management functions, receive and reach goals, make decisions, and take actions to change a system and/or environmental state. For example, a digger, a driver, and their customers and supervisors are all active elements.
    • Resource (RR) - An element role that required for specific management functions, but is not able to perform functions by itself. For example, a shovel is resource for a digger, and fuel and vehicles are resources for a driver.
    • Subject (RS) - An element role on which the actions of active elements are directed. For example, a piece of land is a subject for a digger, and loaded vehicle is a subject for a driver.
    • Observable element (RO) - An element role that supplies information used in a management process. For example, weather is an observable element for a digger, and the road and other vehicles on the road are observable elements for a driver. (Here I must note that the other roles may also supply information and can be considered as observable elements, too.)
    • Group (composite) element (RG) - A group element role. Such a role is useful for the recursive decomposition of a management system. It may represent a division, department, or group as a single super-element and can be broken down into smaller and more specific roles.
  • Now come the Management Functions (F), which are performed by active elements to process information and achieve their management goals. Functions, following the OODA Loop model, can be subdivided into these areas:
    • Observe (FB) - Functions that obtain initial data (basic facts and observations).
    • Orient (FO) - Functions that interpret the obtained data to generate facts useful for decision making.
    • Decide (FD) - Functions that make decisions based on higher-level goals, current situational information, and knowledge about the system structure.
    • Act (FA) - Functions that perform actions to change a system and/or environmental state, or that delegate actions to subordinate active elements at lower management levels.
    • Finally, it is also good to consider Functional Groups (FG), which can be repeatedly broken down into lower-level and more specific functions.
  • Next are the Information Flows (I) that are transmitted between system elements and processed by management functions. They can be divided into:
    • Goals (IG) - what must be achieved in the management process by active elements.
    • Actions (IA) - information on what has to be performed to execute set goals/plans and to change the system and/or environmental state. Based on principles of dualism, actions represent an elementary goal at a certain abstraction level. They show an endpoint in the management flow. Also, they cannot be broken down into smaller goals, while goals can be.
    • Escalations (IE) - signals alerting that set goals cannot be reached in the way they were defined. Escalations are typically transmitted in the opposite direction of their related goals.
    • Interpretations (II) - various facts required for decision making. Interpretations represent the most common form of analytical information.
    • Observations (IO) - initial basic analytical data. Based on principles of dualism, an observation represents an elementary interpretation at a certain abstraction level. They show a starting point in the analytical flow. Additionally, as they’re based on initial data, they cannot be broken down into simpler interpretations, while regular interpretations could be.
    • Results (IR) -  interpretations (including observations) which can be directly linked to specific management goals. Results are used in the feedback loop of a management system.
    • Information about System Structure (IS) - represents information about the structure of a system and environment.
  • Finally, we come to Automations (А), which are various technological tools used to automate management systems. They can be divided into:
    • Automated Systems (AS) - automation solutions which are separately developed, purchased, and installed.
    • Automated Subsystems (AU) - parts of larger automation systems which could be broken down into smaller components.
    • Automated Components (AC) - elementary, indivisible parts of automation systems.

All of these modeling elements have many complex relationships with each other. When not put into a GOMA model, these relationships might look similar to the following semantic map:



It looks like a big mess, doesn’t it? Ideally, a GOMA model will be defined with extremely high precision and details, so it can be sufficient for the design of comprehensive automation solutions--solutions that will completely replace humans in management systems. Obviously, if such complex models are visualized in a single plane, it will be practically impossible to read and understand them. That’s why GOMA models use a number of interrelated graphical views (diagrams).

I’d like to emphasize that the diagrams in GOMA are not models. They are just views of a model to visualize it from a specific angle. Any change in a model shall trigger changes in the diagrams, which will contain the updated elements and relations between them. I’ll give you more details about different types of diagrams in my later posts.

Lastly, I’d like to mention the possibility of formally transitioning between different types of models. In the figure below, we can see the evolution of a model from a Domain/Business model to a Requirements model and finally to an Architecture model.

  • The Domain / Business model defines a system before automation. It describes the system’s structure, element roles, management functions, information flows, and the relationships between them.
  • During the Requirements elicitation, a domain model is extended with automation systems and information about:
    • Active elements to be automated
    • Management functions to be implemented
    • Information flows to be produced or consumed by automation
    • Interfaces with other management systems, and
    • The quantitative and qualitative model characteristics, required for automation, are elaborated.
  • Then, to move to the Architecture model, a requirement model is further elaborated:
    • Each automation system is broken down into subsystems and individual components
    • For each component, the implemented management functions and input and output information flows are defined to outline their interfaces (contacts or surface areas)
    • The technological characteristics of implementation are elaborated
If the resulting architectural model is detailed enough, it can be used to generate code (similar to an MDA - Model Driven Architecture)

With all this information, it is clear that the complexity of GOMA models is usually high. It makes it very hard to create and maintain models without special tools. Thankfully though, the diagrams presented above help to understand them, and additional instrumentation tools are already being developed. (I will discuss these tools in a future post.)

Hopefully, this article has helped you to see the better benefits of GOMA modeling. Though complex, it is much easier to understand than the other typical methods of modeling. It helps us to better understand management automation systems.