Know Why: Systems Thinking and Modeling E-Book

Kai Neumann

• Herausgeber: Books on Demand
• Kategorie: Geisteswissenschaft
• Sprache: Englisch
• Veröffentlichungsjahr: 2015

Seitenzahl: 78

Beschreibung

The complexity of the challenges we are faced with is steadily growing. Our gut feeling cannot predict the future and best practices used in the past do not necessarily work for our individual situations in the present. These facts cause many of us to simply give up. In order to be successful, however, we must understand the interconnections and dynamics at work in these individual situations. With KNOW WHY Thinking and the KNOW WHY Method, Kai Neumann offers two extremely practical approaches with which to handle the complex challenges we encounter in business, in politics and in our personal lives. KNOW WHY Thinking simply asks that we consider the evolutionary pattern of success. The KNOW WHY Method then applies this when we engage in qualitative modeling using what is arguably the most important software in the world – the iMODELER. This book shows you what KNOW WHY is all about and provides you with modeling tips so that you can easily model the following: 1. Personal life: What are the drivers for human happiness? How can we consciously plan to do the things that unconsciously make us feel great? What role do e.g. partners, money, sex, sports, work, nutrition, religion, hormones, products and consciousness play? 2. Management: Why is a product, a company or enterprise, a team, a project, or an employee successful? Why do customers buy certain products? Modeling explains systemic strategy development, project management, marketing, change management, organizational development, and much more. 3. Politics and society: Why aren’t we changing although it is quite clear what needs to change? Wars, poverty, pollution, financial crises, climate change, etc., are easily explained with the help of KNOW WHY. The solutions to our challenges may ultimately not be all that easy, but we can begin to get a handle on them with KNOW WHY Thinking and by modeling.

Leseprobe

Note: For Mac and iPad users there is an interactive and extended version of this work called ‘KNOW-WHY and the iMODELER’ in the Apple iBooks Store. It is enhanced with more illustrations, direct links onto models (running within the book), and a lot of exercises to learn KNOW-WHY-Thinking and the KNOW-WHY-Method.

Content

Our biggest challenge: growing complexity

Giving up: gut feeling and best practices

Why things are: The KNOW-WHY-Thinking

The KNOWWHY of human motivation

Qualitative modeling: beyond mind mapping

The KNOW-WHY-method: systemic and systematic modeling

Tips and tricks on modeling

Quantitative modeling

Free will, EQ and consciousness

Personal happiness: our HIDP

Helping and motivating people: coaching, NLP etc

The KNOWWHY of business success

Idealized System Design

Reflecting on processes and projects

The KNOW-WHY of successful marketing

Our history: merely an unfortunate accident?

The global economy: a closed loop

Sustainability: a concerted action

Global spirit and global politics

What can we do?

KNOWWHY Thinking: a new science science and philosophy?

Useful Literature

1. Our biggest challenge: growing complexity

Our business success, personal happiness and humankind after all depend on our ability to understand the interconnections present within the increasingly complex challenges we face. The economy is on the brink of collapse, there is fierce competition on the job market, more and more people in industrialized nations consider themselves to be unhappier than people in developing countries, and according to studies, earning an annual income of $70,000 or more doesn’t make us any happier. We feel the impact of climate change and suffer from the effects of i.e. pollution, war, poverty and depleting resources – despite the fact that we know which measures we need to take to combat them. We need to understand why we do not change.

In order to be successful, we need to know how things are interconnected and also why they are the way they are. This is crucial because very often we know what should have an impact but we do not know why. We therefore need to apply KNOWWHY Thinking.

When something is complex it means that it is largely unpredictable and that it can only be “approximated.” We can handle complexity in five different ways: ignore it, lessen it, refer to best practices, use our gut feeling, or do our best to examine it through systemic analysis. Systemic (systems thinking, systems theory and systems modeling) means that we observe a part of reality and try to describe it through an interplay of factors that we include in a mental or computer model. It is well known, however, that we are not able to grasp the interplay of more than four factors without the aid of a tool. This is where modeling with the help of a computer comes in.

Modeling can be grouped into two categories: quantitative (system dynamics, agent based modeling, neural networks) and qualitative modeling. Both allow us to analyze the impact of causal chains and feedback loops, which are either reinforcing or balancing and cause dynamic developments.

While quantitative modeling works with data and formulas and results in scenario simulations that predict a system’s behavior over time, the less sophisticated qualitative modeling only roughly describes the connections between factors. It yields matrices that allow us to compare the influences that factors have. Qualitative modeling with a new tool like the iMODELER promises to be the first tool easy enough to be routinely used by planners and decision makers, in fact by all of us from schoolchildren to families to presidents.

The insights we gain from cause and effect modeling depend on how we observe “reality,” the factors that we include and the connections that we see. We need to apply our knowledge, the knowledge from others (e.g. from KNOW-WHY.NET), creativity, our gut feeling even and a method that helps us to find the crucial factors. This is why we should use the KNOWWHY Method, which is based on KNOWWHY Thinking.

Unfortunately, many systems thinkers do not gain insights because they use a tool. Instead, they use a tool to merely demonstrate their view in descriptive models and then go on to claim that their interpretation has authority the same way that many reductionist thinkers do. However, a new tool like the iMODELER – that even runs on your smartphone – together with the KNOW WHY Method and KNOW-WHY.NET allows us all to gain new insights from explorative models on a daily basis.

What we usually do when faced with challenges: reduce the number of factors.

In reality, however, the interconnections of many factors allow small influences to have a large impact.

A small cause and effect model showing reenforcing (more solar energy, more R&D, more efficient modules, more solar energy) and balancing (more solar energy, more no covering of the base load, less integration into the power grid, less (!) more solar energy) feedback loops

2. Giving up: gut feeling and best practices

It is only natural and thus (!) logical that we dislike the uncertainty that accompanies complexity. We also dislike most arduous work – and not just because we lack the time for it. We basically like to keep things simple and to keep the number of factors in our mental or computer model to a minimum, too. We also tend to refer to best practices, or listen to our gut feeling.

But reducing the number of factors is a mistake. Keep it short and simple (KISS) works for communication but not for analysis. The law of variety (W.R. Ashby) states that we need to level the number of possible states that we can take with that of reality. Inconspicuous factors might trigger crucial feedback loops, however, and hence we shouldn’t shy away from building or thinking in terms of very large models. Creativity also increases through the visualization of many factors. We can “bisociate,” meaning that we can form new ideas by combining different associations. Of course, we need a tool that allows us to handle the many factors.

Best practice solutions are based on valuable knowledge. But knowledge that was valid in the past under different circumstances may not necessarily be applicable to a given situation in the present or future. If it were, success could simply be attained by copying from the best.

Our gut feeling, or intuitive intelligence, is a mighty mechanism from nature. We are able to use our experiences from the past to unconsciously reach conclusions in the present. We abstract from what we have learned and develop a feeling for a situation that is similar. It is almost a reflex and therefore very difficult to reconstruct how we got our gut feeling – a feeling that depends on three things: our experiences, our perception of the circumstances surrounding the challenge we face and – as it is a feeling – the emotional state we are in. This is also the reason that our gut feeling might at times be less based on past experiences and biased by our present feelings. If we are in a bad mood we judge things differently than when we are in a good mood. Our unconscious perception of certain aspects is limited to the present. And we cannot predict future outcomes because they depend on aspects from the future. Unfortunately, many publications on gut feelings lack this insight. Last but not least, the benefit provided by a gut feeling in dealing with a complex challenge depends not just on the number of past experiences we have had but also on our experiences with nonlinear developments that arise from feedback loops. We can see such cause and effects when we model.

Cause and effect models often do not include only factual knowledge and hence we need to use our gut feeling to judge the weight of one factor’s effect on another factor. If we have no gut feeling, we simply have to guess. But this is perfectly okay, too, because we aren’t guessing the behavior of an entire system. We can truly only benefit from modeling.

A combination of best practices, gut feeling and modeling works best as they are interdependent of each other.

3. Why things are:The KNOW-WHY-Thinking

There are numerous systems theories out there. Most of them, however, e.g. the viable systems model (Stafford Beer), AGIL (Talcott Parsons) and the ideas of Niklas Luhmann merely describe how some systems work. They don’t describe WHY things work the way they do and so these theories cannot explain why any given system will be successful or fail. Another drawback: many theories are much too complicated to be used in actual planning and decision-making situations.