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THE “THINKING” tC) IN SYSTEMS | THINKING i | Seven Essential Skills < > By BARRY RICHMOND | (ou Tf Ol 0 ul cr oN hat do we mean when we say, “systems thinking”? We can use the phrase to refer to a set of tools—such as causal loop diagrams, stock and flow dia- grams, and simulation models—that help us map and explore dynamic complex- ity. We ean also use it to mean a unique perspective on reality—a perspective that sharpens our awareness of wholes and of how the parts within those wholes in refer to a specific vocabulary with which we express our understanding of dynamic complexity. For example, sys- interrelate. Finally, systems thinking tems thinkers often describe the world in terms of reinforcing and balancing pro- cesses limits, delays, patterns of behavior over time, and so forth, One thing we do know is that systems thinking is also a discipline that requires intensive practice and patience. To that end, there isa series of seven dif ferent cognitive processes that seasoned systems thinkers employ as they address problems of concerns, The seven skills complement one another in that each one is used! at a different point during the eesign of a systems thinking intervention, In this volume, we start with an overview of these thinking skills. We also show how each skill can be mapped onto the four-step systems thinking method, and ‘we contrast the skills with their non~systems thinking counterparts. Then, we explore each skill in detail, one at a time, providing examples, diagrams, and tips for honing the skill, Some of these skills will seem familiar to you and relatively easy to grasp. Others require lots of practice to master. As it turns out, certain aspects of sys- tems thinking just don't come naturally to many of us. The key is to be patient swith yourself This volume should help you to begin honing each of the seven thinking skills. Once you're more familiar with them, you can then experiment with putting them all together to address an issue. As you gain ability and confi- dence, you'll find that you'll begin to see the world in new, more dynamic and holistic ways—which is really the most powerful advantage that systems think- ing offers, lee BALANCING Loop THE LANGUAGE OF exAMPLE ACCUMULATORS 4 Acausa ok between two vas, out epee evant ot 2 unre cnongein Xeauses a cnange pany toad Sates itis be agin f invintne sate recto orwnorese Tl Seoy TEO Sy + isso. LON LON pe 2. Acausal nk between two varabes, who sayeth ee 2 sine henge n Xeauses a chenge i oa id Sin inthe opposite direction, or where a Xewovacts trom ¥. sic stasshon nme on 8 A“>balancing" feedback loop that ovat ‘squats the deared vel, The saeiBar inept ecoack loop that PEGASUS COMMUNICATIONS, INC. WWW.PEGASUSCOM.GOM THE THINKING" IN SYSTEMS THINKING THE THINKING: AN OVERVIEW OF SEVEN SKILLS espite significant advances in Darr npc ante tems thinking software in the 1990s, learning to apply systems thinking effectively remains a tough nut to crack. Many intelligent people con- tinue to struggle far too long with the systems thinking paradigm, thinking process, and methodology, Systems thinking’s steep learning, curve is likely related to the fact that the discipline requires mastering a whole package of thinking skills. Much like the accomplished basket- ball player who is unaware of the many separate skills needed to execute a lay up under game conditions, veteran sys- tems thinkers are unaware of the full set of thinking skills they deploy while or Ce rose onersct Fyporneae ePiiase ypothesie. (Model plano Changs! eminent naertaning Begin by specifying the problem you want to address, Then con to explain the problem. Test your hypotheses by simulating them. Only waen you have an enter- tainable understanding ofthe stuston should you begin to ‘struct hypotheses, or mode! Implement change, “THINKING” ea ek ed executing their erat. By identifying these separate competencies, both new hoop legencls and aspiring systems thinkees can hone each skill in isola tion, before trying to put them all together in reallife applications THE SYSTEMS THINKING METHOD The seven skills that make up systems thinking are best understood when seen in the context of the iterative, four-step process that constitutes the systems thinking method (see “Steps in the Systems Thinking Method”). Each skill plays a role in supporting one or more of these steps. In employing sys- tems thinking, you fiest specify the problem or issue you wish to address You then construct a hypothesis, or model, to explain the cause of the problem. Next, you test the hypothesis by simulating the model. If the model ean gener- ate the problem, you have an entertainable hypothesis. If not, you must modify the model and retest it. Once you have arrived at an entertainable hypothe- sis, you can then com- municate your new-found clarity to others and begin to implement change. When we use the IN SYSTEMS term “model,” we mean something that represents our assumptions (or hypotheses) about how a particular piece of the world works. A model can bea single assumption that we hold in ‘our mind: for example, “The best way to beef up profits isto launch promo- tions.” Or, it can be a complex set of assumptions that we have made explicit asa diagram, a set of mathematical equations, or some other form. One strength of computer simulation mod- cls is that they let you rigorously test how your assumptions and decisions might play out over time. Such models contain equations, written by the model builder, that explicitly express the model builder’ assumptions about how certain variables influence each other. A simple example would be: 2*Promations (*Sales, in dollars is ales = equal to twice the number of dollars invested in promotions"). An important thing to realize about all models is that they are always wrong in some respects. Every model is necessarily an incomplete representa- tion of reality and therefore not “true.” ‘The real test of a model's value is therefore not how “right” itis, but rather how useful it is. Some models are more useful than others because they help us understand reality better than others. There is a tendency in the business world, and in academia, to try to “validate” models, which is to say to anoint them as “true” in the sense of being numerically accurate in a predi PEGASUS COMMUNICATIONS, ING. 701.998.9700,