This blog is part of an online learning platform which includes the Pathways to New Community Paradigms Wiki and a number of other Internet based resources to explore what is termed here 'new community paradigms' which are a transformational change brought about by members of a community.

It is intended to offer resources and explore ideas with the potential of purposefully directing the momentum needed for communities to create their own new community paradigms.

It seeks to help those interested in becoming active participants in the governance of their local communities rather than merely passive consumers of government service output. This blog seeks to assist individuals wanting to redefine their role in producing a more direct democratic form of governance by participating both in defining the political body and establishing the policies that will have an impact their community so that new paradigms for their community can be chosen rather than imposed.

Friday, May 30, 2014

Data Journalism - Another Tool for Creating New Community Paradigms

Besides taking the ongoing STW/STIA Systems Thinking Certification course, there was also an opportunity to participate in a Doing Journalism with Data, First Steps, Skills and Tool course put on by the Data Driven Journalism site as part of the European Journalism Centre’s Data Driven Journalism initiative, a hub for news and resources from the community of journalists, editors, designers and developers who use data in the service of journalism.

The organizer is based in Europe but this blog has had no issue with adopting resources from Europe, Great Britain, Canada or anywhere else if it can help engage and empower communities. There is also a USA version of the course being provided through the Knight Foundation’s Knight Center for Journalism called INVESTIGATIVE JOURNALISM IN THE DIGITAL AGE, but it is much further along and was only just discovered so it will be covered in subsequent posts. The Knight Foundation was featured in a post from 2011 that is one of the building blocks for this effort, Finding the soul of your community and the reason to create your own community paradigms, more later on that as well. 

While there aren’t any plans on becoming a data journalist or having this blog become a data journalism site, there is still an important role that this type of information gathering and dissemination could play in creating new community paradigms. 

The first basic question addressed in the class is ‘What is data journalism?’ It is learning to use the techniques of data journalism (which changes all the time) to find the best possible way to tell a story using numbers.

Data, as  the course points out, has always been a part of how news organizations work. Historically, this also includes those using information to bring about change. Doctor John Snow's mapping of a cholera outbreaks in nineteenth century London changed how we saw how a disease progresses and serves as a model for data journalism today. Another surprising historical example, at least for me, is Florence Nightingale’s key report, ‘Mortality of the British Army’, published in 1858, which presented statistics on the Crimean War regarding war dead. The report documented changes in procedures at military hospitals initiated by Nightingale during the Crimean War and was illustrated using statistical diagrams.

Journalism’s job still remains reporting facts in a manner that people can understand more about issues that matter to them.  The added goal of data journalism is to bring numerical data to life making it possible not only to be understood, but I would add also to make it actionable. 

The basis for this ability is easy access to a variety of often free tools and other resources and their ease of use. This is one of the primary purposes of the New Community Paradigm Wiki
Today data journalism can reveal the numbers behind the news, on a national level as through the Associated Press,  US Election results 2012 interactive visualization. At the local news level, even with relatively little resources, it can tell stories that work for local communities, as  with the  Washington DC income gap (DC Action for Children). It can also be a watchdog on local community politicians, as the Texas Tribune did with it’s Ethics Explorer, A guide to the Financial Interests of Elected Officials.

This depends upon greater availability to open data though that is still an ongoing process. Hopefully, this will mean the development of a greater network of trust through increased transparency between those creating and generating data and those who are depending upon it. 

The skills required for data journalism are a collaboration between coding, designing, and journalism applied to a variety of different media products ranging from visualization to long form articles. It is the process of turning numbers into a story, regardless of whether the story is composed of words or of graphics.

There are different approaches to creating data journalism stories. The Lone Ranger approach is when you are able to do everything by yourself. This is possible because of tools now available such as those found in the Data Driven Journalism ecosystem. These include tools like OpenRefine, Datawrapper, Tableau, Google Fusion Tables, CartoDBYou could also have two person teams, like the Guardian's DDJ team in the USA that created the award winning Guide to gay rights in the US story or a small scale team capable of producing innovative projects quickly like the 'Flooding and Flood Zones' map Hurricane Sandy by WNYC.

It is large teams, like the New York Times that have the resources and the capacity to implement a deliberate strategy to create a new kind of online journalism. They can help with finding ways to tell the story better. An example is their 2012 Olympic Experience.

The size of the team is not everything though. Data journalism, according to the European Journalism Centre Data Journalism course, is about making friends which means it is about community.  The course is designed to  start out with smaller things, what that story will be, how to turn the numbers into stories and give you the basic skills to practice on your own or as part of a team with the potential of becoming part of a movement. The essential thing to remember is that anyone can do it. As the course states, you don't need to compete with the New York Times or The Guardian.  Starting small can lead though to bigger things.  Argentina's La Nacion, considered by many the best data journalism site in South America, started without a programmer using free software. What is important is the information. There are times when quick, messily created pieces can be hits having tremendous impact.  Even visualization is not always required for a compelling story. What is vital is getting the correct facts.

Data, the basic stuff, the building blocks of data journalism, is usually organized for use in commonly used formatted spreadsheets such as Microsoft Excel or Numbers. The key is the selection of the data. The fewer numbers you use to tell the story, the better.  This makes research the most important role but also the most tedious and time consuming, having to dig around in data on the basis of a journalistic hunch that may not pan out. 

If you adopt the Lone Ranger approach then you are going to have to do your own coding. If you have a team, particularly a small team, then your coders can also assist with research on the front end and visualization on the back end. A good team will effectively coordinate who can write and who can code. One notable example sited by the course is Reuter’s Connected China. Designers are those who can make visualizations happen. The example provide by the course is the Guardian’s 99% vs 1%.

At the end of the day though it is the words of the story that give context to the numbers, without context numbers are just numbers.  The Guardian’s Yearly guide to public spending by each government department  helps to explain the data.

“Public spending in 2011-12 was £694.89bn - compared to £689.63bn in 2010-11. That may look like an increase but once inflation is taken into account, it is a real-terms cut of 1.58%, or £10.8bn.”

I will finish up by letting Simon Rogers, one of the instructors for the course, sum it all up in this TEDxPantheonSorbonne video. 

Today, data is increasingly accessible and simple to use, allowing journalists to develop a new way of sharing news. This revolution in the use of data is also accompanied by the increasing importance of data-journalists newsmakers, amateurs, representatives from the crowd ... No specific skills, unfailing motivation: the data-journalists are punks sharing of information.”

Thursday, May 29, 2014

Insight Maker - Understanding Systems Thinking and Systems through Trial and Learning

Hopefully, if only in a broad sense, I have started to have some consider that system thinking could be a helpful approach to addressing many of the challenges facing our communities through the two previous posts. That looking at our communities and the environments in which they exist as systems, more organic in nature than mechanistic in construction, makes sense. At least to the point where some will consider taking the ongoing STW/STIA System Thinking Certification course. Even so, it undoubtedly remains broad and abstract. It needs to be made more hands on.

As mentioned in the previous post, a system is always of something. It may be biological, social, or computer-based, all can be understood as systems even machines are systems, just usually not complex ones. We will find ourselves facing a challenge from a specific type of system, perhaps transportation or perhaps environmental. Each challenge will have a specific occurrence. The specifics of an environmental challenge in Florida will not be the same as those in North Dakota. The specifics of how such individual systems work, how the elements making up those systems actually interact has not really been delved into to any extent. How do you begin to understand the basic dynamic interactions of systems? The most fundamental means of doing so is building models of those systems.

A model is an illustration of the interrelationship between parts or elements of a system that explains how they are related and that can also help in understanding why. A model can be anything from a 3D computer simulation to a paper napkin drawing. So the idea then is to make models.

Is there then some tool that anyone could use to build and experiment with models to test out some of the general ideas of systems thinking and apply them to specific challenges? It would need to be relatively easy to use though capable of doing more complex actions as you learn. It should also be web-based and on the ‘cloud’ rather than be a stand alone desktop program. This would help in at least three ways. One, it would make it more available. Two, there could be a repository of different models and three, it could be possible to let people copy or clone models that they found interesting. Finally, in the best of all possible worlds, it would be free.

There is good news, a little bad and more good news. First, there is such a program called Insight Maker or, which does everything listed above. The little bit of bad news is that this current course is only going to give you the bare basics, though it is a good start. The final good news is that they are planning a course focused on Insight Maker in September of 2014.

Insight Maker has already been introduced through the previous articles on this course and was the means of making the models used so far. It has also been featured on this blog prior to the course. However, because those earlier posts dealt with other issues they may not have been the best means of introducing Insight Maker.

This current STIA certification course is designed to: …provide you with a solid foundation for using Insight Maker to develop rich pictures and causal loop diagrams to depict the relations between elements of a model. A brief introduction to stock & flow simulation models is provided primarily for completeness though not expected to develop an expertise in the development of simulation models.

Once again using a Kumu map, you start at the green circle and move along a path, first to qualitative models which include Rich Pictures and Causal Loop Diagrams and then quantitative models with a brief visit to Stock and Flow diagrams prior to reaching a point at which you are introduced to Your First Simulation and then finishing at the red colored end circle that summarizes the segment. In general terms, the segment seeks to cover the following concepts.

• Model. A simplification of reality intended to promote understanding

• Qualitative Models. Static models depicting the relationships between elements of the model. Rich Pictures and Causal Loop Diagrams are two examples of qualitative models.

• Quantitative Models. Dynamic models allow one to experience the implications of the relationships of the elements of the model over time.

        ◦ Testing every element added to a dynamic model will serve you well.

• Model Development. This tends to be a recursive learning process migrating toward understanding.

• Comments. While it may feel unnecessary, or even an unnatural act, adding comments which embrace your thoughts during the model development will serve you well in the future. You will be surprised how much you forget about the thoughts behind your model in a week, a month, or a year.

The nuts and bolts of the segment though are on using Insightmaker.comInsight Maker was designed as a simulation environment though may be used to do multiple types of models. Which type is most appropriate depends on the situation to be addressed.

It is better to jump right into and play around with it. With some basic instruction, you can start making simple models and test them out. To be fair, I also have Beyond Connecting the Dots, the book written by Gene Bellinger, the instructor for the course and Scott Fortmann-Roe, the creator of Insight Maker. The course material does though give you the basics. You just have to throw yourself into doing some trial and learning. Based on the fundamental premise that we are creating models to get a greater and deeper understanding, the common concept of trial and error never applies, it is always what Gene Bellinger calls trial and learning. You are not going to break anything or cause any systems failure simply by making models with Insight Maker and it can develop that sought after deeper understanding that systems thinking calls for.

Now I am going to say something about modeling that, although it has the potential of driving away people who crave certainty, always needs to be kept in mind. It is a fundamental premise for experienced systems thinkers, pronounced by one of the field’s major thinkers.

All models are wrong but some models are useful - George Box.

The assigned work for this segment called for creating different models. A Rich Picture model and a Causal Loop Diagram in story format were made before. What had not been attempted so far is a Stock and Flow diagram

Following the jumping in and swimming plan of action, the course’s Your First Simulation (IM-13312), which has basic model building blocks, was cloned then copies made (using command-select, command-c and command-v on a Mac) of the three examples provided, which were expanded upon creating Simulation Model Examples (Clone from Your First Simulation Model) then using those same building blocks, stocks, flow and variables to get a better understanding of how they worked.

Stocks represent the ‘stuff’ that can be measured or counted, the inventory of products or census of a population. Stock can also be increased or decreased but not instantaneously. In some cases, it may change extremely quickly but this usually occurs over extended time. Flow is the means of increasing, by flowing in and decreasing by flowing out from stock. Variables influence the rate at which flow occurs by varying the stock or the flow in some manner. Together, these can be constructed into a graphic mathematical formula that serves as the engine for the model. To get a better understanding of how the models (and modeling) were working, I also re-created a particular model's formulas in a spreadsheet format using Numbers.

With this knowledge, my first Stock and Flow diagram was finally finished. Well, completed but not finalized as I still have other ideas. There was a good deal of trial and learning involved, more than I expected, with numerous redesigns and a few restarts. There is the issue of not only making sure your model is actually modeling the real world circumstances that you are considering to the extent needed but also making sure that the internal workings of your model are also consistent with that endeavor. In early versions, the external numbers looked good but a closer examination indicated that it was not calculating what I thought it was. I checked by putting the numbers into a spreadsheet to see if the presumed equations generated by the model could be regenerated there.

Below the model’s description are sliders that you can use to change the number of homes, businesses and other traffic. Pressing the ‘Run Simulation’ button at the top of the page shows the effects based on the numbers over a 20 year time period.  My model ostensibly calculates road wear and tear on community roads due to increased traffic particularly through new development. What it actually considers is the relation between elements within the modeled system.

My model is wrong, remember George Box. It only examines a slice of the world, ignoring other aspects. Its knowledge of the world is not accurate and what it does examine is not realistically portrayed with any precision. I tried to make its limitations clear in the model. Hopefully, though it could be useful, or be the basis for something, if only as a practice run, for something that could become useful.

In writing about the real world understanding mistakes made by Watson, the IBM computer that defeated the greatest champions of the television quiz show Jeopardy at their own game, to explain the difference between knowledge and understanding. Michael Strevens wrote:

“Watson and you both answer questions by seeing connections between things. But they are different kinds of connections. Watson picks up from things it reads that there is a correlation between a sphere’s rotating and a fixed point on its surface having a constantly changing view of the rest of the world. You grasp why this correlation exists, seeing the connection between the opacity of the Earth, light’s traveling in straight lines, and geometry of the sphere itself. For you the statistics are a byproduct of what really matters, the physical and causal relations between things and people and what they do and say. Grasping those relations is what understanding consists in. Watson lives in a world where there are no such relations: all it sees are statistics. It can predict a lot and so it can know a lot, but what it never grasps is why its predictions come true.”

Systems thinking and modeling can help you to understand why.

Saturday, May 17, 2014

Systems Thinking 2nd Segment - Striving for a Better Understanding

This is the second post on the STW/STIA Systems Thinking Certification course. The next segment is titled Systems Thinking. The post on the first segment, Web of Wonder, gave a general introduction to systems, some of their overall  characteristics, described properties of what was termed ‘elements’ or what others have referred to as ‘stuff’ making up the systems, and means as to how systems could be perceived through models or as analogies to plays.

The last segment attempted to get you to perceive the world differently. This segment begins attempting to change how you think about that world, more specifically, in relation to new community paradigms, how you can think about changing that world starting with your community. That is a longer and deeper change and could be daunting. 

The essential point to be made, in relation to new community paradigms, is that changes in perspective, both in what people think and, more importantly, how people think, multiplied by a significant number of people making up a community is a foundation for a paradigm shift within that community. If you read this post to the end then you may either have an affinity for systems thinking or you have an increasing dissatisfaction for the current state of affairs in our communities and are willing to consider radically different alternatives, (putting aside concerns about my writing).

A dissatisfaction with communities can be with processes of complicatedness exhibited by many institutions, particularly by those of the local public sector. There is a form of entrenched institutional behavior is often unresponsive to public concerns and incapable of dealing with the challenges of a complex world. This segment focuses more directly on thinking about systems and this is where things can get messy. Some need convincing that systems thinking as a cure is not worse than the illness.

There is the Kumu map again illustrating the course in general and the current segment specifically.  The Kumu map can illustrate the difficulty that arises when attempting to absorb new information and process it in a new way at the same time. The specific segment being considered is fairly simple, straightforward. Users start at a green circle move along a solid line through various points covering the most relevant material to finish at a red circle that summarizes the segment. The entire course map though, which can always be viewed with a click of the mouse, can look like a bowl of leftover ramen noodles to the uninitiated. Let's stick to the simpler direct path.

The first item along the direct path is a direct question, ‘What is a System?’. We have to understand this to make sense of anything that comes later.   Gene raised this question early on and generated over a hundred different comments reflecting a variety of perspectives in the forum.  Those with experience or a knack for systems thinking can get embroiled in such ambiguous philosophical discussions. This, to my mind, is where many start to have difficulty wrapping their heads around systems thinking because the thinking can get pretty abstract and uncertain. Regardless of how dissatisfied we may claim to be of the processes or systems of complicatedness of our current institutions, we find the 'certainty' they espouse reassuring and often choose the devil we know over a suspect, unknown one. 

My assertion that the question is somewhat ambiguous is that there isn’t any physical occurrence to which you can point of something that is a system, only a system and nothing but a system without having any other attributes. There are a multitude of different phenomena (again an abstract term) that we can observe having the attributes of a system. It is similar to trying to find two in the universe, just two, not two sticks or two rocks but just two. Systems, as an abstract reality (we will get back to a more concrete reality soon), consists of general abstract concepts described above as ‘elements’ or ‘stuff’. Again, abstract terms used as variables to take the place of real things such as money, cars, roads, park benches, sand, water, or anything that can, as explained in the post on the first segment, be changed in quantity or by some metric either directly, exponentially or balanced at a particular level. 

If you have made it this far, I will let you know that there is an operational definition of systems thinking.  "A system is an entity that maintains its existence through the mutual interaction of its parts.", Ludwig Von Bertalanffy

Okay, it is not a concrete object but it is a concrete rule. No matter how many ways there are to think about the many different types of systems and regardless of the number of specific examples for each the rule applies.  A system can be distilled down to just a few very simple concepts.  Any further apparent complexity arises either from multiple interactions iterated over multiple time steps or arguably, at times, is created by systems thinkers for no real beneficial reason. 

Let's then come up with a concrete, hands on example to use in understanding the concepts of a system. The example is this blog post that you are reading. It fulfills the basic definition proposed by Bertalanffy.

Mutual Interaction - A system is made up of parts, those parts interact with each other (they can also interact with the external environment) and the system itself exists via the mutual interaction of these parts. By mutual interaction, it is meant that none of the parts would on their own exhibit the attributes of the system. If you break down water into the elements of oxygen and hydrogen at room temperature, neither gas exhibits any of the properties of water. If you disassemble a car or watch, none of the parts would serve the same function isolated nor would it be easy to discern what was the final purpose of the system. Once the parts are properly assembled though, one has no problem with understanding it as a system. We are more familiar though thinking about the workings of car engine parts and how they make the wheels turn then we are thinking of the more abstract relationships between the parts.

The letters of this post, if placed randomly would be incomprehensible but instead form words which combine into sentences, paragraphs, and the post itself. One could take out words or sentences out of context but the information conveyed by the entire post would be lost. One could also summarize or distill the ideas into a shorter or simpler form, retaining the basic ideas in the post but that is more like creating a model of the post.

Feedback - According to Wikipedia, “…is a process in which information about the past or the present influences the same phenomenon in the present or future. As part of a chain of cause-and-effect that forms a circuit or loop, the event is said to "feedback" into itself.”

The reaction to an initial action, whether within or outside the system,  which in turn influences that initial action, is defined as feedback. The currently featured GapingVoid artwork below this post illustrates this, though the chain can go through a number of steps before coming back to the original action.  If the reaction increases or strengthens the initial action then that is reinforcing or positive feedback. If the reaction produces less or weakens the initial action then that is negative feedback. If it does so to attain a specific level then that is a balancing feedback. Positive and negative have nothing to do with good or bad. Recidivism resulting in an increase in prison populations would be seen as a bad while a measure to lessen or create a negative feedback would be seen as a social good. The important point to remember is that one element's action is some other element's feedback.

This means that the parts of a system are not only connected in a structural or even mechanical sense but also in an organic sense in that the system can evolve. The same is true of this blog post, especially in its creation. Words have to be put together structurally right to be spelled correctly, the subject and predicate of each sentence must work together to express a complete thought, sentences at the beginning of a paragraph are intended to inform sentences that come later. If it is a well constructed system then this post should convey information in an understandable, comprehensive and holistic manner. 

Boundary - Setting the boundary on a system can be a more abstract exercise than discerning the mutual interactions between parts of a system. A car has a braking system, an electrical system, and a fuel system to name a few. Altogether, these subsystems make up a system of self or automobility. That automobile though can be considered a part of a larger transportation system if we are thinking about building a new freeway. A boundary is defined to make explicit, from an operational perspective, what is part of the system and what is part of that system's environment. Boundaries can also be used to explicitly define areas of responsibility among stakeholders within a community system when appropriate.  Boundaries help determine what can be addressed and what needs to be addressed.

There is an obvious boundary for this blog post in terms of length but there is also a boundary on the number of new concepts that will be introduced. This specific system is designed so that you don't have to swallow the entire bowl of ramen in one gulp. However, even if we deal with only the concepts set forth by this blog post we can still extend the boundaries to an even larger system that includes the reader and then those with whom the learning is shared.

Emergence - From the mutual interaction of the parts of a system arise characteristics which cannot be found in any of the individual parts of the system. These characteristics are said to emerge from the interaction of the parts within the system and are responsible for the system's behavior over time. Over time, the emergent characteristics of a system will invariably acquire certain reoccurring patterns (which will be discussed in greater detail at a later time). We often identify systems by these emergent characteristics. Unless there is an obvious problem, we usually don’t give much thought to the workings of the internal combustion engine in our car while being carried toward our destination.  With the writing of this blog post, it is hoped that new ideas and understanding will emerge for those reading it, but being engrossed in the ideas being expressed can make self-editing of the actual words difficult. We see what we expect to be there, missing the components creating a different reality and the potential for unindented consequences arising from someone getting an understanding that we did not mean to convey. 

Now that you have a deeper meaning and hopefully understanding of what a system is what then is systems thinking? How do we think about systems? There are two basic ways to think about systems.

Analysis - is essentially the scientific method of reductionism which means taking things apart, studying those parts, and then attempting to understand the whole from an understanding of its parts.  Such an approach can assume that if we can get all the individual parts of an organization operating correctly then when we put them together they will continue to work correctly through a means of top-down command and control by management. 

This works well in many situations that are complicated in nature or have an algorithmic aspect to them, questions of ‘how?’. It does not do as well in addressing complex situations or in addressing questions of ‘why?’ and if they begin to demonstrate increasing complicatedness then it can be detrimental to the organization's intended purpose.  This does not mean though that in studying complex situations or in determining why something happens to a system within an organization that analysis can be ignored. It is that you must also incorporate synthesis when studying systems.

Synthesis - Is endeavoring to understand something through the nature of its interactions within its larger environment.  The rest of the course will basically be on developing this capacity. 

This then covers the basic elements of this current segment as well as a few offshoot concepts. A model for this segment has been created with (coming soon). There are, however, additional concepts that deal with thinking about systems and going further, those that deal with thinking about systems thinking but for now, we will make this the end boundary for this particular system.

Tuesday, May 6, 2014

Getting Deep into ST - Systems Thinking Certification

Currently, attention is being focused, to an even greater extent than in the past, on systems thinking by participating in the STW/STIA (Systems Thinking in Action) Online Certification Program.  The goal is to better integrate systems thinking into new community paradigms.

This is the first time the course has been offered. It is closed but if all goes well I suspect it will be offered again. It is being guided by Gene Bellinger, who has been cited on these pages most recently in, “How Do I Make Use of this Systems Thinking Stuff?” in Creating New Community Paradigms”. The medium for presenting the lessons is Kumu, a web-based data visualization tool for tracking and visualizing relationships.

The intention is to write about what is taken out of the class and how it evolves my perspective. What it won’t do is to detail or repeat the teachings of the class. Others will have to take the class for themselves to understand how to walk the path. Many of these lessons can also be found in Beyond Connecting the Dots. The difference is that with the class lines up differently than the book in terms of presentation and the interaction with the class is more two-way.

Kumu lays out the course content, usually presented through YouTube videos or InsightMaker models, as points on a visual map which link together the twelve topics making up the course. This allows a visual overview of the entire course or any one of the segments making up the course. My plan is to serve more as a chronicler than as a cartographer as to how systems thinking could be integrated with new community paradigms. Though mind maps such as Kumu don’t really have a time component to them as one is free to start, proceed and jump to whatever path one chooses. The worth of the journey is dependent in part on what the creator provides and links together, and in part on what you put into it.

The first section does not begin with systems thinking in its own right. Instead, it asks you to shift your perspective of the world from how it is usually presented as a machine subject to being broken down into smaller pieces and therefore controllable with the right levers. It asks you to look at your world as an interrelated system or a series of systems. The emphasis, at this point, is not on whether the world is a single system or multiple systems, rather it is how we approach the nature of such a system. 

Such an approach looks to the world and discerns that even before seeking the interconnections making up the world that some of its basic ‘elements’ have common characteristics. They are quantifiable or countable and can be increased or decreased over time. In aggregate, these elements make up complex systems such as communities and how they impact those communities in different ways can be modeled. 

The class makes a point of the common forms of models based in large part on the nature of the elements making up the systems being studied. There are Three Types of Models (IM-8932), Rich Picture, Causal Loop Diagram and Stock and Flow that endeavor at different levels to mirror the essential reality of real world systems through Three Recurring Structures (IM-5138), Independent Growth, Reinforcing Exponential Growth and Balancing Goal Seeking that are ubiquitous within systems. 

Whether it is a matter of funds in the city budget or the number of cars on the road, how that occurs can help determine if communities are worthwhile places to live or not.  These elements can have the additional characteristic of being subject to increase or decrease on an exponential basis. Interest rates on loans can be exponential while revenue from the financed asset could be linear. 

Finally, they can be considered to have optimal levels. Idealistically, city budgets endeavor to reach optimal levels of expenditures to fund the programs which are beneficial to the community. They fund road improvements to increase the capacity of the streets to allow an optimal number of vehicles (The community question is what is that optimal number?) These three properties can interact together in a variety of different way. Built roads in a community can be increased but not easily and not indefinitely. Traffic, particularly if it originates outside of the community, could increase exponentially overtaxing the community’s circulation system. The community could try to find some means of balancing these conflicting influences but its actions might feedback in unintended ways.

It is with these insights that we endeavor to understand the world. The potential to change how we understand the world arises because these insights caution us that we cannot stop at the superficially apparent aspects of the world and need to look deeper at the interconnections defining the nature of these properties. It encourages us to question our assumptions as to why traffic is increasing and not default to past knowledge that may no longer apply. This allows for entirely new considerations such as perhaps a new factory has not only provided employment but also the financial ability and the need for private vehicles to get to work because there is inadequate public transportation.

What the real configuration of interrelationships is must be determined empirically. These depend in very large part on what configuration one seeks for the system or community. Not everyone in a community will be seeking the same configuration as some will seek far less traffic as it hinders their travel while others will seek more because it brings customers to their businesses. Each though could be negatively impacted if they attained what they were seeking to an excessive degree through the detrimental impact it would have on the other group. Too many vehicles would jam the streets turning people away to different routes and too few could result in the community’s downtown dying. Understanding how these all interrelate helps the community come up with viable strategies for finding a balance rather than continually doubling down on a pursuit of increasing growth.

Through this approach one, in collaboration with others, can devise 
a far more likely means of creating a future in which the strategies implemented serve to be of benefit rather than leading to unintended consequences making matters worse or create new problems. It allows one to navigate to such a future even with the challenges of both complex and complicated world with cause and effect separated by both time and space. The building of a freeway along the edge of a community can impact roads within the community if commuters use those roads to bypass traffic jams or as means of ingress or egress. A new road might take traffic away from one business district to another but the impact might not be felt immediately until the stores started going vacant.

This newly acquired insight into the workings of the world allows one to then to acquire and apply the attained knowledge in a new way. More important is that this new way leads to further understanding and knowledge. This new methodology involves the creation of models which will be discussed in far greater detail as the course proceeds. 

Two types of models had been attempted previously. The first, a Causal Loop Diagram model featured in the post, Better Deliberative and Participatory Democratic Community Based Governance through Systems Thinking. The second was a Rich Picture model that served as a pictorial back of the napkin explanation of the detrimental relationships resulting in troubling situations in which we find so many of our communities. The third, more recent, was a Causal Loop Diagram Complexity Assailing Our Communities an extension of one set of relationships set forth in the Rich Picture model. None of the models are viewed as being finalized. Instead, they are seen as means of inspiring further inquiry. 

The course initiates an introduction and provides guidelines for model construction that ties into a very important concept related to the 'Essence of And'. This is not only a central concept but it is also a means of defending the utility of systems thinking. The rationale for why pursue systems thinking and take this class is addressed through the video, “Unleashing Understanding, the Essence of And”. 

The class also offers additional resources through a link to the Model Thinking Course by Scott E. Page, which has been referenced previously on these pages in Systems Thinking as a disciplined process for Community Governance

The course has so far provided elements and models to describe the proper means of discerning and developing the interrelationships and interactions of those elements and their relationships making up systems. This is similar to providing words and grammar. More though must be developed to properly convey reality, by being as accurate and as precise as needed, so that our actions have the desired long-term effect. Our models, because they have multiple aspects potentially impacting different stakeholders in different fashions over time, take on the characteristics of a play.
"More often than not it seems that models that people build are like Cathedrals. In the process of construction they employ a substantial amount of labor, material and staging as they work through the process. Once the Cathedral is completed all that was used to build it is removed and it remains as a structure for others to behold, though more often than not others just find them confusing because of the supporting developmental understanding is missing.
The thought that came to mind was to think of a model like a stage play. I can give the audience a sense of what the play is about so they are interested in coming to the play. Then once they arrive the play actually unfolds one act at a time and some of the actors are in multiple acts.
As such might it be advantageous to consider a model like a play where I provide the audience an overview of the model to develop interest, and then each loop in the model is presented as an act in the play with the elements of the loops being the actors, with relations between them, some of which appear in multiple loops? As such a person’s understanding of the model is developed in a piece-wise coherent manner which attempts to avoid overwhelming the audience.
It was a thought of the moment.”
                                                                                                    Gene Bellinger

This segment of the class offers three different ‘plays’ demonstrating different aspects of systems. The reality that since everything in the web of systems making up the world is connected that it is exceedingly difficult to have only one effect from any action Bird Feeder Dilemma (IM-8872). That the interaction of elements can feedback to their point of origination into a loop changing the nature of those interactions over time Moose and Wolves (IM-8590) and Sustaining the Forest Model (IM-8889). The course uses these ‘plays’ as scaffolding for the other concepts raised in this segment, putting the participant in the role of protagonist and narrator. 

It is realized that this is a hodgepodge of information, overloaded because it comes from so many different levels and different directions, introducing unfamiliar concepts.  How all of this is related is endeavored to be illustrated with the model, Elements, Relationships, Models, and Plays. Hopefully, it will begin to be untangled and be more sensible as the course progresses. 

All of this is seen as creating a Web of Wonder, serving as the port from which the next part of the journey will be taken, an Overview of System Thinking.

“We live in a world much like a giant spider web, where everything is connected to everything else. When something changes in one part of the web it ripples though the entire web. We tend to live in the moment, not realizing how our actions ripple over time through distant parts of the web. When we don't understand the web, things around us seem extremely complicated, confusing, and overwhelming. We feel caught in the web. What we need is a web of understanding for this web of extended interactions.      
                                                                                                   Gene Bellinger

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