Tag: Systems

What is a system?

By Matt Johnson

What is a system? I posted a similar question on The Systems Scientist Facebook page last week. I asked the following question,

In your own words, explain, or describe, what you think a system is?

In response, I received a lot of good and interesting answers from Facebook followers. One Facebook follower stated,

A limited framework of interrelated processes that work toward a mutual output or outcome.

As the reader will see, this answer hits the third axiom of what a system is, and the answer also touches upon the structure and processes of a system as well.

Another Facebook follower hit the mark on two of the systems’ axioms on the first try: the first axiom and the third axiom. As the follower explained,

A system to me is a set of parts, each with a different function, that work in concert, complimentary way towards a common goal.

All together, there were a lot of really good answers and everyone who commented pointed out that a system has a function, or purpose. And many of the Facebook followers added that systems consist of parts and those parts are what compose the system itself, which is correct. So what is a system?

Photo Credit: Wikimedia Commons. Seattle, Washington

To answer this question, we will utilize Donella Meadow’s three conditions from her book Thinking in Systems: A Primer to propose the systems’ axioms, we will be using going forward. And by axiom we mean a statement that is true and will follow our thinking and logic from thusly. Here are the axioms we will be using:

  1. A system consists of a set of elements.
  2. Elements in the system interact.
  3. A system has a function, or purpose.

At first, these axioms seem so obvious and simple, and that’s good, but these axioms are subtly profound. This is because they can be described through mathematics and tested via the scientific method. But perhaps putting the math and science aside for the moment would be beneficial. Instead, a familiar example will suffice.

Again, in order to have a system, the three axioms must be satisfied. For instance, does the United States House of Representatives satisfy each condition?

For the first axiom, all we have to do is count the total number of members that serve in the House, which is 435. In other words, there are 435 elements in our example. Thus, we see that axiom (1) is satisfied. For the second axiom, we should ask ourselves if the members of the House interact with each other?

Is there another answer to a question that has ever seemed so obvious? From the debates on the House floor to Twitter wars, the members of the House of Representatives do indeed interact with each other. There are a plethora of examples to illustrate this point in the form of C-SPAN, MSNBC, FOX, and CNN. Thus, we see that axiom (2) is satisfied.

And finally for the third axiom, does the House of Representatives have a function? It definitely feels like they don’t have a purpose on most days. But they do and this purpose of course derives from the United States Constitution. Thus, we see, although begrudgingly, that axiom (3) is satisfied.

Photo Credit: Chandra X-Ray Observatory. Milky Way Galaxy

Over time and as these blogs progress, we will see that these three axioms will be extremely useful for us. They will allow us to explore cities and economic systems, and most importantly they will allow us to construct describable phenomena via mathematics, test observable data, and make predictions that might not otherwise be accessible through cluttered language, hyperbolic rhetoric, and undefined terms.

One final thing, these three axioms are not the totality of a system. Systems have inputs and outputs, a structure, environment, behaviors, processes, boundaries, and other properties. Together, all of these characteristics are what make a system. But these axioms are a good start and will allow an interested party into the world of Systems Science.

Until the next blog, do the classical science thing and test these axioms for yourself. In other words, try to disprove them. If you can manage to find an example, please do share it in the comments section below so the other readers and commentors can validate your findings.


Matt Johnson is a blogger/writer for The Systems Scientist and the Urban Dynamics blog. He has also contributed to the Iowa State Daily and Our Black News. And he has a Bachelor of Science in Systems Science, with focuses in applied mathematics and economic systems, from Iowa State University. 

You can connect with him directly in the comments section, and follow him on Facebook

You can also follow The Systems Scientist on Twitter or Facebook.


Photo Credit: Wikimedia Commons





Copyright ©2017 – The Systems Scientist



As the European Union celebrates 60 years, can Asia use it as a model for economic integration?

On 25 March 2017, the European Union’s heads of state and government will meet in Rome to celebrate the 60th anniversary of the European project. The date marks the signing of the Treaties of Rome, which established the foundations of European Community that preceded the EU.

While the EU is a unique experiment in integration in many ways, the world abounds in other kinds of regional trade agreements; the World Trade Organization records more than 635. Still, as the most advanced form of market integration in the world, the EU provides a good model for other regions, including Asia.

Why the EU is a good model

Market integration is one of the tools that helped take Europe out of the ashes of the world wars and supported its transition out of the Cold War into peace. It provided a historically fragmented, war-torn, extremely diverse continent with a period of geopolitical stability, and thus brought wealth and prosperity.

Despite Britain’s impending exit from the group, the EU remains the most advanced and successful model for peace through economics in Europe’s history. The bloc continues to attract neighboring countries, having expanded from the original group of six to the current 28, with a combined population of more than 500 million and GDP of more than €14 billion. These countries work together across a single market and carefully selected common policy areas.

The EU’s market integration began with the free circulation of goods, based on the logic that the more states trade with one another and become interdependent, the less they are likely to go to war. It has extended to the free movement of people (stimulating travel, work abroad and cultural exchange), and enhanced economic integration through freer movement of capital and services, the option of joining a common currency, and other joint initiatives and policies.

Later members joined for mainly economic reasons; many others to fill the geopolitical void left by the collapse of the Soviet Union and its regime transition. Central and Eastern European countries, for instance, were supported in their transition to market economy and democracy by joining the EU and various other international institutions.

All signed up to trade with each other, but also to promote shared values of freedom, democracy, human rights, peace, solidarity, strength through diversity and the rule of law. But increasingly negative attitudes towards the EU in some member states, and the EU’s struggle with confidence in its achievements and its future potential is a sign this stability came at the price of dynamic decision-making.

Integration in Asia

Asia is home to more than half of the world’s population and to most of the world’s production. These make it one of the most dynamic regions in the world, with huge economic potential.

Just as for the EU and its members, some countries in the region feel a certain frustration with the lack of progress by the World Trade Organization in dealing with the most urgent economic issues. While this may make regional integration à la EU seem desirable, the scope to achieve similar outcomes in Asia is shaky.

National contexts and ideologies in the region differ as much as economic structures, institutional differences, geopolitical, cultural and historic conditions. The motivation in Asia to work towards greater integration is often subject to the economies’ interdependence through trade and production networks within the global value chain, and is often commercially driven.

Nonetheless, Asia has numerous geo-economic groupings that may lead to EU-like integration including the East Asia Free Trade Agreement (EAFTA), the Comprehensive Economic Partnership in East Asia (CEPEA) and the Association of Southeast Asian Nations (ASEAN). These already make it the world’s second-most integrated region after the EU.

ASEAN also has a network of additional free trade agreements with neighboring countries, such as those between Australia and New Zealand (AANZFTA, China (ACFTA), South Korea (AKFTA), India (AIFTA) and a Comprehensive Economic Partnership with Japan (AJCEP).

Then there is ASEAN+3 – China, Japan, and South Korea, which has an ambitious Master Plan on ASEAN Connectivity, which aims to expand sectors and topics of interaction by 2025.

Countries in the area are also working towards the establishment of a Regional Comprehensive Economic Partnership (RCEP) as an alternative to Trans Pacific Partnership, which has been rejected by US President Donald Trump.

The scene for further economic integration across Asia is clearly set. The RCEP would be a good start, providing the basis for economic cooperation, poverty alleviation, facilitation of trade in products and services and more.

Hurdles for further integration

But significant hurdles would need to be overcome if this project were to succeed along similar lines to the long-term achievements of the EU.

The first involves the question of will for unity in diversity, an idea that guides the EU. The region’s cultures, political regimes, economic systems and religious beliefs are more disparate than Europe. And we can count on many governments resisting sufficient institutional proximity, which would necessarily result in some diluting of sovereignty, non-interference, and territorial integrity.

The second hurdle entails superpower interests in seeing such integration take place – or not – and in what shape. Asia remains under the influence of fiercely competing superpowers, buffeted by the conflicting interests of China, the United States, and Russia. What are the chances the region can achieve equal partnership rather than extending the predominance of major regional actors; of reaching partnership rather than absorption?

There is no power balance between states in Asia as exists in Europe with Germany and France. These countries share a strong belief in European integration, and social and cultural understanding. What would be the parallel historical, ideological and social drivers in Asia? What or who would hold Asian integration together in times of crisis, something the more consolidated and stable EU is currently struggling with?

If Asia could integrate in its own way – most likely much more loosely than the EU and with fewer joint institutions and policies – then the formidable growth potential of the region could become a great driving force for dealing with the biggest challenges of today and tomorrow. These include national security, migration, competition and the re-emergence of protectionism, automation and unemployment, and aging work forces.

Working together to solve these complex challenges would make them much easier to deal with.

In December 2016, the EU and ASEAN celebrated the 40th anniversary of their relationship. As a summary to their underlying beliefs, they stated that “regional integration (is) the most effective way to foster stability, build prosperity and address global challenges.”

Each needs to promote this in its own setting to succeed.

Gabriele Suder, Principal Fellow, Faculty of Business & Economics/Melbourne Business School, University of Melbourne

Photo Credit: Europa.eu

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This article was originally published on The Conversation. Read the original article.

School bus routes are expensive and hard to plan. We calculated a better way

Here’s a math problem even the brightest school districts struggle to solve: getting hordes of elementary, middle and high school students onto buses and to school on time every day. The Conversation

Transporting all of these pupils presents a large and complex problem. Some school districts use existing software systems to develop their bus routes. Others still develop these routes manually.

In such problems, improving operational efficiency even a little could result in great advantages. Each school bus costs school districts somewhere between US$60,000 and $100,000. So, scheduling the buses more efficiently will result in significant monetary savings.

Over the past year, we have been working with the Howard County Public School System (HCPSS) in Maryland to analyze its transportation system and recommend ways to improve it. We have developed a way to optimize school bus routes, thanks to new mathematical models.

Finding the optimal solution to this problem is very valuable, even if that optimal solution is only slightly better than the current plan. A solution that is only one percent worse would require a considerable number of additional buses due to the size of the operation.

By optimizing bus routes, schools can cut down on costs, while still serving all of the children in their district. Our analysis shows that HCPSS can save between five and seven percent on the number of buses needed.

Route planning

A bus trip in the afternoon starts from a given school and visits a sequence of stops, dropping off students until the bus is empty. A route is a sequence of trips from different schools that are linked together to be served by one bus.

Our goal was to reduce both the total time buses run without students on board – also known as aggregate deadhead time – as well as the number of routes. Fewer routes require fewer buses since each route is assigned to a single bus. Our approach uses data analysis and mathematical modeling to find the optimal solution in a relatively short time.

To solve this problem, a computer algorithm considers all of the bus trips in the district. Without modifying the trips, the algorithm assigns them to routes such that the aggregate deadhead time and the number of routes are minimized. Individual routes become longer, allowing the bus to serve more trips in a single route.

Since the trips are fixed, in this way we can decrease the total time the buses are en route. Minimizing the deadhead travel results in cost savings and reductions in air pollution.

The routes that we generated can be viewed as a lower bound to the number of buses needed by school districts. We can find the optimal solution for HCPSS in less than a minute.

Serving all students

While we were working on routes, we decided to also tackle the problem of the bus trips themselves. To do this, we needed to determine what trips are required to serve the students for each school in the system, given bus capacities, stop locations and the number of students at each stop. This has a direct impact on how routes are chosen.

Most existing models aim to minimize either the total travel time or the total number of trips. The belief in such cases is that, by minimizing the number of trips, you can minimize the number of buses needed overall.

However, our work shows that this is not always the case. We found a way to cut down on the number of buses needed to satisfy transportation demands, without trying to minimize either of the above two objectives. Our approach considers not only minimizing the number of trips but also how these trips can be linked together.

New start times

Last October, we presented our work at the Maryland Association of Pupil Transportation conference. An audience member at that conference suggested that we analyze school start and dismissal times. By changing the high school, middle school and elementary school start times, bus operations could potentially be even more efficient. Slight changes in school start times can make it possible to link more trips together in a single bus route, hence decreasing the number of buses needed overall.

We developed a model that optimizes the school bell times, given that each of the elementary, middle and high school start times fall within a prespecified time window. For example, the time window for elementary school start times would be from 8:15 to 9:25 a.m.; for middle schools, from 7:40 to 8:30 a.m.; and all high schools would start at 7:25 a.m.

Our model looks at all of the bus trips and searches for the optimal combination of school dismissal time such that the number of school buses, which is the major contributing factor to costs, is minimized. We found that, in most cases, optimizing the bell times results in significant savings regarding the number of buses.

Next steps

Using our model, we ran many different “what if?” scenarios using different school start and dismissal times for the HCPSS. Four of these are currently under consideration by the Howard County School Board for possible implementation.

We are also continuing to enhance our current school bus transportation models, as well developing new ways to further improve efficiency and reduce costs.

For example, we are building models that can help schools select the right vendors for their transportation needs, as well as minimize the number of hours that buses run per day.

In the future, the type of models we are working on could be bundled into a software system that schools can use by themselves. There is really no impediment in using these types of systems as long as the school systems have an electronic database of their stops, trips, and routes.

Such software could potentially be implemented in all school districts in the nation. Many of these districts would benefit from using such models to evaluate their current operations and determine if any savings can be realized. With many municipalities struggling with budgets, this sort of innovation could save money without degrading service.

Ali Haghani, Professor of Civil & Environmental Engineering, University of Maryland and Ali Shafahi, Ph.D. Candidate in Computer Science, University of Maryland

Photo Credit: Dean Hochman

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This article was originally published on The Conversation. Read the original article.

In Minneapolis, Black poverty is the problem not Wells Fargo

By Matt Johnson

In recent weeks, a lot has been happening in the Minneapolis political arena. Minneapolis Mayor Betsy Hodges has declared her candidacy to run for a second term; and City Council Member Jacob Frey, who represents Minneapolis’ 3rd Ward, has declared his candidacy for Minneapolis Mayor.

Moreover, the Minneapolis City Council recently unanimously passed a staff directive to explore options to discontinue its relationship with Wells Fargo. And this coming 2017, 11 of the 13 council members will be seeking reelection in their respective city wards.

However, none of these individuals will be able to claim they helped reduced black poverty in Minneapolis. This is because it hasn’t decreased. In fact, the number of blacks folks who live in poverty has increased since this current crop of City Hall leaders took office in 2013.

According to the United States Census Bureau, it was estimated that 46.3 percent of Black residents in Minneapolis lived below the poverty level in 2015. That is, about 33 thousand of the approximately 77 thousand black residents in Minneapolis lived below the poverty line in 2015. And that’s up from about 29 thousand in 2013.

Of course, if you’re a regular reader of this blog, you know that the majority of these 33 thousand residents reside on the north side of Minneapolis, specifically in the 5th Ward. And you’re also aware that parts of the north side have the highest unemployment rates in the city along with the highest concentrations of condemned and vacant buildings and foreclosures.

Since this current group of council members, and mayor, have taken office, the poverty rate for black folks in Minneapolis has increased by 2 to 3 percentage points. And that 2 to 3 percent has translated into about 3,000 more black residents living below the poverty line.

Here are the numbers. Note, the United States Census Bureau has not yet published the 2016 numbers.

Year Total  Below Poverty Level  % Below Poverty Level
2013 65,905 29,896 45.4
2014 68,165 32,759 48.1
2015 70,692 32,724 46.3

With the coming 2017 election season for Minneapolis, this blog will continue to utilize multiple data sources to analyze the depressed system in North Minneapolis and the public, economic, and scientific policies that have been put forth to address these continuous urban challenges, although it may be possible the Minneapolis City Council hasn’t passed any such policies.


Matt Johnson is a writer for The Systems Scientist, and a mathematical scientist. You can connect with him directly in the comments section, and follow him on Twitter or on Facebook

You can also follow The Systems Scientist on Twitter or Facebook as well. 

Photo credit: Tony Webster




Copyright ©2016 – The Systems Scientist

Economies, Policies, and Systems

Author’s Note: 

As a scientist, and someone who studies systems, it is not my job to take a political side. Sure, I have my own political and social views. However, as a systems scientist, it is imperative for me to consider the perspective of “the other” in all forms, i.e., economic, political, social, ecological, etc… And I must stress, the questions I pose in this article are but just the beginning of the exploration into the science. Even if my scientific findings suggest disagreement with proposed arguments and policies by policy makers, I still know that the intentions from those who originally proposed such ideas came from a place of empathy and solidarity with those who struggle economically, politically, and socially. 

By Matt Johnson, The Systems Scientist

Policies can affect a city and its inhabitants in different ways. Some of these effects can be positive; some of these effects can be negative; some of the these effects can have no affect at all; and some of these effects can affect a city in a variety of positive and negative ways and combinations. In other words, where a policy, or policies, can change one part of the city in a positive way, it can change another part of the city in a negative way.

Table 1
Table 1

This is important to keep in mind because as this author has demonstrated in previous articles, depressed areas of Minneapolis tend to be more sensitive to systems’ fluctuations than non-depressed areas of Minneapolis. And there are a variety of reasons for why this may be. However, it should be recognized that when it comes to systems, the why is very difficult to ascertain, but some information and knowledge can still be gained.

Since its peak in 2008, the number of foreclosures has been decreasing rather steadily with the exception of a hiccup here and there, according to Table 1. This clearly illustrates a positive behavior for the general system. Taken together with the decreasing unemployment rate, the increase of more than 22 thousand jobs in Minneapolis since 2012, the increase in the number of employed since 2012, the steady increase in weekly wages since 2006, and the decreasing numbers of foreclosures and condemned and vacant buildings over the past 8 years, Minneapolis is showing some economic power, vitality, and stability. However, where some of Minneapolis’ sub-systems (Wards) aren’t necessarily affected or dependent on market fluctuations, other sub-systems are, at least that’s the thinking.

As Table 2 suggests, the 4th and 5th Wards are highly sensitive to market forces; whereas, the 2nd Ward, as can be seen, is not, at least with respect to foreclosures. Why might this be? Well that’s the question.

But here’s an observation from the data. While those on the north side were wrestling with the great recession, it appears that the 2nd Ward was on economic cruise control, although one variable doesn’t tell the entire story. Not even close.

Figure 1
Table 2

Systems are complex entities. In the case of a city like Minneapolis, the general system is composed of an economic system, a political system, and a social system. These systems are further intertwined with the ecological system of Minneapolis, and with each policy implemented, it could have a positive or negative effect, or no effect at all. So the question becomes, should policy makers in Minneapolis be implementing general policies to the entire system? Or should they be focusing on sub-systems within Minneapolis?

As an example, would it make sense to legislate rent control for the entire city of Minneapolis when wages have been steadily increasing and the labor force has been increasing? Would it make sense for policy makers to legislate a $15 minimum wage when wages have been steadily increasing and the labor force has been increasing? And if these policies were implemented to the system, how would the system react? Would the respective sub-systems illustrate similar behavior to that of the foreclosure behavior?

Or would it make more sense to focus in on those depressed areas of Minneapolis and their respective sub-systems? Would it make sense to pass policy that addresses the economic turbulence that those in North Minneapolis, for example, have been experiencing for the past few decades? Wouldn’t development from within be a more viable policy rather than attempting to penetrate the entire system with policies that may or may not be necessary, or that would perpetuate adverse effects?

These questions of course beg more questions, which they ought to. That’s the beauty of science and scientific analysis. If curiosity, exploration, discovery, and patience are emphasized and accepted, then time, data, policies, research, and the scientific method will eventually answer these questions, tell the story, and provide guidance on urban policy.

For further exploration of this subject, please feel free to explore Analyzing a Crime Pattern of a General System and Patterns of the 5th Ward: Unemployment.

**Remember, there is nothing more American than discourse. You are always welcome to post your comments, thoughts, and questions below. Feedback is always appreciated!

The Simple Behaviors of Cities

By Matt Johnson

Cities are complex systems with complex and chaotic behaviors, but yet those same systems as philosopher of science Michael Streven’s explains in his book Bigger than Chaos: Understanding Complexity through Probability can contain simple behaviors as well. As Strevens elucidates, “Simplicity in complex systems’ behavior is everywhere.”

Figure 1
Figure 1

As examples, he uses ecosystems, economic systems, the weather, chemical reactions, and societies to explain such simplicity. In his example of societies, he states

…the familiar positive correlation between a person’s family’s social status or wealth and that person’s success in such areas as educational achievement [is a simple behavior].

We have seen simple systems’ behaviors with the graphical information presented time and time again with respect to the systems research of the City of Minneapolis by Urban Dynamics. For example, in a previous post about the foreclosures in the general system of Minneapolis, we saw that although the total foreclosures in the city peaked out around 900 properties in 2008, there has been a fairly consistent decrease over the past 7 to 8 years. Explicitly this is an example of a simple behavior in the system and Figure 1 illustrates this simple behavior.

We have also been exposed to the simple behaviors of some of the subsystems of Minneapolis. For example, we learned that the foreclosure rates of the 2nd Ward in Southeast Minneapolis, and the 4th and 5th Wards in North Minneapolis exhibited different behaviors in their respective locations as illustrated in Figure 2. But adding a bit more systems language, philosophy, and science to our analysis, we now know that the respective behaviors in these subsystems are also simple in nature.

And finally, if we compare the simple behaviors between the general system of Minneapolis and the respective subsystems of Minneapolis, we can see that there are some differences and some similarities. The contrast of the systems’ rates and behavior can provide us with some worthwhile information.

Figure 1
Figure 2

For example, we see a decreasing foreclosure rate in Figure 1. The General Minneapolis System (let’s call it the GMS) is tending downwards towards the horizontal (the x-axis) of the graph. In addition, both the 4th and 5th Wards are exhibiting similar general systems behaviors in their respective subsystems. It appears as though the foreclosures of the GMS and the 4th and 5th Wards are converging if we compare Figure 1 and Figure 2.

As a consequence of this information, are we to assume that as the city goes, the 4th and 5th Wards go? In other words, does the behavior of the 4th and 5th Wards depend on the behavior of the GMS? Do the simple systems’ behaviors of the 4th and 5th Wards reflect the simple system’s behavior of the GMS? Why would we think this?

As we can also see from Figure 2, the 2nd Ward’s behavior is rather flat over the course of the ten years or so. The simple behavior of the 2nd Ward doesn’t seem to be influenced or dependent on the behavior of the GMS. As the GMS is doing its thing, the 2nd Ward is exhibiting completely different behavior. Why might this be?

We must caution ourselves first before we try to answer this question, or any other questions for that matter. We must caution ourselves before assuming too much from the data. If we try to extract more information from the data than we actually can, we risk drawing conclusions that make little sense. Moreover, our overreaching conclusions could have disastrous effects if applied to policy. This data has limits.

Cities can seem a bit overwhelming sometimes. At the ground level, they seem chaotic, and indeed they are. There are a plethora of interactions and activities taking place every second of the day. But the good news is that systems contain simple behaviors in all of the chaos. And the better news is that this simple behavior can be extracted from the chaos and analyzed to provide citizens and policy makers with some much-needed and worthwhile information.

For further reading on similar subject matter, I invite you to read The General System of Minneapolis: ForeclosuresForeclosure Rates: Wards 2, 4, and 5 from 2006 to 2015 and Patterns of the 5th Ward: “Race”.

Remember, you are always welcome to post your comments, thoughts, and questions below. Feedback is always appreciated.

A Starting Point for Systems Language

By Matt Johnson

One thing I haven’t done very well on this blog is talk about systems and the language of systems. This is important because it provides a common language for those who come from different backgrounds and political affiliations. It is no secret that there is a political divide in this country and it has existed for sometime now.

Liberals view the world one way and conservatives view the world another way. This in turn influences policy decisions and applications. But systems are neither liberal or conservative. Systems do not care if you like Star Wars or if you like Star Trek (you’re writer likes both). Systems do not care if you like chocolate ice cream or vanilla ice cream. The point is that systems have no persona and no agenda. Systems are self-differentiating, complex entities. And very few people understand systems in general.

Why is a common language important? It’s important because it helps to clarify what is meant by “system,” “structure,” and “environment.” We often hear terms like “systemic oppression,” “systemic racism,” and “the patriarchy,” which is another way of saying, “It’s the system.” These terms, when used and maybe for the best of intentions, really just muddy the waters. There is no corollary or definition behind them. They can mean anything. They are ambiguous.

But it makes sense that the current systems language in everyday use is ambiguous. This is because everyday citizens have been forced to adapt and improve in their language. They see something and it’s complex. How does one explain something that seems to possess an absurd amount of variables (or things going on)? So people have to improvise. They have to make sense of it somehow.

Another reason why systems language is ambiguous is because the scientific version of it just doesn’t exist, at least not at the level of physics, chemistry, or biology. Those sciences have been around for so long that the lexicon of those sciences has had time to migrate out to the populace. For example, people use infinity, quantum, calculus, derivatives, atoms, electrons, planets, comets, black holes, gravity, psychoanalysis, and the list goes on and on. These things make at least some sense because there have been scientific practitioners to help aid in the dissemination and understanding of such scientific language, for instance, Carl Sagan and Neil deGrasse Tyson. But systems science has not had this relationship with the general public, nor has it had its Michio Kaku or Bill Nye.

The science of systems is young. It’s origins can be traced back to Ludwig Von Bertalanffy and his book General Systems Theory (see the Mark Twain page for the link) in the middle of the 20th century. Compared to physics and mathematics, this is an extremely young science. But that’s one of the reasons why this blog exists.

It’s here to provide corollary (a proposition established from truth) and definition to the conversation of systems science. It’s here to explain what a system is and what it does. It’s here to explain the difference between what a system is, what a structure is, and what an environment is. And it’s here to explain why the vast majority of systems discussed on Urban Dynamics are probabilistic systems and not causal. It’s here to provide access to an otherwise esoteric field of science.

I will do my best to make sense of these ideas I have shared with you. But it will take time for these ideas to make sense. and I won’t promise that it will happen over night. Habits will need to be broken and some knowledge will need to be accumulated on the part of the reader. But in the end, it will payoff because I will have provided you with a new way to look at the world and a language to describe it.