Category Archives: Network Analysis

High Modernism and Network Science

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It seems appropriate, somehow, that I’m reading James C. Scott’s Seeing Like a State before beginning my Ph.D. studies.

Scott warns against the dangers of a state which undertakes “utopian social engineering.” He sees a recipe for disaster comprised of four elements. The first seems innocuous: “the administrative ordering of society…by themselves, they are the unremarkable tools of modern statecraft.”

But those unremarkable tools, combined with an authoritarian state and a prostrate civil society, can lead to disaster.

The final element Scott warns of, the one that seems most relevant as I begin my studies, is a high modernist ideology. As Scott explains, high modernism is:

…a faith that borrowed, as it were, the legitimacy if science and technology. It was accordingly, uncritical, unskeptical, and thus unscientifically optimistic about the possibilities for the comprehensive planning of human settlement and production.

High modernism is a faith that goes far beyond supporting the scientific process. It is the unwavering belief that humans have the capacity to design utopia.

 

Of course, not just any humans have this capacity, high modernists would have us believe. It is only those who are properly educated, trained, and credentialed. In this technocratic utopia, experts need no local knowledge. Everything can be standardized to translate from one community to the next.

“‘Fiasco’ is too lighthearted a word for the disasters” caused by high modernism, Scott argues. “The Great Leap Forward in China, collectivization in Russia, and compulsory villagization in Tanzania, Mozambique, and Ethiopia are among the great human tragedies of the twentieth century, in terms of both lives lost and lives irretrievably disrupted.”

The high modernism which rocked the last century may be behind us. The world is to complex, too interwoven to believe in simple, standard, solutions.

Yet even as we accept the complexity of the world, we find ways to unravel it. I’m thrilled to be studying networks, an approach which allows for examining and understanding the complex systems which surround us.

So it is with the warning of Scott ringing in my head that I recently read these words about how a network understanding of biology could influence and improve medical practice:

If you suffered from manic depression in recent years, your first visit to the doctor probably started with an hour-long discussion to carefully examine your thoughts and feelings…Twenty years from now things could look quite different. Facing the same doctor, you will have a five minute discussion, just as you do in cases of simple influenza. An assistant will take a few drops of blood and you will walk home empty-handed. In the evening you will pick up the medicine from the nearest pharmacy. The next day you will wake up fresh and happy, as you did before the symptoms appeared. Both the manic and the depressive you will have been washed away.

That doesn’t sound like utopia to me. In fact, it sounds vaguely horrifying.

While there are no doubt many people with serious mental illnesses who would benefit from such an effective treatment, I’d hope it would take more than a five minute conversation before any major personality traits are simply “washed away.”

Furthermore, with such technology at our disposal, we’d be faced with serious dilemmas about what traits to live with and which to wash away. How much depression should a person accept before they undergo such drastic treatment? How soon before authoritarian states started to remove traits of outspokenness and disobedience?

None of this is to say that we should not pursue the science. It is important, fascinating work that is helping to make a little more sense of this mysterious world.

But embracing the science doesn’t mean embracing high modernism – indeed, as Scott argues, that is something we should be very wary of.

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The Revolution Comes in Pieces

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I’ve written before about my skepticism of “scaling up” as the solution to all our social challenges.

That’s not to say there aren’t some solutions which can provide more value by being brought “to scale,” but when it comes to issues of democracy and engagement, I prefer to think of “scaling sideways.” Lots of little, individual programs running parallel within parallel communities.

So I was quite taken with this little snipped from Joshua Miller and Daniel Levine’s recent paper on Reprobation as Shared Inquiry: Teaching the Liberal Arts in Prison:

“We do not know how to spark a revolution that will overthrow mass incarceration all at once and transfigure our society, but we believe that it can be made to fade away through a proliferation of non-carceral practices.”

The paper builds on Miller and Levine’s work with the Jessup Correctional Institution Prison Scholars Program – which you can support here.

Essentially, Miller and Levine argue that in order to build a truly just and effective prison system, we have to radically shift our society, doing away with our current systems of dominance and subordinance.

It’s not just a moral problem that “for the past 30 years, between 40 and 60 percent of prison inmates were below the federal poverty line,” or that “at midyear 1998, approximately 16 percent of inmates in US state prisons and 7 percent of inmates in federal prisons had a mental illness.” And it is not just a moral problem that the US “incarcerates Blacks and Latinos at disproportionate rates.”

Those are serious, moral problems within our society, but…those deep inequities also render our criminal justice system ineffective.

That is, “it is morally unreasonable to expect an offender to be moved by condemnations coming from agents of a system that routinely subjects him to injustice it is unwilling to recognize as such.”

Miller and Levine offer the Liberal Arts as a tool to break this dominant/subordinate cycle, a resource for engaging incarcerated people – not as subordinates in the ultimate system of domination – but as agents in reflecting on the “the nature of value, and the proper way to relate to other human beings in society.”

“Prison classrooms,” they write, “become political spaces at the heart of an institution where politics is disallowed.”

They acknowledge that their own work is small compared to the vastness of the challenge, but argue that “the utopian vision of a society in which the whole encounter between currently-dominant and currently-subordinated social groups is transformed is likely to be made up of a multitude of small, piecemeal encounters like this.”

Scaling sideways.

And that’s the thing: democracy requires individual engagement. It requires engagement from the individuals within a society, but more deeply, it requires that those individuals are engaged…individually. As autonomous beings, as agents of their own destiny and desires.

The challenges of democracy are challenges of collective action, to be sure – how to work together across differences and interests, how to divide and distribute limited resources.

But at its heart, democratic values are about the individual. The belief that every person’s voice has value, that all people are created equal and that all people demand your respect.

It’s not a simple case of rugged individualism, but rather a subtle interplay of individual and collectivist thought: all voices have value, and therefore we each have a responsibility to ensure that all voices are heard.

But a focus on individual agents requires programs that are small and flexible, developed for a local context and shaped by local knowledge.

You can’t scale up something like that without losing what gives it value.

But we can tackle the problem piece by piece, through networks of small efforts and regional connections.

We can scale these solutions sideways and little by little we can radically transform our society, making our deep inequities and injustices fade away through a proliferation of better practice.

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Predictive Social Science

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One of the great sources of despair in the social sciences is the lack of predictive theories.

Physics can tell us what will happen when we throw a ball in the air, or when we drop two objects simultaneously. Why can’t the social sciences provide similar trajectories for human behavior?

Put another way by economist Richard R. Nelson, “If you can land a man on the moon, why can’t you solve the social problems of the ghetto?”

One argument is that the social sciences are quantitatively stunted compared to their natural science peers; that the science of social has not yet developed to it’s full potential.

Those feeling more kind may argue that human affairs are simply more complex than those of levers and pulleys; that civil society is infinitely more intricate than a Grand Unified Theory. It’s not so much an issue of scientific chops, but rather that there is so much more work to do to solve social problems.

I find both of these arguments rather uninspiring, but what’s notable is that they each lend themselves to the same solution: more data, more formalism, more math, more “science.”

As if predictive social science is just around the corner. As if the solution to poverty is one Einstein riding a wave of light away.

To be fair, the social sciences have made remarkable quantitative advances. In 2008, Nate Silver correctly predicted the presidential contest in 49 states, and the winner of all 35 U.S. Senate races.

Fueled by the promise of better sales and better customers, the field of predictive analyics is on the rise – helping companies better identify what their customers want. Or perhaps, more accurately, what they can get their customers to buy.

In 2012, for example, Target used their big data mining to figure out a teen girl was pregnant – before her father did. It wasn’t that complicated, as it turns out, just watch for the purchase of certain vitamins and you could have a lucrative customer for life.

But creeping on a teenager – or even predicting elections – is a far cry from solving our most pressing social problems.

Why can’t you solve the social problems of the ghetto?

Perhaps our first mistake is to think there is an analytical solution.

Bent Flyvbjerg, a Danish urban planner, argues that a predictive theory approach to the social sciences is “a wasteful dead-end.” Instead we should “promote social sciences that are strong where natural science is weak – that is, in reflexive analysis and deliberation about values and interests.”

Flyvbjerg calls this approach the phronetic model, explaining, “At the core of phronetic social science stands the Aristotelian maxim that social issues are best decided by means of the public sphere, not by science. Though imperfect, no better device than public deliberation following the rules of constitutional democracy has been arrived at for settling social issues, so far as human history can show.”

I’m not sure I agree with Flyvbjerg that “no predictive theories have been arrived at in social science, despite centuries of trying.” Surely, we have not solved poverty, but we’ve come disturbingly close to predicting the patterns of an individual.

But just because we could have predictive theories of social science does not mean that is all we should aim for.

There is important knowledge, valuable knowledge, in quantitative understandings of society. We should pursue those understandings fully, but we should not deign to stop there.

Why can’t you solve the social problems of the ghetto?

Surely, one white, male economist cannot. No matter how much data he has.

But perhaps we can.

Predictive social science, assuming it exists, is only one tool towards a solution. Without phronetic social science – dialogue and deliberation between all members of a society – it is worth nothing.

Of course, this phronetic social science ought to be informed by predictive social science, just as predictive social science ought to be informed by phronetic social science.

The two aren’t competing paths towards the same end – we must pursue them both.

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Network Science

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I am thrilled to share that I’ve been accepted into Northeastern’s Network Science Ph.D. program, and I will begin there full-time this fall.

As the website describes, this “is a new interdisciplinary program that provides the tools and concepts for understanding the structure and dynamics of networks across diverse domains, such as human behavior, socio-technical infrastructures, or biological agents.”

Networks can be seen and understood in a range of different settings. There’s the network of your Facebook friends, and the network of roads that weave through your town. Networks can be used to understand the spread of disease, the narrative of a story, the development of professional knowledge, or the process of a person’s moral reasoning.

I plan to apply Network Science specifically to political science questions. I’m interested in understanding how individuals interact through a network lens; how institutions interact; how individuals in institutions interact; how local, regional, national, and global levels interact –

I could go on.

I’ve been interested in these questions for a long time. I suppose one of the reasons I’ve pursued an interdisciplinary background – my Bachelor’s is in physics and Japanese, my Master’s in marketing – is because no single field seemed to answer all these questions. Or fully seek to address them.

Most disciplines seem to focus on just one way of looking at the world.

As an undergraduate, my Sociology 101 professor said that sociology is like trying to understand the world by looking down on a bustling street. A psychologist watches individuals, a sociologist watches the crowd.

I’m not sure whether others would agree with that assessment, but it always seemed an excellent argument for why psychologists and sociologists ought not to be siloed.

Both perspectives are crucial.

To me, network science is a step back from that level. It’s about seeking understanding both on an individual and collective level. Seeing how things fit together, how they are connected or not connected. Zooming in to a micro level and zooming out to a macro level.

One could easily argue that this approach is still too limiting. In her recent book, Forms, Caroline Levine uses the techniques of literary analysis to argue that the world can be understood through the colliding of different forms, namely: whole, rhythm, hierarchy, and network. So perhaps “networks” are but one of many forms which can help us understand the world.

But I, at least for the time being, think of all those forms in network terms and I’m eager to explore their colliding.

When you slam particles together, surprising things emerge. And when networks collide the result is no less surprising.

So this is a real thing that is happening. And I’m thrilled and humbled to have the opportunity to explore these questions.

And over the next five years, you all can come along for the ride.

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Why Read Dead Authors?

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There are all sorts of clichéd arguments for why one ought to study the past or explore the wisdom of long dead scholars.

Yes, yes, those who cannot remember the past are condemned to repeat it.

Or, perhaps, with so much wisdom in our collective past, we shouldn’t waste our time reinventing the wheel.

Sigh.

It’s not that those aren’t good arguments. They’re perfectly fine arguments, and perfectly fine reasons for studying ancient works.

But. There’s something more –

The way I perceive and understand the world is deeply rooted in my given place and time. The way I think is shaped not only by my individual experiences, but my broader cultural context.

That is to say, not only can an individual’s morals be considered as a network, but the ideas a person understands can be considered as a network. There are plenty of values which I don’t hold as my own, but when I meet someone with those values I understand where they are coming from.

In some ways, this understanding is simply a feature of my own network – when someone holds a value different from my own, I naturally try to understand it using the network of values I do hold.

But I’m not sure relying on our existing network provides a broad enough perspective.

Thales of Miletus is famously recorded as having thought that archê, the ultimate principle, was water.

Did you miss that?

Everything is water.

What does that mean?

I’ve read many (inconsistent) explanations of what that means, and I suppose I understand it enough to try to explain it. But, really…it’s kind of crazy talk. Right? I remember learning about Thales in high school and laughing to myself. Man, those ancient Greeks were crazy.

But his argument was also important.

Interpreting his belief quite literally, in the physics realm, Thales of Miletus is credited with being the first (in recorded, Western, history) to conceive of the idea of a fundamental particle. That is to say, with his argument that “everything is water,” Thales led humanity down a path of thought which brought us to molecules, atoms, protons, quarks, and leptons.

There’s a moral in there about how we should always listen to our crazy elders because you never know what nugget of wisdom will propel you forward –

But that’s not my point.

“Everything is water” sounds crazy because I have no context through which to interpret that phrase. Being more accurate that “archê is water” doesn’t help.

But it made sense at the time.

In Metaphysics, Artistole explains simply:

Thales, the founder of this type of philosophy, says the principle is water (for which reason he declared that the earth rests on water), getting the notion perhaps from seeing that the nutriment of all things is moist, and that heat itself is generated from the moist and kept alive by it (and that from which they come to be is a principle of all things). He got his notion from this fact, and from the fact that the seeds of all things have a moist nature, and that water is the origin of the nature of moist things.

And then he moves on, as if that’s all you might ever need to know about someone who thought that water was the essence of the universe.

Perhaps Thales is a trivial example – it may not be all that relevant exactly what Thales thought or meant. But I don’t think I’ve ever come across a more foreign idea than that.

And that’s the reason why I like to study dead authors from around the world.

Understandings of public and private, political and social, citizen and society have varied not only across the globe but across time.

It’s hard to see the assumptions of your culture when you are a part of it. But trying to understand someone else’s perspective – not only a moral system, but a whole framework and way of thinking that is foreign to you – expands your capacity to think, to examine, or perhaps simply…to consider the possibilities.

And that has real value.

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The Adjustments Between Individuals

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What is society? What does that word describe?

The first dictionary definition I ran across describes society as, “the aggregate of people living together in a more or less ordered community.”

Without over thinking it, that sounds about right. A society is a group of people. They may be in the same physical place, and they may have some means of communicating with each other. They may share certain values or have other characteristics in common.

Those are details over which reasonable people are right to quibble, but the fundamental concept is the same: a society is a group of people.

But what if that fundamental concept is a myth? An oversimplification, or, perhaps a convenient lie? What if society is not a group of people?

Well, then, what should we conceive it to be?

In his 1925 book the Phantom Public, Walter Lippmann argued that we ought to “think of society not as the name of a thing but as the name of all the adjustments between individuals and their things.”

That is to say, society is not a group of people – it is a group of relationships. Relationships between people, between objects, between issues. A complex web describing how each person interacts with the word, and by extension, how we interact with each other.

As Lippmann bemoans:
We have been taught to think of society as a body, with a mind, a soul, and a purpose, not as a collection of men, women and children whose minds, souls and purposes are variously related. Instead of being allowed to think realistically of a complex of social relations, we have had foisted upon us by various great propagative movements the notion of a mythical entity, called Society, the Nation, the Community.

In Lippmann’s account, the error of taking society to be Society is more than an issue of semantics, and it is more than an innocent oversimplification. A theory of democracy which personifies society as a coherent whole, rather than a network of individuals and relationships, is not only mistaken – it is dangerous.

In post-World War I America, Lippmann looked out and saw the challenges of an increasingly globalized, centralized and professionalized world:

To defend themselves against the economic powers of darkness, against the great monopolies or a devastating competition, the farmers set up great centralized selling agencies. Businessmen form great trade associations. Everybody organizes, until the number of committees and their paid secretaries cannot be computed. The tendency is pervasive.

The concern, of course, is not necessarily with the centralization per se. Rather:

The men who make decisions at these central points are remote from the men they govern and the facts with which they deal. Even if they conscientiously regard themselves as agents or trustees, it is a pure fiction to say that they are carrying out the will of the people. They may govern the people wisely. They are not governing with the active consultation of the people.

Whether these people are elected, appointed, or otherwise endowed with power makes little difference in the end. Those with power are the ones who have power – everyone else is left out.

Yet the myth of Society, allows this to be so. A democratic people would never accept a king imbued by God – but they will accept government anointed by Society.

The people have spoken, they say. They cheer in victory or moan in disagreement, but the sentiment is the same. It is the Will of The People.

But “The People” is not a collective whole. Society has no unified will – and the myth that it does only allows those in power to falsely view themselves as benevolent actors of the people.

It would be impractical to do away with representative government, but what would it look like, I wonder, if we could divorce ourselves from this collective notion? If we could see society not as a unitary object, but as a messy web of relationships? If we truly saw our elected officials not divinely as the Voice of People, but as individuals themselves – given power not by social fiat, but simply for necessity’s sake.

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Scaling (not) Up

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When people talk about “scaling” they seem to generally mean “scaling up.” This is often used particularly within the business context – how can we scale up our business model to serve more customers? Or, as perhaps the more skeptical add, to make more money.

“Scaling up” is also prevalent within the non-profit sector – how can we broaden our reach? How can we connect more people with our services or convince more people of our message?

Scaling up is, perhaps, a litmus test, which divides strong companies from the weak. Great idea, I’ve heard people say, but will it scale?

It is, perhaps, nice to do something at a small, local, level, but if you can’t effectively scale up, conventional wisdom seems to say, there’s not really much point. Or at least, the conversation then turns into a (worthwhile but secondary) debate over whether it’s okay to improve one life rather than many.

But is scaling up really the only way to go?

I’m not intrinsically opposed to scaling up, but I question the assumption that it’s the only way to go – that success and upwards scale are inextricably linked.

As someone recently commented to me, perhaps some efforts could benefit from scaling down.

I am particularly intrigued by what I can only describe as scaling laterally – connecting local work in one place to local work in another place.

Scale, I suppose, is at its essence a navigation problem. How does information, or perhaps commands, get from one place to another?

The typical model of scaling up tackles this problem more or less effectively. Some centralized governing body oversees a network of smaller entities. A well articulated company brand or character can greatly help in making sure that all the pieces are working together, but it tends to be a very vertical solution.

Perhaps that is the easiest solution, and perhaps it is the best solution – I am certainly in no position to judge.

But it is not the only solution.

A central governing body is not inherently necessary. A vertical structure is not inherently necessary. What’s necessary is that information can get from point A to point B. And this information needs to flow in a timely enough matter that the two can truly communicate.

…But what kind of scale is that?

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A World with no Friction

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In physics, it is common to tackle complex problems by starting with a simplification of the scenario.

Want to understand how an object move along a surface? Start in a world with no friction. Assume a standard downward force, g, and understand the simplest version of what is going to occur.

Once you have a simple formula for the simple situation, then you can add friction and other real-world complications. Little by little you can expand your simple model into a complex model, slowly but surely adding the detail that’s needed to understand how things really work.

This is one of the beautiful things about the mathematics of science. When you truly come to understand the equations, you can see how clearly g, the force of gravity on Earth, is derived from G, the gravitational force of the universe. You can see how the formula for an object traveling at the speed of light is actually just the same as an object moving at an every day speed – it’s just that for every day purposes the complex factors become so small they are irrelevant.

There is nothing wrong with the world without friction. This model is a crucial first step for deeper understanding. It’s the place you have to start, the model you have to truly understand before you can move forward.

It is not uncommon to criticize the social sciences for their lack of a predictive model. Physics can describe the future trajectory of a moving object, why can political science describe the future trajectory of a government.

Frankly, I don’t find that concern all that compelling. I am rather relieved that social sciences can’t predict my every move, and I am dismayed as a matter of principle at big data analytics which seem to move in that direction.

But, from my vantage point far outside these fields, the social sciences do seem to be stuck in – or perhaps, slowly moving out of – a world without friction.

I’ve been glad to see the growth of network analysis within the social sciences. Still in its nascent stages, perhaps, but slowly adding the complexities of reality onto social science models.

People interact with each other. Organizations interact with each other. Organizations, governments, and yes, even corporations, are made of people interacting with each other.

A government doesn’t exist in a vacuum. There is – as we well know – friction within our society. Using network analysis to get at these more subtle interactions is a critical step in moving social science understanding beyond the simple – but valuable model – of a world with no friction.

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Powers of Ten

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You know that amazing 1977 science video Powers of Ten? If you haven’t seen it, go ahead and take a minute to watch it at the link. It might blow your mind.

Okay, well, maybe not, but this was just about my favorite movie when I was in elementary school.

I found myself thinking back to this video today after an engaging conversation with some of my colleagues about the power and role of network analysis.

With the advent of the Internet and especially of social media, the idea of “social networks” has entered – or become more prominent – within the popular lexicon.

These social networks have always existed, of course, but they now seem easier to navigate and quantify. In Facebook terms, I can tell you exactly how many friends I have, and I can also occasionally discover when two people – whom I know from different networks – know each other.

Perhaps more interestingly, the ghost in the Facebook machine has a birds eye view of everyone’s network. Not only am I individually acutely aware of the vast network of people who exist beyond my own, local network, but one could chart the social networks of everyone on Facebook as one giant, global network.

So, that’s pretty cool.

But of course, a social network of this type isn’t the only kind of network governing our world. In a social network, the people are nodes and the relationships between them are edges.

But we could zoom out a level – see where the powers of ten video comes in – and think about a community, not as a network of individuals, but a network of institutions and organizations.

And you could think of these institutional networks at different levels as well. The city I live in has a dense network of organizational ties, but we could also move outwards to look at regional organizational ties, or state-wide ties. We could look at national or international networks of relationships.

We could look at communication networks, transportation networks, relational networks, and many other types of networks operating at these macro levels.

And of course, we can zoom in as well. Thinking of an individual not as a node in a network, but as the network.

In a very literal sense, this could be the network of veins and arteries, the network of nerves, or other biological networks that keep us alive and functioning.

But we can also consider a person’s ideas as a network.

David Williamson Shaffer does this in his work on Epistemic Games. Professional training, he argues, is essentially the process of developing a specialized way of thinking – a network. A lawyer may have to learn many facts and figures, but more deeply, they learn an approach. A way to address and explore new problems.

Not only can you model this networked way of thinking in professionals, you can watch a network develop in novices.

Perhaps an individual’s morals can also be conceived as a network. This is certainly more appealing than concerning a set list of rules to follow – situations are, after all, complex and context in everything.

(While I’ll leave my zooming there, I do feel compelled to clarify that I don’t mean that to imply that we have reached the fundamental particles of human existence. I prefer to think of morals as complex, uncertain things rather than a simple, discrete point.)

So if you zoom in that far, if you consider a network where a person’s ideas are nodes – does that individual network have any connections beyond the person who contains them?

Perhaps.

Ideas are more free than blood cells, and just because I have an idea doesn’t mean you can’t have it to.

An idea may be a node within my network, but I am a node within a human network. I am a node within social networks and I am a node within institutional networks. Local institutions and, ultimately, global institutions, too – though you may not be able to spot my blip on that network map.

And that’s why I like the Powers of Ten video. Because all these different levels, all these different ways of looking at things – they’re not isolated. It’s no accident that atoms make stars.

And it is not only understanding each level that matters, it is understanding how all these levels are connected. How they build to form a whole that looks radically different from its component parts.

Understanding a single network is valuable, but understanding the levels of networks, and the network between them – well, that, my friends, would be a thing of beauty.

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Taxonomy

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So, I don’t know what you did for fun over the long weekend, but I took advantage of the extra time to finally create categories and tag my blog entries.

I’d intentionally not done this at the beginning – when I began blogging, I had a general sense of the types of things I thought about, but not a coherent sense of what I’d end up writing about. So, I didn’t want to limit myself by category.

But I also didn’t want to create categories as I went – in my experience that just leads to a long list of poorly delimited categories which may or may not actually be helpful for navigating content.

So I waited over a year, and, having a somewhat overdeveloped love of process, I put a not insubstantial amount of thought into the development of categories and the tagging of posts.

For those of you who are moved by such things, here’s how I handled the process. While I’m not likely to repeat it eminently, I am, of course, interested in any changes or amendments you might suggest.

I started with a draft list of categories. A short list of things that I’m pretty sure I write about a lot. Since I personally have written all the posts on this blog, I found this step rather easier than when I have previously attempted this exercise for communal blogs or organizational website. I more or less know what I write about.

Then I made some changes and amendments as I tagged each post. Separate categories for “Citizens” and “Institutions” become one category of “Citizens and Institutions,” which eventually became “Citizens and Civil Society.”

I kept that category separate from “Civic Studies” which, while certainly overlapping, has a more academic lens. Random musings about what it means to be a good citizen based off a conversation I had with a stranger on the bus – that went under “Citizens and Institutions.” If I quoted Robert Putnam, Elinor Ostrom, or even Robert A. Heinlein, that probably got a “Civic Studies.”

And, both of those categories stayed separate from “community” which, while also intertwined with the above, tends to focus more on my communities – organizations I work with or, occasionally, interactions in the cities I visit.

“Justice” served as an umbrella category – though I was tempted at times to break it down. Racial justice, economic justice, and LGBT rights seem to be my most common topics within this category. I’m not sure how often I articulate a connection between these topics, but keeping them together felt right.

Interestingly, I believe many of my posts about gender equity ended up under “social norms.” Perhaps I’m too tired of fighting those battles and have devolved to simply being annoyed. A sort of, did you hear what society says we should do? sort of sarcasm.

History, Marketing Communications, and Physics (or, perhaps more generally, STEM) each earned their own categories as the starting point for much of my thought – being formally trained in two and generally interested in the other.

Perhaps my biggest struggle was around morality – as it were. I don’t have many declarations of what it means to be moral, but I do spend several posts exploring what it means to be a good citizen – and, almost by default, what it means to be a good person.

I ended up putting these posts under the “Citizens and Civil Society” banner. I couldn’t quite bring myself to declare “morality” as a core interest, and…I’m not sure that I’m concerned about morality, per se. I’m concerned about being a good person, and I’m concerned about being a good citizen. And I’m concerned about being the best person and best citizen I can be…but, morality? Some how that didn’t feel like the right word for it.

So, with all those categories declared and all my posts tagged, this is how things shook out for 249 posts, many of which have multiple tags:

Citizens & Civil Society  – 108 posts
Miscellaneous Musings  -  75 posts
Civic Studies – 60 posts
Community  – 53 posts
Justice – 51posts
Social Norms – 39 posts
History -  31 posts
Unpopular Opinions  – 20 posts
Marketing Communications – 19 posts
Mental Health  – 16 posts
Physics  – 14 posts
Meaninglessness  – 10 posts
Utopia  -  10 posts
Network Analysis  -  9 posts

Once I had completed this process, I couldn’t help but take a look at something -

I’ve written before about moral networking – a process by which use network analysis to interpret your moral views. This can be a helpful process for self-reflection and a helpful process for deliberation.

But I find myself skeptical of its use as a quantitative, network analysis tool. It’s too…soft. Too driven by gut feelings and what you’re thinking of at a given moment. Combining individual networks into a collective network presents an even greater challenge – what does it mean for two nodes to be the same?

If we use the same word, do we mean the same thing?

David Williamson Shaffer’s work on Epistemic Network Analysis can provide some guidance here. Shaffer argues that the way professionals think can be modeled as a network – being an urban planner doesn’t mean you’ve memorized a set of facts, it means having crafted approaches and ways of thinking which help you address the topics you encounter.

The “scientific method” aren’t just steps you memorize, it’s a way of thinking.

Shaffer carefully constructs models of a professional’s network, then tracks the development of a personal network in a novice training to be a professional.

The key here, is that to develop the networks, Shaffer and his team conduct in-depth interviews with professionals and novices, record training conversations between professionals and novices, and then systematically code all this information.

They look not only for ideas, but specific ways of thinking.

I’d love to try something like that out for moral networking, but, lacking the time and resources to do this properly, I’m left to play with the poor man’s coding in my little sandbox.

I’ve previously played around with using simple word counts as a way to visualize the connections between my blog posts.

That, of course, has many challenges, including, for example, the many meanings of the word “just.”

So, recognizing the imperfection and probable meaninglessness of this next analysis, once I had my posts tagged, I had to map them:

Blog_categories

Nodes are sized by the frequency of use, and edges are sized by the number of times linked categories appeared together.

As the chart above indicates, Citizens and Civil Society (shorted above to “Citizens”), was by far the most frequent category, with 108 tagged posts. It also had the highest degree (linked nodes), with 13 connected nodes – out of 14 total for the network. It is also most central to the network, with a betweenness centrality of 0.063.

There are a total of 68 edges.

The network is fairly connected, with an average path length – the distance from one node to any other node – of 1.25. The network diameter is 2 – if Kevin Bacon were one of the nodes, no other node would be more than 2 degrees from Kevin Bacon.

Marketing Communication and Network Analysis both have the fewest connections – each with a degree of 5. However, I wrote 19 post about marketing and only 9 (now 10) on networks.

This is, of course, still a very soft analysis. Still very based off my own biases and gut decisions.

But it’s a fun project for a holiday.

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