Category Archives: Physics

Now We Are All Sons of —

On July 16, 1945, 35 miles southeast of Socorro, New Mexico, the world’s first nuclear weapon was detonated at 5:29 am.

The test was code-named Trinity by J. Robert Oppenheimer. There is no definitive explanation for why Oppenheimer chose the name, but it is widely believed to be a reference to John Donne’s Holy Sonnets: Batter my heart, three-person’d God.

Oppenheimer had previously been introduced to Donne’s work by his mistress, Jean Tatlock, before she committed suicide the year before Trinity.

Donne’s poem reads:

Batter my heart, three-person’d God, for you 
As yet but knock, breathe, shine, and seek to mend; 
That I may rise and stand, o’erthrow me, and bend 
Your force to break, blow, burn, and make me new. 
I, like an usurp’d town to another due, 
Labor to admit you, but oh, to no end; 
Reason, your viceroy in me, me should defend, 
But is captiv’d, and proves weak or untrue. 
Yet dearly I love you, and would be lov’d fain, 
But am betroth’d unto your enemy; 
Divorce me, untie or break that knot again, 
Take me to you, imprison me, for I, 
Except you enthrall me, never shall be free, 
Nor ever chaste, except you ravish me.

Upon witnessing the detonation, Oppenheimer recalled being inspired by a line from the Bhagavad-Gita: “Now I am become Death, the destroyer of worlds.”

It is unclear whether Oppenheimer actually uttered those words in the early morning hours of July 16, so perhaps the most memorial line from that day goes to another scientist on the project, director Kenneth Bainbridge:

Now we are all sons of bitches.

In a letter to Oppenheimer, Bainbridge later tried to clarify his words:

The reasons for my statement were complex but two predominated. I was saying in effect that we had all worked hard to complete a weapon which would shorten the war but posterity would not consider that phase of it and would judge the effort as the creation of an unspeakable weapon by unfeeling people. I was also saying that the weapon was terrible and those who contributed to its development must share in any condemnation of it. Those who object to the language certainly could not have lived at Trinity for any length of time.

In the May 1975 issue of Bulletin of the Atomic Scientists, Bainbridge shares Oppenheimer’s reply:

Years later [Oppenheimer] recalled my words and wrote me, “We do not have to explain them to anyone.” I think I will always respect his statement, although there have been some imaginative people who somehow can’t or won’t put the statement in context and get the whole interpretation. Oppenheimer told my younger daughter in 1966 that it was the best thing anyone said after the test.

In the same article, Bainbridge describes the detonation in careful detail:

The bomb detonated at T = 0 = 5:29:45 a.m. I felt the heat on the back of my neck, disturbingly warm. Much more light was emitted by the bomb than predicted, the only important prediction which was off by a good factor. When the reflected flare died down, I looked at Oscuro Peak which was nearer Zero. When the reflected light diminished there I looked directly at the ball of fire through the googles. Finally I could remove the goggles and watch the ball of fire rise rapidly. It was surrounded by a huge cloud of transparent purplish air produced in part by the radiations from the bomb and its fission products. No one who saw it could forget it, a foul and awesome display.

A few weeks later, the United States dropped atomic weapons on Hiroshima and then Nagasaki; shaking the world with their devastation.

The U.S. won the war, but the horrors unleashed by humanity that day can never be put back in the box. We made something great and terrible; a remarkable tribute to the accomplishments of science and tragic testament to the destructive power of mankind.

Now we are all sons of bitches indeed.

Citation Networks

In his seminal work “Networks of Scientific Papers,” Derek J. de Solla Price argues the citation networks provide a broad picture which “tells us something about the papers themselves as well as something about the practice of citation.”

This sentiment is echoed in later works.

Franc Mali, Luka Kronegger, Patrick Doreian, and Anuska Ferligoj, for example, write: “Understanding science as a social system implies considering science as fundamentally relational, and as a community-based social activity.”

In their work on Citation Networks, Filippo Radicchi, Santo Fortunato, and Alessandro Vespignani further argue, “citation networks in the last several years have become one of the prototypical examples of complex network evolution.”

What is particularly interesting is that citation networks are complex systems. As L.A.N. Amarala and J.M. Ottino define it:

“A complex system is a system with a large number of elements, building blocks or agents, capable of interacting with each other and with their environment. The interaction between elements may occur only with immediate neighbors or with distant ones; the agents can be all iden- tical or different; they may move in space or occupy fixed positions, and can be in one of two states or of multiple states. The common characteristic of all complex systems is that they display organization without any external organizing principle being applied. The whole is much more that the sum of its parts.”

Citation networks certainly meet this definition.

Another interesting element of citation networks is that aging often – but not always – has adverse effects. As deSolla Price finds in his study of a relatively well bounded citation network, “half the references are to a research front of recent papers and the other half are to papers scatter uniformly through the literature.”

Intuitively, this makes sense – research seeks to push forward a frontier of knowledge and thus most citations are to relatively new research developments.

However, despite this trend, there are still the very successful papers – the classics – which scholars return to and cite time and time again.

On Calls for Unity and Disturbing Appointments

In his election night victory speech, Donald Trump took on a more moderate tone, proclaiming: “I pledge to every citizen of our land that I will be president for all Americans, and this is so important to me. For those who have chosen not to support me in the past, of which there were a few people. . ..I’m reaching out to you for your guidance and your help so that we can work together and unify our great country.”

The Democratic party also took the high road – conceding the election even though Clinton received at least a million more votes than Trump. There were calls for unity and respectful meetings between the president and the president-elect. While some chose to take to the street in “Not My President” protests, the resounding message from the Democratic establishment was clear: Democrats have a civic duty to give the president-elect the benefit of the doubt.

And perhaps we did, but as the work of his transition team gets underway Trump has made it clear what kind of President he will be.

Perhaps Representative Katherine Clark put it best when she wrote: “A ‘President for all Americans’ doesn’t appoint an anti-Semitic, racist, homophobic misogynist as senior advisor.”

I’ve heard from a lot of Trump supporters that his dramatic campaign rhetoric really was just rhetoric. They don’t really expect him to build a wall or undertake any of the more troubling policy proposals. Candidate Trump wasn’t as terrifying as liberals thought because his campaign  commitments weren’t intended to be taken literally.

But even if this argument allays an impression of Trump as a bigot, the appointment of Stephen Bannon as chief White House strategist and senior counselor cannot be so easily explained away. As chairman of the alt-right Breitbart News, Bannon has given a voice and a platform to the neo-nazis and extremists of America.

His appointment is cause for grave concern.

There are many great articles detailing Bannon’s more serious flaws, but I’ll quote here from the National Review, a “conservative weekly journal of opinion”:

The Left, with its endless accusations of “racism” and “xenophobia” and the like, has blurred the line between genuine racists and the millions of Americans who voted for Donald Trump because of a desire for greater social solidarity and cultural consensus. It is not “racist” to want to strengthen the bonds uniting citizens to their country

But the alt-right is not a “fabrication” of the media. The alt-right is a hodgepodge of philosophies that, at their heart, reject the fundamental principle that “all men are created equal, endowed by their creator with certain unalienable rights.” The alt-right embraces an ethno-nationalism that has its counterparts in the worst of the European far-right…

..The problem is not whether Bannon himself subscribes to a noxious strain of political nuttery; it’s that his de facto endorsement of it enables it to spread and to claim legitimacy, and that what is now a vicious fringe could, over time, become mainstream…No, Steve Bannon is not Josef Goebbels. But he has provided a forum for people who spend their days photoshopping pictures of conservatives into ovens.

This is why I find the appointment of Bannon so horrifying. When true conservatives agree that this is a disconcerting turn of events, it’s pretty clear that something is wrong. Our republic truly is in danger.

Now is indeed a time for unity; but not the unity of blindly supporting the President-Elect. It’s a time for liberals and conservatives alike to unite in denouncing hate in all its forms; of making it clear in no uncertain terms that equality and respect for all people are core American values on which our country will not compromise.

Election Modeling

I’ve been spending a lot of my time working on a class assignment in which we are asked to model the U.S. presidential election. The model is by necessity fairly rudimentary – I’m afraid I won’t be giving Nate Silver a run for his money any time soon – but it’s nonetheless been very interesting to think through the various steps and factors which influence how election results play out.

The basic approach is borrowed from the compartmental models of epidemiology. Essentially, you treat all people as statically equivalent and allow for transitions between discrete compartments of behavior.

Consider a simple model with the flu: you start with a large pool of susceptible people and a few infectious people. With some probability, a susceptible person will come in contact with an infectious person and become infected. At some average rate, and infectious person will recover. Thus, you can separate people into compartments, Susceptible, Infectious, Recovered, and with average transition rates can estimate the number of people in each compartment at each time step.

Of course, sophisticated epidemic models can be much more complicated then this, and trying to interpret the complexity of an electoral system through such a simple model has proven to be challenging.

First there’s the question of how to transfer this metaphor to electoral politics – what does it mean to be ‘susceptible’, ‘infectious’, or ‘recovered’ in this context?

But perhaps the piece I have found most interesting is trying to understand the system’s “initial conditions.” I am not an epidemiologist, but a simplified model of disease spreading where some people start susceptible and a few people start infected makes intuitive sense to me. We even worked out mathematically how moving people from “susceptible” to “recovered” via vaccination helps prevent a serious outbreak of a disease. (PSA: get your flu shot.)

But I’ve had a much harder time wrapping my head around what initial compartment a voter might belong in.

There’s an idealized version of politics in which all eligible voters start with a completely open mind – a clean slate ready to be filled with thoughtful judgements and reflections on the merits of each candidate’s policies.

But that’s not really how electoral politics works.

I, for example, have always been a staunch partisan, and while it perhaps would be better if I entered an election season as a clean slate – I always enter with a whole host of biases and preconceptions. The debates and TV ads were never going to change my mind.

So what has been most striking in the process is how little movement actually takes place – especially considering just how long this election has gone on.

When you take the partisan leaning of Independents into account, Pew estimates the current population of registered voters as 44% Republican/lean Republican, 48% Democrat/lean Democrat, and 8% no leaning/other party.

FiveThiryEight‘s weighted average of national polls shows some fluctuations over the last six months, but currently puts Clinton at 45.8%, Trump at 39.4%, and Johnson at 5.8%. That’s not a direct correlation to the raw partisan leaning, but it’s close enough to show that – in the epistemological framework – relatively few transitions are happening.

In fact, the earliest FiveThiryEight numbers, from June 8 of this year put Clinton at 42%, Trump at 38%, and Johnson just shy of 8%. So I guess this makes me wonder:

…Couldn’t we have held this election back in June and saved ourselves the trouble?

Democratic Distributions

Gaussian, Poisson, and other bell-shaped distributions are some times called “democratic.” This colloquial term is intended to indicate an important feature: an average value is a typical value.

Compare this to heavy-tailed distributions which follow generally the so-called 80/20 rule: 80% of your business comes from 20% of your clients, 80% of the wealth is controlled by 20% of the population. Indeed, this principle was originally illustrated by Italian economist Vilfredo Pareto when he demonstrated that 80% of the land in Italy was owned by 20% of the population.

In these distributions, an average value is not typical: the average household income doesn’t mean much when a small group of people are vastly more wealthy than the rest. This skew can be shown mathematically: in a bell curve, the variance – which measures the spread of a distribution – is well defined, while it diverges for a heavy-tailed distribution.

Yet while heavy-tailed distributions are clearly not democratic, I’m still struck by the use of the term for normal distributions. I’m not sure I’d call those distributions democratic either.

I’m particularly intrigued by the use of the word “democratic” to nod to the idea of things being the same. Indeed, such bell-shaped distributions are known primarily for being statistically homogeneous.

That’s starting to border on some Harrison Bergeron imagery, with a Handicapper General tasked with making sure that no outliers are too intelligent or too pretty.

That’s not democratic at all. Not really.

This, of course, leads me to the question: what would a “democratic” distribution really look like?

I don’t have a good answer for that, but this does raise an broader point about democracy: most real-world systems are heavy-tailed. Properties like hight and weight follow normal distributions, but power, money, and fame are heavy-tailed.

So the real question isn’t what a democratic distribution looks like; it is how do we design a democratic system in a complex system that is inherently undemocratic?

Phenomenology and Physics

The philosophical field known as phenomenology, initiated by Edmund Husserl in the early 20th century, can perhaps most simplistically be described as the charge to describe phenomena; to describe, as the Stanford Encyclopedia of Philosophy explains, the “experience of or about some object.”

A core principle of practicing phenomenology is what Husserl called phenomenological reduction or epoché, a term borrowed from the Skeptics. In order to properly describe a phenomena, he argued, we must first bracket out certain biasing factors.

As Sarah Bakewell describes in At the Existentialist Café, phenomenology frees us “from ideologies, political and otherwise…forcing us to be loyal to experience.”

In other words, ” the Husserlian ‘bracketing out’ or epoché allows the phenomenologist to temporarily ignore the question, ‘But is it real?’, in order to ask how a person experiences his or her world. Phenomenology gives a formal mode of access to human experience.”

Phenomenology is a rigorous philosophical exploration, but on its surface it seems far removed from the basic tenants of physics. Phenomenology seeks to describe the world as it’s experienced, physics attempts to describe the world as it is.

So I was struck yesterday, when a professor described the process of developing a physics model in somewhat phenomenological terms; to model a complex system, we must strip away all the noise, focusing on those elements which really matter.

Interestingly, while similar in practice, the approaches are very different in interpretation. In phenomenology, this reduction is something of a purification process. Focusing on the core elements of an object, freed of ideologies and constructs, allows a phenomenologist to more truly describe a phenomenon.

The hard sciences have a different understanding: all models are wrong, but some are useful. The reduction process of modeling is logistically and computationally necessary, but it removes an element of truth from your understanding. The goal is to always come up with more and more complex models; methodically adding more elements to the model in order to better describe complex, real-world systems.

In some ways, phenomenology is an important topic of debate in physics. Schrödinger’s famous thought experiment where a cat is both alive and dead until observed was intended to illustrate the limits of the Copenhagen interpretation of quantum mechanics. That model indicates that wave forms collapse when an observation takes place; that the cat is both alive and dead until observed.

This strikes me as a phenomenologically valid reality; no phenomena really exists outside the experience of that phenomena. But this seems scientifically unlikely; a cat cannot be both alive and dead.

The fields of phenomenology and physics have some stark differences, but it seems they may have more in common than one might initially think. And it raises interesting questions for the social sciences – which continually tries to be more and more like it’s hard science peers. But in stripping away the noise and complexity, in reducing too far to a mathematical model, we strip the phenomenological aspects of reality; we think we’ve modeled the world as it exists but neglected the world as its experienced.

Knowledge and Wonder

In his autobiography, Life on the Mississippi, Samuel Clemens – better known as Mark Twain – describes his changing relationship with the great river.

He grew up along the Mississippi, working as a typesetter and dreaming of some day becoming a steamboat pilot. In fact, his chosen pen name, “Mark Twain” is a steamboat cry, indicating a safe depth of 2 fathoms. In his early 20s, Twain was taken on as an apprentice pilot and he spent the next two years learning everything there was to know about the Mississippi.

He describes a magnificent sunset which left him bewitched in when steam boating was new to him, and he describes the awe he felt at the secret knowledge he was learning to glean from the river’s captivating surface.

The face of the water, in time, became a wonderful book ‐ a book that was a dead language to the uneducated passenger, but which told its mind to me without reserve, delivering its most cherished secrets as clearly as if it uttered them with a voice. And it was not a book to be read once and thrown aside, for it had a new story to tell every day. Throughout the long twelve hundred miles there was never a page that was void of interest, never one that you could leave unread without loss, never one that you would want to skip, thinking you could find higher enjoyment in some other thing. There never was so wonderful a book written by man; never one whose interest was so absorbing, so unflagging, so sparklingly renewed with every reperusal. The passenger who could not read it was charmed with a peculiar sort of faint dimple on its surface (on the rare occasions when he did not overlook it altogether); but to the pilot that was an italicized passage; indeed, it was more than that, it was a legend of the largest capitals, with a string of shouting exclamation points at the end of it, for it meant that a wreck or a rock was buried there that could tear the life out of the strongest vessel that ever floated. It is the faintest and simplest expression the water ever makes, and the most hideous to a pilotʹs eye. In truth, the passenger who could not read this book saw nothing but all manner of pretty pictures in it, painted by the sun and shaded by the clouds, whereas to the trained eye these were not pictures at all, but the grimmest and most dread‐earnest of reading matter.

Twain knew something the “uneducated passenger” didn’t know. He could see more and feel more as his knowledge of the river deepened. But, eventually, something changed:

Now when I had mastered the language of this water and has come to know every trifling feature that bordered the great river as familiarly as I knew the letters of the alphabet, I had made a valuable acquisition. But I had lost something, too. I had lost something which could never be restored to me while I lived. All the grace, the beauty, the poetry, had gone out of the majestic river!

…No, the romance and beauty were all gone from the river. All the value any feature of it had for me now was the amount of usefulness it could furnish toward compassing the safe piloting of a steamboat. Since those days, I have pitied doctors from my heart. What does the lovely flush in a beautyʹs cheek mean to a doctor but a ʺbreakʺ that ripples above some deadly disease? Are not all her visible charms sown think with what are to him the signs and symbols of hidden decay? Does he ever see her beauty at all, or doesnʹt he simply view her professionally, and comment upon her unwholesome condition all to himself? And doesnʹt he sometimes wonder whether he has gained most or lost most by learning his trade?

Gaining full knowledge of the river removed the mystery, removed the wonder. The river was no long a thing a beauty – it was an object to be analyzed factually.

Interestingly, Henry Thoreau expressed something similar as he worried about his work as a surveyor and found himself complicit in defining the wilderness of land as private property:

I have lately been surveying the Walden woods so extensively and minutely that I now see it mapped in my mind’s eye – as, indeed, on paper – as so many men’s wood-lots, and am aware when I walk there that I am at any given moment passing from such a one’s wood-lot to another’s. I fear this particular dry knowledge may affect my imagination and fancy, that it will not be easy to see so much wildness and native vigor there as formerly. No thicket will seem so unexplored now that I know that a stake and stones may be found in it.

As Kent Ryden describes in Landscape With Figures, “In the end, Thoreau viewed his profession of surveyor with a profound and deep-seated ambivalence, in that it simultaneously sustained and destroyed the visual, spiritual, emotional, and imaginative relationships with landscape and nature that he valued so highly.”

Knowledge has practical purpose and value, both Twain and Thoreau seem to find, but it also destroys something greater; knowledge is incompatible with beauty and wonder.

I don’t believe I could disagree with that sentiment more strongly.

In his autobiography, A Mathematician’s Apology, the brilliant G. H. Hardy wrote: “It may be very hard to define mathematical beauty, but that is just as true of beauty of any kind — we may not know quite what we mean by a beautiful poem, but that does not prevent us from recognizing one when we read it.”

Physicist and Nobel laureate Frank Wilczek has written extensively on the beauty of natural laws, which he argues is a sentiment with deep historical roots in physics:

The nineteenth-century physicist Heinrich Hertz once described his feeling that James Clerk Maxwell’s equations, which depict the fundamentals of electricity and magnetism, “have an independent existence and an intelligence of their own, that they are wiser…even than their discoverers, that we get more out of them than was originally put into them.” Not long after, Albert Einstein called Niels Bohr’s atomic model “the highest form of musicality in the sphere of thought.” More recently, the late Nobel laureate Richard Feynman, describing his discovery of new laws of physics, declared, “You can recognize truth by its beauty and simplicity.” Similar sentiments are all but universal among modern physicists.

Both Twain and Thoreau describe the loss of beauty through a process of learning, but more importantly, through a process of objectification. Through their respective work they come to see nature as a thing to be conquered, an object which can be possessed. They come to view the river or the woods through completely utilitarian means. They domesticate the natural world.

Real knowledge isn’t about that. It is about understanding the world, about reading the wonderful book as Mark Twain so eloquently describes; but ultimately it’s about constantly unlocking deeper levels of mystery, finding new layers of awe.

Knowledge builds beauty; the book never ends.

Adventures in Network Science

Every time someone asks me how school is going, I have the tendency to reply with an enthusiastic but nondescript, “AWESOME!” Or, as one of my classmates has taken to saying, “WHAT A TIME TO BE ALIVE!”

Truly, it is a privilege to be able to experience such awe.

As it turns out, however, these superlatives aren’t particularly informative. And while I’ve struggled to express the reasons for my raw enthusiasm in more coherent terms, I will to attempt to do so here.

First, my selected field of study, network science, is uniquely interdisciplinary. I can practically feel you rolling your eyes at that tiredly clichéd turn of phrase – yes, yes, every program in higher education is unique interdisciplinary these days – but, please, bear with me.

I work on a floor with physicists, social scientists, and computer scientists; with people who study group dynamics, disease spreading, communication, machine learning, social structures, neuroscience, and numerous other things I haven’t even discovered yet. Every single person is doing something interesting and cool.

I like to joke that the only thing on my to-do list is to rapidly acquire all of human knowledge.

In the past year, I have taken classes in physics, mathematics, computer science, and social science. I have read books on philosophy, linguistics, social theory, and computational complexity – as well as, of course, some good fiction.

I can now trade nerdy jokes with people from any discipline.

And I’ve been glad to develop this broad and deep knowledge base. In my own work, I am interested in the role of people in their communities. More specifically, I’m looking at deliberation, opinion change, and collective action. That is – we each are a part of many communities, and our interactions with other people in those communities fundamentally shape the policies, institutions, and personalities of those communities.

These topics have been tackled in numerous disciplines, but in disparate efforts which have not sufficiently learned from each other’s progress. Deliberative theory has thought deeply about what good political dialogue looks like; behavioral economics has studied how individual choices result in larger implications and institutions; and computer science has learned how to identify startling patterns in complex datasets. But only network science brings all these elements together; only network science draws on the full richness of this knowledge base to look more deeply at interaction, connection, dynamics, and complexity.

But perhaps the most exciting thing about this program is that it truly allows me to find my own path. I’m not training to replicate some remarkable scholar who already exists – I am learning from many brilliant scholars what valuable contributions I will uniquely be able to make.

Because as much as I have to learn from everyone I meet – we all have something to learn from each other.

There are other programs in data science or network analysis, but this is the only place in the world where I can truly explore the breadth of network science and discover what kind of scholar I want to be.


I joke about trying to acquire all of human knowledge because, of course, I cannot learn everything – no one person can. But we can each cultivate our own rich understanding of the puzzle. And through the shared language of network science, we can share our knowledge, work together, and continue to chip away at understanding the great mysterious of the universe.


A common theme in community work is questioning what it means to be an expert. 

Given the complex and technical issues our communities face, it seems reasonable, perhaps, to rely on the knowledge of experts. After all, there’s a reason why people undertake years of schooling to become urban planners, architects, or other types of experts.

A prevalent challenge to this model is that it over looks the knowledge which “average” community members have. An architect may know how to design a building that won’t fall down, but the ‘community’, broadly speaking, knows what aesthetics and functionality are most important and needed. They are experts in their own right.

I was reminded of this debate earlier this week though, surprisingly, an article in Nature about quantum physics work by J. J. W. H. Sørensen et al.:

With particles that can exist in two places at once, the quantum world is often considered to be inherently counterintuitive. Now, a group of scientists has created a video game that follows the laws of quantum mechanics, but at which non-physicist human players excel.

There are few interesting points here. First, the work is advancing human understanding of quantum physics. Second, the human brain seems to be more capable of understanding quantum physics than we previously thought.

Finally – and germane to the point above – the physicists on the team who designed the game…found it extremely challenging. Being a physicist, or having expertise in physics, didn’t determine someone’s ability to succeed at this quantum game. Gamers, on the other hand, when their own type of expertise, did better than the physicists and the computer models combined.

Fitness Landscapes and Probability Distributions

Imagine trying to solve a problem of unknown complexity. You have to start somewhere, so you try a solution more or less at random. If you’re lucky, you know enough about the situation to start with an educated guess.

Regardless of how successful – or unsuccessful – your attempt was, you learn something about the best way to tackle the problem.

Next time you do a little bit better.

Perhaps there are other people around you trying to solve the same or similar problems. You can learn from their efforts as well.

Eventually you converge on what seems like the best possible solution, and then, problem solved, you keep deploying the same solution.

In several disciplines, this process can be described as exploring a fitness landscape. There are optimal solutions, really bad solutions, and everything in-between. Some combination of a priori knowledge and learned exploration gives you an intuition of what the fitness looks like.

Imagine the quick calculations you do in your head when trying to figure out how long it will take you to get somewhere. If you’ve gone there before, you might have a sense of the average length of travel. If you’re familiar with an areas traffic patterns you might have a sense of how much traffic to expect, or what routes to avoid. You may also have a sense of whether it would be more socially proper to arrive a little bit late or a little bit early.

You almost effortlessly predict an optimal solution to a complex problem.

There’s a great deal of interesting research being done to understand how individuals and groups explore or exploit these complex landscapes. As a matter of simplicity in an already challenging problem, it is common to study problems for which an optimal solution is universally an optimal solution.

That is – if every person had perfect knowledge of the fitness landscape, they would each make the same normative judgements about what solutions are “good.”

For my own research interests, this is an important piece of the challenge. One’s definition of “good” or “optimal” is a crucial piece of what policy solutions one might seek – or, more generally, how one might interpret the “fitness landscape.”

If two or more people are exploring the same landscape but have different normative judgements as to what is optimal, this poses a huge challenge.

One solution to this challenge is to hope for the convergence of opinion – so a group may not start with normative agreement on the fitness landscape, but with good deliberation they will come to collective agreement eventually.

There’s a great deal of social science research looking at how consensus forms in groups, with an eye towards possible biases and poorly-formed consensus. Does a group agree with the loudest voice in the room? Does it converge on whatever idea was most popular before discussion began? Did it give full attention and weight to all possible alternative before a final decision was reached?

Yet, on top of all the things that could go wrong in consensus forming, one of the most disconcerting thoughts is that such ideal consensus is not possible at all.

Examining such a question means understanding just what causes a person’s opinion to form in they first place – an understanding, I’m afraid, we are quite far from.

Some opinions may be formed on the spot, with no clear reason why. Other opinions may have cemented through some past series of experiences.

But here’s a thought experiment – imagine dozens of clones of the same person, each starting their life in an identical setting. With every event they encounter – on whatever time scale you prefer – the effect of that experience on them is given by some probability distribution.

If one were so inclined, you could build your favorite sense of nature vs. nurture into this probability distribution.

After some series of n events, the people produced…would be different?

If that were to be the case – even among a starting group with all the same initial conditions, it would pose a significant challenge to the idea of consensus, and ultimately would require some method to make sense of overlapping conceptions of a similar fitness landscape.