Lean In Review

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A good bordering on great book, underrated as a result of overly contentious discussions about Sandberg’s privilege. This is true even given her recent comments after her husband’s death. I wonder how many critics actually read the book. The book is clearly meant for a particular audience, but isn’t everything?

Take this section from the introduction, for example.

Some, especially other women in business, have cautioned me about speaking out publicly on these issues. When I have spoken out anyway, several of my comments have upset people of both genders. I know some believe that by focusing on what women can change themselves — pressing them to lean in — it seems like I am letting our institutions off the hook. Or even worse, they accuse me of blaming the victim. Far from blaming the victim, I believe that female leaders are key to the solution. Some critics will also point out that it is much easier for me to lean in, since my financial resources allow me to afford any help I need. My intention is to offer advice that would have been useful to me long before I had heard of Google or Facebook and that will resonate with women in a broad range of circumstances…

I am also acutely aware that the vast majority of women are struggling to make ends meet and take care of their families. Parts of this book will be most relevant to women fortunate enough to have choices about how much and when and where to work; other parts apply to situations that women face in every workplace, within every community, and in every home. If we can succeed in adding more female voices at the highest levels, we will expand opportunities and extend fairer treatment to all.

Overall there was a lot of clear thinking for both men and women in the workplace and interesting data. I especially like the detailed sections on the challenges of childbirth and childcare, on being more assertive in the workplace, and on learning not to be too hard on one’s self.

Durant to the Warriors

In breaking news (that I myself did not break) Kevin Durant has signed with the Golden State Warriors.

Thoughts

1. Haters gonna hate.
The haters have already come out en mass calling Durant a traitor, saying that his exit is worse than The Decision (LeBron James’s announcement that he was leaving Cleveland to go to the Miami Heat). I always find this point of view strange. Try applying the logic to anything else in life and it sounds absurd: You graduate college. You don’t get to choose where you get a job, instead you are “drafted” by Microsoft. You try diligently for nine years to overtake Apple. You fail. Your teammates are great, but an even better team awaits at Facebook that has an even better chance of overtaking Apple as the world’s top technology company. You decide to leave. Who will call you a traitor?

On the other hand there is this:

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2. Sports matter.
The thought experiment above is just a reframing of the idea that sports really matter to people. Their brains turn off, tribal affiliation and emotions kick in. I always find it silly when non-sports fans deride enthusiasm toward sport and suggest we devote that energy to “something that matters.” Sports matter. As much as anything in our society sports matter. To millions (billions?) of people around the world a fan’s home team is a part of their identity and rooting for another team is as unimaginable as adopting another family. In a very real sense their home team and their home team’s fans are a part of their family.

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3. The best ever.
The Warriors’ starting lineup is now considered the best ever. Last year — without Durant — they won 73 games, the most in NBA history! More than teams that included Michael Jordan, Magic Johnson, LeBron James. Durant is the second best player of his generation behind LeBron and one of the best players of all time. Steph Curry is the best player of his generation. The Warriors already had arguably the two best 3-point shooters of all time in Curry and Thompson. Now they have three of the top — what? Maybe 10 or 20 — shooters of all time! Draymond Green is one of the best all around players in the league, perhaps of all time by the time he retires (he finished second in NBA Defensive Player of the Year Award voting in 2016 and second in triple doubles). Three of the Warriors’ new starting five received regular season MVP votes last year. Between Durant and Curry they’ve won the past three regular season MVPs. Iguodala came in second for the NBA’s Sixth Man Award this year (and won the Finals MVP a year ago). Has any team like that ever been assembled?  The Warriors’ 12-man lineup includes many solid roll players so even if you replace Iguodala with Bogut or Livingston you still create the greatest lineup ever (Update: Bogut will likely be traded to clear up cap space for Durant).

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4. But remember…
The Championship was handed to the Miami Heat after LeBron, Wade, and Bosh joined forces in 2010. That team went 2-2 in the Finals. An accomplishment to be sure, but it’s not like we could just pencil them in as champions every year. Remember when Howard and Nash joined the Lakers? They became a favorite to get to the finals; they didn’t even make the playoffs. Let’s not speak too soon about the success of these new Warriors.

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5. Russell Westbrook must be PISSED.
Steph Curry is one of Westbrook’s most hated foes and now Durant — the man that once called Westbrook a brother — has left to play with that foe. Ouch!

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Testing Tony Kornheiser’s Football (Soccer) Population Theory

Fans of the daily ESPN show Pardon the Interruption (PTI) will be familiar with the co-host’s frequent “Population Theory.” The theory has a few formulations; it is sometimes asserted that when two countries compete in international football the country with the larger population will win, while at other times it’s stated that the more populous country should win.

The “Population Theory” sometimes also incorporates the resources of the country. So, for example, Kornheiser recently stated that the United States should be performing better in international football both because the country has a large population, but also because it has spent a large sum of money on its football infrastructure.

I decided to test this theory by creating a dataset that combines football scores from SoccerLotto.com with population and per capita GDP data from various sources. Because of the SoccerLott.com formatting the page wasn’t easily scraped by R or copied and pasted into Excel, so a fair amount of manual work was involved. Here’s a picture of me doing that manual work to breakup this text 🙂

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The dataset included 537 international football games that took place between 30 June 2015 and 27 June 2016. The most recent game in the dataset was the shocking Iceland upset over England. The population and per capita GDP data used whatever source was available. Because official government statistics are not collected annually the exact year differs. I’ve uploaded the data into a public Dropbox folder here. Feel free to use it. R code is provided below.

Per capita GDP is perhaps the most readily available proxy for national football resources, though admittedly it’s imperfect. Football is immensely popular globally and so many poor countries may spend disproportionately large sums on developing their football programs. A more useful statistic might be average age of first football participation, but as of yet I don’t have access to this type of data.

Results

So how does Kornheiser’s theory hold up to the data? Well, Kornheiser is right…but just barely. Over the past year the more populous country has won 51.6% of the time. So if you have to guess the outcome of an international football match and all you’re given is the population of the two countries involved then you should indeed bet on the more populous country.

Of the 537 games, 81 occurred on a neutral field. More populous countries fared poorly on neutral fields, winning only 43.2% of the time. While at home the more populous country won 53.1% of their matches.

Richer countries fared even worse, losing more than half their games (53.8%). Both at home and at neutral fields they also fared poorly (winning only 45.8% and 48.1% of their matches respectively).

The best predictor of international football matches (at least in the data I had available) was whether the team was playing at home: home teams won 60.1% of the time.

To look more closely at population and winning I plotted teams that had played more than three international matches in the past year against their population. There were 410 total games that met this criteria. I also plotted a linear trend line in red, which as the figures above suggest, slopes upward ever so slightly.

population_vs_winning_perct.png

Although 527 games is a lot, it’s only a single year’s worth of data. It may be possible that this year was an anomaly and I’m working on collecting a larger set of data. As the chart above suggests many countries have a population around 100 million or less and so it would perhaps be surprising if countries with a few million more or fewer people had significantly different outcomes in their matches. But we can test this too…

When two countries whose population difference is less than 1 million play against one another the more populous country actually losses 55.9% of the time. When two countries are separated by less than 5 million people the more populous country wins slightly more than random chance with a winning percentage of 52.1%. But large population differences (greater than 50 million inhabitants) does not translate into more victories. They win just 51.2% of the time. So perhaps surprisingly the small sample of data I have suggests that population differences matter more when the differences are smaller (of course this could be spurious).

This can be further seen below in a slightly different view of the chart above that exchanges the axes and limits teams to those countries with less than 100 million people.

population_vs_winning_perct_smaller.png

R code provided below:

###################################################################################################
# James McCammon
# International Football and Population Analysis
# 7/1/2016
# Version 1.0
###################################################################################################
 
# Import Data
setwd("~/Soccer Data")
soccer_data = read.csv('soccer_data.csv', header=TRUE, stringsAsFactors=FALSE)
population_data = read.csv('population.csv', header=TRUE, stringsAsFactors=FALSE)
 
 
################################################################################################
# Calculate summary data
################################################################################################
# Subset home field and neutral field games
nuetral_field = subset(soccer_data, Neutral=='Yes')
home_field = subset(soccer_data, Neutral=='No')
 
# Calculate % that larger country won
(sum(soccer_data[['Bigger.Country.Won']])/nrow(soccer_data)) * 100
# What about at neutral field?
(sum(nuetral_field[['Bigger.Country.Won']])/nrow(nuetral_field)) * 100
# What about at a home field?
(sum(home_field[['Bigger.Country.Won']])/nrow(home_field)) * 100
 
# Calculate % that richer country won
(sum(soccer_data[['Richer.Country.Won']])/nrow(soccer_data)) * 100
# What about at neutral field?
(sum(nuetral_field[['Richer.Country.Won']])/nrow(nuetral_field)) * 100
# What about at a home field?
(sum(home_field[['Richer.Country.Won']])/nrow(home_field)) * 100
 
# Calculate home field advantage
home_field_winner = subset(home_field, !is.na(Winner))
(sum(home_field_winner[['Home.Team']] == home_field_winner[['Winner']])/nrow(home_field_winner)) * 100
 
# Calculate % that larger country won when pop diff is less than 1 million
ulatra_small_pop_diff_mathes = subset(soccer_data, abs(Home.Team.Population - Away.Team.Population) < 1000000)
(sum(ulatra_small_pop_diff_mathes[['Bigger.Country.Won']])/nrow(ulatra_small_pop_diff_mathes)) * 100
#Calculate % that larger country won when pop diff is less than 5 million
small_pop_diff_mathes = subset(soccer_data, abs(Home.Team.Population - Away.Team.Population) < 5000000)
(sum(small_pop_diff_mathes[['Bigger.Country.Won']])/nrow(small_pop_diff_mathes)) * 100
#Calculate % that larger country won when pop diff is larger than 50 million
big_pop_diff_mathes = subset(soccer_data, abs(Home.Team.Population - Away.Team.Population) > 50000000)
(sum(big_pop_diff_mathes[['Bigger.Country.Won']])/nrow(big_pop_diff_mathes)) * 100
 
 
################################################################################################
# Chart winning percentage vs. population
################################################################################################
library(dplyr)
library(reshape2)
 
base_data = 
  soccer_data %>%
  filter(!is.na(Winner)) %>%
  select(Home.Team, Away.Team, Winner) %>%
  melt(id.vars = c('Winner'), value.name='Team')
 
games_played = 
  base_data %>%
  group_by(Team) %>%
  summarize(Games.Played = n())
 
games_won = 
  base_data %>%
  mutate(Result = ifelse(Team == Winner,1,0)) %>%
  group_by(Team) %>%
  summarise(Games.Won = sum(Result))
 
team_results = 
  merge(games_won, games_played, by='Team') %>%
  filter(Games.Played > 2) %>%
  mutate(Win.Perct = Games.Won/Games.Played)
 
team_results = merge(team_results, population_data, by='Team')
 
# Plot all countries
library(ggplot2)
library(ggthemes)
ggplot(team_results, aes(x=Win.Perct, y=Population)) +
  geom_point(size=3, color='#4EB7CD') +
  geom_smooth(method='lm', se=FALSE, color='#FF6B6B', size=.75, alpha=.7) +
  theme_fivethirtyeight() +
  theme(axis.title=element_text(size=14)) +
  scale_y_continuous(labels = scales::comma) +
  xlab('Winning Percentage') +
  ylab('Population') +
  ggtitle(expression(atop('International Soccer Results Since June 2015', 
                     atop(italic('Teams With Three or More Games Played (410 Total Games)'), ""))))
ggsave('population_vs_winning_perct.png')
 
# Plot countries smaller than 100 million
ggplot(subset(team_results,Population<100000000), aes(y=Win.Perct, x=Population)) +
  geom_point(size=3, color='#4EB7CD') +
  geom_smooth(method='lm', se=FALSE, color='#FF6B6B', size=.75, alpha=.7) +
  theme_fivethirtyeight() +
  theme(axis.title=element_text(size=14)) +
  scale_x_continuous(labels = scales::comma) +
  ylab('Winning Percentage') +
  xlab('Population') +
  ggtitle(expression(atop('International Soccer Results Since June 2015', 
                          atop(italic('Excluding Countries with a Population Greater than 100 Million'), ""))))
ggsave('population_vs_winning_perct_smaller.png')

Created by Pretty R at inside-R.org

Poor Design Choice by Apple

Why is the “Add to Dictionary” option so close to the actual spell check corrections? So many times my hand slips, goes a little too far, and adds a misspelled word to the computer’s dictionary. Cmd + Z does not seem to undo this misstep. And the internet tells me it is quite hard to go in and change the computer’s dictionary to correct the mistake. What’s worse is that after the word is added to the dictionary the red underline indicating a mistype disappears so that you don’t know if you corrected the word or accidentally added it to the dictionary. I then have to copy and paste the word into Google to determine which of the two cases occurred since Google has a quite robust spell check feature.

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What is the Best Characteristic for Business Leaders?

There are many candidates, but perhaps something like: “Strongly stated weak priors” (priors are a Bayesian statistics concept that roughly translates to: “How strongly do you believe what you believe based on prior evidence?”). Leaders must be flexible enough to quickly change their mind in the face of contradictory information or the emergence of a better plan from colleagues, but must sound confident so that subordinates will feel motivated to undertake the given direction and not too often challenge the leader. A leader that has weakly stated weak priors will too often induce endless vacillation. But making a wrong decision and gaining momentum is often a better alternative than making no decision at all or to making a series of quick direction changes whenever a new piece of information emerges. As Gary Vaynerchuk said just yesterday on his Snapchat channel, it’s better to make a wrong decision and then adapt to it than to make a slow decision. A leader with strong priors will too often ignore contradictory evidence and lead the business down a wrong path without the flexibility to adapt.

Or perhaps an even better candidate for a characteristic is “Strongly stated weak priors wrapped around a core of genuinely strongly held beliefs.” As I stated in my last post many of the most famous business leaders in recent memory likely held as their most worthwhile talent shaping the world to their vision whether or not that vision had any real merit to begin with. But during the long road to bringing their vision to light they had to both lead and follow the recommendations of their lieutenants (as long as the lieutenants views didn’t contradict one of their strongly held beliefs.”)

My Answer to Peter Thiel’s Question

Here is my answer to Peter Thiel’s now famous appeal to entrepreneurs to “tell me something that’s true that almost nobody agrees with.” Although it’s not explicitly stated in the question if you read Peter’s work it’s probably better if the assertion is forward-looking.

My Answer:
Most people think the future of business is about big data, but the future of business is about simply heuristics.

Why:
The answer is simple. At the highest level business has always been about simple heuristics. If you read any of the biographies about prominent business leaders of the past, say, 30 years – Jobs, Gates, Bezos, Musk – what stands out is that they follow their intuition. Often the intuition goes against all prevailing signals and their real talent is not seeing the future, but rather stubbornly creating it, often at great personal, and sometimes professional, expense.

The rule of heuristics often holds true for entrepreneurs as this quote below from the New Yorker’s profile of Mark Andreessen demonstrates (notice that both the old and new model of startup funding use heuristics):

When a startup is just an idea and a few employees, it looks for seed-round funding. When it has a product that early adopters like—or when it’s run through its seed-round money—it tries to raise an A round. Once the product catches on, it’s time for a B round, and on the rounds go. Most V.C.s contemplating an investment in one of these early rounds consider the same factors. “The bottom seventy per cent of V.C.s just go down a checklist,” Jordan Cooper, a New York entrepreneur and V.C., said. “Monthly recurring revenue? Founder with experience? Good sales pipeline? X per cent of month-over-month growth?” V.C.s also pattern-match. If the kids are into Snapchat, fund things like it: Yik Yak, Streetchat, ooVoo. Or, at a slightly deeper level, if two dropouts from Stanford’s computer-science Ph.D. program created Google, fund more Stanford C.S.P. dropouts, because they blend superior capacity with monetizable dissatisfaction.

Venture capitalists with a knack for the 1,000x know that true innovations don’t follow a pattern. The future is always stranger than we expect: mobile phones and the Internet, not flying cars. Doug Leone, one of the leaders of Sequoia Capital, by consensus Silicon Valley’s top firm, said, “The biggest outcomes come when you break your previous mental model. The black-swan events of the past forty years—the PC, the router, the Internet, the iPhone—nobody had theses around those. So what’s useful to us is having Dumbo ears.”* A great V.C. keeps his ears pricked for a disturbing story with the elements of a fairy tale. This tale begins in another age (which happens to be the future), and features a lowborn hero who knows a secret from his hardscrabble experience. The hero encounters royalty (the V.C.s) who test him, and he harnesses magic (technology) to prevail. The tale ends in heaping treasure chests for all, borne home on the unicorn’s back.

This doesn’t mean big data won’t have an impact on business, but that it’s not the future of business. When to fund big data projects and the collection of the massive amounts of data necessary to feed them, when to replace or augment existing business systems with big data systems, when big data is overkill and unnecessary, when data needs an injection of personal intuition, when business questions are formatted in a way that’s difficult for computer’s to understand, and many other questions hinge on the decisions of business leaders, which will inevitably are made using simple heuristics. Output from big data is just one more source of information that will be leveraged by business decision makers using simple heuristics.

You may get the impression that I’ve made the argument that almost no one believes that big data is the future of business. But alas any amount of magazine, blog, or newspaper reading, or even a large portion of listening to business leaders, will show you that my view is decidedly in the minority. Business leaders are using simple heuristics in their approach to big data even if they do not always realize it.

Things I Do and Don’t Hear (in Seattle)

In the category of “signs of mood affiliation” here is a short list of things I do and don’t hear in Seattle:

Do hear: “Who funded that study showing GMOs were safe?”
Don’t hear: “Who funded that study showing GMOs weren’t safe?”

Do hear: “We should be believe in climate change because nearly every major scientific organization (and many individual studies) have shown it’s real and a serious problem.”
Don’t hear: “We should believe in the efficacy of GMOs because nearly every major scientific organization (and many individual studies) have shown both their safety and benefits.”

Do hear: “Are you aware of the methodological and measurement problems of GDP?”
Don’t hear: “Are you aware of the methodological and measurement problems of the inequality data?”
Don’t hear: GDP isn’t perfect, but it correlates with nearly everything else we do care about and so as a single measure it’s not bad.

Do hear: “That scathing op-ed in the New York Times by the ex-Wall Street executive really hit the nail on the head.”
Don’t hear: “That scathing op-ed in the New York Times by the ex-Wall Street executive was an ‘n’ of 1. Hundreds of thousands of people work on Wall Street, surely some of them find the work challenging and rewarding.”
Do hear: “That interview with the Iraqi teenager who supported America’s invasion was an ‘n’ of 1. Surely, there are many other contrary opinions among Iraqis.”

Do hear: “You can’t trust the corporate billionaire’s position on tax reform, she’s totally biased!”
Don’t hear:
“You can’t trust the social justice activist who’s entire identity is wrapped up in opposing corporations writ large, she’s totally biased.”

Do hear: “We need to learn from and honor the traditional practices of the Tanzanian farmer.”
Don’t hear: “We need to learn from and honor the scientific techniques of the modern American farmer.”

Do hear: “Justice Antonin Scalia was a horrible person. His interpretation of the law is crazy. He’s allowing corporations to take over elections because he subscribes to corporate personhood.”
Don’t hear: “I’ve actually read Citizen’s United or any other judicial opinion/concurrence/dissent from Scalia.”
Also hear: “Why are you even quoting the bible if you haven’t read the whole thing cover to cover? Why don’t you read something before forming an opinion.”

Do hear: “Justice Antonin Scalia was a horrible person. His interpretation of the law is crazy. He’s allowing corporations to take over elections because he subscribes to corporate personhood.”
Rarely hear: “I have any knowledge at all of the law or legal history.”
Don’t hear: “I’m aware that most of the econometric work on elections shows that spending has little effect on election outcomes and since the Citizen’s United decision campaign spending has actually gone down.”

Do hear: “Supreme court justices on the right are obviously biased about gay marriage and should recuse themselves.”
Don’t hear: “Ruth Bader Ginsburg presided over a same-sex wedding and should recuse herself in gay marriage cases.”

Will probably hear: The person that wrote this post is obviously a republican and pro corporation/Monsanto/a bunch of other stuff and anti same-sex marriage/equality/a bunch of other stuff.
Will probably not hear: The person that wrote this post has views that do not comport with either political party, but as he lives in Seattle he tends to hear more liberal hypocrisy than conservative hypocrisy, of which there is undoubtedly mounds.

How long until we view M2F sexual reassignment surgery like we do breast implants?

This question arises because I am currently in London and for the first time noticed several women on OKCupid that had listed their gender as transsexual. This follows a pattern of more transsexuals being open about their gender reassignment in other dating apps I use.

I say that one day gender reassignment will be viewed more or less like breast implants are today. How long? My guess is two to three generations at most (about 50 years or so).

So what are the similarities?

Surgery
Of course, both involve body modification surgery.

Authenticity
Both types of body modification face criticisms of inauthenticity. We ask women if “they’re real” as if having breast implants is illegitimate. The discrimination trans women face today is much worse, but I anticipate in the future comments will slowly move in the same direction so that the main criticisms of trans women will revolve around their “fakeness” rather than the harsh and detesting discrimination they currently face.

Ambiguity
Of course, it is often not so easy to tell if a woman has had breast implants, especially when she has clothes on. More and more the story is the same with transgender women. I actually dated a woman for a several months recently and to this day do not know if she had breast implants (I know that sounds ridiculous, but it’s true; there were signs in opposing directions). Many, many times on dating apps I see a woman and I cannot tell if she is trans or just happens to have masculine features or has a preference for a particular type of makeup application. Sometimes I cannot tell at all and only know from her openness about it on her profile. I’ve been on a date with a woman that was particularly tall with a deep voice and a less curvy figure; but she also had many feminine features. I still have no idea whether she was trans.

Femininity
Both M2F sexual reassignment and breast implants are a move toward femininity, which is to say both procedures move in the same direction. Neither procedure may conform to everyone’s view of femininity, but it is at least the view of those undergoing the procedure.

Kids
In a discussion about the comparison to breast implants a friend pointed out that one difference is that transsexual women will never be able to bear children. I think eventually science will overcome this problem, transplanting more sexual organs into gender reassignment recipients, but I admit this is likely more than 50 years off (perhaps the first experimental procedures of this nature will take place in 50 years). But from a practical point of view I think men are less intent on having their own children than women, and so “settling” for adoption isn’t so bad.

So while breast implants and gender reassignment differ in that the former doesn’t affect the ability to reproduce, on the whole I view them both as compromises one may have to make: “Well, I’m not that into breast implants, but I love you so let’s be together” vs. “Well, I would have liked to have biological children, but I love you so we can adopt.” And note that of course many relationships today survive various female infertility problems with what I suspect is not to much heartache on the side of the man.

Taste
Breast implants are not for everyone. Some men don’t like the way they look or feel. Some fraction of men prefer breast implants for the same reasons. In 50 years dating a transsexual woman will be viewed the same way. A few men will have a trans fetish, as they do now, and some won’t be able to get over the aesthetic aspects. But on the whole most men will be willing to compromise and trans discrimination will be limited to whispers that “someone’s had some work done” rather than prohibition on North Carolina bathroom usage. This is not to say subtle forms of discrimination are not hurtful, but again that transsexual discrimination in 50 years will be equivalent to the whispers and judgement those with breast implants get today.

And by the way, note that many women today may or may not have masculine attributes such as sharp facial features or prominent facial hair (ex. eyebrows) and men are correspondingly attracted, or not, according to their taste. Because (especially non-Asian) trans women tend to have more masculine features men will or will not be attracted to them on the same grounds they are or are not attracted to more masculine women today.

Sex
You may be tempted to point out that the anatomical changes necessary for breast implants are far less than those necessary for gender reassignment and so people’s views about having sex with a tans woman, even in 50 years, will be different than our views about having sex with females that have had breast augmentation today.

I think many people over estimate the anatomical differences however, and they are only likely to diminish over time as surgical techniques improve. If anything, the fact that transsexual vaginas are sculpted from “scratch” may mean that men will find them more pleasurable. To this point, a trans female acquaintance once told me “trans pussy is the best pussy” (and no I’m not making that up).

And note that STDs are still an issue. Trans women have high rates of STDs. In this way the early trans movement is similar to the emergence of gay culture in the 1980s.

Not Caring
Most men probably don’t care too much one way or another whether their partner has breast implants. It may not be their preference, but for most men breast implants aren’t a deal breaker. In 50 years dating a transsexual will be viewed the same way. Rather than be a deal breaker for most men, as the situation stands today, my grandchildren (or perhaps their children) will mostly not care. The important thing will be whether they love their partner, they’ll have to make certain compromises — as we all do — to find an ideal mate and their partner’s previous gender will be one such minor compromise that may arise.

I suspect that the younger the person when they underwent surgery the easier it will be to accept a partner’s new gender. Dating someone that had reassignment surgery at 18 somehow seems easier to stomach than dating someone that had the same surgery at 40. In some ways I simply view this as the fact that we find people with their shit together more attractive and so the sooner someone “finds themself” and has gender reassignment surgery the better. We all know someone in their mid-40’s starting their 4th career and wonder what’s going on. The other component, of course, is that for whatever reason the longer someone has lived with their current gender the more legitimate it seems.

Representation
Some women are quite open about their breast implants. Others do not open up about it immediately. Some get what you might call “breast implants as a badge,” what look to me to be ridiculous and audacious implants that announce to the world what is living inside their breasts. In 50 years transsexual women will behave the same way.

In fact, various stages of openness are already starting to occur. I have met a transsexual woman who feels it is not her responsibility to inform her partner because she no longer self-identifies as a women. Many other women on dating apps like Tinder and OKCupid change their profile gender to “trans” or announce in their profile something along the lines of “I’m a post-op transsexual women. If that bothers you please keep your comments to yourself and move along.” Some trans women seem to intentionally project themselves to make it obvious they have had gender reassignment surgery.

Identity
There is a question as to whether there is a greater sense of identity wrapped up in gender reassignment. Gender is part of identity to be sure, but I’m not convinced it is drastically more important to our identity than our occupation; national, state, city, or neighborhood affiliation; physical appearance; or political ideology. For a particular type of person “political reassignment surgery” is just as drastic a change as gender reassignment surgery. More broadly, for many Americans “reassignment” away from being “a Boston cop” or “a religious Texas rancher” is no less drastic or unthinkable than M2F reassignment. This is all to say I think there is a similar sense of identity involved in both sorts of surgery where body modification is an attempt to transform one’s physical appearance to comport with the way in which one imagines themselves, and that gender transformation is not necessarily anymore intertwined with identity than many other aspects of ourselves.

A Stochastic, Interrogative Approach to Medium-Sized Project Execution

Oh hush, the title is just for fun.

I am happy to write this post as it crystallizes the approach I take to problem solving; an approach that until recently I had trouble expressing. To bring the approach into clearer relief we can take a hypothetical project example: updating a product support website.

Their Approach

The standard approach is to first assign a team to the project: a project manager, someone in charge of strategy, a client-facing manager, designers and UX reviewers, perhaps a data analyst and maybe a web engineer.

Next a framework of some kind is applied. Perhaps there are five phases, most of them sequential, each depending on the completion of some prior step. The phases in turn have many sub-steps to be completed. Team assignments are made as to who will do what. The project manager is certain to require status checks and keep track of logged hours.

The phased approach is sure to contain a structured research component, the creation of personas, a stage involving a careful UX audit, long discussions with the client about KPIs and getting really specific about their needs. An initial presentation of the phases will be made as well as follow-on presentations about how each of the sub-processes are progressing.

The approach aims to present a carefully crafted final product by moving slowly one step at a time in progression. The project phases, structure, and path are more or less laid out from the beginning. This is the approach everyone seems to take. It is taught in business schools. Questions are asked about it in interviews (“Tell me the approach you would take to solve this problem. What steps are needed?”). It is often encouraged by the client (because, after all, many of them went to business school). But it is a process I deplore. To say it does not come naturally to me is like saying flying does not come naturally to a fish.

My Approach

Instead of aiming to minimize errors by completing the project once carefully, I aim to minimize errors by completing the project quickly multiple times. Each time I learn from my mistakes and find out what is missing, gradually inducing structure as I continue to iterate. The word “stochastic” in the title wasn’t purely a flourish, it describes where I begin: anywhere I like; nor is the term “interrogative,” which describes in my process the act of putting on “the client hat” after each iteration and integrating myself about what is missing from the final output.

So how does it work?

The first step is to reduce the project down to the lowest number of dimensions, its essence. For the project above the assignment might be to “make the support site better and give me some wire frames.” And for my process that’s all the direction I need. What “better” means in practice isn’t so important at this stage, it will become apparent over time. Nor is a defined scope of work necessary, which I personally care little about. The scope becomes whatever I determine it to be as I iterate.

Next I pick a random place to start research. This might be to start looking at competitor sites, it might be reading about UX best practices, it might be examining academic articles in a business journal. Again, the starting point is stochastic. I simply start at whatever entry point seems exciting and relevant. I do some research for a while and if I don’t find anything or get tired of research I quit and start a new research thread. I take mental notes, begin inducing an organizational structure for the data I find and final client recommendations, and start thinking about what a wireframe might look like. I take screenshots and write down notes.

Then after some amount of research, whatever feels right (but in the case above probably a few days), I start to create a wireframe. I produce it as if I’m giving it to the client. As I create it my mind is filled with questions. Is addition X of the wireframe really needed? Why did I add it in the first place? What quantitative piece of data would support adding it? Can I get that data myself or does the client have that data? What if the data turns out to oppose my intuition? This begins the stage of interrogation as I aim to create a first draft product that withstands client review. As I play the role of the client I interrogate the output (the wireframe in this case). Can I truthfully say I did all the research I could in area Y to justify the removal of something currently on the support site? What are some of the themes that various aspects of the research fall under that would unify my approach? What KPIs would making these changes really drive? Do my changes work for all users or just a subset?

As my self-interrogation begins to make my output crumple and I begin to take notes about where it needs to be shored up, how I can shore it up, where I need to do more research, different conclusions I would draw depending on what various data look like, and so on. I have a list of things to tackle. What do I do? I start over. I pick a random item on the list that excites me. I again research, this time in more depth or in areas I know I’ve missed. I take new notes. A new picture is formed in my head. It’s not completely new, but rather a more robust and complete version. I remake the wireframe and re-interrogate myself. I continue the process until I have an answer for every question I can think of, until I pass my self-interrogation. Along the way I will have induced everything I need about how to structure the various aspects of the final deliverable and recommendations.

The Good and the Bad

My process is iconoclastic because I’m naturally a contrarian. I despise standard processes and opinions for no other reason than they are standard. I think it’s better to be interesting than right in almost all cases.

The process I just described did not arise from intention. It’s just the path I take naturally. My mind cannot wrap its head around what needs to be done a priori or how to do it without jumping in with two feet. Nor am I necessarily convinced I’m doing something new. The process I use is somewhat akin to rapid prototyping and has analogies to stochastic computer science optimization algorithms.

In a lot of ways I admire and am jealous of those that can clearly see the path set out before them and create the structured, phased, carefully crafted plan. But that’s not me. I used to want to imitate them, but I’m not sure I ever can. Perhaps it’s better to carve out a small space for myself using my off-beat approach.

My approach has some drawbacks of course. First, it takes a certain personality type (like mine) that combines intelligence, creativity, and impatience. To the outsider it seems as though I’ve skipped something, played fast and loose in an environment that demands caution and foresight.

Second, it’s almost impossible to use this process in a team setting. It creates a small space for a sole endeavor, which is why it doesn’t scale well. Medium-sized projects of the type I described are about as big as you can go (though often large tasks have small components which can utilize this process).

It can also be inefficient. Having to induce a new framework every time you start a project is perhaps not the most efficient way of doing things. Nor is stopping midway through a research thread and starting a new one simply because you were bored. There are very real switching costs to stopping your current task and starting a new one, only to later have to reorient yourself to the old task once interrogation is complete and you remember there are holes in your original research. And yes, you can miss things.

Because it has its own type of inefficiency my process benefits from soft deadlines. The process has no clear completion date, it’s done when you pass the self-interrogation and that can sometimes take longer than desired, so ultimatums like “Get this done by June 3rd” create artificial cycles and derail the process’s freedom.

Because it is less structured it can be hard to give status during the phase in-between draft outputs. Project managers are sure to hate it. In the standard approach we must all be comfortable all of the time, at the first sign of discomfort or ambiguity the project manager swoops in. We have to find out who is doing what, when they will be done, what resources they need, what the dependencies are, a list of next steps, ad infinitum.

But my process also has its benefits.

Just as surely as it is inefficient in certain respects, it is very efficient in others. It allows a single individual to do the work of many. In fact, it demands it. It takes a single individual with diverse thoughts crashing together in their head for the process to be truly effective.

As much as it seems unfocused from the outside, it is hyper-focused from the inside. It does everything at once — the UX audit, the data research, the competitive analysis. And as the one executing it, my mind is always on. I can work harder and longer than most people because the diversity of the approach allows me to stay fresh and hungry. I quickly begin to see the various strands of research combining in my mind and forming a unified story. I begin to see structure and patterns, which in turn combine with whatever new (or old) ideas I’ve encountered in podcasts, books, journal articles, interviews, movies, and conversations with friends; the approach has strong returns to analogy and rewards the combination of disparate thoughts and disciplines.

I’m bounded by no constraints and so can think more creatively and quickly than the alternative, structured path. The phrase “make the support site better and give me some wire frames” is enough direction because the process will figure out the details as it moves along. In this way it’s better at handling ambiguity.

My approach also better abides by Sister Corita Kent’s advice: “Do not try to create and analyze at the same time. They are different process.” The alternative approach fails in this respect because its goal is to simultaneously analyze the problem and create a path toward solution with predefined guardrails long before true creation has ever begun.

Because I don’t have to worry about process, I save up-front planning time. Because I work alone I save time on meetings and coordination. I get to a first draft product quickly, but one that is already coherent and rich with ideas; some of them are wild and may get culled out in later revisions. I can share these early versions with the client, and they are pleased with my progress. I can archive the wild ideas I’ve culled and present them coherently at the end of the project under the heading “Thinking Radically.” I go down dead-end research paths, yes; but so does the alternative approach. I get to fail quickly many times; the alternative approach can hardly afford to fail at all. And because I don’t start out with a pre-defined structure I induce only what I feel is essential rather than letting standard, business school-style frameworks dictate my path and thinking.

Just One Last Thing

Many will still view my process illegitimate, the ex-post justification of a hyperactive manic. I disagree, of course. They say that writing is thinking and I think articulating the process clearly here will only sharpen my skills at it.

The next step is to try to integrate my way of doing things into the standard business practices of my current employer. I will report back.