Shane (09:16)
the, writing in itself leads to.

unknown, leading to an unknown destination is kind cool. how would my character respond to a situation like this versus how does the character need to respond to get to a fixed endpoint?

Rui (09:32)
Mm.

Shane (09:32)
lot of rambling and windy and sometimes loop -de -looping stories

Rui (09:39)
have you ever as you were writing and you stumble upon a piece where it's very surprising to you either intellectually or emotionally? I would be actually very interested in the piece if you ever got this indescribable feeling or you just start crying.

Shane (09:57)
something that I have written or something that somebody else has written.

Rui (10:00)
something that while you're writing.

Shane (10:02)
think so, although I have definitely, I do have strong feelings about like some of the characters that

grown fond of, but I don't think that I have the point of.

sort of aha moment or some sort of emotional.

for me or any of the characters, but maybe that's something to shoot for. Because I've definitely been very attached to other people's characters and had strong emotional reactions to developments in other books, whether it's a character being killed off or a character finding, achieving some goal of theirs or some sort of the, some sort of relationship between characters. That definitely all happens. I wonder if it's hard to, harder to do in your own writing or I'm just not there yet.

Because, know, some of it's the surprise factor, some of it's the buildup and the journey. And if you're, you're the one creating the journey, then you sort of know where it's going. Maybe that's a cop out there.

Rui (10:59)
Hmm.

Yeah, I would be very curious if one day you find yourself in a very surprising emotional land, because that might be a key to unlock some stuff that you don't even know. But that's also extremely scary

I'm pretty sure I don't read as much as you do. But when I do sometimes read really good fictions, I find that the reason why the book is considered as classic, is because this author has extremely deep insight and understanding of human nature.

how do you usually deepen your understanding of human nature?

Shane (11:41)
I that's part of, that goes back to one of your questions from earlier, which was was getting in the way? And it's partly, I guess, is just like reading other stuff and being like, there's no way I could actually up with these insights about human character or a of mine could some large swath of human society in some way that helps people gain clarity. of my favorite books of all time is by

of the worst people of all time is Orson Scott Collard, who wrote Ender's Game. And he's just one of the best character writers of to think that of my characters that I have thrown down on the page could ever someday rival Ender or Bean or any of the other great characters in this series is to believe for me. that's, I guess, product of a lot of work and a lot of practice that I not put in.

But yeah, those characters things about people and about the power of, for example, the power of empathy and the power of understanding companions. And mine are always going on fun adventures and having good times once telling some stories. It doesn't even feel like they should be mentioned in the same sentence.

you read Ender's Game? should definitely read Ender's Game. Arguably the best science fiction book of all time. like I said, he's a terrible person, homophobic, among other things.

at the end, he was talking about his writing process, about how the way he thinks about characters is that every character in his story is a different person when they interact with each other character in his story. So not only do you have to have, let's say, five characters, but you have to have five choose two characters. There's the math. Because every character interacts with each other character in a unique way. seems like so much work. Are you kidding me?

part of the reason that they come off as authentic, seeming characters, like these are real people, these are interesting people, these are people you care about, is because they are developed with such care and thought and precision. And it's very intimidating.

Rui (13:37)
People who are awful based on a lot of moral ethics judgment that we have, but they create great artwork that somehow peers through humanity, and resonate with people. how do you differentiate? Like, they're actually awful people if they can, you know, resonate with the rest of us so strongly.

Shane (13:42)
Mm

Okay, yeah, definitely called me out in that, probably shouldn't have been making sweeping statements, Scott Card is on the record as being a homophobe. And for me, that makes you a lower quality individual. That being said, to your earlier question, his books remain some of my favorites and that's not going to change. And so I do.

I am in some case able to separate the art from the artist when it is advantageous for me.

Rui (14:26)
You

Shane (14:27)
Another example of this is often, because we haven't talked about sports enough often as a fan of baseball or football players, you run into the situation where the players on your favorite team are not necessarily characters in their own right. So you have the question of how can I root for the team if I don't like the players on the team?

for example, there was a player had an issue with domestic violence, again, making you a less than upstanding person. Some people might say a bad person. And then he was traded to my favorite team and he used to be one of my favorite players. And now I'm like, can I still root for this person who beat his significant other? And the answer is no, you can't.

but you still root for the team that he's on, it makes it very hazy and it's uncomfortable. And the easy answer is you just have to stop pretending that the, it's helpful to pretend that they're not real people, that they're just like video game characters or book characters, because real people are complicated and they get in the way of enjoying books or sports or many other things.

Rui (15:22)
Thank

Right,

I'm gonna switch the topic a little bit. I really enjoyed the chat about art and writing, but I'm just gonna venture a little bit maybe into your sports. As a transition point of like sport, your sports internship and full -time job at the major league baseball.

Shane (15:41)
Now we have to talk about math and science or sports. Sports is good too.

It's been too liberal arts for too long.

Yes, so my first job coming out of college was working at Major League Baseball at the office of the commissioner in New York.

didn't really know what to look, even look for when I was starting the job search process. feel comfortable thinking about the future of it often. And so a lot of times I find myself having to sort of improvise. And so as I was doing job searching, I was like, well, some people always say that you have to love what you do. And I love sports. So this was an obvious answer. And I to run into a useful connection there.

And ended up interning there for a summer and then turning it into a full -time job for about two years. And it was a great first job for sure. I had worked on a lot of really fun projects. our, group initially started as kind of a long -term strategic planning task force where we were looking at different ways of improving. engagement in the game, whether that is more people showing up to games, more people watching on TV or just.

changing the way that people interact with baseball in a variety of creative ways, most of which were terrible ideas. And it was then our job to quantitatively show that those were terrible ideas. Although not all of them are terrible ideas. So for example, two years ago now they changed the schedule so that every team plays every other team every year, which was not always the case. This was ostentatly in the name of competitive.

Rui (17:16)
Hahaha

Shane (17:32)
balance so that it's more fair. But in reality, it was a cash grab like everything else. They were trying to produce more interesting games not repeating games as often. And therefore more people would, would tune in and show up. And people did for that and a variety of other reasons. was not just a one -to -one effect, but we showed that we showed in our analysis that there would be a slight increase in attendance.

implemented the policy and the attendance did increase. So that was cool.

Rui (18:00)
I see, don't you think this is just a common sense to rotate the content?

Shane (18:06)
Common sense is not necessarily the mode of decision -making in baseball, especially baseball is a sport with a long history. Professional baseball originated in the 1860s or so. And it's a sport that is mostly followed by older men, older men being very stubborn and resistant to change. I say this as a young...

youngish man who is also stubborn and resistant to change. And part of that is my baseball fandom and my baseball upbringing. But if you try to change other sports, it goes a lot easier than it goes if you try to change baseball just because the tradition, the tradition aspect of it is so strong. And so it wasn't even until 1996, I want to say somewhere in that range that even the two halves of the leagues, the American league and the national league started playing games against one another.

And was a huge change at the time. People hated it. And now people assume it's normal, but it took about 30 years to get there. So yes, common sense is that you should put the best matchups on the field and have all your teams play all the other teams like, like the other sports do. But then baseball and baseball fandom gets in the way of that and a lot of other things. And so it took some, took some hard selling. was not my problem. I was just the one who built, built some models to say that, yeah, this would probably.

Rui (19:06)
Hahaha

Shane (19:27)
lead to some increased attendance. analysis was like a very early exploration and then a lot of other teams did their work and made it possible to actually make that schedule change. But I like to at least say that were one of the first, first groups to explore that change.

Rui (19:42)
I never thought of you as someone who would do marketing related work.

Shane (19:49)
Yeah. So after a lovely year of doing long -term strategic planning, real life got in the way and the company, the league brought in McKinsey, I think to reorganize the entire company. And so our little team got merged in with the marketing analytics team, which also had already had a large group of data scientists and analysts on the team. And so why have two separate data teams when you could just have one big data team?

And they sold it to me as like a growth opportunity. marketing is a really important skill in today's business world. Everyone should have this, have this chance and it'll get you to get the opportunity to work with a lot of big data. And there was, there was a ton of data, that's for sure. ⁓ but I very quickly myself bouncing off it. ⁓ you know, putting a lot of time and effort, a project where the, end goal was putting, ⁓

⁓ changing the order of like search results on Google or putting a spam email in someone's inbox or changing whether changing the strategy of the people posting on Instagram not important to me.

so I left

Rui (20:52)
when you say sports analytics, I would think about, you actually look at the stats of the players and the angle of the bats you know, all the fancy stuff you can do with the players and team strategy. Was that you had in mind? anyone?

on the team doing that type of job.

Shane (21:11)
So that was the goal, right? state of the art analytics going on right now in sports as being for the most part, especially in baseball done by the teams and their employees. And so each team has a large and growing analytics department people like you and me building out systems and optimization models and all that good stuff to enhance the process of putting.

players on the field to get the best possible team. And there's so much that can go that goes into this now, even over the last five years, since I trying to do this stuff. the amount of science that has gone to like where your fingers go on the ball and what angle you're releasing the ball changes how the ball is spinning by a marginal fractional degree. And that changes how effective it is at fooling hitters.

so much going on. So that was always the goal was to get into that world. ⁓ and I sent some applications out, had some interviews. got to interview in Yankee stadium. I got to interview in Dodger stadium. Some, some nice bragging. It was very cool. I didn't get either of those jobs, but I thought the, the office, the commissioner job that I ended up

Rui (22:15)
You

Shane (22:19)
did getting would be a good launch into, into that world.

Rui (22:22)
I'm just imagining, let's say, I don't know, 50 years down the road, and every baseball team has a super stellar data analytics team attached to them. And the players basically become human robots. We're telling them exactly how you position your fingers, your toes, even. And that's kind of weird to think about. I think potentially at that point in time, the great players are the ones who can defy the rationalities and still

make a great win despite whatever the statistics say. At least that's my imagination.

Shane (22:58)
Yeah, I of the holdups that I ended up getting was as you do more research into these analysis teams are doing, what they're focusing on, you can see that more and more work is going into smaller and smaller marginal gains, where the low hanging fruit has been achieved, right? We now have different statistics than we had 20 years ago, and we know we're looking for different things, and there's different ways of...

of measuring skills than the word before. And that's all hugely important, but now all the teams have that. And so now you go to the next level down, you have to dig deeper and dig deeper and dig deeper. And so it takes more and more effort to make smaller and smaller gains. And that sort of was one of the holdups is I like, do I really want to spend all this time and effort on a fraction of 1 % of a win a made up step, but, ⁓ and the answer was no, is like, I would like to be focusing on slightly bigger.

things that feel use of my time among other things.

Rui (23:55)
Yeah, I don't know.

at that point in time, maybe what you should consider is to assemble a team of robotic baseball players and you just program all of those predictions and let the adversarial models just play against each other.

Shane (24:10)
I sincerely believe that that is the future of the NFL for football. I don't know if we'll get to that point in baseball, but at some point, football players are gonna wanna stop getting concussions and we'll end up with robotic football. That way for the best.

Rui (24:22)
or a cyborg,

Shane (24:23)
if you could also just like have the human players and then replace their brains with robots after they get broken, that would work too.

Rui (24:30)
Okay, that's a little bit dark. after grad school, you took a turn into the environmental data science role. Could you please maybe show a little bit more about that realm of work?

Shane (24:46)
Yeah.

I was reaching the end of grad school and coming back to this decision point, and like I said, I don't really like planning or making decisions. So I reached these points and forced them to make decisions. A couple of different things happened. Number one was So at that point during COVID, all of the sports teams were pretending that they were broke. And so were not hiring. And so it forced me to think about other things. And I had conversations with our

career advisor, Tracy, and she was like, what do want to do? And I was like, I don't know. I thought I was going to back into sports. ⁓ but I ended up going with was like, I knew something that made me really upset. And that was working for stuff that I felt didn't matter. And so what does matter? And would that make me happier? And so I had a lot of good friends at that point and who still working in climate and environmental fields. And that's something going back to our conversation about the outdoors, like it's a really

The two things are very linked. People who feel passionately about spending time outdoors tend to also feel passionately about environmental issues, I'm no exception to that. And so it made a lot of sense as something to care about.

Rui (25:55)
That's awesome.

maybe you share a project story.

Shane (25:59)
projects I was most proud of were building optimization models for certain sectors of the environment to represent certain sectors of the economy. So the two that I spent the most time on were the electric sector and the industrial sector and specifically the iron and steel manufacturing the industrial sector. So electricity and iron and steel manufacturing are both sectors that produce a lot of carbon emissions.

and other types of emissions and areas where we know to time to reduce carbon emissions

so the EPA, the Department of Energy and the of the government been, and will continue to be regulating these sectors with the, the goal of producing policies that effectively reduce emissions here. And you need tools like this to essentially help you craft those policies to like policies will work, which ones will not. If there are multiple that work, which ones are better.

So as a concrete example, the Inflation Reduction Act was passed in 2022 by the Biden administration and is the biggest climate law that has ever been passed by any country ever. It's a big deal. I feel like it's my duty to communicate that to your audience. It was a big deal. We were not involved in the passage of that law, but people who were involved in the passage of that law use the same kind of models that we work with.

to essentially say, here's which provisions are important for this law. So as part of the lawmaking process, you propose something and then real life comes and you have to pull things out. So you can't have everything you want. And so they would just go back and forth to these modeling teams. And if we pulled out provision A or provision B,

one should we include to lead to the biggest reductions in carbon? And they'd run the models and back with an answer. it's like, you should keep provision B. You can lose provision A. And so what ended up happening was the best they could do to reduce as much carbon pollution as possible while satisfying all of these political

the reason I use that as an example is because I'm very excited to be transitioning jobs and the company that I am going to start working for next month is the company that built those models and was working with Biden administration to make those decisions, as part of the inflation reduction act process. And hopefully as additional laws.

and regulations and climate rules get passed, we'll be a larger part of that process. And the tools that we build will continue to be useful.

Rui (28:30)
That ice awesome dude. I'm so excited for you. Quick question.

Shane (28:32)
That's the goal at least.

Thank you.

Rui (28:40)
Could you please give me an example for what is a provision? What does a provision look like?

Shane (28:47)
⁓ sure. A really easy example is if you buy an electric vehicle now, you get a $7 ,500 tax credit on that. it is incentivizing people to buy electric cars instead of gas cars. Similarly, the IRA, Inflation Reduction Act, was very much incentive heavy. It's not like rules. It's generally guiding people.

through giving them money so they're allowed incentives to build out solar and wind power plants instead of gas and coal power plants as well, some of the big ones. And those are the ones that were kept in because these models, these analytical tools, were saying that those were the most effective strategies among others proposed.

Rui (29:22)
Okay.

And how do you really figure out what kind of provisions that you can take out and which ones to keep there? Do you do simulations? Like what kind of analytics techniques you ended up using?

Shane (29:44)
Yeah, these are simulation models. so generally speaking, they're optimization models that are representing either a certain sector or the entire economy. And so you'll run the economy with a certain law or provision activated. You'll run the economy without the same thing activated, and you'll see how the carbon emissions from that sector change. You'll also simultaneously, depending on the model, be able to see the changes in prices.

changes in quantities of, for example, electricity produced from wind or solar or from fossil plants. so depending on the tool, you can get a lot of valuable information about how these different decisions are going to affect you and me and the economy at large in an ideal case. there's always that caveat, right? These are very high level simplifications of very complex systems.

And

Rui (30:42)
so when you were doing this type of simulations, do you examine quite a bit of the assumptions? Or in general, assumptions are given, and then you're just gonna play with the constraints on top of it.

Shane (30:59)
⁓ it's not a, not an easy yes or no answer. There's a lot of, a lot of assumptions that get involved in a lot of us things that are just sort of ignored. One of the, one of the ones that I like to use as an example is if you read any analysis about the inflation reduction act, it will talk about how much carbon is going to get reduced from the electric sector if we, if we shift to wind and solar, right? That's, that's a given.

If you produce more electricity from wind and solar and less electricity from burning coal and burning gas, then there are going to be less emissions. And all of the models show this. A major headache that we have, we as a society have to deal with over the next, really now is electricity transmission, which means connecting sources of electricity to demand centers for electricity, AKA like our houses.

And so and solar have to be built in very specific places, AKA places where you can have sunlight and have wind. And that's often not anywhere close to where electricity is being demanded. And so transmission is the wires and stuff that you see everywhere that's connecting these things. And so a lot of these models will basically say like, we're assuming that transmission gets built. And if it doesn't, none of this works.

Rui (32:25)
And.

Shane (32:25)
And

it's in there, it's there, it's in the small print, but it's not easy. And there's absolutely no guarantee that any of the necessary infrastructure does get built up the next 20, 30, 40 years or sooner, preferably. That's a big, that's a big assumption.

Rui (32:37)
Right.

do you see that this will be a government -owned infrastructure project for the US, or this will be a private sector sort of effort?

Shane (32:52)
I am definitely not the person who should be answering this question, but it's going to be a bit of both. It's an interaction between the government that regulates private and investor owned utilities. And the main question essentially as always is who's going to pay for these necessary improvements. Usually it's us, the consumers. the utilities, like all big companies have a lot of power in in the policy making.

and also in the pricing room.

⁓ and so it's a, back and forth of how much we can, how much we can get built without, cause you know, don't want public opinion to turn against transmission projects, right? If people see it as like transmission or, or solar and wind is like raising their prices, which some of the models show will happen in some of the models do not. Then suddenly you lose all this momentum that we've gotten and like things slow down. We really can't afford that to happen.

so getting this stuff to be built while keeping prices at a reasonable level and wrangling utilities to actually pay for these improvements under their own dime is not an easy problem. And I'd love to offer optimistic solutions to that, but, we're still working on it.

Rui (34:11)
Wow, okay. There's a lot of pieces going into it. I mean, even just working within a tech company just to get specific project done is already like a huge headache. I cannot imagine the amount of hurdle and agreement alignment that this type of project needs to get to. And also you're touching a lot of people's interest right?

Shane (34:23)
Mm

Rui (34:34)
I'm actually just curious a little bit about the tea. What kind of political constraints you ended up running into that you really don't want to deal with, but you kind of have to just make adjustments because of that.

Shane (34:51)
have good juicy at this point. Like we've been pretty insulated from a lot of the politics so far, at least in my old job. I don't know if the same will be true at my new job, but I'll get back to you if there's any fun stories about running run -ins with politicians or lobbyists or anything.

Rui (35:08)
Fair, fair enough. I guess what keeps you going? The answer is sort of obvious, but I still want to hear it.

Shane (35:16)
What do you think the answer is?

Rui (35:17)
you care, you just care deeply.

Shane (35:20)
what I was hoping, you know, would be the motivation, right? I was at the baseball job doing the digital marketing, I was like, I need to care more and that will make it easier to go to work eight hours a day, five days a week, most of the days per year. And it definitely has, but it hasn't gotten me all the way there. Like work is still a grind. And there were days that I was like, this doesn't feel like it matters at all. So I'm still working, still trying to figure it out, hoping the next job will be better than the last, look for incremental improvement. But.

Yes, I think a certain amount of like large scale motivation has helped me be more excited about what I'm doing and feel like I am not wasting my time as much,

Rui (36:02)
every choice has its own cost and suffering attached to it. It's just a matter of what type of suffering you're willing to choose, right? You care about this issue enough to stay in the game right now.