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[Assistant] There's nothing wrong with that. That's the way the world works. There's interests that motivate people to go forward.


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[Assistant] But our unique role here should be, at MIT, we are the honest broker. We are not trying to make it. And so, when people come into EIRs, they have to basically agree to an agreement that says they will not pay your company, they will not invest in your company, they will not try to get a consulting job with your company, they won't accept a position in your company.


[User] Please transcribe this text

[Assistant] That's the rigor that we're putting in there to make the entrepreneurship be different than any other place.


[User] Please transcribe this text

[Assistant] And that makes us different. And that's, I believe, and I hear it over and over again, that's extraordinarily valuable to you. And by the way, it's extraordinarily valuable to you because it's extraordinarily valuable to the engineers on the other side of campus who have not trusted their interactions with Sloan in the past.


[User] Please transcribe this text

[Assistant] Because they're always out trying to grab something. So can they get, go to a place, what we call the DMZ Zone, the Demilitarized Zone, where they can get honest advice and help meet up with other nice business people?


[User] Please transcribe this text

[Assistant] Where the geeks can meet nice suits who will help them, right?


[User] Please transcribe this text

[Assistant] And then the last thing is the Mensa Montis. We have an extraordinary group of academics who are doing rigorous research here, and we have practitioners. This is the mind in the hand. Everything we do, exploiting that model.


[User] Please transcribe this text

[Assistant] Those are the values by which the entrepreneurship center, and if you go through, and this is a case where you can look at it,


[User] Please transcribe this text

[Assistant] Did every single thing that we do meet these values? Do the people we hire, we just hired Elliot Cohn, does he meet all those values? As we interview the people now, do they meet all those values? There are things that we do that we deselect.


[User] Please transcribe this text

[Assistant] When a venture capitalist calls up, a very good venture capitalist says I want to come in and have office hours.


[User] Please transcribe this text

[Assistant] So look, here's the deal, you know, we'd love to do that, I'm sure the students would like it, but,


[User] Please transcribe this text

[Assistant] Were you willing to agree to this ethics agreement? I don't think you are because it means you can't invest in them. No, they're not willing to do that.


[User] Please transcribe this text

[Assistant] Because that's what they're trying to do. So then that violates our honest broker scenario. So they have to be able to go someplace else and do something else.


[User] Please transcribe this text

[Assistant] And, and, and so everything that we do, we, we measure against these. And that's an example that should be very close home to you is how to do values. Is it working?


[User] Please transcribe this text

[Assistant] I think it's very clear now. We've made a number of decisions on things that have been pretty controversial.


[User] Please transcribe this text

[Assistant] Our faculty is half what it was last year. The faculty here are very committed to the practitioner. Faculty are very committed to these values.


[User] Please transcribe this text

[Assistant] And we have a group that's coalescing that's doing very, very well. Alright, so that was, uh, sorry, that was probably longer than it should be.


[User] Please transcribe this text

[Assistant] But that I think wraps up. Is culture important? This is what I was talking about. Culture eats strategy for breakfast. And then it eats technology for lunch. And it eats products for dinner.


[User] Please transcribe this text

[Assistant] And everything else, it's, it's sooner or later. Culture is the most, Lou Gerstner, who's not the most entrepreneurial guy in the world said, when I came to IBM, I thought culture was important.


[User] Please transcribe this text

[Assistant] When I left, I realized it was the game. It was everything. And I think you'll find that out as you go through this and you see them. I don't know if you've seen this in your companies, Jeffrey, but the companies that have strong culture, nine times out of ten are the ones that end up doing very well.


[User] Please transcribe this text

[Assistant] They will attract people, they will motivate people, they will be aligned in what they do, they will be highly efficient, they will take pride in it, in what they do, and they'll follow up. So, let me just see, I think that's


[User] Please transcribe this text

[Assistant] And this is what leads into this afternoon. As you start a company and you're a leader, what are you doing to make clear what your values are? And how are you going to enforce them? You know?


[User] Please transcribe this text

[Assistant] Do you do that? You can get up and give speeches. That's great. Uhm, that is not sufficient.


[User] Please transcribe this text

[Assistant] What we did is we put it, built it into our processes in the evaluations, said, what are you doing to make your customers more successful? What are you doing to improve your reputation? What, you know, what are you doing to, uhm, compete and win? You know?


[User] Please transcribe this text

[Assistant] And all those things we went through. And when we did those, guess what? The bottom line took care of itself. As I said, the company went from 50 million


[User] Please transcribe this text

[Assistant] The cash flow positive and half a billion dollars in two and a half years.


[User] Please transcribe this text

[Assistant] And it wasn't because we had such a clever strategy. We had a good strategy. We had a great culture. And that's what makes a difference. And that, and that


[User] Please transcribe this text

[Assistant] Fundamentally is leadership. Leadership sets help sets the values.


[User] Please transcribe this text

[Assistant] Put in place ways to enforce those values and each day takes the risk. I'll tell you one other one. At Sensible, we had, we were raising our Series A.


[User] Please transcribe this text

[Assistant] And we had a VC firm that came in and they seemed very gre- they seemed terrific. They were very, very nice. Guy Bavo came in. We spent a good amount of time with them. We signed the term sheet for 15 million dollar pre-money value.


[User] Please transcribe this text

[Assistant] $50 million post-money valuation for $5 million. This is in 1990, this is in, let's call it 1999. $5 million, we literally thought we could change the rotation of the earth. And it was a lot of money, and there were four of us in a room.


[User] Please transcribe this text

[Assistant] And so as we went through the process, they signed the term sheet and they said,


[User] Please transcribe this text

[Assistant] Then they switched. Bob was out and Gary was in and he started trying to change the rules of the game and they didn't do the due diligence and they had a no-shot clause for 60 days. So we couldn't go out and talk to anybody.


[User] Please transcribe this text

[Assistant] And then they started trying to chip away at the deal. Well, we didn't mean 5 million all at once, we meant 5 million over 2.


[User] Please transcribe this text

[Assistant] Two tranches. Well that's not the same thing at all. That's not the deal. You're breaking the deal here. And so what we did is they didn't even start to do due diligence. So even though we didn't know what we were going to do, we had a board meeting and we made the decision that


[User] Please transcribe this text

[Assistant] We don't know what will happen, but we're walking away from this deal. And uhm,


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[Assistant] And I'll never forget it, Bob Coleman, you know, who wasn't the chairman, but he on the board, he said,


[User] Please transcribe this text

[Assistant] That's fantastic, because if you start compromising your values now, this company's done. And we didn't.


[User] Please transcribe this text

[Assistant] And we turned around and said at the end, we're sorry, we can't do this deal anymore. Our values are not a match.


[User] Please transcribe this text

[Assistant] Uhm, after that, we ended up, someone else watched us, Advent International said, wow, that was very impressive. That was great leadership. We would like to invest. They invested.


[User] Please transcribe this text

[Assistant] And the company went on and did very, very well. The other person went out and tried to badmouth us, and it backfired on them in a big way. They didn't get involved in many deals after that in Boston.


[User] Please transcribe this text

[Assistant] And it was, but the key was we stayed strong. We stuck to our values and that set the tone for the company that we meant business about our values. Okay, so it's noon. We'll be back at one o'clock and, uh, Ed Shine


[User] Please transcribe this text

[Assistant] Uh, we'll be, Matt, Matt Marks will be running it, Ned Schein will be here. Thank you very much.


[User] Please transcribe this text

[Assistant] There we are. Alright. So my name is Matt Marks. Uhm, so I'm fairly new here. I've been here for about a year. Uhm, I'm, but I'm old for a new professor and that's because I spent about 10 years doing startups before I came back to academia. And so one of them was right here in Kendall Square.


[User] Please transcribe this text

[Assistant] And that was eventually acquired by Microsoft. And then I came back here, finished up the MBA.


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[Assistant] I did a PhD and now I study startups and also entrepreneurial environments and what, uh, and what areas like Boston can do to increase the amount of entrepreneurial activity and startups.


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[Assistant] So that's my background. Uhm, but this morning is Bill's show and he's gonna take you through his experience with, uh, challenges of leadership in entrepreneurial startups. Uhm, I'll be here and I'll jump in from time to time. And this afternoon we've got Ed Schein for you who is kind of the father of studies of culture and leadership and, uh, that'll be a real treat to have this afternoon. Uhm, so schedule's up here on the board. We'll take a break at 12 for lunch and, uh, back to you Bill.


[User] Please transcribe this text

[Assistant] So uhm, just to go back to what Bill said about leadership and crisis, uhm, I don't want you to come away from this thinking that leadership is all about dealing with crises. In fact, sometimes crises are easy because everyone knows you need to change.


[User] Please transcribe this text

[Assistant] And one day at our executive offsite, uh, our CEO, Mike McHugh, if you have an iPad and you've seen Flipboard, Mike's running that now.


[User] Please transcribe this text

[Assistant] He said, this is not good enough. We're like, Mike, what are you talking about? We're profitable, we got through the tech crash, you know, we're, we're doing well.


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[Assistant] You know, people are happy, what are you talking about? And he said, you know, it's not, it's not enough. And he said, I want 20 people pulled off of their projects to go and build this new thing. And I don't know totally what it is yet, but we're going to build this new thing.


[User] Please transcribe this text

[Assistant] And we all thought he was completely insane because we thought we'd built and learned to manage a good business.


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[Assistant] So as you think about leadership, think about not just when, oh no, the competitors here, you know, we have to change things, it's knowing when to change and convincing the whole company that they need to follow you.


[User] Please transcribe this text

[Assistant] Bill, can I chime in on this real quick? Please. So there's a, just on this notion there's a fascinating study that was published last year where


[User] Please transcribe this text

[Assistant] Uh, researchers did two things. They went and measured, uh, company performance and trying to relate it to the CEO's ego. And for that they, you know, how do you measure that? Well, they did two proxies. First is, how much more does the CEO earn than the rest of the executive team? Like, what's the multiplier?


[User] Please transcribe this text

[Assistant] And the second one was, how big is the picture of the CEO in the annual report?


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[Assistant] You would think that's a randomly var- no. It's not randomly varying. It's very closely tied to their, to their compensation and it's negatively correlated with the company's performance. Very strong results year after year. So it ties back to this. In fact, I sometimes wonder


[User] Please transcribe this text

[Assistant] You know, the CEOs are always going around and giving talks and lectures. I mean, who's doing their job? You gotta wonder. That's perfect, Matt.


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[Assistant] When you look, and the thing is we hear about the outliers, we hear about Bill Gates all the time, we think I gotta be like him when I grow up, but the fact is when you look across thousands of companies over many, many years,


[User] Please transcribe this text

[Assistant] That's actually, you know in general it's not the most effective thing to do.


[User] Please transcribe this text

[Assistant] Not that it can't work, but in general, it doesn't.


[User] Please transcribe this text

[Assistant] So what is the difference between put and patch?


[User] Please transcribe this text

[Assistant] Well, here's a good analogy. Recently I went onto Amazon and I found a really nice looking bicycle.


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[Assistant] And because I really wanted to poison my lungs by exercising around London, I clicked on the buy now button. So there I was super happy, super excited for my bicycle to arrive. And then on the day of arrival, I opened up that box and my bicycle was...


[User] Please transcribe this text

[Assistant] And I wanted to try and figure out how we can solve this problem. So there were two ways that they could fix this problem. Option one was they send me an entire new bike. So this is the equivalent of put.


[User] Please transcribe this text

[Assistant] You're updating your database by sending an entire entry to replace the previous one.


[User] Please transcribe this text

[Assistant] Now, the other option Amazon offered was for them to simply send me a new tire. That was the only thing that was broken. The rest of the bike was fine. And in order to save the world from carbon emissions, shipping a wheel is much better than shipping an entire bicycle.


[User] Please transcribe this text

[Assistant] And this is the same as patch. So when you're sending a patch request to the server, you're only sending the piece of data.


[User] Please transcribe this text

[Assistant] That needs to be updated. Instead of the entire entry that will be replaced, you're simply just updating the thing that needs to be updated. So when we see this in practice, I want you to think back to this bicycle analogy and maybe it'll help you try and understand the difference between these two words.


[User] Please transcribe this text

[Assistant] You've got, you've got a company set up to... A foundation, a foundation. A foundation. Not for profit. Alright, well thank you so much for your talk. Thank you. Thank you.


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[Assistant] The grant agreement you signed has strict safety provisions. This isn't your first violation.


[User] Please transcribe this text

[Assistant] Thank you for your great service to our country.


[User] Please transcribe this text

[Assistant] These folks will escort you to Oscorp Robotics, where you'll receive the latest in prosthetics.


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[Assistant] I'm trying to help you, Otto. You're free to continue your work in a secure environment. At Oscorp. You always were the smartest guy in the room. You haven't changed a bit. Neither have you.


[User] Please transcribe this text

[Assistant] Harry will be coming back from Europe early next year. Maybe the two of you can start that business you always talked about. This is opportunity knocking.


[User] Please transcribe this text

[Assistant] This is an icon of National Geographic, an Afghan refugee taken by Steve McCurry, but the Harvard Lampoon is about to come out with a parody of National Geographic and I shudder to think what they are going to do to this photograph. Oh, the wrath of Photoshop.


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[Assistant] Poetic Image for a Story on Tolstoy by Sam Abel.


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[Assistant] Pygmies in the DRC by Randy Olson. I love this photograph because it reminds me of Degas' bronze sculpture as a little dancer.


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[Assistant] Paul Nicklin. Polar bears need ice to be able to move back and forth. They're not very good swimmers and we know what's happening to the ice.


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[Assistant] These are camels moving across the Rift Valley in Africa, photographed by Chris Johns. Shot straight down, so these are the shadows of the camels.


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[Assistant] This is a rancher in Texas by William Albert Allard, a great portraitist.


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[Assistant] These are sea lions, uh, in Australia doing their own dance by David Dubelais.


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[Assistant] And finally, The Bow of the Titanic, without movie stars, photographed by Emery Kristoff.


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[Assistant] Because photographs emulate the way that our mind freezes a significant moment. Here's an example. Four years ago, I was at the beach with my son and he was learning how to swim in this relatively soft surf of the Delaware beaches. Uhm, but I turned away for a moment and he got caught into a riptide and started to be pulled out towards a jetty. I went, I can stand here right now and


[User] Please transcribe this text

[Assistant] See, as I go tearing into the water after him, the moment slowing down and freezing into this arrangement


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[Assistant] I got him, the wave broke over us, we got back on shore, he was fine, we were a little bit rattled.


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[Assistant] But this flash bulb memory, as it's called, is when all the elements came together to define not just the event, but my emotional connection to it.


[User] Please transcribe this text

[Assistant] And this is what a photograph taps into when it makes its own powerful connection to a viewer. Now, I have to tell you, I was talking to Kyle last week, uhm, about this, that I was gonna tell this story, and he said, oh yeah, I remember that too. I remember my image of you was that you were up on the shore yelling at me.


[User] Please transcribe this text

[Assistant] So this represents, this is a cross sample of some remarkable images taken by some of the world's greatest photojournalists working at the very top of their craft, except one.


[User] Please transcribe this text

[Assistant] This photograph was taken by Dr. Ewan Mason in New Zealand last year and it was submitted and published in National Geographic. Last year we added a section to our website called Your Shot where anyone can submit photographs for possible publication and it has become a wild success tapping into the enthusiast photography community.


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[Assistant] The quality of these amateur photographs can at times be amazing. And seeing this reinforces for me that every one of us has at least one or two great photographs in them.


[User] Please transcribe this text

[Assistant] But, to be a great photojournalist, you have to have more than just one or two great photographs in you. You've got to be able to make them all the time.


[User] Please transcribe this text

[Assistant] But even more importantly, you need to know how to create a visual narrative. You need to know how to tell a story. So I'm going to share with you some coverages that I feel demonstrate the storytelling power of photography.


[User] Please transcribe this text

[Assistant] The original intent was to travel there and bring back a classic story of diverse species of an exotic locale. And that is what Nick did up to a point. This is a serval cat. He's actually taking his own picture shot with what's called a camera trap. There's an infrared beam that's gunning across and he's stepped into the beam and taken his photograph.


[User] Please transcribe this text

[Assistant] A lion having a late night snack notice he's got a broken tooth.


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[Assistant] But the centerpiece species of Zacoma are the elephants. It's one of the largest intact herds in this part of Africa. Here is a photograph shot in moonlight, something that digital photography has made a big difference for. It was with the elephants that this story pivoted.


[User] Please transcribe this text

[Assistant] Nick, along with researcher, uhm, Dr. Michael Fay, collared the matriarch of the herd, they named her Annie, and they began tracking her movements. The herd was safe within the confines of the park because of this dedicated group of park rangers.


[User] Please transcribe this text

[Assistant] For outside the safety of the park were poachers who would hunt them down only for the value of their ivory tusks.


[User] Please transcribe this text

[Assistant] The matriarch that they were radio tracking after weeks of moving back and forth in and out of the park came to a halt outside the park. Annie had been killed along with 20 members of her herd.


[User] Please transcribe this text

[Assistant] Was, he brought back, was a story that went beyond the old school method of just straight, isn't this an amazing world? And instead, created a story that touched our audiences deeply. Instead of just knowledge of this park, he created an understanding and an empathy for the elephants, the rangers, and the many issues surrounding human-wildlife conflicts.


[User] Please transcribe this text

[Assistant] Now let's go over to India. Sometimes you can tell a broad story in a focused way. We were looking at the same issue that Richard Rerman touches upon in his New World Population Project for the first time in history.


[User] Please transcribe this text

[Assistant] More people live in urban rather than rural environs. And most of that growth is not in the cities, but in the slums that surround them.


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[Assistant] Jonas Bendixson, a very energetic photographer, came to me and said, we need to document this and here's my, here's my proposal. Let's go all over the world and photograph every single slum around the world. And I said, well, you know, that might be a bit ambitious for our budget.


[User] Please transcribe this text

[Assistant] So instead, what we did was we decided to, instead of going out and doing what would result in what we consider sort of a survey story, where you just go in and see just a little bit of everything, we put Jonas into Dharavi, which is part of Mumbai, India, and let him stay there and really get into the heart and soul of this, uh,


[User] Please transcribe this text

[Assistant] What Jonas did was not just go and do a surface look at the awful conditions that exist in such places. He saw that this was a living and breathing and vital part of how the entire urban area functioned.


[User] Please transcribe this text

[Assistant] Sometimes though, the only way to tell a story is with a sweeping picture. We teamed up underwater photographer Brian Scarry and photojournalist Randy Olson to document the depletion of the world's fisheries. We weren't the only ones to tackle this subject.


[User] Please transcribe this text

[Assistant] But the photographs that Brian and Randy created are among the best to capture both the human and natural devastation of overfishing. Here in a photo by Brian, a seemingly crucified shark is caught up in a gill net off of Baja.


[User] Please transcribe this text

[Assistant] I've seen sort of okay pictures of, uh, bycatch, the animals accidentally scooped up while fishing for a specific species. But here, Brian captured a unique view by positioning himself underneath the boat when they threw, uh, the, uh, waste overboard.


[User] Please transcribe this text

[Assistant] And Brian then went on to even greater risk to get this never before made photograph of a troll net scraping the ocean bottom.


[User] Please transcribe this text

[Assistant] Back on land, Randy Olson photographed a makeshift fish market in Africa where the remains of filleted fish were sold to the locals, the main parts having already been sent to Europe.


[User] Please transcribe this text

[Assistant] And here in China, Randy shot a jellyfish market. As prime food sources are depleted, the harvest goes deeper into the oceans and brings in more such sources of protein. This is called fishing down the food chain. But there are also glimmers of hope.


[User] Please transcribe this text

[Assistant] And I think anytime we're doing a big, big story on this, we're not only going, we're not, we don't really want to go and just look at all the problems. We also want to look for solutions. And Brian photographed a marine sanctuary in New Zealand where commercial fishing had been banned.


[User] Please transcribe this text

[Assistant] The result being that the overfished species have been restored and with them a possible solution for sustainable fisheries.


[User] Please transcribe this text

[Assistant] Took a look at the sweep of the medical system that is utilized to handle the American wounded coming out of Iraq. It is like a tube where a wounded soldier enters on one end and exits back home on the other. Jim started in the battlefield. Here, a medical technician attends to a wounded soldier on the helicopter ride back to the field hospital.


[User] Please transcribe this text

[Assistant] And then back to the states to recuperate at veterans hospitals such as here in Walter Reed. And finally, often fitted with high-tech prosthesis, they exit the medical system and attempt to regain their pre-war lives. Jim took what could have been a straight-up medical science story and gave it a human dimension that touched our readers deeply.


[User] Please transcribe this text

[Assistant] Now these stories are great examples of how photography can be used to address some of our most important topics.


[User] Please transcribe this text

[Assistant] But there are also times when photographers simply encounter things that are, when it comes down to it, just plain fun. Photographer Paul Nicklin traveled to Antarctica to shoot a story on leopard seals. They have been rarely photographed, partly because they are considered one of the most dangerous predators in the ocean.


[User] Please transcribe this text

[Assistant] Here a penguin goes up to the edge and looks out to see if the coast is clear and then everybody kinda runs out and goes out.


[User] Please transcribe this text

[Assistant] So she is pretty significant in size. And Paul said he was never really afraid because she was more curious about him than threatened. This mouthing behavior on the right was really her way of saying to him, hey, look how, look how big I am. Or you know, my, what big teeth you have.


[User] Please transcribe this text

[Assistant] Then Paul thinks that she simply took pity on him. To her, here was this big goofy creature in the water that for some reason didn't seem to be interested in chasing penguins. So what she did was she started to bring penguins to him alive and put them in front of him.


[User] Please transcribe this text

[Assistant] Puffing out, uh, you know, snorting out in sort of disgust, uhm, and lost interest with him and went back to what she does best. Paul set out to photograph a relatively mysterious and unknown creature and came back with not just a collection of photographs, but an amazing experience and a great story.


[User] Please transcribe this text

[Assistant] It is these kinds of stories, ones that go beyond the immediate or just the superficial, that demonstrate the power of photojournalism.


[User] Please transcribe this text

[Assistant] I believe that photography can make a real connection to people and can be employed as a positive agent for understanding the challenges and opportunities facing our world today. Thank you.


[User] Please transcribe this text

[Assistant] How do you, how do you know that? We don't have much time, Isaac. Tell me his name.


[User] Please transcribe this text

[Assistant] Now that you've seen that we can write code in our server, work out some sort of value or get some piece of data and then we can send it over to our template to be rendered.


[User] Please transcribe this text

[Assistant] I've got a challenge for you to test some of the things that you've learned from previous days.


[User] Please transcribe this text

[Assistant] I found a pretty interesting API called ageify and there's also genderize.


[User] Please transcribe this text

[Assistant] This is a really simple API. It doesn't require you to sign up and it doesn't require any authentication. What you can do with it is you can hit up the URL of the API.


[User] Please transcribe this text

[Assistant] And then you can pass over a value for name and it's going to predict the age of a name. You can go ahead and hit try me to see what you get. You can see it's a very simple JSON that you get back.


[User] Please transcribe this text

[Assistant] And this describes the name that you entered.


[User] Please transcribe this text

[Assistant] The estimated age of that person with that name.


[User] Please transcribe this text

[Assistant] And also how many times they've sampled this name when they were calculating this data.


[User] Please transcribe this text

[Assistant] Now, another one is genderize where you can pass in a name and it'll give you the predicted gender.


[User] Please transcribe this text

[Assistant] So I have to say from testing that I think genderize is a lot more accurate than ageify, but maybe I'm just a little bit upset because it says that people with my name on average are 63 years old.


[User] Please transcribe this text

[Assistant] But hey, you know, I'm doing pretty well for a 63 year old. So I can't complain.


[User] Please transcribe this text

[Assistant] Now what I want you to do is to turn this into a dynamic Flask application.


[User] Please transcribe this text

[Assistant] We're going to set up a new route in our application and it's under the forward slash guess route.


[User] Please transcribe this text

[Assistant] Now after forward slash guess, we're going to put in a name. So for example, forward slash guess, forward slash Angela. And what it should give us is hey, and then the name that was entered into the URL.


[User] Please transcribe this text

[Assistant] And then it should say, I think you are, and then the predicted gender from genderized.io.


[User] Please transcribe this text

[Assistant] And then it should tell me how old it predicts that name to be.


[User] Please transcribe this text

[Assistant] And it doesn't really matter what I enter here. This piece of data come from these two APIs.


[User] Please transcribe this text

[Assistant] Now you might have to think back a little bit when we learned about how to use the request module to get hold of the data that we want from an API.


[User] Please transcribe this text

[Assistant] And then you can incorporate it into a website that you can design however you want.


[User] Please transcribe this text

[Assistant] But be aware that the first line needs to have the name that was entered into the URL.


[User] Please transcribe this text

[Assistant] And the name should ideally be capitalized into title case like this, and then it should have the predicted gender and the predicted age.


[User] Please transcribe this text

[Assistant] So this is your challenge and it might take you a little while to work out all the components, but I think you can do it.


[User] Please transcribe this text

[Assistant] Pause the video and spend at least 15 or 20 minutes working out this challenge and see if you can complete it.


[User] Please transcribe this text

[Assistant] Remember that you've always got the help of the documentation. So there's the Flask documentation, there's the Ginger documentation, and also there's the documentation from the Agify and genderize.io.


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[Assistant] And if there's anything else, you can always search through the course for the part where we dealt with APIs and getting hold of data from APIs.


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[Assistant] As well as using Google and Stack Overflow to help you achieve this. Don't just give up if you get stuck. Try to work like a real world developer and work out the solution to your problems.


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[Assistant] Alright, so the first thing we have to do is set up this forward slash guess, forward slash name route.


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[Assistant] Back inside our server after where we've defined our home route, let's go ahead and create app.route and here we're going to target forward slash guess and then forward slash name.


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[Assistant] Now because we need to parse the URL to get hold of the name that the user entered, if we take a look at the Flask documentation, you might remember that yesterday we covered this idea of variable rules and parsing a URL.


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[Assistant] The way we do it is by adding some angle brackets around the part of the URL, which is going to be taken in as a variable. And here's their example.


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[Assistant] So in our case, we're going to put this name part of the URL inside some angle brackets to turn it into a variable. And then we can create our function.


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[Assistant] Which I'll just call guess. Um, it doesn't really matter what you call it. And then inside this guess method, I'm going to put in this name.


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[Assistant] So essentially when my guess function goes through this decorator method, it's going to give it a value for this name variable.


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[Assistant] And then I'm going to catch onto that name and I'm going to send it over when I render my template.


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[Assistant] So let's go ahead and create a new template to render. So it's going to be an HTML file, which I'll just call guest.html.


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[Assistant] And I'll leave the title as Guess. Now inside the body, I want to be able to type the name. So it was Hey, and then the name.


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[Assistant] And then it was, I think you are, and then the gender.


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[Assistant] So something like this. Now we just need to replace the name, the gender and the age. So I'm going to keep those names and I'm just going to wrap them like this.


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[Assistant] Now remembering the names of these variables, I'm going to go back to my server and I'm going to try and render my guest dot html.


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[Assistant] So it was a render template, the name of the template is called


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[Assistant] Guest.html and then I'm going to add a bunch of keyword arguments.


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[Assistant] So the first one was the name. So this is the name of the keyword argument. And then the value is also going to be name because it's going to come from this URL. If you find that confusing, you can always change this. For example, we could change this to person name.


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[Assistant] Now you might be able to see that this name comes from the URL, which gets passed in through the input.


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[Assistant] And this person name is the one that we use when we put it into the template. Let's go ahead and test this out. Let's comment out these two lines of code.


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[Assistant] By hitting command forward slash or control forward slash and then hit save and run the code and let's try going to this URL.


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[Assistant] So you can see it says hey, and then it says whatever it is that we typed after guess. So if we type a whole bunch of gobbledygook, then that's also what we're gonna see right here.


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[Assistant] Now one of the things about the name is it'll be nice to have it in title case. So we can change things to title case by using the Python title method where we simply just say text.title.


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[Assistant] We can do that inside our guest.html because we know that we can evaluate Python expressions in between the double curly quotes. We can say person name dot title, and then it's the parentheses.


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[Assistant] So now if we save that coming back over here and we go back to our Katie and hit enter, you can see it's now capitalized at K.


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[Assistant] And the first character is always now going to be capitalized.


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[Assistant] We've solved the first part, now we need to figure out the gender and the age.


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[Assistant] And to do that, we're going to need the help of these two APIs, genderize and ageify.


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[Assistant] So the API is probably the simplest API you've worked with. You've worked with a lot more complex APIs in the past, so this shouldn't be too hard. Now what we want to do is we want to replace this part that says Peter with whatever it is that the user typed in to the URL right here. We're going to need to take this entire string and we're going to paste it in and this is going to be the URL.


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[Assistant] Now we're gonna replace this part by using an F string. So we have an F in front of the string and then inside the curly braces, we can put in the name which got passed from the URL. So that's the gender URL.


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[Assistant] And in order to get the data from that URL, we're going to need the requests library. So let's go ahead and import requests.


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[Assistant] Now once it's installed, we can now use requests.get and we can get hold of the data at this gender URL. And this is going to be our gender response.


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[Assistant] Now from this gender response, we can get hold of the gender data and we want it in JSON format. So we can say gender response dot JSON.


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[Assistant] I'm not going to explain this too much because we've gone into excruciating detail before on how to work with JSONs, how to work with APIs and how to work with the request library. I'm hoping that at this stage it's just a bit of revision and you just had to look up some of the methods, but you've already seen this before and it should be quite familiar.


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[Assistant] So now finally from the gender data, we want to extract the part of that dictionary that is interesting, which is actually just the one under the key gender.


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[Assistant] So let's go ahead and add our square brackets and then put gender in here. And this is going to be equal to our gender. And that is what we're going to pass over. So we're going to say gender equals gender.


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[Assistant] And that means that once we get into the guess.html, it'll be able to put the value into this placeholder. Now all that's left is work out the age. So let's get our ageify URL.


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[Assistant] And this is going to be pretty straightforward because it's going to be pretty much the same as what we did for the gender.


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[Assistant] And if you take a look at the JSON you get back from calling this API,


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[Assistant] A new variable called age and the value is going to come from right here.


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[Assistant] So now if we hit save and we go back to our website and we hit enter,


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[Assistant] It should say, hey Katie, I think you're female and maybe 32 years old. Why is Katie younger than me? Anyways.


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[Assistant] I hope you managed to get that to work. If not, take a look at the final code in the course resources and see where you went wrong.


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[Assistant] And maybe revise the section in the course on APIs.


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[Assistant] I was standing there looking at it, and I realized that Velazquez was looking at me. And I thought, oh, I'm the subject of the painting. And I thought, I'm not gonna be able to do that painting. I, you know, I was to the point where I was using a stopwatch and painting squares out of, uh,


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[Assistant] It's, uh, you know, she's, uh, it's, uh, it's complicated.


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[Assistant] Yuri, where are you? Call me when you get this. I just hope the devil's breath is still there, and still secure. If the demons got to that truck, this just went from bad to catastrophic.


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[Assistant] Love the optimism. But in my experience, when it looks bad, it's usually worse.


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[Assistant] Me? Just trapped in a prison with every criminal I've put away in Atlantic.


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[Assistant] Which means I have 192 non-broken ones. Thanks for taking care of me, by the way.


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[Assistant] I'm fine, Mei. Stayin' safe. I'm more worried about you.


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[Assistant] Let's hope it stays that way. I'll stop by when I can, May. Love you.


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[Assistant] Yuri, complicated was an understatement. I'm gonna have to disable these transformers to free your men.


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[Assistant] Yuri, precinct is secure. And I just found out what it means to be Sable's priority target. Not fun.


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[Assistant] Guess so. Okay, I need to sign off for a bit. Call me if things go south.


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[Assistant] I'm sorry I made you feel like you couldn't. Still partners?


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[Assistant] MJ, Ms. Watson and I were just talking strategy.


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[Assistant] Ozcorp's developing something, but there's no way they can keep it safe from Octavius and Lee. We need to find the cure and protect it.


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[Assistant] Gang, a costume nutjobs is taking the city apart piece by piece. It's time I return the favor.


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[Assistant] Ms. Watson can give you my number. Good luck team.


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[Assistant] Miles is watching Feast, MJ is tracking a Devil's Breath cure. Time for me to focus on finding Otto. Stopping whatever he has planned. Yuri, we need to start tracking the big boys. Still no leads on Octavius?


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[Assistant] Cause you keep leaving me tasks? That's kinda on the nose.


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[Assistant] News reports say this is where auto-release devils breath. Might have left some residue I can track.


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[Assistant] But evolutionarily, two million years is nothing, and yet in two million years, the human brain has nearly tripled in mass. Going from the one and a quarter pound brain of our ancestor here, habilis, to the almost three pound meatloaf that everybody here has between their ears. What is it?


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[Assistant] Well it turns out when brains triple in size they don't just get three times bigger, they gain new structures. And one of the main reasons that our brain got so big is because it got a new part called the frontal lobe and particularly a part called the prefrontal cortex.


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[Assistant] Now what does a prefrontal cortex do for you that should justify the entire architectural overhaul of the human skull in the blink of evolutionary time? Well, turns out the prefrontal cortex does lots of things, but one of the most important things it does is it is an experience simulator.


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[Assistant] So just give it a moment of thought. You probably don't feel like you need a moment of thought. And interestingly, there are data on these two groups of people. Data on how happy they are. And this is exactly what you expected, isn't it?


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[Assistant] These aren't the data. I made these up. These are the data.


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[Assistant] You failed the pop quiz and you're hardly five minutes into the lecture because the fact is that a year after losing the use of their legs and a year after winning the lotto, lottery winners and paraplegics are equally happy with their lives.


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[Assistant] For the simulator to make you believe that different outcomes are more different than in fact they really are. From field studies to laboratory studies, we see that winning or losing an election, gaining or losing a romantic partner, getting or not getting a promotion, passing or not passing a college test, on and on.


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[Assistant] Help them change their views of the world so that they can feel better about the worlds in which they find themselves. Like Sir Thomas, you have this machine. Unlike Sir Thomas, you seem not to know it.


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[Assistant] Maurice Bickham is somebody you've never heard of. Maurice Bickham uttered these words upon being released. He was 78 years old. He'd spent 37 years in Louisiana State Penitentiary for a crime he didn't commit. He was ultimately exonerated at the age of 78 through DNA evidence. And what did he have to say about his experience? I don't have one minute's regret. It was a glorious experience. Glorious!


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[Assistant] He wouldn't lend the money, and of course six months later Ray Kroc had exactly the same idea. It turns out people do eat hamburgers, and Ray Kroc for a while became the richest man in America.


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[Assistant] There's something important to be learned from these people and it is the secret of happiness. Here it is finally to be revealed.


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[Assistant] Because when people synthesize happiness as these gentlemen seem to have done, we all smile at them, but we kind of roll our eyes and say, yeah right, you never really wanted the job, oh yeah right, you really didn't have that much in common with her, and you figured that out just about the time she threw the engagement ring in your face.


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[Assistant] What kind of economic engine would keep churning?


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[Assistant] With all apologies to my friend Mathieu Ricard, a shopping mall full of zen monks is not going to be particularly profitable because they don't want stuff enough.


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[Assistant] I wanna suggest to you that synthetic happiness is every bit as real and enduring as the kind of happiness you stumble upon when you get exactly what you were aiming for. Now I'm a scientist, so I'm gonna do this not with rhetoric, but by marinating you in a little bit of data.


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[Assistant] Let me first show you an experimental paradigm that is used to demonstrate the synthesis of happiness among regular old folks. And this isn't mine, this is a 50 year old paradigm called the free choice paradigm. It's very simple. You bring in, say, six objects.


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[Assistant] And you ask a subject to rank them from the most to the least liked. In this case, because the experiment I'm gonna tell you about uses them, these are Monet prints. So everybody can rank these Monet prints from the one they like the most to the one they like the least. Now, we give you a choice.


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[Assistant] We happen to have some extra prints in the closet and we're gonna give you one as your prize to take home. We happen to have number three and number four. We tell the subject.


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[Assistant] It is a bit of a difficult choice, because neither one is preferred strongly to the other, but naturally people tend to pick number three because they liked it a little better than number four.


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[Assistant] Sometime later, it could be 15 minutes, it could be 15 days, the same stimuli are put before the subject and the subject is asked to re-rank the stimuli. Tell us how much you like them now. What happens? Watch as happiness is synthesized. This is the result that has been replicated over and over again. You're watching happiness be synthesized. Would you like to see it again?


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[Assistant] Patience to rank them from the one they like the most to the one they like the least. We then gave them the choice between number three and number four. Like everybody else, they said, gee, thanks doc, that's great, I could use a new print, I'll take number three.


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[Assistant] We ask these amnesiac patients to tell us which one they own. Which one they chose last time. Which one is theirs. And what we find is amnesiac patients just guess.


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[Assistant] These are normal controls. If I did this with you, all of you would know which print you chose. But if I do this with amnesiac patients, they don't have a clue. They can't pick their print out of a lineup.


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[Assistant] Here's what normal controls do. They synthesize happiness, right? This is the change in liking score. The change from the first time they ranked to the second time they ranked. Normal controls show, that was the magic I showed you, now I'm showing it to you in graphical form.


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[Assistant] The one I own is better than I thought, the one I didn't own, the one I left behind is not as good as I thought.


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[Assistant] You know that they are showing you averages of lots of people. And yet, all of us have this psychological immune system, this capacity to synthesize happiness, but some of us do this trick better than others. And some situations allow anybody to do it more effectively than other situations do. It turns out that freedom, the ability to make up your mind and change your mind,


[User] Please transcribe this text

[Assistant] So we gave them cameras, they went around campus, they took twelve pictures of their favorite professors in their dorm room and their, you know, their dog and all the other things they wanted to have hundred memories of.


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[Assistant] They bring us the camera, we make up a contact sheet, they figure out which are the two best pictures, and we now spend six hours teaching them about dark rooms, and they blow two of them up and they have two gorgeous 8x10 glossies of meaningful things to them, and we say, which one would you like to give up?


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[Assistant] I have to give one up. Oh yes, we need one as evidence of the class project. So you have to give me one, you have to make a choice, you get to keep one, and I get to keep one.


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[Assistant] There are two conditions in this experiment. In one case, the students are told, but you know, if you want to change your mind, I'll always have the other one here, and in the next four days before I actually mail it to headquarters.


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[Assistant] Half of the students in each of these conditions are asked to make predictions about how much they're going to come to like the picture that they keep and the picture they leave behind. Other students are just sent back to their little dorm rooms and they are measured over the next three to six days on their liking and satisfaction with the pictures. Look at what we find. First of all, here's what students think is going to happen.


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[Assistant] They think they're gonna maybe come to like the picture they chose a little more than the one they left behind.


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[Assistant] These are not statistically significant differences. It really, it's a very small increase, and it doesn't much matter whether they were in the reversible or irreversible condition. Wrong-o. Bad simulators. Because here's what's really happening.


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[Assistant] Right before the swap and five days later, people who are stuck with that picture, who have no choice, who can never change their mind, like it a lot.


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[Assistant] So here's the final piece of this experiment. We bring in a whole new group of naive Harvard students and we say,


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[Assistant] We could do it so that when you take the two pictures, you'd have four days to change your mind. Or we're doing another course where you take the two pictures and you make up your mind right away and you can never change it. Which course would you like to be in? Duh-hah-hah-hah!


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[Assistant] 66% of the students, 2 thirds, prefer to be in the course where they have the opportunity to change their mind. Hello? 66% of the students choose to be in the course in which they will ultimately be deeply dissatisfied with the picture.


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[Assistant] They can't be exactly the same. In more turgid prose, but closer to the truth, was the father of modern capitalism, Adam Smith, and he said this. This is worth contemplating. The great source of both the misery and disorders of human life.


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[Assistant] Seems to arise from overrating the difference between one permanent situation and another. Some of these situations may no doubt deserve to be preferred to others.


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[Assistant] But when those preferences drive us too hard and too fast because we have overrated the difference between these futures, we are at risk. When our ambition is bounded,


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[Assistant] It leads us to work joyfully. When our ambition is unbounded, it leads us to lie, to cheat, to steal, to hurt others, to sacrifice things of real value. When our fears are bounded, we're prudent, we're cautious, we're thoughtful. When our fears are unbounded and overblown, we're reckless and we're cowardly.


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[Assistant] The lesson I want to leave you with from these data is that our longings and our worries are both to some degree overblown because we have within us the capacity to manufacture the very commodity we are constantly chasing when we choose experience. Thank you.


[User] Please transcribe this text

[Assistant] Today we're gonna continue our discussion of consumer choice, uh, and we're gonna talk now about what happens when we take that unconstrained choice we talked about on Monday and impose budget constraints. We'll talk about what budget constraints are.


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[Assistant] We'll then come to talking about how consumers make constrained choices, and then we'll end with an example of food stamps. So let's talk, start by talking about budget constraints.


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[Assistant] The construction of budget constraints. So basically, uhm,


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[Assistant] Last time we talked about the fundamental axiom of consumer choice that more is better. So what stops people from just binging on everything? It's their budget constraint. It's their limited resources.


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[Assistant] Now, for most of this course, we're gonna make a simplifying assumption that your budget, that is what you spend, equals your income. That is what you earn. Okay? That is, there won't be any savings or borrowing. Okay?


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[Assistant] Now, that is a simplifying assumption and indeed we'll spend a couple lectures at the end of the semester talking about what happens when people can save or borrow. Uhm, that said, this is not a terrible description of most Americans. The median American household has $400 in the bank.


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[Assistant] So this is not kind of a terrible description of-


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[Assistant] The way most people live their lives in America, which is what they earn each week is what they spend each week.


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[Assistant] So, uh, that's what we'll do. It also might not be sort of a terrible description of your life.


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[Assistant] Uh, I presume in college you're not doing a lot of savings. You maybe do a little borrowing. But not a lot of savings are borrowing.


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[Assistant] So what we're gonna do is we're gonna assume that's true for you as well. We're gonna assume your parents have given you some amount of money to spend. We'll call it Y. Your income Y is the amount of money your parents have given you to spend, uh, for say the semester or the month.


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[Assistant] And once again, let's say all you spend your money on is pizza and cookies. Okay, that's all you want to spend your money on.


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[Assistant] We write the budget constraint as saying that your resources, your income, why?


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[Assistant] Can be spent on either pizza or cookies. And the constraint is that you could spend it that, that budget has to be divided between pizza


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[Assistant] Or cookies, we have the price per cookie times the number of cookies. So P sub P is the price per slice of pizza. P sub C is the price per cookie. P is the number of pizzas and C is the number of cookies.


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[Assistant] That's your budget constraint. You can essentially devote your income.


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[Assistant] Is your income divided by the price of cookies? That is, if you decide to devote all your income to cookies, then how many cookies can you have? Y over PC. If your income is 100 and cookies are 10 bucks, that means you're going to insomnia cookies.


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[Assistant] Then, uh, then you can only have ten cookies, etc. Likewise, the y-intercept,


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[Assistant] Is, uh, the income divided by the price of pizza. That's how many pizzas you can have. The budget constraint represents, uh, the budget constraint. The slope of the budget constraint is the price ratio.


[User] Please transcribe this text

[Assistant] The negative of the price ratio, because it's a downward sloping line. PC over PP. That is, uhm, every extra cookie that you buy, holding your income constant, lowers the amount of pizza you can have by P sub P. Okay? So, let's consider an example.


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[Assistant] $12 for pizza, this is like downtown San Francisco. Or New York.


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[Assistant] $96 income, $12 for a slice of pizza, $6 for a cookie. Okay? Uh, I'm sorry, Y is gonna, we're gonna make Y72, my bad. It's Y72. Your income is $72. Okay? And you can spend it on pizza and cookies and those are the prices. Now.


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[Assistant] What that means is if you want to just pizza, you can get 6 pizzas. If you want to just cook it, you can get 12 cookies.


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[Assistant] And generally, the rate at which you can trade off pizza for cookies is minus a half.


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[Assistant] Okay, that is every additional cookie would require giving up half a slice of pizza.


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[Assistant] Okay, every additional cookie requires giving half a slice of pizza. That's why the slope would be negative a half. Okay?


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[Assistant] So basically, we're gonna call the slope of the budget constraint.


[User] Please transcribe this text

[Assistant] The slope, we're gonna call the marginal rate of transformation.


[User] Please transcribe this text

[Assistant] The MRT, last time with the MRS, the marginal rate of substitution. Now we're going to have the MRT, the marginal rate of transformation, which is equal to minus PC over PP, or the slope of the budget constraint.


[User] Please transcribe this text

[Assistant] Okay, that is the marginal rate of transformation. Now, this class is not alchemy. We are not literally transforming.


[User] Please transcribe this text

[Assistant] Pizza into cookies, that would be kind of cool, but we're not doing that. That's somewhere else at MIT. Okay? But, if effectively doing the same thing, what we're doing is, given that we have fixed amount of money, and given that we're gonna spend it all,


[User] Please transcribe this text

[Assistant] The more you spend on pizza, the less you spend on cookies. So you're effectively transforming pizza into cookies and vice versa. Because you're gonna spend all your money.


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[Assistant] You gotta spend it on something. So the more you spend on one, the less you get of another.


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[Assistant] So that's the center which we call the marginal rate of transportation, of transformation.


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[Assistant] So basically this comes back to the key concept we talked about in the very first lecture, opportunity cost.


[User] Please transcribe this text

[Assistant] The opportunity cost of a slice of pizza is two cookies.


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[Assistant] Remember, opportunity cost is the value of the next best alternative.


[User] Please transcribe this text

[Assistant] Okay, the opportunity cost is by the next best alternative. Well here you only have two alternatives, pizza and cookies.


[User] Please transcribe this text

[Assistant] So the opportunity cost of a slice of pizza is two cookies.


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[Assistant] And that's the sense in which you're transforming pizza into cookies or cookies into pizza. Okay? Now.


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[Assistant] This seems kind of abstract, but let's actually think of an organization which has taken this principle to heart.


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[Assistant] To develop the best method of weight loss in America, which is Weight Watchers.


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[Assistant] Okay, Weight Watchers. Now, it turns out that dieting is super hard and basically doesn't work.


[User] Please transcribe this text

[Assistant] Okay, there's a large literature which says that people go on diets all the time, then they stop them and they gain their weight back. Okay, dieting is incredibly hard and basically doesn't work.


[User] Please transcribe this text

[Assistant] Okay, but a much more successful approach has been established by Weight Watchers. It's not the only approach, but it's been proven to be much more successful.


[User] Please transcribe this text

[Assistant] Okay, and essentially what does Weight Watchers do? They set up a budget constraint and ask you to follow it. So for example,


[User] Please transcribe this text

[Assistant] They essentially assign point values to every good you might consume. You go on the website and everything in the world you might want to eat has a point value.


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[Assistant] What's your age and gender? That stuff matters for weight loss. And what weight do you want to achieve?


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[Assistant] And they say if you want to achieve a weight loss of X over Y days, then you've got to limit yourself to Z points.


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[Assistant] Okay, so essentially your goal is to lose weight. So we're gonna give you the budget constraint. We're not gonna tell you what to eat. That's why it's better than dieting.


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[Assistant] Because once again, Adam Smith was right. People like to have choices. They like to let choice drive things. But we are going to tell you your total budget.


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[Assistant] So for example, ve- vegetables are like zero points, Snickers bars are like six points, etc. They have various point systems.


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[Assistant] Okay, so for example, suppose your budget's 30 points, which would be pretty typical. Okay?


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[Assistant] Suppose you go to McDonald's for lunch, and you get a number one. The number one at McDonald's is a Big Mac, which has 14 points, fries, which have 10 points, and a Coke, which has 6 points. That's 30 points, and it's only lunch. Okay, you've blown your whole budget for the day on lunch.


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[Assistant] Now, you could just get depressed and say screw it, I'll just be fat. But clearly looking around the room you guys have not made that choice. Uhm, or you could look at the budget constraint and say well what else can I get?


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[Assistant] Well it turns out you can get a 10 piece nugget, which is 12 points.


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[Assistant] Apple slices, which is 1 point, and a Diet Coke, which is 0 points, for total only 13 points. Now you have 13 points and plenty of room for dinner.


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[Assistant] Now to be honest, anyone who tells you that second lunch is because that first lunch is a liar.


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[Assistant] Okay, I'd much rather have a Big Mac and fries and a Coke than nuggets and apple slices and Diet Coke. Gimme a break. But, I'd also much rather have dinner.


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[Assistant] Okay, so basically this lets you make the trade-off by imposing a budget constraint. By setting relative prices across goods. The points are like utils, they're not meaningful. They're only meaningful relatively. Let's just say relative price across goods.


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[Assistant] And then it lets you essentially optimize across those various, uh, across those various goods. So budget constraints essentially, by setting up this marginal rate of transformation, help, can help with a lot of kind of decisions in life. Okay? Questions about that?


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[Assistant] Okay, now what happens if we shock the budget constraints? So we talked about constructing them. What about shocking the budget constraints?


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[Assistant] We're gonna do a lot in this class of what we call comparative statics, which is essentially making changes in one thing or another and seeing what it does to the system. So let's talk about shocking the budget constraint. Let's start first with a change in prices. Suppose the price of pizza goes from $12 up to $18. This is a really good slice of pizza. Okay? Well what happens to the budget constraint? Let's look at figure 3-2.


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[Assistant] Figure 3-2 shows what happens. Your original budget strength BC1. The equation of that line is 12P plus 6C equals 72. Okay, the price of pizza, the number of slices of pizza plus the price of cookies times the number of cookies equals 72. Now the price of pizza has gone up.


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[Assistant] What that's done is that has pivoted inward your budget constraint to BC2. It has flattened the budget constraint. Because the slope remembers the ratio


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[Assistant] Of the price of cookies to the price of pizza.


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[Assistant] Right? That's our ratio. Well that ratio has just fallen. It used to be a half, now it's a third.


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[Assistant] Negative a half, well it used to be a half, now it's a third. So slope has fallen from negative a half to negative a third.


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[Assistant] So what's happened is, you can still have as many cookies as you had before. The y-intercept has not changed. But you can have fewer slices of pizza. That's why it's a pivot.


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[Assistant] Because one price has not changed, only the other price, so it's a pivot inward.


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[Assistant] The other thing here you'll notice, we have all these funny dots and stuff. Okay?


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[Assistant] That represents what has happened to what we call your opportunity set.


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[Assistant] Your opportunity set, which is an important concept. Okay?


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[Assistant] Your opportunity set is the set of choices available to you, given your income and market prices.


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[Assistant] The set of choices available to you, given your income and market prices.


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[Assistant] So your opportunity set initially was the black dots plus the red dots. Now your opportunity set has shrunk. Your opportunity set is now just the black dots. Given your income,


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[Assistant] You can now get less stuff. Same amount of cookies, but less pizza.


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[Assistant] And you are worse off. Your opportunity stat has shrunk. Even though your parents are still sending you the same check. You are worse off. Because you can now buy less pizza with it. Okay?


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[Assistant] So that's, that's what happens to the opportunity set when a price changes. And likewise, you should show yourself the same thing will happen when the price of cookies change. In that case, you'll get an increase in the steepness of the budget constraint.


[User] Please transcribe this text

[Assistant] Okay, but your opportunity set will still, your opportunity set will still shrink. Okay. Now, what about, yeaah.


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[Assistant] That's a good point, we're gonna come back to that. We haven't, we assume they're spending all their money, but it's just a way of representing, you can think of the line being lower as the same thing.


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[Assistant] So the oper- we, we care about, we just care about the area because it represents the set. But you're right, you could just focus on the line and say the line is everywhere lower. So they're, they're worse off. That's another way to look at it.


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[Assistant] But we like to think about it as a set. It comes in handy later for various reasons. Okay? That's a good question.


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[Assistant] Now let's ask about a second thing. What if your income goes up? What if prices are back to 12 and 6, but your parents decide to send you more money?


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[Assistant] Suppose your parents, uh, or send you less money.


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[Assistant] Turns out you haven't been paying enough attention in 1401, your parents are mad. They're monitoring you, that's why we have the camera here. This goes directly to all your parents. Okay.


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[Assistant] Uh, I'm sort of joking. Uhm, and, uh, and so let's say a parent's cut your allowance to $60. Okay? Well, what does that do? That's in figure 3-3.


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[Assistant] Okay, in figure 3-3, the old budget constraint was that you get


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[Assistant] Pizzas and cookies at a price of 6 and 12 and you get them till you spend 72 dollars. Now you can only get them till you spend 60 dollars.


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[Assistant] Now what we see is not a pivot in the budget constraint, but an inward shift in the budget constraint.


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[Assistant] Because the relative price of pizza cookies has not changed. Therefore the slope hasn't changed. Okay, the slope is dictated solely


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[Assistant] Okay, and once again, now instead of being able to buy say 12 cookies and 6 pizzas, now you can only buy say 10 cookies and 5 pizzas. That's the most you can get.


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[Assistant] Okay, so once again your opportunity set has been restricted, but in a different kind of way. Through this pivot inward.


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[Assistant] Okay, so that's how we sort of manipulate these budget constraints, and we're gonna come back to that, uh, next lecture, that'll be important. Yeah.


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[Assistant] That's a great question and we're gonna actually answer that question next lecture very explicitly. So hold on to that question and we'll talk about, we're gonna compare explicitly why income changes differ from price changes and what are the underlying mechanisms. Yeah.


[User] Please transcribe this text

[Assistant] Great, great question. So as I said, we always are going to start with simplifying assumptions to make life easy. There's no reason that this couldn't be written in three dimensions, and you'd have relative marginal rates of transformation, rates at which you're willing to trade off various things. So you could just extend the math to more dimensions. It wouldn't add any richness to just make your head spin.


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[Assistant] So, but the basic, so the basic, all the basic ideas can come across with two goods. But it would be the same mechanics with more goods.


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[Assistant] Okay, you essentially, when we get to the constrained optimization, you essentially have more first order conditions in your constrained optimization. That's the way to think about it.


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[Assistant] Okay, so let's access a great segue. Let's turn to the second part, which is how we use budge constraints and


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[Assistant] The utility function we learned out last time to actually decide, describe how consumers make choices.


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[Assistant] So we take utility, remember, I said last time, consumers are going to maximize the utility, subject to a budget constraint. Well now we've taught you about utility, we've taught you about budget constraints, let's put them together. Okay? How to consume, how do consumers put them together? Well, graphically, the representation of preferences was our indifference curves.


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[Assistant] That represented people's indifference with further out indifference curves made people happy. Right? That was last time.


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[Assistant] So essentially what we're going to ask graphically is, what is the highest indifference curve you can achieve given your budget?


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[Assistant] Right, we know you want to be the highest indifference curve possible, by more is better.


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[Assistant] So we're simply gonna ask, what is the highest indifference curve you can reach, given your budget?


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[Assistant] Okay, so let's consider the same utility from last time. Utility is square root of p times c. Okay? And let's consider the same budget.


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[Assistant] Where can you go with that? So let's turn to figure 3-4 and do it graphically. We'll do it mathematically in a minute. Okay? So in figure 3-4,


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[Assistant] You have our budget constraint, which runs from 6 pizzas to 12 cookies.


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[Assistant] That's the original budget constraint. And you have a series of indifference curves. And these indifference curves, i1, i2, i3, i4, they all come directly from this utility function.


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[Assistant] So simply I've solved this utility function. I'll talk about the math in a little bit. You'll do more math in section on Friday.


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[Assistant] Okay, but essentially you can solve, we'll show you, you'll derive on Friday.


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[Assistant] How you take this utility function, literally can draw the indifference curves from it. Okay?


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[Assistant] For now, take my word that these indifference curves represent this utility function.


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[Assistant] Is the furthest out indifference curve you can achieve while still meeting your budget, while still meeting your budget constraint.


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[Assistant] And therefore, we say that the optimum graphically is the tangency.


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[Assistant] Between your indifference curve and your budget constraint is the optimal constrained bundle.


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[Assistant] You see how we brought, last time we talked about further round difference curves make you happier. This, today we talked about the fact you're limited by your budget. So we have the furthest indifference curve you can get to is going to be definitionally at the tangent of the indifference curve and the budget constraint.


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[Assistant] And once again, that gives you, we realize we don't want to measure utils, but just for mathematical, for mathematical purposes, that gives you utility at that tangency of square root of 18. Okay? At that point, you are choosing six cookies and three pizzas. That is the choice you are making. That is the best off you can get given your budget.


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[Assistant] And to see this, let's talk about some other points and why they're not better.


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[Assistant] Okay, let's talk about point A. Why isn't point A better? Why isn't it better to have


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[Assistant] Two, maybe you just, maybe you like cookies a lot and don't like, or like pizza a lot and don't like cookies that much. How can we say that point D is better than point A? Yeah.


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[Assistant] It's on a higher indifference curve. So point D dominates point A because it's on a higher indifference curve. Well fine, same person, by that logic why not choose point E?


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[Assistant] So the bottom line is, you can see graphically why the tangency is the best you're going, uh, is the best you're going to do.


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[Assistant] Okay, likewise point C you wouldn't choose. Point C is, has this, has the same slope.


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[Assistant] It has the same slope as point D. In other words, the slope's minus a half at point C. You drew a line tangent to point C.


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[Assistant] The slope will be minus a half, just like it is a point D. But you wouldn't be spending all your money. So you wouldn't choose that point either. Yeaah.


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[Assistant] We're, we're gonna, we're gonna come to, we're gonna, we're just, first of all, we're not gonna have discreet, well, we can have lines, and the lines could end up


[User] Please transcribe this text

[Assistant] You could end up lying along, you could end up lying along a budget constraint, for example, or you could have, you could even have utility functions which judge the budget strain at one extreme or another. And we'll talk about those cases.


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[Assistant] Okay, let's come to the mathematical derivation. So that's the graphics, let's come to the math. Okay, now.


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[Assistant] Always a bit of a tightrope act when I'm doing math up here on the board, so bear with me. Okay?


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[Assistant] But the key thing is the math of constraint optimization is all about the marginal decision. Remember, it's hard to say how many cookies you want. It's easier to say, should I have the next cookie?


[User] Please transcribe this text

[Assistant] Okay, it's about constraint optimization. And what we want to ask is, we essentially want to compare, how do you feel


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[Assistant] About trading off pizzas versus cookies. Versus what will the market let you do?


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[Assistant] Instead of trading off pizzas versus cookies. That is, we, the optimum is going to occur when we set your marginal rate of substitution.


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[Assistant] Which remember, we defined as minus muc over mup.


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[Assistant] Your marginal rate of transformation, which we defined as minus PC over PEP.


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[Assistant] And this is the fundamental equation of consumer choice.


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[Assistant] If you understand this equation, you can solve virtually every consumer choice problem I'll give you. Okay? That basically, at the optimum,


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[Assistant] The ratio of margin utilities equals the ratio of prices. That is, the rate at which you want to trade off pizza for cookies is the rate at which the market will allow you to trade off pizza for cookies. Okay?


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[Assistant] Basically, it's saying the ratio of the benefits. Think of this as the benefits and this as the costs.


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[Assistant] Think of the MRS as the benefits. It's what you want. MRT is the cost. It's where you're constrained. You want to set the ratio of the benefits equal to the ratio of the cost.


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[Assistant] Okay, now I find it actually easier to think of it this way. If you just rearrange terms, you can write it as muc over pc equals mup over p sub p. I like this way of writing it because I call this the bang for the buck equation.


[User] Please transcribe this text

[Assistant] What this is saying, your marginal happiness per dollar should be equal.


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[Assistant] This is sort of the happiness per dollar spent on cookies. This is the happiness per dollar spent on pizza. And you want those to be equal. You want the bang for the, you want to put your next dollar where it's gonna make you happiest. Okay?


[User] Please transcribe this text

[Assistant] And so basically, think of that as your bang for your buck. So for example, suppose you were in a position.


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[Assistant] Where the MRS was greater than the MRT. You're in a position where the margin utility of cookies


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[Assistant] And I'm getting rid of the negatives, there's a negative on both sides, so I'm just gonna get rid of the negatives, okay? The marginal utility of cookies over the marginal utility of pizza was greater than the price of cookies over the price of pizza. Okay?


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[Assistant] That is, the slope of the indifference curve was greater than the slope of the budget constraint. This is the slope of the indifference curve. Okay, the slope of the indifference curve, this is the slope of the budget constraint.


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[Assistant] In absolute value, the slope of the difference curve is greater in absolute value than the slope of the budget constraint. Okay? That'd be true at points like point A.


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[Assistant] Point A where you intersect, where you basically intersect from above.


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[Assistant] The, uhm, uh, the budget constraint by the indifference curve. So point like A, point A has a steeper slope of the indifference curve than it does the budget constraint.


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[Assistant] What that says is intuitively, and once again I want you to understand intuition, the rate at which you are willing to give up


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[Assistant] The rate at which you are willing to give up cookies for pizzas.


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[Assistant] I'm sorry, let me say it a better way. The marginal benefit to you of another cookie relative to another pizza


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[Assistant] Is higher than what the market will charge you.


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[Assistant] To turn pizza into cookies. Let me say it again. The marginal benefit to you of another cookie.


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[Assistant] Which is this. This is how much more you want the next cookie. Relative to how much more you want the next pizza. It's greater than what the market's gonna charge you to trade in your pizza for cookies. Therefore, you should trade in your pizza for cookies. Okay? So let's see this mathematically. At a point like A, point A, okay, you have your margin utility for pizza


[User] Please transcribe this text

[Assistant] Is the derivative of the utility function with respect to the number of slices of pizza. Marginal utilities, derivative of the utility function. So it's du, dp, okay, which is equal to 0.5 times c over square root of p times c. Okay.


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[Assistant] Uhm, so, and at point A, at point A, we had, uh, we had, uhm, two cookies and five pizzas. At point A, P was five, C was two. Okay, that's true point A. So we can evaluate this marginal utility UDP, which equals 0.5 times C over square root of P times C. So that's one over the square root of ten. Okay? That's the marginal utility of the next slice of pizza.


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[Assistant] The next slice of pizza makes you 1 over square root of 10 happy. Once again, that number is meaningless. We only care about in ratios.


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[Assistant] Okay, so the Mars utility of pizza is 1 over square root of 10.


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[Assistant] Marginality of cookies is 2.5 to the square root of 10. Therefore, your marginal rate of substitution


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[Assistant] Is minus 2.5. Remember, marginal rate of substitution is muc over mup.


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[Assistant] So your marginal rate of substitution is minus 2.5. What does that mean? Can anyone tell me what that means? Your marginal rate of substitution is 2.5.


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[Assistant] You're willing to trade exactly, you're willing to give up 2.5 slices of pizza for one cookie.


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[Assistant] That's what that number means. And that is a meaningful number. That's not an order. That's cardinal. We can use that. You're willing to give up.


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[Assistant] 2.5 slices of pizza to get one cookie. What is the market asking you to give up? How much pizza do you have to give up to get one cookie?


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[Assistant] You are happy to give up two and a half slices of pizza to get a cookie. But the market's saying we'll let you have a cookie for half a slice of pizza. So what should you do? Eat less pizza, eat more cookies.


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[Assistant] That will unambiguously make you happier. And that's why you should move from point A towards point D. Okay, that's the intuition.


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[Assistant] Okay, you basically want to trade pizza for cookies until these things are equal. Indeed, I'd like you to go home and do the same math starting at point B.


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[Assistant] If you do the main same as start at point B, you'll find the MRS is much below a half.


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[Assistant] That is, at that point, you are happy to give up tons of cookies to get pizza. Because jeez, you got ten cookies in one slice of pizza. You give up tons of cookies to get pizza. But the market says you only have to give up two cookies to get pizza. So you'll happily do it. And you move back towards point D.


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[Assistant] Okay, that is hard and very important. If you understand this, you're, you're sort of done with consumer theory.


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[Assistant] Okay, this is sort of the core of what consumer theory is all about. It's all about this balancing act. The whole course is fundamentally all about one equation, which is marginal benefits equals marginal costs. Okay, everything we do,


[User] Please transcribe this text

[Assistant] It's about weighing the marginal benefit of an activity against its marginal cost.


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[Assistant] The, if we take the next step, what's the benefit, what's the cost? Well here the marginal benefits the MRS, the marginal costs the MRT, we want to set them equal. And this sort of example I hope explains why. Okay? So that is how we think about constrained choice. Now I want to apply it.


[User] Please transcribe this text

[Assistant] I want to apply it by looking at the example of food stamps.


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[Assistant] Okay, now food stamps are not actually called food stamps anymore. Uh, when I was a kid they were called food stamps. It's basically a program the government has.


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[Assistant] That provides money for individuals to buy food if they're low income. Essentially, uh, we have in the US what's called the poverty line.


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[Assistant] The poverty line for an individual is about $14,000. Okay, for a family of four it's about $28,000.


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[Assistant] I feel that number obviously going to depend on where you're from. If you're in Boston, you'll say that's insane. If you're from some rural part of the country, you think, yeah, that's poor but manageable.


[User] Please transcribe this text

[Assistant] Okay, we'll talk later about the poverty line, what's good and bad about it. But in any case, if you're below the poverty line in America, roughly speaking, you get help with buying food. And that comes to the program we now call SNAP. It used to be called Food Stance, I gotta update my notes.


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[Assistant] Uhm, supplemental nutrition, I don't know. I know the answer for nutrition.


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[Assistant] Uhm, okay, so basically what the SNAP program does,


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[Assistant] Is it gives you a debit card. If you qualify in income grounds, you get a debit card. And that debit card can be used to buy food and food only. Okay, so essentially a debit card from the government that you can use to buy food if you're poor enough. And they give you sort of a fixed amount every month and that month can be used to purchase food. So here's the question.


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[Assistant] Why code through this rigmarole? Why not just give people cash? It's a fancy thing. If we want to give poor people money, why not just give them money? And we're gonna, I don't want the answer yet. Okay?


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[Assistant] What I want to do is show you graphically how we think about the trade-off and then we'll come to the answer. So hold your thoughts. So let's actually graph how we think about food stamps. Let's go to figure 3-5a. And let's start with a cash transfer. So here's the setup. Imagine people


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[Assistant] Start with an income of $5,000. That's super poor, okay? $5,000 is a whole family income for the year. Okay?


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[Assistant] And let's say all they can spend it on is food or shelter. Remember, as this gentleman pointed out, in life there's more than two goods, but it makes it a lot easier to have two goods. So imagine in this case your two goods are food and shelter.


[User] Please transcribe this text

[Assistant] And actually, quite frankly, if you're that poor, that probably isn't too good. You have to, you can worry about it at that level of income.


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[Assistant] Okay, it's food and shelter. So you have $5,000 to vote to food and shelter. So you have some original budget line.


[User] Please transcribe this text

[Assistant] Okay, which is, uh, labeled there, original budget line.


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[Assistant] That runs from 5,000 of food to 5,000 of shelter and then you could have some of in between, some, some of, some along the way.


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[Assistant] Okay, now let's say we give someone $500 in cash. Obviously this graph is not to scale. Okay, it looks like you're doubling his income, it's only $500. Just, this just sort of makes it easier, not to scale graph. Let's say we give someone, we say to them, look, you're poor, we're gonna give you $500 in cash. Well now, all we've done is shift out your budget constraint from $5,000 to $5,500. Okay, we've shifted out your budget constraint from $5,000 to $5,500. What does that do to your choices?


[User] Please transcribe this text

[Assistant] We'll consider two different types of people. Person Y


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[Assistant] Okay, they used to be on Difference Curve I0. They used to spend almost all their income on food and not a lot on shelter. They're probably homeless.


[User] Please transcribe this text

[Assistant] Okay, so they spent all their money on food, and were basically homeless.


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[Assistant] Now what do they do? Well they spend a little more on food and a lot more on shelter.


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[Assistant] Okay, so basically, that's what they do. That's their constrained optimization.


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[Assistant] We're not saying it's right or wrong, this is not normal economics, it's positive.


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[Assistant] The positive thing is, given their utility function, they move from point y1 to y2. Now imagine someone like individual x. They're different. Their tastes are such that they don't need to eat, they just want to have shelter. So they're up at point x1, initially.


[User] Please transcribe this text

[Assistant] And you give them that $500 and they spend just a little bit more of it on food and even more of it on shelter. They just love their shelter. Okay, and they're just super, they're super weight watchers. They don't eat. Okay?


[User] Please transcribe this text

[Assistant] So basically, they move from x1 to x2. Once again.


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[Assistant] Not normative right or wrong, it's just these are feasible choices people could make given the opportunity set with which they're faced. And that's how to get the $500 in cash. Questions about what I did here on this graph alone.


[User] Please transcribe this text

[Assistant] Uhm, okay, but, but do people understand what the cash transfer is? How it works. Okay, now let's go to Snap.


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[Assistant] And let's say with Snap, instead of giving them $500 to give them the debit card, instead of handing them a $500 check,


[User] Please transcribe this text

[Assistant] We give them a debit card with $500 on it that can only be used on food.


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[Assistant] How does this affect their budget constraint? Now we see where budget constraints start to get interesting and fun and the kind of challenges you're going to face in this course in drawing budget constraints.


[User] Please transcribe this text

[Assistant] The original budget constraint continues to be the original budget line running from 5,000 to 5,000. The new budget constraint is this kinked line.


[User] Please transcribe this text

[Assistant] Okay, at 5,000 on the y-axis, so it starts at 5,000 on the y-axis, 0 on the x-axis. There's a flat line that goes to 5,000 on the y-axis, 500 on the x-axis, and then it slopes down, parallel to the original budget constraint, to 5,500.


[User] Please transcribe this text

[Assistant] Could someone explain to me why that's the new budget constraint? Yeah.


[User] Please transcribe this text

[Assistant] To spend at least $500, compared to cash, we can do whatever the hell you want. We are forcing you to spend $500 of your money on food.


[User] Please transcribe this text

[Assistant] Coming to the question back there, it doesn't have to be a specifically labeled 500, it could be any 500, but we're forcing you to spend at least $500 on food. Well, what does that do to your choices? Well, for person Y,


[User] Please transcribe this text

[Assistant] It makes no difference whether they get cash.


[User] Please transcribe this text

[Assistant] Or whether they get food stamps. Now the person, light blue shirt, turquoise shirt, asked that question. Why does it make no difference?


[User] Please transcribe this text

[Assistant] Yeah, why does it, whatever, greenish, I don't know what you're, you, why does it make no difference for person Y if they give them food stamps or cash?


[User] Please transcribe this text

[Assistant] Exactly. He can just reallocate his money. Okay? That's exactly right. So for person Y, there's no difference. Look, they're already spending, what?