University of Manchester. I retired a few years ago now, a couple of two, three years ago, and since then I've been working in a sort of EL tech type thing, because I like to keep myself busy. So I think Richard has reminded me that we are going to be recording this presentation, so just be aware and hopefully you're happy with that. Obviously as we're joining, we've got some opening screens that was about future events that are coming on, which I'll put at the end as well. So if everyone can give me a thumbs up if they can hear me, and you're seeing my presentation all looking good, so nothing obviously wrong. So as I said, Richard is staying in the background, so I'm just going to carry on. So please, if you have anything that's important, want to say anything, use the chat. I'm monitoring the chat on the right hand side, but I'm sharing my screen at the moment, so I'll switch my camera off so you don't have to look at me all the time. We're now looking at my main screen. Obviously you're at an alt-sig webinar series. This is just the usual advertising that we have at the beginning and the end of the presentations, and a little bit about alt-sig if you want to join check the thing and just check over the background. So obviously watch out for the what's in the chat. There's plenty of announcements and things going on in the chat in the background. Anyway, let me carry on. It was a strange sort of time when I found that I was approached by an organisation called IACL, and essentially it was a website of resources, and they've had this for many years, and they wanted it to be converted a little bit more into a learning platform. So luckily, I knew one of the key people there when I worked at Manchester, and they approached me to say if I could work as a consultant to help develop this particular platform. So a little bit of background about IACL as an organisation. They're a not-for-profit organisation dealing with contact lens educators. So these are all people involved in optometry who do contact lens education. It's about 800 members, or it's about 850 at the moment, in about 78 countries. So it's quite a big organisation in that respect. So I'm dealing with people that are based in Canada, South America, India, China, and my main colleague who helps run this with me, and he's the main person, I'm just the consultant, is a person called Craig Wood, Wood's, who's based in Brisbane in Australia. So I have a weekly meeting every Monday morning with my colleague in Australia. Anyway, this was the website, or roughly the website, that they put all their learning resources into, and it became unwieldy because it was literally just you dump things all the time. You just kept putting things in, free resources, and it was only a repository of resources.  Whoops, not that one. You got to another window with all the resources. It was very, very confusing. Anyway, the main thing that, overall, we had to choose a learning platform, and I think that was my first task. What do we use for delivering all this material? And we ended up going through with Canvas, basically because they could deal with a small organisation. The other thing that I'd like to introduce at this stage before I go into a lot more detail on things is the types of software I've used to build this whole thing together. It's been great because I've had no sort of people telling me, I must do it this way. I have been given a completely free hand to work out the best way to deliver this content. All the rules about organisations where you might be did not count because we deliver to multiple countries, so it became an absolute dream in some respects because we just did it. I just did it with my colleague in Brisbane. The type of software we used was basically, I used iSpring for creation of scorn packages, which I'll explain a little bit more later. Something that you may never have come across is Swivel Cloud, and it's a swivel robot, which you may well have heard about and sure you have, but there's also a link to that. It's a video storage solution, which is completely free, and we ended up choosing Swivel Cloud purely because we can make sure that the videos that were stored there were then embedded in Canvas and could not be easily downloaded, so it's just made it really difficult. We also used Jitsi Meet for some conference software, I'll explain that a little bit later. I also used Neapod, and coupled with that I used Google Slides and Neapod. I also used Google Slides and AutoCraft to create certificates, and we also used towards the end of the last year Proctorial, and we actually ran a fellowship exam under Procted Conditions. So, I accl, and here's our first lesson and first challenge. We set it up two years ago, 850 members, it was very successful, people were invoing, and then we started opening it up to institutes around the world, and when we started it opening up to institutes around the world, we ended up, by the time we finished at the count, 18 1,800 students, which unfortunately went way over our licence, which caused a little bit of embarrassment when we got a rather large bill from Canvas. So lessons learned, make sure that you read the small print on the number of users that you have on a learning management system, because in Canvas we got caught out by accumulative within the year. So if anyone student clicks it in one year, that class does one of our actual licences. So that was something to bear in mind if you ever do this again. That's one of our big lessons learned. We have sorted it, we're not changing, and we're managing it in a different way now. So this is, we ended up producing this platform and we called it IACLtop, which stands for Teaching Online Platform. In a very simple form, it is a set of areas like members, web lectures, flashcards, ICL 2020 case reports, DLP, student areas, regional hubs and so on, and conferences. That was basically grabbing everything that was off the website and redeveloping it. So one of the first things we had to do was develop a members area so that people can come and visit. So they came into the members area. They got a welcome video and the other thing we had to link into the members area was a calendar. So we put the calendar there and the calendar or might look fairly simple, but we actually used a piece of software called Tokify, which works really well for integrating events. So when you look at the Tokify website that you can see at the moment, you can see Submit Event. So any of the members could submit all the content that they want for an event in the calendar and it automatically populates it. They also have a bit to subscribe. That particular piece of software has really worked really well and it's embedded in the main website as well as in IACL top. So if you go back to the main page again, you can see there the members area and the events. The other thing to bear in mind, we are also multi language, so that's another challenge. We developed all the materials in multiple languages and the challenge on that one would be, how do you get it all translated in the correct way? So one of the quick wins that we did, we used everything in English first, we used Google translate to all the various languages, then those translations went back to the unit coordinators for each of the areas around the world. They then corrected those and then we uploaded those directly into IACL top. So and it worked very well using Google translate. Very handy solution and a big challenge. The other challenge we had, a lot of resources we had were actually images, PDFs and things that way. So we actually used in Canvas, which is something that's really really very useful, is the IACL library and that's called the Canvas Commons and we built that down to just being involved with just our content. Nobody else can see it, just our members. So when you see the Commons like you see now, you'll see results which are fully searchable, we can share complete courses through this, we can share PDFs, videos, everything through this whole system, which works extremely well and when you look at the detail you can get the image there and then the member can actually download it or import it into another course and we also have the ability to put in the detailed descriptions. So we actually got a complete library of resources built into Canvas which made it extremely useful and the other bit that we were trying to, how do we get people together and we did it through what they call regional hubs and regional hubs, we actually created virtual coffee rooms and you can see one there and this is part of Canvas where you allow Canvas to have a group and they have their own areas and this is the English hub, we have a Chinese hub, a Spanish hub, a Chinese Korean hub and in the coffee room, this is where we use Jitsie and Jitsie, anyone can go into that coffee room, it's a unique ID that way, they go into the room and they'll take and chat with someone, it's the rooms always open and it's been that was quite useful and it was the different languages, we can share files with the people so it becomes a community. We also put a very quick guide in and as you can see on here at the moment you can find what you've got in IACL top, I made it fully interactive so that you can click any of these areas, it told you what the area was and you can click it and you go straight to the course rather than having to go through your dashboard so let me just, that was the members area so that's really just a place where people come together but what about the resources? I say I'm happy to take any questions later but I'll get through quite a bit of this at the moment because we're, so I'm sorry if I'm going on quite quickly, there is a big area, it's quite a big area so we've talked about the web lectures, this is where we use swivel cloud, we had a very simple course, the monthly webinars were here and this is a swivel cloud embedded in a page, once the webinars have been embedded within this page they are then added to the commons or the library so all these webinars are then downloadable or viewable or being able to put into different areas so that was quite useful, it's been very simple, minimal maintenance, we also did flash cards, this is still ongoing but the flash cards gain very simple but in multiple languages as you can see we've got English, Chinese, Indonesian, Korean, Spanish and each one of those will go to the separate flash cards and each flash card has also been developed within Neapod as well so they can be used for teaching resources. Going through now onto the ICL 2020, these are the contact lens course, again multiple languages, this was a series of powerpoint files, some of them were 230 slides long so we have the different language versions as you can see, I don't know what they all mean but I'm sure they worked all right and I converted all of these using iSpring to create its own learning pods so you can see at the moment there's a little download on the top corner so you the member can download a powerpoint and a pdf version of the actual content, there was notes so you can tag between the slides and the notes and you can just expand to full screen, this has worked out really well rather than having lots of slide decks and as you can see this one we're looking at at the moment is the basics of contact lenses A1, A2 and there was seven or eight different areas, I'm going on to case reports, case reports as we know with most case reports relatively straightforward they were a multiple choice type questions delivered through a set of modules but we did them in a sequential way so you had to complete all items so as you went through all the various items you got feedback and you couldn't progress until you got 100% when you got to the end of series one which was case report seven and you managed to complete all items you then have an ability to sign up for a certificate of completion so that worked out really well and we've had about two thousand two two and a half thousand downloads of certificates on the system I might add this was series one case report seven there's actually 35 case reports and nine series so it's a lot of material I also created a set of neopods for the same content so it becomes more interactive and then the DLP this is the distance learning program again it was in multiple languages and it was practice for time based exams and the DLP you can see just a different language very simple multiple choice but these are what are used to help our members go into what they call the fellowship exam and the fellowship exam is a very high profile time constrained three hour exam and quite strict rules about how you actually do it which is why we ended up using the fellowship proctored using proctorial so you get an idea and when we went into proctorial that was a whole first time for me using a proctored solution it worked extremely well and that was in different countries we did we ran the exam at different time zones and it was a bit worrying at some points but equally it was an extremely handy and useful way to go through right I'm going to come to the end I've been going on from quick I want to give everyone opportunity to ask any questions because I know it's only meant to be half an hour it's a very much a an in-depth look at what we've been doing and to be fair it was a it has been very interesting and a lot more work is still needs to be done so I'm happy to take any questions at the moment anyone speaking to me yeah we did have we right for people using proctorial we did have that real problem about people may or may not want the privacy issues and all that involved but it was their choice and a lot of the members ended up sometimes having to go and sit this exam in another country which would have cost them maybe up to two to three thousand dollars to travel to go and do this exam in one of our in one of the centres so the resistance we had none because we it was in their interest to do it we weren't forcing them they had to pay to do the exam and so it it worked out really well so I have no problems I understand all the issues with proctorial and things so another question was did we consider any other lms beyond canvas um I we looked at obviously blackboard was not really going to work for us um it's because we're such a small institution we only had a membership of around about 800 members and at the time when we were searching we didn't want to go to an enormous amount of money um the one thing I did look at which is a possibility of future times is talent lms um that would do exactly what we're doing now but would be in theory a slightly more economical one and would work really quite well for expansion without being penalised but the general feeling for most people and the institutions or the other um tutors that have used canvas around the world have found it extremely useful they've we've had very little problems whatsoever you know I'm the admin overall so I am the field admin if anything goes wrong with canvas I think I've had maybe 10 things that have been alerted to me in the last two years so I have no problems about recommending canvas that way but like everything lms and everything you have to work out what you're trying to do and what you're trying to do with the package we also considered had to be careful because we were going to multiple countries as well and obviously canvas does come under the problems with band areas like Iraq is still banned because you can't deliver to our members in Iraq because the US government bans that but those sorts of things that we obviously had to measure but the nice thing about it worked really well in in china and that's one of our main areas and so that all worked quite well so any other questions at the moment so a little bit one thing that you may have the one thing that we did if we go back to the just go back to the area let me just show you something on the members page again one thing that might be of useful or something we have a set of sponsors which you can see which are alcan and cooper vision and johnson and johnson they help to sponsor everything but our problem one of my challenges was how do we get the sponsors on everything we ever do so we have a standard page and the sponsors page is done in the package i'm using at the moment which is called vis me and all i need to do and i is to if we need to change a sponsor i've changed the vis me file and it automatically updates it in all the courses that we have everything but the nice thing about it as well is that the vis me dashboard or vis me analytics can also show you everything or the action every time anyone's viewed any of these logos so i actually have a vis me report that shows like google analytics but it actually does a nice bit of graph to show you how many people have gone to all the various areas around the world where it's coming from so that's also very useful and it saved me an awful lot of time because if you don't have to remember to put it in each course it's automatic so any other questions at the moment i say i'm trying to keep it short precise as i said the the one thing i should mention a little bit about the we do have additional support for this and catheter who works in in india and she does quite a lot of the background information for us and also keeping the supporting the institutions overall so um but we're at the beginning of yet more work that's going to come this way and we're likely to move things around a little bit as we go through the year so that's about me finished richard do you want to join in a minute thanks you um it was very interesting i think it's uh a very complex it is complex to go through in 30 minutes yes i appreciate what i was trying to do it was a trying to explain it's a challenge to bring something from what is a a website into a learning package but the nice thing about it i had a free reign yeah and that's something none of you have it is something i'm very jealous of very hamstrong with some of the things that you've been able to probably for any quickly this complete presentation so it's interactive if if that helps because basically sharing i can share this application with everyone um so that you can look at it again which makes it interactive and you'll see some of the a bit more of the detail rather than just watching the recording


No, no, no, no, no, no. No, no, no, no, no, no, no, no. All right. Don't forget we have a little recap on all. Probably you're watching on YouTube, but we do a little recap each week. So this was last week's. And if you missed last week's products, you can always catch up because we have a lot going on. That's just videos that we put together. This week we have some new stuff. OK. We've got some Edge Lighting LED strip. This is just a warm white and cool white LEDs. This is the warm white that we're showing here. This is why it looks so warm. It's just plain 12 volts plug it in. And it's like ultra high density LEDs. And it looks like just one large, warm strip of light. We've had these before that the light comes out the front. These come, the light comes out the side. So you can see only the top edge is lit. So this would be good for edging something. Like if you want to have a thin layer of light go around something architectural or a project where you have to go around the corners. This way the light will go. That's warm. What if I wanted cool? We have cool to do. So cool light. Again, we have the version where the light comes out the front. And that's good for like high resolution. But the edge lighting is also very, very useful. So again, you want to bend it around because flexible you want to bend it around. Oh, my cool side cold and my warm side warm. Got both warm and cool. And the next week we'll have RGB as well. But just as a note, it's all one LED, like one big light. You power it, the whole thing lights up. It's not indressable. You can't animate the individual pixel. All of a sudden. One ginormous LED that you drive 12 volts. OK, next up. Yet another Raspberry Pi camera this week. It's the global shutter camera. So this is kind of interesting. This is used in photography. Instead of scanning through the pixels to take a photo, like most high resolution cameras do, this is a global shutter, which means that it grabs all the pixels at once. So that means that some people know if you're trying to take photos or something that's going very fast. You're trying to speed photography, sports photography, some wildlife or nature or whatever, something's moving very fast. You'll get like a skewing effect. Like if you take a photo of a propeller, you know, it's a common thing that you'll see that as the scan lines get scanned, like the image looks stuttery. With the global shutter, the photo is taken all at once, so you don't have that stuttering effect. There's no change between, you know, there's no motion between the lines, but it's a low resolution. I think it's only like, you know, one point or like two megapixels. It's not like, you know, five or 10 or 12 megapixels. That's the trade-off. But if you want global shutter, you want to take the photo all at once. This camera's for you. However. Want to note, it doesn't come with lenses. You'll have to pick up a lens. See these things? They don't come with it. You're going to need it. See how it doesn't come with a lens? You're going to need a lens. And if we say you need a lens and we have a picture of a lens, don't say, well, there's a picture of a lens. I should have got a lens. No, you need a lens. These are what lenses look like. That's right. So you'll need the lens. It's a C or CS lens. Can be strong with this, yeah. You screw on. Have fun with it. See at the wide angle or the telephoto, and that's how you take photos of the thing either far away or really close. Yeah. All right, next up. This is coming soon, but we are excited. It's going to soon, but a lot of people asked us, they wanted to know when these would be available. So we figured we'd put them in the shop. This is the DVI Output RP-20. They're going to go fast. They're going to go fast, and they're very cool. So it has a DVI Output port, also known as HDMI compatible. It doesn't have audio. Although we're kind of looking into, maybe we could have audio. Who knows? But for right now, it's DVI only, and we've got an Arduino library that we recommend people use where you have a frame buffer. And this is the 8-4 GFX demo code that we've got. It draws circles and lines and squares and fonts and text. So it's a 320 by 240 internal, or 400 by 240 internal frame buffer 16-bit color. Let me see some color here. And that's because that's how much memory the RP-2040 has. So the RP-2040 only has 264K of RAM. So if you are using 320 by 240, that's 155K. 400 by 240, that's 190HK. So you want to have someone re-lift over, and those are kind of standard DVI resolutions. You know, there's some Pico DVI projects where they like automatically on the fly generate graphics. And so you can go to higher resolution, but then you don't have a frame buffer kind of like on the fly trying to generate the graphics. But you know what, you check it out. I'm sure you'll, the folks here who are excited about doing DVI hacking. What's just really neat is it's all in one. You get all the feather pins labeled at the top, and it's like the standard feather GPIO as well. I squared C, eight megabytes of flash, USB-C. The DVI output at the HDMI port using eight consecutive pins. You also get the hot plug detect utility and CEC pins if you'd like. And the I squared C coming from the DVI, right? That's the EDID reading code. So you can tell what monitor's connected to. That's level shifted and you get that on the I squared C pins as well. So good video hacking board that will be coming soon. Figured to let people sign up for it. Sign up, look fast. Week or two we'll have them in the shop. We can go to the next one. Okay, so we're getting close to the start of the show, but to get ready, we're gonna have a can pal. This is a board that I designed a few years ago when we were doing some can experimentation and then I couldn't get anything. But the components are finally available and back in stock. I can't believe we couldn't even get like can bus transceivers. That's how bad it was last few years, but now they're plentiful. So this is a can bus transceiver. This is used with a microcontroller that has a can peripheral. The can peripheral is the thing that generates the bits and the transceiver is what converts it from those bits into the differential signal required to be on a can bus. If your microcontroller doesn't have a can peripheral, you can't use this. This is only for those, again, those microcontrollers that have, if it says some of the data sheet, I have can bus, you can use this and you connect the RX and TX pins to the two can pins, power it with three to five volts and it uses whatever logic level you power it with. Some chips that do have can peripheral ESP32 in series, STM32 F405, SAM E51, some chips that don't have can peripheral at Megas, SAMD21, SAMD51, RP2040, although somebody once did a PIO hacked version, but pretty much it doesn't have a peripheral built in. NRI 52 series doesn't have can. The IMX RT-1011 doesn't have can built in. The TNT-4 does have can, so you wouldn't use this with the TNT-4. Again, you have to have that peripheral, connect the RX and TX on this board. We also have a little boost converter on the left that'll generate the five volts required from three volts power. So you don't need to have a separate five volt power input and there's a switch on it for the termination. So it's a good idea to have termination at the beginning and end of your can line because you have all these devices on it. If you want to add termination, flip the switch, and it says 60 ohms or 60 ohms on each side, high and low, it's 120 ohms total. I want to clarify that because I think it might be confusing. So on the other side, you get the terminal blocks with low, high and common ground. So that's the CAN bus transceiver. If you're bored and my controller have can peripheral. Start of the show, beside it too, Lady Aida, our team, our customers, our community, everybody who makes this thing go is, there's your role, here he is. Okay, but let's say your microcontroller doesn't have a can peripheral and you want to add can peripheral. Okay, you've come to that place. You want to use the CAN bus wing, which features the MCP 2515 over SPI. The CAN 2515, it's a slightly older chip, but you know what? It's reliable, it's inexpensive and it's available right now, which is really important. So this chip is partnered with a TJA 1051 dash T dash slash three, sorry. TJA 1051 T slash three, which is a three-volt logic level CAN transceiver. We also have a little five-volt boost generator to give you a clean five-volt so it can communicate on the CAN bus lines. And this basically adds a CAN peripheral to any microcontroller that doesn't have it built in. As I mentioned for the CAN PAL, ESP 32 has CAN peripheral, ESP 13C4 has CAN peripheral, SAM E51 does as well and the STM 32 F405, but most low-cost mode controllers do not. In which case, you plug this in, it communicates over the SPI lines, plus it needs a CS pin, an interrupt pin, and boom, you have now CAN bus for any microcontroller board. So let's go to the overhead. Boom. And I will show you what it looks like. Okay, so let me... Zoom. Exactly. And focus. Okay, so this is it. I just plugged it onto... You know, this is a... Let's see, this is a SAM D21 or NR50... Sorry, it's at MEGA 32U4 Feather, a perfect example of a chip that does not have CAN bus, but let's say you wanna send messages back and forth. You plug this in, it communicates with the MCP 25.5. There's code in every platform has code for this chip. It's extremely common. It's kinda like the CAN transceiver chip that everyone uses. And then it sends messages to and from this. And then over here is a terminal block. You can use the wires here. You plug into H and L through the transceiver. This is the transceiver. The CAN bus lines, you can connect to a CAN bus and send and receive messages very nicely up to, I think, one megabit per second speed. So very, very fast. Over here, if you want, you can get a DE9.  They straddle. They can get this version. It's linked off the product page. You solder it in and then you can connect this to like an ODB port. A lot of them either have a... Or CAN devices have a DE9, DB, D sub connector. Or you can connect it to an ODB cable if you want to connect to a car. There's onboard termination here. This is like the 120 ohm. And then you can cut this if you don't want to have the termination because you're adding this into an existing CAN bus line that's only terminated. And then we also have the reset and silent pins. You know, you probably don't need them but if you do want them. And then on the back, we connect the chip selected interrupt pins automatically to GPO five and six, but you can pet them and we assign those pins if you like, just solder a jumper from these pads over to whatever you like. So this is, you know, any feather you want to use with CAN bus, very common in robotics. Again, automotive, automation, but also it's, you know, it's great because I'm like, I squared C and these device can send a signal anytime. So it's great for large networks where you don't want to have to have like one polling controller and multiple peripherals. Unlike your, you can have multiple devices and there's a priority queue built in. It's differential, so it's great in noisy environments. And, you know, it's a pretty low cost way to connect to existing devices that use CAN, very commonly used. You know, a lot of electronics and automotive robotics and such, but I think it could also be used by in general makers who want to connect multiple microcontrollers together using only two wires. We asked, can we do it? We did. We can. Test and test. Test and test.


Historic vegetable moment on the space station, presented by Science at NASA. On August 10, astronauts on the International Space Station sampled their first space-grown salad and pronounced it good. They were treated to freshly harvested red romaine lettuce grown in the veggie plant growth chamber, a special structure designed to make gardens flourish in weightlessness. On Earth, garden florae battle such foes as cutworms, aphids, slugs, root maggots, blights and rots. But weightlessness is one thing no earthly vegetation encounters. In a weightless environment, there is no up and down, so roots grow in all directions. Water and substrate, the materials used to anchor these plants and allow for root growth, tend to float away. With veggie, these problems are solved by using plant pillows. Bags of substrate with space dirt and controlled release fertilizer. Wicks are implanted in the bags to draw water into the substrate and provide a place to glue the seeds, which are oriented so roots will grow down into the substrate and shoots that emerge will push out of the bag. LEDs furnish light for photosynthesis and give the shoots a sense of direction so they keep growing upward. The walls of the veggie chamber can expand to make room for the growing crop. According to astronaut Scott Kelly's taste buds, this special space-tailored arrangement works. He explains the importance of growing such culinary delights in space. If we're going to Mars someday, there's going to be a long period of time where we're going to have to be self-sufficient, and having the ability for us to grow our own food is a big step in that direction. The astronauts celebrated this historic vegetable moment by toasting their lettuce leaves and shouting cheers. Before long, they'll be toasting cabbage, tomatoes, and more. The next SpaceX delivery will supply some of the seeds. We will be sending up seeds for a small cabbage that is very highly rated for flavor and additional red romaine lettuce, says Joy Amasa, veggie payload scientist at Kennedy Space Center. We are working on crop selection with dwarf tomatoes and dwarf peppers. Upcoming experiments will use various ratios of red and blue lights and different fertilizers in attempts to improve crop yield, nutrition, and flavor both on Earth and in space. Other botanical treats are planned for the astronauts as well. We have additional seeds on board the station, says Massa. These are zinnias, really pretty daisy-like flowers that help us understand longer-duration growing plants that have to flower in space such as tomato. That kind of enjoyment is expected to be an important added bonus to veggie. I think the psychological benefit of growing plants in space will be incalculable, she says. Having living plants can help with stress and increase the crew's enjoyment. Growing plants can provide the sights, smells, and tastes of Earth. Fresh produce could add a variety in interest to the diet and the textures of fresh vegetables, crunchy, juicy, could add a new dimension to the packaged diet. Scottish poet Alexander Smith said it best. How deeply seeded in the human heart is the liking for gardens and gardening. Apparently, the same is true of human hearts in space. For more from the International Space Station, go to www.nasa.gov. For more nutritious news from Earth Orbit and beyond, stay tuned to science.nasa.gov.


And they may well ask, why 35 years ago fly the Atlantic? Why does rice play Texas? We choose to go to the moon. We choose to go to the moon. We choose to go to the moon in this decade and do the other thing, not because they are easy, but because they are hard. Because that goal will serve to organize and measure the best of our energies and skills. Because that challenge is one that we're willing to accept. One, we are unwilling to postpone. And one, we intend to win. And the other's too. Thing looks OK up here. Here you go, Tom Dodd. Roger, how does it look? There you go. Go ahead and win. Roger, Neil, we got a, if you give us food and data, we got the loads for you. That's 40 feet down, two and a half. Wrapping up some guts. 30 feet, two and a half down. Take data. Forward, forward, drifting to the right level. On backlight. OK, engine stop. We copy you down, Eagle. Kristen, Tranquillity Base here, the Eagle has landed. Roger, Tranquillity. We copy you on the ground.  We're breathing again. Thanks a lot. Thank you. OK, Neil, we can see you coming down the ladder now. OK, I just checked to get back up to that first step. It's not even collapsed too far. Roger, we copy you. Thanks for bringing us little jumps. Yeah, now step off the limb now. That's one. We got a beautiful picture of you guys up down there. Let me tell you, Bob, this flag is a beautiful picture. It's been a beautiful.  I guarantee you. I was rolling on the moon one day. How are you, sir? Thanks for good. Back start. Take over. I hear you have good products. Looks like you've been flying well up there, partner. The spacecraft looks good. Oh, you betcha. Here we go. A super flying machine. Many years ago, the great British explorer George Mallory, who was to die on Mount Everest, was asked why did he want to climb it. He said because it is there, well, space is there. And we're going to climb it. And the moon and the planets are there. And new hopes for knowledge and peace are there. And therefore, as we sexail, we ask God's blessing on the most hazardous and dangerous and greatest adventure on which man has ever embarked. Thank you. And the flags are waving, and the cigars are being lit up.


Thank you so much, Chiara, and thank you to all the organizers for inviting me. I'm really excited to be here to present this paper on entry into two-sided markets shaped by platform-guided search. And just a fact, this is joint work with Kwakau Lee, who's in the audience, and he's just in the year below me at Princeton, which means he'll be a fantastic student on the job market next year that you guys might be interested in. So the other day, I needed to purchase a new water bottle. So I went to the usual suspects, the e-commerce apps on my phone. I typed in the words water bottle, and this is what I saw. Now notice on the left hand side how eBay actually tells you it has more than 280,000 results for water bottles available that you can peruse if you so choose. Now I don't know about you, but certainly even during a pandemic, I feel like I have better things to do with my time than to painstakingly evaluate 280,000 different water bottle options. And so it occurred to me that how these platforms choose to rank these search results, in general their search ranking and recommendation algorithms, they certainly seem to have an outsized influence over what product I eventually end up purchasing. Now I'm hardly the first person to have noticed this. In fact, in a report in 2018 to the Steakless Center at Chicago Booth, a group of economists noted that defaults can direct a consumer to the choice that is most profitable for the platform. And the US Senate investigation of competition and digital markets just a few years after that put a rather finer point on it, noting that Amazon specifically, Amazon can give itself favorable treatment relative to competing sellers. And it has done so through its control over the buy box, which is just another one of these search ranking and recommendation algorithms that I will go over in much more detail later in the talk. However, before we get there, note that a merchant selling on Amazon had a rather different take on the experience, noting that Amazon seemed to rotate the buy box, switching off sellers from time to time and hypothesizing that this might be to keep sellers from getting discouraged and leaving the site. So this is our first indication that entry interacts with this topic of search ranking and recommendation algorithms in a perhaps interesting way. Now I want to do two things today and that is I want to ask and answer two key questions in this context. The first question is what is the effect of search guidance? To answer this question, we will need to examine the interplay between Amazon's need to attract merchants on the one hand and its ability to guide consumer search on the other hand. The key trade-off facing the platform is that between creating competition between merchants thereby lowering prices and attracting consumers and making sure that these merchants are adequately incentivized to join the platform in the first place because a platform without product variety, a platform without many offers is certainly also not going to attract any consumers. What we will find is that empirically this entry channel matters in that if we ignore the entry channel, we estimate a static 51 billion dollar consumer surplus gain, a really staggering consumer surplus gain from the effect of the particular search guidance algorithm we will examine today. However, this major, major gain is actually offset down to a minor gain of only one billion dollars by a reduction in entry that the search guidance algorithm entails essentially because the search guidance makes consumers more price conscious, creates tougher competition on the platform and therefore makes the platform less attractive for entrance. Now the second question I want to ask today is what is the effect of self-preferencing? The regulatory concern here is that firms which participate on their own platforms as sellers make revenue not only from intermediation fees but also from their own sales and these firms may thus be tempted to self-preference that is they may be tempted to preferentially guide consumers towards their own offers. Several theories of harm have been advanced in this context but a key theory is that self-preferencing could act as a barrier to entry, effectively depriving consumers of choice in the long run as merchants opt not to participate in a platform that will just divert demand away from them. Empirically however our model suggests that when we estimate it we find no harm and instead we find a moderate to small five billion dollar consumer surplus gain from the self-preferencing and that is because consumers once we adequately account for the influence of recommendations on their behavior still prefer the platform's offers and that means that consumers would benefit from being shown the platform's offers more frequently or in more prominent spots which is exactly what our model suggests. Furthermore we evaluate this theory that self-preferencing could act as a barrier to entry and we find that actually there is a only a minuscule entry reduction that comes about from self-preferencing which we attribute to the fact that consumers have this strong preference already without the platform sort of influencing their demand and thus merchants already anticipate that they will not be able to outcompete on products that the platform was also selling. There is a major caveat to our analysis here however which is that we don't model Amazon's pricing decision because there's been a lot of controversy over whether the platform is really profit maximizing whether it has some more dynamic long run goal and so we don't want to take a stance on this profit maximization objective. Now if Amazon raises prices by more than 7.8% due to its self-preferencing then we find that our wealth of results flip and then the consumers are actually worse off just because of this Amazon raising its own prices effect not so much because of the effect that Amazon's behavior discourages third party merchants. In fact the third party merchants are more excited when Amazon raises prices they can also raise their prices and that increases their profit margins. Now the agenda for today's talk is going to proceed as follows. I'm going to start by introducing the setting in a bit more detail and providing some descriptive statistics. Then together we will build a novel structural model of intermediation power and by intermediation power I just mean the platform's ability to steer consumers which we will operationalize using the modeling technique of consideration sets at Aguri. In our model firms make an entry decision and then choose prices. The platform chooses some offers to recommend over others and finally consumers make their choice and just to be clear the key innovation of this model is this entry stage that allows us to assess for instance the trade-off that the platform faces between lowering prices by intensifying competition but also lowering entry by intensifying competition. We will take our model and we will estimate it on extensive proprietary data. We will estimate demand and recommendations using a maximum likelihood approach and fixed and unit costs using a simulated method of moments procedure and we will find that Amazon indeed has intermediation power as about 26% of consumers will only consider recommended offers. The platform uses this power to intensify price competition increasing the demand elasticity from around three to around four and to self-preference giving itself a recommendation advantage that's roughly equivalent to pricing 9% cheaper. Now just to be clear when I say self-preference it is a totally valid explanation of these results and I'm sure Amazon would say that they are just giving themselves an advantage because they do better on things that to me are unobservable right like for instance the number of times that when they ship a package there is actually a valid tracking code on it. Things like this are unobservable to me they are real quality differences and in fact this explanation is most consistent with my results which finds that consumers also have a strong preference for Amazon which might be exactly because of these to me unobservable factors. Finally we will use our model to evaluate the effect of self-preferencing and the effect of search guidance yielding the counterfactuals I described on the previous slide just a quick reminder self-preferencing seems to be good for consumers and search guidance is also good for consumers although our assessment of search guidance massively depends on modeling this entry stage. If there are no questions at this point I think it makes most sense for me to delve straight into the setting perfect so that we understand the context a bit more. So the setting I'm going to talk about today is Amazon Marketplace which is a website I think we all know but what we might not know is that third parties co-list here amongst Amazon's own offers and in fact 60% of sales were sales of co-listed offers and they generated some 25 billion dollars in third party profits in 2020 so these are not no small numbers this is a significant part of Amazon Marketplace. I showed you earlier this picture of my search for a water bottle and I do think that the search algorithm is perhaps the main algorithm that influences consumer choices on these platforms however the search algorithm also brings with it some challenges some econometric and these challenges are mostly summarized by the fact that the search algorithm conflates relevance and recommendation and it also has the issue that it gives you access to products that differ and that differ perhaps in ways that are hard for the econometrician to disentangle. So rather than focus on the search algorithm let us focus and set on this algorithm that has been repeatedly mentioned by for instance the US Senate antitrust proceedings to get to this algorithm we will click on one particular product here and when we click on this product we arrive on a product page it looks like this it has big add to cart and buy now buttons but it also has this little note down here that tells you there's actually seven different sellers who are willing to sell you this item now if you click on buy now or if you click on add to cart what you're going to get is amazon choosing the seller in fact amazon has already chosen the seller who owns these buttons on the website it looks like this there is a little box around it and I guess that saves it the moniker the buy box so that is the recommendation algorithm we will look at today just to be super clear what it does is it maps a list of offers for the very same product into the identity of a recommended author and I really like to think of it as econometrician's paradise because product characteristics here are going to cancel we'll still have to deal with offer characteristics but you're choosing between different offers on the exact same product this exact same UPC code level product now we will tackle the setting using novel high frequency high fidelity data that we sourced from a company repricing listings for third parties we have a panel of around 200 000 products that we observe over around 450 days and for these products we have prices recommendations sales ranks and for what I believe maybe the first time sales across a large variety of merchants although that data comes with its own challenges that we will discuss later today we will focus just on all the products we observe in the fashion category around 10 000 products to be perfectly honest the the estimation is very computationally challenging so we wanted to to limit our sample and we wanted to make sure it's sort of homogenous so we picked one category this is the category with the most sales observations so the first question you might have about the status certainly the first question I had is well what actually determines recommendation status and your intuition might be as it was for me that price should really matter a whole lot and so this is what I'm showing you on the left-hand side here is that your intuition is right on the y-axis you see the fraction of offers that are in the buy box the fraction of offers that are recommended and on the x-axis you see the price rank of the offer so the cheapest offer price rank number one is recommended the line share of the time around 56 but you still see that the second cheapest offer the third cheapest offer and so on these are all still recommended relatively frequently so it doesn't seem to be the case that it's just price that matters price certainly seems to have an outside influence but something else also matters what is that something else well here's one of them feedback count so feedback count you can think of as a noisy measure of seller experience every time you sell something there's a certain probability you get an additional review and I'm not looking at the quality of the review right now I'm just looking at the count now if you allow me a causal interpretation for now and I agree that there is some trouble here but we will discuss it later if you allow me a causal interpretation imagine you're starting off as a seller down here with a feedback count around 20 now you hustle over years and you build up to a feedback count of a hundred thousand so you really conduct a lot of sales well then this graph is showing you how much can you raise your price and still be recommended the same fraction of the time and what we can see is that you can raise your price by around two and a half percent so I think what I find interesting here is that these are sort of figures of a plausible magnitude right you would think that you know a 2.5 percent additional margin that is like quite a bit of additional margin but it's also not an unrealistic amount to incentivize people to become experienced sellers now we can look at the table where I've done this for various offer characteristics so for each characteristic moving from the first to the 99th percentile in the data I'm showing you by how much you can raise your price turns out if you use fulfillment by amazon you can raise your price a lot cynics might say this is because amazon makes more money of you but I would counter that it's also true that your fulfillment services have changed a lot your shipping is going to be faster your consumers are going to have a better experience now additionally somewhat metaphysically challenging but if you manage to become amazon you can raise your prices by an additional nine percent the feedback count we just talked about positive feedback also matters a little bit and what's really bad is if you take a long time to ship then you're going to have to cut your prices by a lot now with this basic understanding of what determines recommendation status I think at this point it's going to be most useful for us to delve into the model first before we go to estimation so we have sort of an understanding about how to structure the data and how to think about this in a structured way the model from a bird's eye perspective has four stages there's going to be an entry stage successful entrants have to make a pricing decision this pricing decision will determine recommendation status and jointly the pricing decision and the recommendation status will determine the consumer choice now we will start at the bottom of the model and work our way back up in this modeling section so before we go there however let me just emphasize that this model will play out in parallel across many markets and here a market is going to be a product page so we will assume we will get our all of our consistency by saying this plays out in parallel across these product pages so starting with consumer choice right here at the bottom of this model and we are going to use the most box standard choice model you can think of which is a loaded choice model I'll make fat in 1973 so given a market T consumers are going to face some alternatives call them J the alternatives always include an outside option and the alternatives have some observed characteristics XJT so think of these as things like shipping time or the amount of positive feedback that a seller got there is some unobserved XJT this is because each seller has a name and maybe you associate something with that name that IS an econometrician can't see and then of course there's a price PJT they combine in the usual linear fashion into a mean utility delta JT and then once we shock that mean utility with a consumer specific loaded shock we get our loaded choice model now here is where we depart a little bit from this box standard choice model by introducing consideration sets at a GURI and in more or less the same way as dynastine and al in 2018 did this although adapted to our setting of course so here what we'd note is that one alternative call it jr one alternative on each market T is recommended and the fraction a fraction row of consumers we will call sophisticated sophisticated consumers consider only sorry consider the entirety of the choices available to them so essentially they click on this little link that I showed you earlier to evaluate all offers fraction one minus row of consumers are naive and they will only consider the recommended offer as well as the outside option right and just to be clear I'm a naive consumer so this is not meant as as judgment once we write down this model it becomes clear that market shares are just going to take this very natural form of a convex combination of choice probabilities amongst sophisticated consumers those are standard loaded probabilities and amongst naive consumers also a standard loaded probability but not a naive consumer can only choose something that is being recommended or the outside option so naturally the next thing we have to explain is how do we actually get these choice probabilities here or sorry these recommendation probabilities here now the recommendations again are just going to be a discrete choice problem only this time the platform is solving this good choice problem right and in particular we will use a nested loger model here for each market T the platform is going to choose one offer to recommend note the platform can actually recommend the outside option that is something that does happen in the data and it's actually a motivating piece of evidence in my paper now because that happens we want a nested loger model because we want to exclude the out we want to basically emphasize that the inside options are better substitutes so we want nests that are just the outside option on the one hand and all inside options on the other hand basically from the platform's perspective if I recommend the outside option these naive consumers are never going to buy and so I only want to do that in extreme circumstances beyond there's a question from jack let me just read it so that will be fast I find it very strange to use a standard choice model for the choice of amazon vendors I certainly and I assume most people have no idea who the vendors apart from amazon are and what type of service they offer to put it in another way are there any sophisticated consumers yeah that's an excellent question I I find that there is quite a few sophisticated consumers but I also bias my estimation in the other direction so basically I make identifying assumptions to be super robust against people claiming oh Leon really all consumers are sophisticated because that was sort of the reaction I was expecting from people that you know all these platforms don't actually influence our choices so I you know I find even then that there is still naive consumers so your question goes in the opposite direction is it maybe is everyone a naive consumer now of course I could estimate that that would be a potential result my model could find that the fraction of naive consumers is one so in another way another way to say this is this fraction of role is just going to be estimated so that that should be in the model thank you great question okay so moving on to the pricing stage now we have to start making some more assumptions so we will assume that we have single alternative firms and that means that the subscript j doesn't just index alternatives it also indexes firms and that these firms have constant marginal costs which I would urge you to interpret as wholesale costs these firms are not generally producers they are firms that often you know the typical firm sort of buys stuff on Ali Express or one of the Chinese websites has a delivered either directly to an amazon warehouse or to their own warehouse and then resells it in the US so these firms are going to play a Nash Bertrand pricing game where the type of a firm is going to be given by its marginal cost its various characteristics right like how fast do I share and these unobserved characteristics the firms are going to simultaneously choose prices to solve a profit maximization problem right like in a standard Nash Bertrand setting with two twists the first one is that the platform gets a certain share of the revenue that's where there's five parameters there and in fact it's around 15 percent here but it varies a bit across categories and then the second twist is that these market shares include the effect of recommendations on sales so in particular if I'm a merchant I might say well should I lower my price well amongst sophisticated consumers it's not really going to make a difference but if I lower my price I'm more likely to get the recommendation and if I get the recommendation I am much more likely to make a sale amongst the naive consumers so it's still worth lowering my price that mechanism that entire mechanism is contained in these SJC. Final part of the model is the entry stage here we will assume that each market T has a set of potential entrance script NT and an associated fixed cost that doesn't vary across firms FT we will assume that markets are separable so that no entrant is considering entering on multiple markets at once which is going to be crucial for us to be able to treat these as separate decisions and to get consistency in our estimation. Now comes the major assumption of the paper that arguably I will have to defend a bit and this is an assumption on the information set in particular we will assume that each firm at the entry stage only knows their own marginal cost as well as the fixed cost associated with this market so that assumption really comes in two parts the first part is that firms don't know anything about other firms right so in particular I don't know that Chiara is currently selling these stocks for this price when I'm entering because if I knew that and a new demand I could invert it to her costs and I don't know these costs that sounds a bit crazy on the first read through but on the second read through keep in mind that what Chiara is doing might be irrelevant if she is not going to stick on this product right even though Chiara is currently selling these stocks there's a very high turnover in this data so is she still going to be selling these stocks in three months or on the relevant time horizon where I'm making my entry decisions that seems quite unlikely in this data set so I think this is actually quite a reasonable simplification and you can see why we needed to avoid this classic issue with entry being a game of strategic substitutes and multiple equilibria emerging so this part of the assumption I think yeah yeah there's a there's a question from Luis Cabral am I right that many of the sellers are we selling the same product they purchase from the same source so almost same costs for everybody that's how I interpret the question I think I think that is a good assumption I would focus on the almost the same costs so I think in general this is a good assumption yeah they are probably sourcing from either the same source of very similar sources quite frequently and so I guess an implication so a follow-up is from me so how can we assume that they don't know anything about other competitors costs well they will not not know anything they know that costs are how costs are distributed right so they just don't know the specific realization yeah perfect thank you and then a question from Justin Johnson if you look at websites that give advice to third-party sellers the suggestion is that sellers spend a lot of effort figuring out which markets are likely to be more profitable and have less competition I guess in answer to your assumption about the information that yeah no but that's a that's a great point and that is definitely in my model because that's basically amounts to the market size mattering right they want to know where's the market size high relative to the number of entrants at the moment and that's that's exactly what they do yeah so the second part of this assumption is I think a bit harder to defend and this is the part of the assumption where I say firms don't even know their own vertical characteristics right like how fast they will ship and so on so there's two things I can say here firstly the recommendation algorithm is a bit opaque so they might know their own characteristics they might just not know how it plays out in the recommendation algorithm the second thing I can say is that this is currently necessary for computational reasons to keep this computable and we're working on on relaxing this finally we will assume that firms play symmetric equilibrium and taking all these assumptions together gives us this very nice intuitive equilibrium where firms will enter if and only if their marginal cost draws sufficiently low where sufficiently low of course varies market by market right bigger markets can support more entrance crucially that means that the model allows for selection on cost why is that important well one of the counterfactuals we'd like to run is what if we make the recommendation algorithm more price elastic of course that makes the market more competitive and we will have less entrance but which entrance do we lose that's going to be really important for welfare right do we lose the cheapest most efficient entrance or do we lose a random draw across the cost distribution no we lose the least efficient entrance and this model is able to capture that now with the model out of the way let us go into the empirical application the empirical application I will discuss today I will discuss exclusively recommendations and choice because that's that's where the the interesting estimation happens wholesale and fixed costs are very naturally identified wholesale costs by prices fixed costs by the entry patterns but actually most of the work goes into estimating those because that's a massive simulated method on moments procedure but I think it's less interesting to talk about so recommendation on choice and we can talk about them together to start with the data and market definition are going to be as follows the data are recommendations and third-party sales in the amazon fashion category and a market is going to be a product page p on a day towel so that means that the alternatives jt just to emphasize again are going to be offers for the very same product and the first challenge we have is what I call pooling which is that usually you have like cross sections of the same of different geographical markets where the same products are on sale or time series of the same market over time but here we really have different markets with different products on sale and we need to pool the information across them to identify our coefficients and that is mostly fine the one place where it's not fine is the price coefficient people react differently to price of different markets and what we find in the data is that basically they care about block price which we emulate with with sort of an exogenous measure of price because we want them the price in game to still have a unique equilibrium and then we need to talk about partial observability this is the really a sort of exciting unique challenge of this data set so I'm really excited that we have demand data from amazon at all but we of course don't actually have demand data sourced from amazon we have it sourced from third party merchants and usually on each product on amazon that we have data on there is one third party merchant for which we have the sales data and then we have the price data for all the merchants so that means we have sales for exactly one offer in each market turns out that under this loget a structure we're still identified as long as we know the total market size and what we do for the total market size is the classic trick using the sales rank and shovel year and gold space observation that sales are essentially power law distributed that is that challenge the final challenge is going to be that this is high frequency disaggregate demand data so the berry 94 lock transformation is going to yield severe zero share buyers right on most days most merchants see zero sales so what could we do we could aggregate aggregation across products doesn't really make sense here aggregation over time is going to yield measurement error and attenuation bias because prices move over time essentially we'd be smoothing over exactly the variation you want to use to identify these coefficients so instead we're going to proceed by maximum likelihood maximum likelihood means that warning belts are probably going off because of endogeneity i'm going to have to do this slide real quick because i'm running out of time but basically what i do to deal with endogeneity is that i am going to assume that unobservable offer quality is time and variant so i'm going to allow endogeneity at the offer level as long as it's time and variant and then i verify that for the part of offer quality that i can observe the observable quality really is time and variant and the rest of the slide i'm going to skip my apologies so with that we will have enough time to do the counterfactuals which is really the exciting part right now we've eaten our vegetables now dessert is on the table so the first counterfactual that i want to do is the effect of self-preferencing how do outcomes change due to self-preferencing and i will do all my counterfactuals in three stages i will look at the short run where prices and entry decisions stay fixed at the medium run the prices are allowed to vary but entry is still fixed and in the long run prices and entry are both allowed to vary now two caveats the first one for both this and the next counterfactual you see some big numbers here that's because i'm cheating i multiplied the welfare numbers for the 10 000 markets where i compute welfare for and i scale them up to the entirety of amazon marketplace so you can get some feel for what the size of this numbers is please don't take this numbers to be exactly estimated for the entirety of amazon marketplace and the second caveat is that across both counterfactuals i'm going to keep amazon's behavior so when i say prices are allowed to vary i mean third party merchants prices are allowed to vary and the same for entry decisions because of this thing that i mentioned earlier where we don't know whether amazon is really maximizing profits in the short run okay so with all of that out of the way let's look at how outcomes change due to self-preferencing i can do that because i estimate in the model how much more likely amazon's offers are to be recommended controlling for the other things i observe and i can just make them as likely to be recommended as the other offers in the short run i see that self-preferencing raises consumer surplus exclusively amongst the naive consumers what what is going on here well consumers prefer amazon's offers sophisticated consumers they don't care about the recommendations so they don't care about the self-preferencing but naive consumers they have to be helped by these recommendations and basically what you want to do is you want to show them recommendations for offers that they're actually likely to purchase and so that makes them better off in the medium run you might worry that there is going to be a slight price effect because now you're emphasizing something in the recommendation system that's not priced and indeed we find this this rather minuscule effect here and then in the long run we see that there is some entry displacement but the entry displacement is really tiny there is around five to six entrants on these markets on average so basically what this is telling me is our our model is not failing the basics maltest that it predicts the right direction of the of the entry entry must go down but it barely goes down so it seems like there isn't really entry displacement due to self-preferencing then the second counterfactual i want to run is the effect of search guidance so here i evaluate the impact of the algorithm compared to a no recommendation baseline and what i'm going to say about a no recommendation baseline is i'm going to treat it exactly the same as if there was a random recommendation so this is a bit of a reduced form search model here in the background basically what i'm assuming is that these naive consumers they don't suddenly become sophisticated and gain the ability to evaluate all the offers they're just going to stick at their ability to evaluate one offer but now they have to evaluate a random offer so you might say that this is a bit extreme so take it with a little grade of salt but i do think it does well to illustrate sort of the underlying mechanisms and tradeoffs here so in the short run as prices and entry decisions stay fixed we see that the recommendation algorithm creates a vast gain of consumer surplus and note that this really is a trifecta of great outcomes for the platform the consumers and the producers what's going on well you should show consumers things that they want to buy if you show consumers other things they're just not going to buy anything and that's going to be bad for the consumer that's going to be bad for the platform and that's going to be bad for the merchants however in the medium run we get some inclination some idea of what it means to show consumers what they want to buy because it means showing consumers cheap offers we saw that the recommendation algorithm loads really heavily on price in fact the price elasticity of the recommendation algorithm is minus 19 so with a price elasticity of minus 19 we see that the prices are going to fall by around 14 in the medium run so this is really a rather large decrease in prices and of course the consumers love this but the merchants not so much and the platform not so much now in the long run you might have anticipated this already what if prices fall so much is there going to be an entry effect yes there's going to be a large entry effect the presence of the recommendation algorithm reduces the amount of entry on the platform to the tune of around two merchants on an average of five to six merchants that's a rather large reduction so what's going on here is you know if you make consumers a lot more price elastic to this recommendation algorithm then entry is going to be reduced and people usually get more excited about the self-preferencing part of the paper I think this is actually a part of the paper that really excites me because you can really see like a market design question here usually we don't think of consumers preferences as part of the policy that the market designer can influence but here we see how at least the part that matters to the pricing decision therefore the entry decision is something that the platform can influence in online settings so in conclusion then there are three main takeaways that I would like you to go home with today the first one is that we found that defaults matter and intermediation power is real so I skipped a bit too quickly to the estimation part so that we could get to the counterfactuals but what I find in the paper is that 26 percent of consumers will only consider the recommended offer and that is a finding after I stack the deck is sort of in against finding that naive consumers exist so if anything it should be higher when I talk to industry sources they say oh it should it should be quite a bit higher so we find that defaults matter intermediation power is real so that means careful monitoring of algorithms shaping market outcomes is that will remain crucial going forward if you're a business note that the short run and the long run impact when it comes to recommendations are extremely different right I showed you in the short run it's great you should just make them super price elastic everyone benefits but in the long run there's a real entry trade-off that you have to consider and even in the medium run these price elasticity's are redistributive so that means you should reduce your aligns on short run AB tests and try to supplement them with models or long run tests finally if you're a regulator note that self-preferencing can be welfare enhancing that's what we found here in this particular setting with this particular data I don't want to overemphasize that I'm certainly not saying it's always welfare enhancing what I'm saying is a much simpler point that is really rather obvious which is consumer preferences matter when consumers have the same preference for a platform's own office that the platform does then the platform's preference for this office and a recommendation algorithm raises consumer welfare and that means that this really this issue of self-preferencing that we're trying to sometimes address sort of by the stroke of law it's really an issue where we have to decide on a case by case basis using the data that is available and it's an empirical question whether banning will increase or decrease welfare in future research I'm extremely interested in looking at paying for recommendations what other people call sponsored search and I would call sponsored search guidance I think it adds an interesting complexity to the situation which I'm very grateful for the fact that with the buy box algorithm I didn't have to worry about this but in other settings I think that could yield even worse outcomes as merchants essentially compete not on price on lowering prices but on upping their bids for the consumer's attention thereby much decreasing the efficiency of the market thank you so much and I guess I'll turn it over to Mike for the discussion awesome thank you Mike take it away great thanks a lot for having me I'm really excited to let's see the work and presentation form and also discuss it as you can probably tell from the presentation or guess the paper is also very well written and so I think it's a really nice read for everyone it kind of shines through in two ways first it's extremely clear about the assumptions in the model kind of refreshingly so and very clear about which ones matter and which ones might be harder to digest and then second it really develops a theory of harm around entry I was admittedly a little bit skeptical to begin with my experience in platform markets online platform markets is a little bit more about ebay where you have a proliferation of products and it's kind of hard to believe that the marginal products are that valuable or the marginal listings I should say I think Leon and co-author pretty convincingly established in the paper that entry could be a big deal here they're not that many products to begin with and so it makes sense to investigate it so I think that that's really nice I'm also I mean this is just a central question not just in platform economics but probably the focus of a lot of antitrust economics at the moment so I think it's a really important question to answer and so I think a lot of people have been trying to answer this question and it's been really hard to get good data and so I think the data here is not perfect but Leon kind of highlighted where there are really improvements over what else you could find and in fact often thinking about how to evaluate these platforms and potentially regulate them in the future you know they own the data Leon's trying to estimate the recommendation model which is very much proprietary information and kind of the central part of their business and so trying to infer these things as hard and I kind of congratulate the authors on how much progress they're able to make I'm especially impressed by the considerations that model I'm often a bit skeptical of papers that are imposing pretty strong econometric assumptions to identify consideration sets as opposed to obscene them in the data Leon doesn't see them directly in the data but given the institutional environment and the data he does have I think it's quite excuse me convincing where they arrive I want to just in my few minutes bring up a few thoughts about on kind of directions they could take or or just how to think about this research you know in terms of thinking about what self-preferencing is I think you know other researchers have had a slightly different definition which is not whether Amazon or the platform gives a preference to their own products in the recommendation algorithm whether that preference exceeds kind of the demand difference condition on being shown both products and so I mean Leon makes the point that a reason why this is beneficial the self-preferencing is because it's steering people towards the products they seem to demand I'd be curious whether their steer is there more or less steering than demand might warrant and that might be a more kind of direct antitrust question given I mean given the results I don't think it'll change then the conclusions so I think it's a bit more of an academic exercise but in terms of interacting with the antitrust law that's where I would go excuse me I also wanted to you know bring up in the bigger counterfactual which is let's change the whole algorithm entry is going to matter a lot and so I think there are a few assumptions built into the model that's um that are working against this as Leon mentioned but there are also a few that are working maybe towards entry mattering a lot and we might think you know we want to investigate further um the first Leon was very upfront about but it was um the marginal entrance may not in an ex ante sense may not have lower quality because entrants don't know their qualities when they enter and so I would encourage and it sounds like the authors are working on this um that seems like a really valuable path to take in and if it requires having fewer products I would probably go for it and I appreciate that um you're not doing one product like I did in my paper but um I think you could do fewer products maybe to save on computation but add some more potential selection on quality um otherwise you know the marginal entrance is going to usually be a reasonably high quality type um this second thing is it was kind of related to the first question that Jacques asked which is the demand model so um it's a flat loge um so the you know the next product's probably pretty valuable and we in part infer that because we see kind of noisy choices in the data we see products that aren't in the buy box still have decent market share so we interpret that's okay first off they're a decent number of naive consumers or no sorry sophisticated consumers because they reach these products not in the buy box and second they value those choices and I think that second part's kind of a strong assumption about is this are people choosing things and really they want that different product or was it a pretty close call and I don't really have a better sense of how to infer it from the data but I do think interpreting the loge at choices in the welfare framework is you know driving the results a lot especially in the bigger counterfactual where you remove all of the search um the last thing I just wanted to bring up at the very end was I personally have found entry models are pretty challenging to fit across lots of different markets in terms of fit um and so you know the in data fit here is the in sample fit here um it's hard to pick up why does this market have 12 and why does this other market have two entrants um and so kind of the conometric predictions usually much more closer to the mean and that's that's what the authors are finding it's not obvious to me it's going to lead them to make incorrect conclusions about the change in entry in response to these policies I found that often kind of matching the cross sectional distribution of entry across markets doesn't always give give great fit on on treatment effects when you do see them um but uh kind of the lack of in sample fit is something I would think about a bit in terms of about how much does the model have to explain entry across markets um so with that I'm kind of curious what other people think in terms of the Q&A


What's going on everybody? Got Kyle and Shawn here relating sports cards. We're ripping, this is a six box NFL mixer, the Immaculate Luminance of Elite Patches six box mixer. This one's got one iMac collegiate, two plays and patches, one Daunress Elite and two Luminance. Good luck here guys. Let's do it. Let's do it. Let's do it. Next break with spots is going to be our half case six box breaks of gold standard both at 10 to go. Both at 10 to go. iMac collegiate is going to be next and then seven to go on PYT NT. Teams on sale for all three of those PYTs. All teams on sale. Let's start with the elites. Good luck here guys. The Immaculate Luminance Elite Patches six box mixer. Golfing out with this Shawn. Play around with golf after this. Yeah. Start off with a Josh Allen. Elitist. Elitist. All card ship on this one. Okay Jeffrey taking the Steelers and NC my man. Jeff thank you sir. Thank you Jeffrey. Appreciate you. Elitist of Allen. Filler and base.  So we'll go through the base a little bit quicker. And exactly Steve exactly 299 or 999 Christian Gonzalez for the Patriots to 399 we got a rookie Ventrell Miller Jags.  It's probably for us. There's a rookie Devon Achan rookie on deck. Kamaya Raiders rookie to 399 on the orange. Base base base. The fake Josh Allen. Yeah the real ones for the Jags. It is a two elite deck. The dolphins. E. E. We play I mean I have that one. We play the 2k 2k one. Yeah we've been playing the 2k one. It was uh it was cheap slash free at one point. So everyone snagged it. I have that. I haven't played that one. 399 we got Fred Warner Niners. There's Kelsey. The golf game is pretty dope dude. Some cod. Ain't nothing wrong with that.  Bigsby Miller game you're good at that's fair. I'm not good at golf. Say that. I mean understatement to uh 349 full throttle as Rashad Bateman raided. Shady I'll play you sometime. I have that one too. Yeah I haven't played that one Shady. I haven't played that one. That was fun. The Masters. I think it's better than 2k. Derek Henry. But nobody wanted to buy it so. Fair. I got it for like I get any game. Yeah 75 spellbound. You tell me about that. It was a great ending. Yeah I mean got some action going for sure. Not great if you're a Saints fan that was. One in one here. B. John Robinson blew to 99. We are uh one and one at this table on the pickle. Okay I'm going to write it down right now. 2149 is Drake. I'm actually going to take a picture of it. Falcons.  It's a rookie Tyree Wilson Raiders on the elitist. Hey my name is for Shizzle. For Shizz. 2011. Thank you Todd buying some uh gold standard spots. 999 Brian Breece. Saints and on card auto. This one's going to be Cedric Tillman. Penn Powell's purple ink there for the Browns. It's Tillman rookie out of Tennessee. Tennessee. Old Tennessee. Tennessee. Thank you Todd taking the Jags and um gold standard number nine. We'll get after it. 2 49 got Diane Henley. That's going to be rookie for the um Chargers. Purple to 49. Base Vets. I mean you're home right now. Here I'm going to type it in. Just hit me friend request for me. 2 999 Byron Young. And we're a little forgetful when we get home so it's a Jared Hall on the um uh Vikings working on card. Penn Powell's nice one there. The backup QB. Shall about to have 50 PSN requests when he gets. Hey if anybody wants to address me hit me up. Just make sure you that I'm ready. Yes you are. I dropped my Instagram in uh in the chat yesterday. I got like eight followers. I was like okay. All right. Just leave me a little note. Playmakers a lobby. I'm down to play with everybody man. I love it. I love it. Yeah Sean's a gamer. 74 Charbonnet. Nice one there on Seahawks rookie. And base base base. Any guys let's do luminance now. Save that iMac for last. Four shizzle. Nice at it. He is pretty good. I'm nice at it. He is pretty good. I'm working on them irons. Oh yeah you just I don't know like I gotta see your setup like what do you have to do. Shorter to 50. You said you're using the controller. Yeah I'm on the controller. Oh sorry about that. Points 400. What does the random that the end. Unforgettable Davante Smith. Because I'm watching you don't do full slaves. Yeah I just don't go all the way back I guess. And then half the time it like it doesn't like when I do that so it. Yeah that game is all. It's tuned to 50 cardinals. What's it called. Dynamic Johnson. I wouldn't say anything. Timing. Yeah swing time.  Got ETN. Ooh nice one there on the auto. It's a 50 nice one there. Jags. Good running back for the Jags. And CD lamb. We will sasquatch but I already have enough ordered. I'm already full for tonight. We'll have a little bit of time I have looks like Tom Drew Richie another one for Tom and then Jonathan L. I believe that's all I'm going to have time for today. We'll have some more tomorrow. Thank you Brett taking the buck spot. Yeah so I'll have some more tomorrow with Forest but no more room for tonight. There's a patch. That one's going to be AJ Brown on the jumbo jersey. That's nice the 25. There you go birds. It's a little four color patch. That is sick 12 to 25. For the Eagles. It's a nice little patch there. Jameer Gibbs is 75 Lions. Got Marvin Mims to 175 Broncos and unforgettable to 100 CD lamb. That was a good pack of luminance right there. Renumbered cards and the AJ Brown. Yep tend to go on gold standard. Go Dolphins. Go birds. Vroom. This guy out of here. I'm a pro gamer. Really saying well hit me up. Are you really a programmer? Josh Downs got Luke Schoonmaker. That's for the Cowboys. Their rookie tight end to 100 and Jackson Smith. Nice one there on the on-card year one auto. That'll go to the Seahawks. Jackson Smith and Jigba. Dynamic of Mr. Gibbs. Oh we got the Badaard show on. Honour Badaard. There he is right there. Honour Badaard. The next goat. What they're saying. There's Josh Allen to 250. Bill's. Nice man what do you play? Dolton Kincaid to 100 for the Beals. Unforgettable. No I believe him. I believe him. And what do you play Saint? Thank you Tyler taking the Ravens in gold standard. Second one's down to 7. First one's still at 10. He's 89 I believe. Or 98 one of the two. He got 10 with the ball over the puck. Just had it. Yeah 98 right there. He's pretty nice. He's pretty nice. Yeah he's like the Blackhawks. So 150. Kenneth Walker on the Blackhawks. A Kindred Miller to 100. Saints on the rookie receiver. On the cubic. Dynamic is C.J. Stroud. This one there on the C.J. So the second one does have 7. First one has 10. It'd be the same case if you guys do buy both on those. Peyton Manning nice one there on the Player Warrant Jersey. That's a vintage materials for the Colts. Rollercoaster Tycoon. That's a good game bro. You're good at it. Say Dante Banks to 150 for the Giants. And rookie to 175 Miles Murphy Bangles. Alrighty guys now we'll do the plates and patches. Tyler taking the Jags in N.T. Really that's pretty cool saying it. Counter strike there we go. That's awesome dude. Let's go. His son's been betting on counter strike games. Doing pretty good at it too. I put my gamer tag up there in chat. Hit me up. 35 is Hutch. Nice one there. Lions. Big Hutch. Beast of Ante Adams for the Raiders. Full coverage. Jumbo Jersey 59. That one's going to be Will Levis Titans. 259. Interesting numbering there. Redemption is Deuce Vaughn. Rookie Patch Autos Blue. That one's going to go to the Cowboys. Cowboys on their rookie. Running back Deuce Vaughn. Patch Autos Blue. And there's Sean's boy. Marquis Marx is Jalen Waddle. 7 out of 10. Nice one there for the Finns. Let's go Dolphins. That adds up to the Jersey number. 7 out of 10. Hey let's go. Nice one there. Marquis Marx. And the Funky Bunch. Jesus. Nice dude. Yeah that's I would consider that broke. Yeah. By all means. Counter strike tournament check. Yeah. I just need a test copy. Yeah. He's good at it. Yeah. Oh I love it. Here's a Herber. It's a 35. Nice one there on that metal. Go Chargers. Got a Justin Tucker base. Swag tuck. Let's play man. Jersey to 75. Double coverage. Zae Flowers Ravens. All the boys play here. Everybody plays. And Jake Hayner on the RPA to 75 this time for the Saints back up QB. Little three color patch. And on the back another nice one is 75. It's Raven. Zae Flowers. April showers. I did see that. I did see that. 75 Zae Flowers. I sure did. Alrighty guys. Last one in this one is iMac. iMac collegiate. We'll get a full case of iMac going next. Also got to do one random for a 400 points. 400 points card. Impressive. I feel Sam as a beast gamer. You feel wrong. Stay chippy. Stay chippy. Let's go Skullface. Skullface killer. Let's go Skullface. Everybody win 100k around here. Let's go. I know I get in on some. Let's go boys. I know Shipe won 100k in Draft Kings. Damn. You got Saint won 100k in Counter Strike. We got our boy Skullface won 100k in Cod. Let's go. Let's go boys. There's a 35 Ken Walker. That one's going to be for the Seahawks. A boy butternut won $3.50 in the 4-9 one here. Let's go. 350. We'll take it. A boy butternut squash. Just a nice one. A gloves jersey to 75 Anthony Richardson. Nice one. They're Colts on the AR Gloves Relic. Kind of is Tucker Kraft. That one's going to be to 99. That's a Packers rookie. Tucker Kraft. Let me make sure actually. Pretty sure that's right. Yep. Packers on the rookie. Tucker Kraft introductions to 99. Right on. Right on. Auto to 49. Will Anderson, Jr. Nice one there. Texans. Texans on the rookie auto. I don't play Todd Dillon though. Dug into 99 on the RPA. Nice one there. That's Chargers rookie QB 18 and 99. My son Dillon's pretty nasty in all those games. Blackjack, you took. Oh my goodness. My goodness. He's taking everybody for their money. Taking everybody for their money. Let's go. That's what I'm saying. I'm getting his advice on the game. There we go. Oh nice for the Colts again. This one is Anthony Richardson to 99 on these Signature Moves auto. We will mag that for the Colts. Very nice. Had a patch Richardson and the auto. That'll do. Very nice one there. We'll mag that Colts. All righty guys. Let me do a quick recap. I'll do the random first. 400 points. Let me roll two dice. We'll do a recap right after. Seven times. Seven times on the random. One moment. Let's go, Shite. You show that casino whose boss. All righty guys. Seven times here on this random. Got all teams in. Good luck. Cardinals down to Jets. One, two, three, four, five, six, seven. It is Dolphins. Dolphins. Taking home the 400 points. That's so sick, Shite. So sick. Yeah, that's pretty sick. All right guys. Let's do a quick recap and then we are moving on to iMac full case. Oh no, I only got the head space for that. No chance. Too cloudy. Need to code and take in the Rams in our gold standard. 75 is Richardson. 75 is a Flowers. 59 will Leviss. Payton Manning Jersey. There's a AJ Brown 25. RPA Max Duggan in 99. Got a Will Anderson of 49. This one's Tucker Kratz in 99 for the Packers. Matthew, we're full tonight on personal blood. Yeah, Matthew, we are full for tonight. If you get a order in now, you'll be one of the first up with Forest Tomorrow, but we are going to be full for tonight. So a Jake Hayner RPA to 75 Saints. Zay Flowers to 75. There's Waddle to 10. Nice one. Dolphins. Marquis Marks. Deuce Vaughn. Cowboys. Got Kendrae Miller to 100. Bills is Dalton Kincaid to 100 on the auto. Base auto of year one. Jackson Smith to 50. We got Travis E.T. and Jags. Another 50. Clayton Toon. Got Penpal Auto. Jaren Hall and Penpal Cedric Tillman. Purple Ink. And 299. Immaculate Signature Moves is going to be Anthony Richardson. There we go. Colts. All right. All right guys. That'll do it for Immaculate Luminance of Elite Patches, 6 box number 9. We will get, yeah, we'll get Immac full case going here next.