So one day this donkey fell into an open well, which was dry. It went and fell inside, thirty feet down and started crying piteously wanting to come out and no way to come.     Working ass, all right. Retired ass, nobody is interested.  So all of them brought their shovels and shovel after shovel they started throwing the earth into the well.     Every time a shovel of earth goes in, one step up, one step up, he's coming. And then he climbed out.   Then the farmer also, the old farmer also thought, wow, he suddenly felt pride. He said, see my donkey, such a smart donkey. And he tried to go and caress the donkey.   It'll come back and kick you in the face one day.  Today is the day to get rid of the nonsense, not tomorrow.  And if you every day say today is the day and one layer goes, in a very brief time, when I come back after a few months, I must see shining beautiful faces, not… What? What? Yes! Better. You better be, okay? There are certain things you will know only by plunging in. If you just breathe this energy which is all over the place, even the physical process of life could be reversed in many ways. We are only talking about moving from the mundane to the mystical, from the logical to the magical. It will hasten one's journey towards consciousness.


Okay We are here Another week has flown by of course I say the same thing All the time hello to everybody Welcome to Progressive discussions. I'm your host James P. Madonna of Megalife 21 and I am here at the newsletter censored research center in northeastern, New Jersey With my co-host and mentor the one and only the Reverend doctor William J. Eisenman. How are you feeling good? How are you feeling this week, sir? I guess you had an extreme fall this week He fell and he hit the the bathtub of his upper arm Wacked into the bathtub and he was lucky not to have a broken arm exactly You weren't even taking bumps in a pro wrestling ring You you just fell in the not that he was a wrestler, but I Had my share back when man Bob back. So all right. So it is The month of the eyes of March or is it starch coming up starch the month of starch March goes in like a lion and comes out like a lamb Something like that. Yeah, I think I say this every year when March rolls around in like a lamb out like a lion, but I will say this Yeah, anything can happen to March weather wise But I will say this come March 17th if the same thing occurs that happened the past couple of years and nobody has Or you can eat good quality brisket corned beef and cabbage. I have my own a Nice big one that I got and I'm gonna make my own for the first time.  I don't like sweet desserts. I like bland desserts I'm not into that's a dessert sweet. I don't know what it is. I heard soda bread is a dessert I'm not too sure about that to me. It is because there is some raisins in it But there is there is some sugar and I were so ah some Light though.  It's got a lot of carbs, too Yeah, this guy. That's why I eat the mild one if during holidays the one with all the sesame seeds on the outside It's oblong and it has a lot of with Sesame seeds, it's not as sweet as the other but anyway, yeah, we're getting too frivolous with our conversation Yeah, yeah, yeah Because we got a lot of heavy-duty things to say first of all first of all as first first off I'm only gonna give greetings to those that Care about me and my show and I respect this There are people out there. We have people like that. There are people There are people out there. There are people that respect the show that that are Progressives or who claim to be progressives who are actually some of them are very dedicated progressive warriors I have to admit they're good, but I have to also say that they are narcissistic egomaniacs because They post everything on their own page their own profile They do not contribute to my ever-growing large political group called Uncensored hard-hitting truth, which is on Facebook They don't contribute to my largest group, which is holistic health talk, which is on Facebook They think they're gonna get discovered They have Way before old James all over here. Let me tell you something. They got no pizzazz They have no charisma whatsoever. They have they have bland personalities and a bland face They may have a good face for radio. Well, let me tell you something. They don't hold the candle to me and the Reverend Dr. William J. Eisenman who Can't hold the candle unless he's pouring hot wax on some ladies nipples. Oh Hold on Liberty bells By the way, my other hotel bell broke because I got it at a dollar store But the Reverend Dr. William J. Eisenman's supplier of props professional props From the West Coast. I got a new hotel bell Louder crisper heavy-duty. I love it. Listen Now that's that's a beautiful sound. Thank you. Thank you Oh, my my black thorn chilele is ready to connect with the skulls of teabaggers and Republicans and sell out Traderous hold on I Would love to kick this was a living entity. I would hit this too. But anyway Traderous sell out blue dog Demon crats that are on the take and we know who they are. All right now right away before you forget I know you're gonna say something there is a debate Tomorrow night Now listen to this debate. All right tomorrow Sunday It is gonna be on Fox News with Bernie Sanders and Hillary Clinton and Since it's on Fox, you know, they're going to try to instigate a knockdown drag-out No holds barred battle between chillery and Feel the burn Let me start because I got stuff I Got stuff man Stuff remember buff Bagwell the wrestler on Wcw buff has the stuff Okay, he's coming to ring with a top hat black top hat Whoa, I had a picture of himself on the top and then when he used to lean forward he Had a smiling face of himself.   I have first-hand knowledge by our IT director and and technician All they did all they did for him was remind them that he had to keep on renewing The Norton and they did nothing but pester him telling him constantly every little thing that's going on in his computer And it did not catch one Trojan or one virus nothing He actually has superior results with all the free Internet security Out there that is available on the internet For free you go to file hippo comm and you can see they all apprehended apprehended Trojans and the computer possible Computer viruses everything all the above and the was it the out of where and the and the malware and Tupperware and I What is that that now where fights? Yeah. Yeah, the that other term they use I'm talking about a real Real a wear program anything out of where anything unwanted that comes into your computer the free programs intercepted them Spyware I Was thinking of that word. I yeah, you know, it's by word to anyway shame on you Norton But what else is new in the capitalist system of the United States of America, right? I Want a bash?    I don't we're doing some serious thinking It's all about deception Reverend Bill the capitalist system is pure deception Okay, well, it's pure deception that people understand it from their point of view and think that it's a Good economic system.  If somebody if you're getting into a debate with a tea bagger there, they'll never understand this but with social programs helping the poor and The poor having money in their pocket That they didn't have before Including food stamps they put that money back into The economy either by they have to spend it all in there in it on food So the markets make money from food stamps and spending money from Social programs, which only make up like one to two percent of the total budget really You know, but a tea bagger would think that it's like they believe the lies that makes up the largest portion of the budget Which is military waste and bloke 57% 57% but they all that money in the pocket of the po-folk goes back into the economy to further stimulate it so You know the old story how many refrigerators can the rich buy? The rich do not spend as much money as the poor and the black Simple as that and and if you if you have experts Really analyze the flat tax that and the consumption tax that republicans want They want to do away do away with the IRS of course It it arranged the way it turns out at the end is that the rich pay even less taxes than they're paying Now and we pay more and the middle class pays more So all right Okay, I want to say shame on you to the oil rich countries of the Middle East like Kuwait For exploiting workers from the Philippines poor people from the third-world country of the Philippines are get our receiving a Maximum because sometimes it's less a maximum of Lousy 100 us dollars per week Okay, I found this out You know how much money countries like Kuwait have To throw around or any of those countries Dubai United Arab Emirates, whatever They have they are filthy rich, and they pay these poor souls allows the 100 us dollars per week and If you throw this up to their face though, they'll probably say it's a lot more Then they'll make they're making in the Philippines, but guess what I blame the Employment agencies in the Philippines for hooking them up with these exploitive jobs in The oil rich country, so they're probably taking a commission and just sticking people there, you know And then of course they'll throw it up to your face. Oh, well, you're making more money than you did before You know, that's like a Barbara Bush saying hey You should be thankful you're sleeping in the in the Superdome football field.   Thank you now Bill Clinton Super mega chiseled soul of shame shame on you you goosenecked a bulbous red nose Corporatist Talk about sellout corporatist blue dog Democrats He'll Clinton showed up at four separate polling area areas that I believe in the Boston metropolitan area The mayor of Boston allowed him to do this He naturally caused a a public scene which prohibited People from voting in the Massachusetts primary Or I made it very difficult to get through the crowds Four separate polling areas what he did is illegal But when the state of the state of Massachusetts claims he did not do anything wrong and so I smell cronyism and Favors paid for favor system. Yeah, hey paid paid for favors that are paid for I Have a feeling that the super PACs I think I think behind closed doors all this is planned To do everything they possibly can between, you know, the campaign You're okay, that's it. I told you croaked on me the campaign Contributors super PACs for Hillary Clinton and Hillary Clinton and her husband. I think they're all they've all planned every Which way possible including the mainstream corporate control media? to prevent Bernie Sanders from getting the nomination and the election of course they don't They don't know now they're mentioning Bernie Sanders name more because they kind of have to because he's he's you know There's a hell of a lot of more people feeling the burn nowadays and he's like Coming out with the big guy kills me God bless you.  Oh, yeah, but I don't like CNN they deliberately don't want to mention him But yeah, hey, you know, you got a what what is a Hillary Clinton running against the ghost? Yeah, you gotta mention what you're taking what Bill Clinton did and it's illegal in Massachusetts. Okay That's all that's all you have it's electioneering. That's what it's called electioneering electioneering Is that is that as bad as Jerry Mandarin or is it no Jerry Mandarin is this is mapping out Areas, okay. This is electioneering in the old days. They used to go in and tell you who to vote for That was electioneering and that's what he's doing He's not supposed to be in the room. He can be outside. I believe but he cannot be inside He was elected. Oh, he went inside too. Yes. He went inside the room. So he was in and oh Not at all. He was in them. Oh underhanded tactics, uh slick Willy underhanded tactics Yeah Now I saved the best for last should have been arrested on site It's amazing how Certain people have clothing of Teflon that that now they're not held accountable Nobody's held accountable these days anymore. You see anybody going to jail from Wall Street. No, okay And and and all these people online that claim I have I have the dirt To totally bring down this other person All right, I you know I can I can blow to whistle on this person on Hillary I got so much dirt on Hillary blah blah blah blah threats statements Don't just talk about it. If you really have the dirt Just do it Do it. Let's see it man. Don't just make claims and statements to to the online media to People interviewing you just do it surprise everyone Surprise I just brought down Hillary's campaign surprise do it if you really want to do it you don't just You know get the publicity that you know so well They want their 15 minutes of fame or maybe they want their 15 Maybe they want their 15 minutes of fame, you know and see if Hillary's people pay them off to shut up if they really have anything if They really have it. What I'm saying is we if you don't if you don't have to show it You're just looking for your 15 minutes of fame, right?  You could see the apple did not fall far from the tree No, I did his father who is crazier than him. That's true He has a puffy face like the guy that does the e harmony dating commercials, you know He has the same way of talking or working out But he harmony. Yeah, no, I don't I don't the one the new commercial the one. Oh, it's no one year old It's nauseating. I'm on a dating service and something How's that working out? Oh, yeah, he says that you get on the harmony. Well now the match because you don't want it to make all the She don't want to fill out all that stuff. She want to fill out the the questionnaire You wanted do you want it fast or you want forever? Let me tell you something. I was told there's no questions. There's nothing about hooking you up with somebody you're attracted to in terms of How they look and as far as I'm concerned physical chemistry comes way before friendship and Compatibility and if you don't have the physical chemistry You're gonna end up just being buddies, you know, I mean hey, but I mean I Haven't seen a good-looking person on any e harmony Or match comm commercial, but anyway getting back to yeah, Bill Clinton. Bill Clinton did was illegal People are just not held accountable now Chris Christie seems to be the Donald Trump shadow.   Oh, yeah He says I can't do it's too late. I can't I can't do I can't call him off. I Can't call him up. You know what that's a good answer because I wouldn't want to see them be called off. I Said that that's a blessing right first of all Donald Trump is a blessing for Bernie Sanders in disguise Well, if they if they haven't been called off or etc. Then there must be something there Well, yeah, she keeps saying there wasn't they're not they're not gonna They're not gonna be done is the guy who set up the server and etc. They've given him immunity So that now he can testify, but they're bringing in people to under under oath, right? They're bringing people into the interview that guy Pagliani or what?  They're all being Consult with a console Okay, now listen to this very carefully. This is my last section of the monologue This past Super Tuesday two million 500,000 two and a half million more voters showed up for the Republicans the GOP then for the Democrats on Super Tuesday two and a half million more more voters showed up to vote for Republicans then The Democrats this is very very very bad sign. It's a bad sign.  That's my first question. If you don't vote I'm not interested in hearing what you have to say. This is bad. This is pathetic and it shows that all the excited Millennials the young people that are feeling the burn are still not showing up at the primaries to vote Okay Shit or get off the pot Don't get excited over over the the election in 2016.    She's sounding more and more like a Republican She is the probably the best Republican the Democratic Party ever had or seen And you know, I believe it was Grover Cleveland who once said Former president back in the day. He says America will receive exactly the kind of government it deserves Americans will get The kind of government it deserves, and if you don't participate, well You only have yourself to blame, but it's a pattern Reverend Bill It's a pattern every time election comes people talk so much shit on on the internet and they don't show up I'm talking about the so-called progressives the nuts show up


Hey guys, welcome back to my YouTube channel. This is Daniel at Rosal here for today's video I'm going to do a demonstration of how you can sync audio and video in post-production in Caden live on a bunch of Linux and doing that really using the manual method So Caden live somewhat recently did introduce this feature for setting your reference audio and then automatically Sinking a second audio layer the method I'm going to be showing this video is how to do that completely manually if you just want to do it The simple way you want to record your video using one device Perhaps it's got sound as well But you don't want to actually use that as the audio in your final take you want to use a Different microphone so in the example today What I'm going to be doing is reading a little bit of text on from Wikipedia About Jerusalem just to give us a short random text. I'm going to be recording that using the webcam I'm actually recording this video using that's a Logitech 930c. That does have a built-in microphone So I'm going to be availing of that fact to use that as reference audio Then I'm going to be rigging up a zoom h1n Audio recorder using a lavalier microphone very similar to the one I'm wearing now I'm going to be then importing that audio file into the same timeline and I'm going to be using the built-in audio from the webcam as a reference and Setting the actual audio we want to use from the zoom h1n lab by reference to that And then exporting that as our finished project. So let me show you how this can be done The first thing I'm going to be doing here is using OBS studio. I've set up my webcam I've set up the audio source to be the built-in microphone on the webcam and I'm going to hit record when I'm ready to roll and Simultaneously, I've got my lavalier microphone Clipped on to me here going into the zoom h1n and ready to record on that source as well Going to start recording here then punch the button on the zoom h1n and when both sources are done We're going to sync up in that Caden life Jerusalem is a city in Western Asia Situated on a plateau in the Judean Mountains between the Mediterranean and the Dead Sea It is one of the oldest cities in the world and is considered holy for the three major Abrahamic religions Okay, so what I've done now is I popped the memory card out of the zoom h1n And this is the card that's going to have the file that we grabbed from our lavalier of the same audio me reading the Wikipedia page about Jerusalem, so I'm just going to open it up here and There's only one Wav file saved on the zoom currently, so I'm going to open this up in Audacity and This should be me reading the same text. Okay, so here's this file in Audacity I'm going to play it now just so you can hear that is it is the same audio Jerusalem is a city in Western Asia Situated on a plateau in the Judean Mountains between the Mediterranean and the Dead Sea It is one of the oldest cities in the world Okay, so here's where the fun is going to start. So I've opened up a new Project here in Caden live, and I'm just going to drag in The clips here so you can see what we're working with so the first file here is Test narration webcam video on audio.flv that is Me speaking into the webcam, and that's the audio from the webcam So what I'm going to do firstly is drag that into our project bin, and I'm just going to flip over the profile Next clip we had there in the bin is zoom 001.wav, so I'm going to put that as well Into the project bin. So now what I'm going to do is this follows. I'm going to drag Me and the webcam into the timeline, and then I'm going to drag this into the timeline And the rest is actually really really simple So when I said this was the manual method, I wasn't kidding So what I want to do is let is just mute the audio on the reference track here And you can see how incredibly simple this actually is all I need to do Is make sure that the way that the waveforms are matched and this should play pretty much in sync You can be as exact as you can be so I'm going into this is as far as I can zoom in on kden live And you can you're making frame by frame adjustments here to the waveforms, but and then just play a bit and just see how that sounds So that looks totally fine to me There is no perceptible lag between video and audio at least not least to my eyes as a non-professional video editor Again, this is kind of a quick and dirty method So, um, you know, there is an automatic feature for this But if you're just doing one take of an interview and you're just literally grabbing an sd card out of a zoom popping that in and All you have to do is align the two So you basically use your original audio as a um as an alignment track and then you add in your extra audio Don't forget that you need to put this to mute and the beautiful thing is that this will if you want to at any time check You can unmute We actually have two microphones layered one on top of the other there But they're so in sync that you actually can't hear that even if they're one frame out of sync or a few frames listen to now It sounds like a mess So, uh, the good thing is that once you put this on mute and you render out that's going to render without audio Track one, so you're just going to get audio track two So in other words, you can just use this for guiding the sync in post-production And this is a really easy albeit manual way of syncing your video and audio from a separate microphone In post-production using cate in life Jerusalem is a city in western asia Situated on a plateau in the judain mountains between the mediterranean and the dead sea It is one of the oldest cities in the world and is considered holy for the three major abrahamic religions Hope this video was useful. If you'd like to get more videos for me Please feel free to subscribe to this youtube channel. Thank you very much for watching


Hi Trish. Hey Krista, what's up? I'm good. How was your weekend? What's good? What about yours? It's possible. I'm going to buy you a bomb. What's up with you? You wanted to work too? That's how they pay you on foot yesterday and talk about your business? Yes sir. You see the two of you all? You see the two of you all? Just leave me alone. Really? Hey, hey, Krista. What's the matter? What's the question about it? Do you want it again? I've been taking it for a lot lately. My mind, I've worked, everything. It's not 100 percent. You know what? What if I sign you up with the EAP? EAP? Yes, the Employee Assistance Program. Oh yes, yes, yes. I remember, I remember. Yes, that would be great. That would be great. Can I come into my office with you? Sure. Hi, good afternoon Chris. Good afternoon. Welcome to the Employee Assistance Program. Hello. I just came to say thank you for recommending me the EAP session that I did. The sixth session, it was really, really helpful. Sure. I'm really happy that you apologize to me. Yes. Thank you.  You also. Thank you. Hey, morning, Krista. I just came to apologize for my reaction the other day. Really? Yes, I did come to apologize and I do hope that you accept my apology. It's okay. Your stuff happens. Thanks. You have a nice day. Thank you sir. All right. You changed it. If you believe that you are victim of bullying or you are the bully, then seek help. Seek counseling. Call the EAP at 468-2269. EAP works. Let it work for you.


Thank you very much. And Imka is also here, so she can help spread the burden or share the burden of interesting questions. Well, it's the light to be to be here in Toulouse without Castle A. So I want to talk about this paper about framework for detection measurement welfare analysis of platform bias. And again, with Imka. So everybody in this, you know, the seminar knows platforms and regulators, it's all on our minds are all kinds of headlines. And in particular, the, we now have laws either being contemplated or put into effect that ban self-preferencing. There are a lot of other concerns about platforms, but, you know, today we want to be focusing on this question of self-preferencing that I think a lot of us have been interested in as well. Now, there's a, you know, there's a, excuse me, there's a sense in which regulatory action is way out ahead of research. And I think there's an urgent need to, to, to, you know, to detect and measure the welfare consequences of self-preferencing. But the thing is identifying unwarranted self-preferencing is not at all straightforward. I mean, it sounds kind of straightforward, but I think in some sense it isn't. So the basic questions that we want to address or try to address in this paper, first of all, what is self-preferencing? Secondly, how might we detect it? And then third, what is its welfare cost? Or at least what I should say is how might we go about measuring its welfare cost? I want to be clear that this paper, isn't it so much about the phenomena as it is about how, how to study the phenomena? Because the, as you'll see as we go, the data we have are all quite imperfect. But there's going to be a silver lining, so don't, don't tune out just yet. All right, so the generic setup that I think the self-preferencing question is typically about is some kind of ordered listing of products. Like search result rankings are maybe the most common kind of example, but you can also think of just the display of products on product pages. And users choose among some ranked list of products. And the platform in the background is choosing the ranks to serve some objective that might involve the welfare of consumers, the welfare of sellers, or the welfare of the platform itself. Now self-preferencing, I'll give a vague definition now, but we'll get more specific as we go. Self-preferencing arises if the platform ranks its own products higher, meaning better, it's a lower number, but higher in the ranking, then would maximize some combination of seller and consumer surplus. So we're going to think about self-preferencing as deviating some sort of frontier involving the interests of buyers and sellers. A roadmap for the 40-minute talk, maybe 35 left.   And we'll talk about two approaches that are, you know, being used, conditioning on observables approaches versus what we call outcome-based approaches. And we'll show with Monte Carlo evidence the advantages of the outcome-based approach. But to be clear, it's not that one is good and one is bad. It really depends on what data you can have. What we're trying to talk about here is not so much to, you know, to criticize, but rather to point out what are the advantages if you had different kinds of data and you could do different kinds of things. Then we'll go to some actual empirical data from Amazon and Spotify and Expedia to do some empirical comparisons showing how you can get conflicting results from these different approaches. And then finally, we'll implement the framework directly estimating simple versions of the structural model that gives rise to both estimates of the rank bias as well as the welfare effects of whatever bias we detect. And we will see meaningful differences across the settings that we study, which, again, is not so much about whether there's bias in the world, but rather that the methods, I think, can reveal some interesting things. Alright, so the model. Two parts to the model. Consumers are choosing among ranked products and better we're going to build in the idea that's both plausible and evidence-based that better rankings give rise to higher purchase probabilities. The platform in the background is choosing if they're end products to rank, it's choosing among basically end factorial possible rank orderings in what it chooses, how it chooses to depict the products. And in so doing, the platform is deciding, first of all, how to balance the interests of consumers versus sellers, and secondly, how much to advance its own interest at the expense of the consumers and the sellers. That's how we're going to think about a division of the problem. Now without self-preferencing, we're going to think about search rankings, for example, leading to a welfare frontier between maximal consumer surplus and maximal producer surplus. I think where that's the producers here are the third party sellers, of course, when there's also, you know, Amazon selling its own products, it's a producer and a platform, but whatever. With platform bias, the rankings are going to give rise to a departure from that frontier. That's the idea. Now, before I get into the implementation, let's just say what does one need an implementation and what we need from a demand model is a way to map product characteristics, prices and platform chosen ranks, kind of as primitives. Into the quantity sold of each product, therefore the revenue for each product, and the total revenue from the choice set, as well as the consumer surplus from the choice set. There are a lot of ways one might do this. You know, again, in principle, especially if one had the right data, one might think about search models, one might think about limited information choice models. So we're going to illustrate this partly for simplicity of illustration and partly just because our data aren't maybe what we wish they were, we're going to go forward, you know, go forth with pretty straightforward illustrations via logit and nested logit. But again, you know, put in your favorite choice model here, because our idea isn't about the particular demand model, we didn't invent that. Our idea, rather, is how to define self-preferencing, you know, with this welfare frontier idea. Okay, so on the consumer side, consumer I is going to choose among now I just we called it J, we called it and a minute ago, sorry about that, among J rank products and consumer I has this utility function. Now what we've done here is we've separated out this delta J zero, which we're going to call rank independent quality of the products from the part that's causally determined by the rank location of the product. Okay, so that's that's the causal rank effect. And so mean utility of a product is again you bar J is delta J zero plus the gamma times r J, but this rank independent mean utility is delta J zero. Now I want to be clear so if you think about rankings, think about a search ranking or something. There are two different ways in which the rank is related to the product quality. First, there's a potentially causal impact of the rank on purchase like as demonstrated in Ursus nice paper with that randomization. But there's also another aspect here which is that the platform is going to rank better and unobservably better products higher as well. So big note here in a box. Delta J is also related to our J beyond the causal effect, because the platform ranks better products higher, but we do want to isolate out that causal part because that's the part which by re ranking the platform could cause you know different things to be sold and different degrees of consumer surplus to be experienced. Okay, so outcomes depend on the ranking we'll use capital R just to describe the whole ranking of the products. And again, in logit land. So SJ is the is the choice probability for product J and it's got this familiar form. I think all we're doing here with the yellow stuff is just to bring out the part that's induced the causal effect induced by the rank choice. And the gross seller surplus of cross products going to be P my not now let me also make a simplification that I should make clear right now. We're going to go forward pretending and it's also true to be usually for our context that price and variable profit are the same thing. So we'll talk about revenue and variable profit interchangeably, but let's realize or remember then some context that won't be true. And so we've done a little thinking about how to deal with that problem, but it raises some different issues, but anyway, I'm going to start getting sloppy. Instead of writing P minus MC. I'll start to be talking about revenue. In any event producer surplus looks like this in a logic and consumers surplus looks like this. But the point is, all of these things are manipulable by the choice of the ranking.  So the platforms ranking choice again is a big combinator problem. Now we realize that this is related to some things people have been thinking about about, you know, optimal rankings serving interests of platforms and so forth. Rankings depend on the platforms disposition toward consumers versus sellers as well as bias. And then a couple of extremes and what we're going to call the welfare frontier maximizing CS. Now that one's kind of straightforward, because after all this e to the gamma term, it just is a is a bigger, bigger markdown, right, the worst you rank things. So if you wanted to maximize that some and therefore maximize CS you just put the best in the sense of highest delta J zero products at the highest ranks, and you would just order by delta J zero. That one's kind of straightforward maximizing consumer surplus arises from simply ranking the products by delta J zero. Remember delta J zero has in it. The effects of the product attributes as well as the price already like the minus alpha P in some sense in the usual model has already been subtracted out. The other maximizing PS or revenue that's a little harder, but we're going to propose that you could just rank by, let me just use a shorthand and say PJ times e to the delta J. So as it's going to turn out, that's, that's not as straightforward as it sounds, but it turns out that it works really, really well. The reason it's not as straightforward as it sounds is because, well, this, this, this ranking by, well, maybe a formula help to describe it, but but rather than argue against myself, let me just say we've done lots of numerical experiments and this simple approximation works really well. Okay. So the welfare frontier ranking according to PJ times e to the delta J zero is what's going to maximize revenue or producers surplus and ranking according to e to the delta J zero maximizes CS. And so this is just the downward sloping portion of this welfare frontier. So what we're going to do is define essentially the supply side of this problem via this this this function or this this this relationship so I J star is going to equal Kappa one times the natural log of PJ plus Kappa two times delta J zero. So if you think about that, this is essentially, we're essentially describing some family of functions that involve just ranking according to consumer surplus versus ranking according to something like P times Q. So if you think about values of Kappa, if Kappa one equals Kappa two, this is equivalent to ranking by something that's monotonically, you know, monotonically, or monotonic with revenue P times Q in some sense. And if Kappa one equals zero and Kappa two is positive, it's just ranking according to consumer surplus all that matters for the ranking is again the quality of the products. So, the way we're going to think about bias then is to add in another term to this supply function. There's a box here in this this plus I guess that's a side times delta DJ DJ is just an indicator for the product, the product being, let's say a platform product, or in other contexts it might be a platform preferred product you know but whatever it's some kind of product about which we suspect bias. So we're going to allow for for this this additional term in the supply function that takes you off the frontier. So if size not equal to zero there's bias and it's going to change the ranking, and it's going to deliver a solution to this problem that's interior to the frontier. Okay, so something like this is just hypothetical. So something interior, and then you can think about the welfare cost being the some measure of the distance between that term and the frontier. Okay, so let's let's try to implement or use this idea, go from theory of bias tests. So the supply function and bias detection that we're going to call this the conditioning on observables approach.  Now, if one were to imagine that this, this supply function were both cardinal and linear, hence the joke here, but cardinal and linear. Then one could just write the following, it would be that is our supply function would be equivalent to just the rank is equal to some Kappa prime times L and PJ some Kappa two times delta J zero plus some psi prime times D. One could regress ranks on these terms and the coefficient on DJ would well reflect bias. So this is the conditioning on observables approach through the lens of our setup. I think, in reality, the delta J zero is hard to observe because the delta J zero is it's easy to observe if you have quantity it's like a mean utility that you get out of a demand model but if you don't observe quantity, then it's kind of hard. So, you know what what people tend to do in reality for understandable reasons is they'll run a regression of RJ zero or RJ excuse me on some controls X, and maybe they'll put the price in there or not. That's not super important. I don't think and then an indicator for the for platform products. And so, so I provides a measure of bias effects controls for all the effects, or at least that's actually too strong a statement but if it controls and doesn't leave out uncorrelated stuff. But the thing is the side could also reflect on observe platform brand characteristics. You know, so for example, Amazon basics batteries might be desirable more desirable than their observable characteristics reflect or less desirable whatever. Yeah, there's some Amazon basics products. Okay. Now the other approach. So, so again, the just a, you know, to depart from the conditioning on observable approach. It's an utterly sensible thing to do if, as is quite typical, you can observe ranks you can observe characteristics. And so it's I think it's very much of interest to know, you know, whether there's differential ranks of Amazon, for example, products conditional on stuff, but there's a lingering worry that there are some observables that that are also correlated with the with the platform dummy. So the other approach, the outcome based approach, you know, is to is to basically ask whether conditional on the ranks that platforms assigned whether products sell differently and it turns out I think we can interpret that approach through this lens as well. So in the platform product, it's going to have well what's shown here Kappa one times Alan pj plus capital to and so forth, whereas a non platform product is going to have the same thing without the bias term. So in the absence of bias of course size equal to zero, they'd have the same quality expected quality, if size positive than the platform product at the same rank would be would be a worse product. If, if gamma that is if the causal effect of the rank is the same for platform and non platform products, then this gives rise to a kind of a very simple outcome base test for bias. Just ask the quantity essentially pursuant to the ranking, the log quantity stick in rank fix effects so conditional on rank and again you could control for price to be fully consistent with this framework, although I don't think it matters terribly conditional on rank to the platform and non product platform products sell differently kind of pursuant to the impact of their ranking on their on their sales. Now, the outcome based intuition, you know with revenue maximization the platform would assign ranks based on expected quality and better quality, better products would get better ranks, and the effective rank on sales is multiplicative. If there's no bias that you'd have the same expected sales for platform and non platform products conditional on rank. If they're self referencing you'd have a lower effective rank on sales for platform versus non platform products in some ways this slide is more intuitive than the last. All right, so implementation and data needs and this is not you know for all the kinds of approaches we're thinking about. One needs rankings and platform identifiers and then for the conditioning on observables approach one needs like characteristics of the products that you know the ones that belong in there. For the outcome based approach one also needs to observe the outcomes that the ranks in part cause and in part are correlated with like quantities, quantity sold to do the welfare analysis. So everything above plus an ability to get causal rank estimates because in a counterfactual you want to change ranks and say something sensible about what would happen with counterfactual ranks. Yes, so it's time for horse race. Now, so one thing we're going to do here is a Monte Carlo simulation with for the following idea. So let's suppose that the expected sales of well to try to try to see when the conditioning on observables approach works versus when the outcome based approach works or really when one when one is vulnerable. So expected sales depend on beta X plus tau Z and on and on the rank assigned. Now let's suppose Z is observed by the platform but not by the researcher. So these depend on X and Z as well as the platform indicator, but here's the problem. The platform indicators potentially correlated with the unobserved thing. So we can, we can set up a little Monte Carlo in which we, we have all these features and we can run the regressions the conditioning on observables regressions and the outcome based progressions. So what we do is we simulate data for a range of biases degrees of bias and a range of correlations between the unobserved product characteristics unobserved to the researcher observed to the platform and the platform indicator itself. And, you know what we end up with the sort of a pretty picture. There's a good color on this picture and things that aren't yellow or less good. So what this is saying is that the conditioning on observable approaches are on the left hand side, and the purple purple what those are our values to detect bias. Yeah, these are essentially the test getting it to test getting it wrong. This is, yeah, this is all conditioning on observable, excuse me, I misspoke about one thing. The whole thing is conditioning on observables. The lower right is one where there isn't anything unobserved that is the, we assume that the Z can be controlled for and observe the full information set up. So if, if we do it with the outcome based approach, we see that we do as well essentially is observing everything, even though we don't observe everything so the right hand side picture here which is yellow is showing us getting us results as good, essentially as if we could observe everything, even though we can't observe everything. Okay, so again I mean I don't know if we needed a Monte Carlo for that the point is unobservables could threaten one kind of test if you could in addition to observing ranks also observe some outcome that the rank is meant to affect, then there's another approach available to you. So some real world illustrations and we have illustrative data but all these data have have weaknesses to go with whatever advantages they have. So if we have the Amazon Kindle daily deals, what is that what that is is every day Amazon promotes 50 books, and they rank them and they post this ranking on a page they also send out emails to subscribers. Now, it's an interesting context in part because Amazon is a big publisher. And so self-preferencing is a real potential concern and interesting concern here about 20% of the books that they promote are published through Amazon Direct or Amazon publishing. So again, there's the possibility of self-preferencing. We also have the Expedia hotel searches that many people have used these are old data now from 2013. We have like 400,000 searches and 8 million listings. Now what's interesting about these data is a bunch of the searches are randomized. Now there's no self-preferencing in this context because Expedia is not a hotel company that said there, there might be bias with respect to chains. I don't think that's not so much a credible concern as an illustrative as an illustrative idea. Then we have Spotify New Music Friday data which I've analyzed in the past with Luis Aguirre and another person, another wall folder I guess. So there we look at 20 songs by country ranked each week and there are whatever 20,000 listings and of these about 6600 eventually appear in the top 200 streaming songs. And the question there is, is there possible bias with respect to the major record labels who are partial owners of Spotify. So again, it's not quite a self-preferencing context, but it is one where it's a platform that's doing stuff and you might worry about its bias. Okay, so I'm going to talk at least about part of this. The first column, so this is the Amazon regressions. The first column is just a regression of log rank on in this case the preferred indicator is, is it an Amazon product? And there are a bunch of controls that we're not reporting in this table. But after accounting for stuff, Amazon products have lower that is better ranks. So that looks like self-preferencing. The second column is the is the outcome based attack outcome based approach. So what this is is a regression of log expose quantity on rank indicators, the price and I forget if we have anything else in there. This one says this was negative as well so they both indicate self-preferencing they both indicates that the direction of, you know, of the measures are the same for this test for both Amazon tests are the same for this context. I mean that said that the sizes of the estimated the sizes are rather different, but the directions are the same. Okay, I for time I'm not going to talk about those last two columns although I suggested that I just gave you some suggestion from them. Here's the Expedia context. So here ranks sorry chains in the first column. This is again a regression of rank on whether your chain and some other stuff ranks get worse. Sorry chains get worse that is higher but worse ranks. And the second column is the outcome based approach. This again indicates, if anything, anti chain bias. Both find that chain hotels are ranked too low, although again the magnitudes when you convert the magnitude of the of the outcome based test into a rank effect which for which you need to make a parametric assumption about the effective rank, you get pretty different sizes but the same direction. Over at Spotify. Now the first column shows that this is major major label music is being preferred according to the conditioning on observables tests again there's a long list of observables with funny names like dance ability, because these are songs, dance ability and liveness and whatever, but a lot of Conditional conditional on those the ranks are better for major label music in the outcome based approach by contrast. The, the, there's bias that there appears to be bias against major label music because it streams better conditional on the rank to which it is assigned so that we're getting different not just different magnitudes but different different signs from these two tests in this context. So the bottom line is that the field data, at least provides some evidence for the Monte Carlo results and concerns about the context in which the conditioning that there are contexts in which conditioning on observables might give you the wrong answer. Okay, so structural but again I mean, there's no point in criticizing something, unless you have an alternative and you only have an alternative if you have quantity data and so forth so we want to be clear that it's not like we think that's a hopeless approach in fact, I suspect you'll see us doing more stuff with that as I think we just want to understand when it's when it's best applied and when you know when it's best justified and whatnot. All right, so the structural approach. So let's start with the. So here we'll just do Amazon and Expedia because we don't have price data that the Spotify context doesn't have a product price there's a subscription price. So it really is not so amenable to this to this stuff if we want to do usual sorts of welfare analysis, but we'll do Amazon and Expedia. And at Amazon, we're going to estimate a plain logic, almost without apology. And so X is going to contain an Amazon dummy and some pre promotion sales. And so the estimated values minus, you know, causal gamma are going to give us our Delta J zero hat. On the supply side what we're going to do is we're going to look at the relationship between the ranks that the platform assigns to the products, the prices and then these these Delta J zero hats. And then we'll put in the platform indicator as well. Okay, and we're going to think about, you know, rank ordered logit as the way to do this because we want to be, you know, ordinal not cardinal and so forth, although it's going to turn out that doesn't terribly much matter. So first of all, the Amazon estimates. So if we just run this run this regression and do this this logic. We get this rank effect of minus point 405, but we also want to get the what we would think of as a plausibly causal gamma. So for that we estimate a different regression that has product fixed effects and we make use of the fact that the same book gets promoted on different days at different ranks. So we take our product fixed effect. And when we do that we get the causal part of the rank effect as shown here is point 335. So I guess that should be a negative sign. So we distinguish between this overall relationship with our and the causal part that we want to use in the counterfactuals. So the supply side, we're going to do three things first column two is not the rank order loaded. It's just the linear regression of the rank on whether it's a platform product, the log price and the rank independent, excuse me, mean utility. It's intuitive just because, well, you can see the sign. So if it's a platform product, the rank is is better, lower, lower means better. That's also confusing about ranks is worse means higher but whatever. So the platform products get better ranks, the higher prices give you better ranks, and higher quality gives you better ranks now that might seem counterintuitive but remember, higher quality giving you better ranks that makes sense consumers like that and it drives demand higher prices giving you better ranks reflects the fact, the idea that the platform likes revenue. Okay, so so it's not it's not weird it's a it's only weird if the post office ran the platform. The third column is the rank order loaded and it's just, it's, we use the normalization so things are positive instead of negative but it's the same kind of results. The other thing maybe to note just as the magnitude of the estimates. The platform product in the third column has a coefficient of 12. Where's the other co I mean just think about how that's kind of big in some sense compared to the other two coefficients, and that'll maybe be more meaningful when you look at an Expedia where everything's going to be infinitesimal, or that is the bias looking stuff's going to be kind of an infinitesimal. So for an Expedia, we have a slightly different context was on the one hand we have micro data so it's super cool we have search level data we see choices. Now, people are confronted with a list of hotels, and they look at them and maybe they choose one so we have to do something a little bit more complicated we're going to do a conditional logic on what thing gets purchased if something gets purchased. So at the upper level, we'll do just a straight logic on the relationship between the decision to book something and the inclusive value from the lower level so it's kind of a four persons fixed. Well, it's a proper nested logic, but estimated in two steps. And so, but again I should mention some some all of these data are in some sense wonderful because they're micro data at the search level. They're also not wonderful for a variety of reasons, one of which I mentioned they're super old but the other data that were given away for this this hacking contest over sampled searches and which purchases took place so you really shouldn't. I'm not sure you should believe everything about the estimates, or at least the size of some of the parameters, but that said, the estimates kind of appear to make sense. In the first column you see for example that people don't like prices. You know, people do like things that are better reviewed and more stars people a little bit like chains, and so forth so that the estimates don't not make sense. The third and fourth columns then are again these rank order loges and here are things, you know, look in many ways like they looked over at Amazon, that the platform likes better products that is likes to rank better products higher. Lower but better sorry, the platform likes prices because it likes revenue, the platform seems to dislike chains. Now the size of this dislike if you look at the third fourth column excuse me the chain coefficient is minus point six. The other coefficients, you know, just think about relative magnitudes that's pretty small compared to the other coefficients in relation to what was going on at Amazon. I'm just trying to put in in our heads and expectation about what the pictures might look like. Okay, so what we can do with the model and can have a little bit of fun, we can calculate rankings, what we can do is we can, you know, take the estimated bias and remove it and then recalculate the what rankings, the would have ensued or whatever risen if the platform had psi equal to zero. So what this picture shows is three things. There's, there's the actual data, which is the solid line. There's the models version of the actual data. This is the distribution of the Amazon products ranks. And then there's the model de bias that is that thing way to the right. If one were to de bias these rankings you'd see that Amazon products will be basically at the very bottom of the heap. Okay, but now, again, I want to issue this disclaimer there there are a lot of issues with these data so this is not so much a claim about Amazon today as it as it is a claim about the the approach. So this is not, you know, this is not don't take this as much in the way of evidence about Amazon. Over at Expedia, all three distributions look quite similar, because after all we found rather tiny bias, but one other thing I'm one of their minor victory lap we might want to claim here is just that model actual and actual actual have very similar distributions, so that it would be distressing if that weren't true. Yeah, so but it isn't not true so that's less distressing. Okay, then then a nice sort of place to look at the results is one of these, one of these welfare frontiers. The Amazon Kindle daily deal data. So the circle is the de biased version of what we actually see. So in some sense, that's telling you how them how Amazon trades off consumers versus producers. And what's a little bit interesting is that it's way over over near consumer land right so the the bias, you know another way to say it. Well, no, let me not do that. Just just think about like the slope of the function at that point, or think about, like what share of maximal consumer surplus is the device point versus what share of maximal revenue. And it's much closer to maximal consumer surplus than it is to maximal revenue. So the point though of course is the difference between the plus sign which is the actual and the device. And so you can see the extent to which actual bias or the actual rankings are in this case in the second bullet for going 3% of the bias CS and 5.3% of the bias producers surplus or revenue. You can also think of it relative to the relative to those 100% endpoints. You can't really see that there are two dots here because there isn't much in the way of bias according to chains and not that we expected there to be so again it's an illustration that shows that maybe if there isn't bias you don't find it. But what is a little bit interesting is that there's again that this thing the circle on the frontier lives pretty close to consumers and farther from from producers. Okay, so I'm going to come in for landing ahead of schedule so lots of time for questions. So where are we, you know, we need ways to test for and evaluate bias and many people in this zoom room are thinking about that and we're thinking all about it together and that's great. This paper presents an equilibrium framework that gives ways to compare testing approaches, and it shows the context in which the outcome based approaches advantageous. These illustrative estimates of the model and that they give us ranked bias estimates platform consumer surplus and producers surplus and the MR the basically the MRS between them, or at least something like that, as well as measures of the of the welfare effects. And I guess we don't need backup slides but I do want to say one thing, which is one of the motivations for doing this, even though we currently, I would say have imperfect data is that the digital Circus Services Act has a provision for vetted access to the kind of data well to some kind of data we'll see in the future, but if one had the right data. It might work very well be possible to implement our kind of our wish list of approaches and get answers that are both sort of transparent but also believable so we're excited about this as an approach more than we're excited about these as results. Because we think there's both a need and an opportunity to study to study this. Okay, I'm going to. I'll stop it there. I'm a few minutes early. That's because I talked fast. It was the coffee. Thanks very much Joe also for keeping so well to time. Now over to Chiara. All right, I can use the sum of those minutes. And then I'll leave the other minutes for questions. Thank you Joel for for presenting this and thank you in candle for for doing this. It's a great paper. I'm literally it's a night to do to use it with with with a project with Andre Prappian and Alex McKay. And it's a great paper because it really tries to bridge the gap between regulation and its implementation, and it doesn't do it. 510 years later, the timeliness is quite impeccable so I think it's particularly important that we have this paper now in the context of the digital markets act where there's a lot of apparently innocuous prohibitions such as no self-preferencing, but then you know their implementation is not as straightforward. So I think the major contribution of these papers is truly methodological and so I want to highlight its value and and clarify, you know, some where it may or may not apply in in the context that we care about. So, the idea I think is pretty intuitive right consumers derive utilities from goods, sellers derive profits from the sale of those goods and these create sort of that parental frontier that you've seen where depending on the way we assign to consumer and producer surplus we obtain different rankings in which those options those goods are presented to consumers right any deviation from this frontier is a form of platform bias so this is where the definition of bias comes in. And this framework is really able to detect and quantify these these bias right. These framework is also extremely appealing, because it helps us make sense of to more reduced form approaches to the tech bias that have already been used in in the literature, the conditioning on on observables approach, and the outcome based approach. And so the first, you know the conditioning on observable approach is pretty simple right you regress rank on observables you have access to and a dummy for the type of products that you want to evaluate bias. And if you have a coefficient on that dummy that's different from zero then you have vice for or against depending on the sign right. The second thing is that it's very likely that you have access to all the product characteristics of I think consumers choice. And so that coefficient can be due right that coefficient on the dummy that you care about can be due to both on observables or bias. Now, I don't think people who have used these approach are using these as a definitive test of bias. I know at least, you know, I have used this with Andre and and Alex. And we're very explicit in that in that very short paper that these should not be taken as a definitive test of bias. So the question that I have for for him can and Joel is whether their model can actually rationalize this condition on observables approaches a first step in the detection of bias. Why do I ask this for two main reasons. The first one is that this is truly the easiest test to implement. You don't need demand, right. You only need rankings and a large chunk of observables, but this is something that can often be straight. You don't need the collaboration of the platforms or other intricate way to collect data right so it's extremely appealing. The second reason is that, of course, this test is not always right, but not all errors are the same. So I would want to see right a better qualification of the errors that these tasks makes as a function obviously of the correlation structure between the unobservables and the dummy right. For example, if it's the case that the test under some correlational structures tends to err on the side of false positives right, meaning that it detects bias when non exist, but when it exists, it detects it right. Well, then it could be a first step in a multi step detection and correction process right and and remember that we can get a sense of that correlational structure between the dummy and the observables why we do have a set of observables right already that we can progressively add to that regression. The second reduced a form approach that their structural approach rationalizes or evaluates is an outcome based test right for which you have you have to have data on demand so this is a total order right. And that's where you know the digital markets act with the with the with the ability of researchers to ask data for these platforms really becomes valuable. And the idea is that conditional on the same rank if one product has lower demand than the other than the platform is faith is biased in favor of that product. Okay, so that's that's the intuition for that test. The second approach actually, both with Monte Carlo simulations and the more structural analysis that in Kendall have done with Amazon and Expedia. It seems to be very closely aligned to the settings to to give very close results to the more structural approach so I, I really sort of enjoy this because, you know you don't you don't need the regulators to make a complicated structural model you can really do do the outcome based approach and be done right. But these brings me to the second point that I want to make which is where these approach can or cannot be a be applied because if we assume that the platform ignores producer surplus right so we don't care about the the profits that sellers make. Then this approach is very broadly applicable. But if we allow for the platform to consider producer surplus, then the assumptions on the supply side are somewhat strong and need for their explanation I think Joe hinted at this, but I want to sort of qualify this because marginal costs are assumed in a simplest form are assumed to be equal to zero and prices set before ranking takes place. That means your price as a function of your product is placed right now, what kind of supply model would rationalize different prices with identical marginal costs. One that I have in mind is a Bertrand model of competition with differentiated products, but here's the catch. When the ranking of prices. In that context, the ranking of prices to be consistent with the rankings of the ranking dependent mean utilities. And so there's wouldn't be a trade off, I think, between focusing relatively more on seller surplus or consumer surplus. There's only a trade off to the extent to which, you know, the high prices are for products that consumers like less. Right. But if the high prices are for products that consumers like more. You don't have that trade off. And, and so, you know, to the extent that there is a trade off I would like to try and have a micro foundation for where that may arise with no marginal costs right and I think this is quite important and see I'm using a little minute, because in this paper the seller surplus maximizing ranking should prioritize products with high prices. But if those high prices come from higher marginal costs, and if Amazon for example, right, at lower marginal costs at a minimum there's no double marginalization or there may be economies of scale, a ranking prioritizing Amazon products because of lower marginal costs right and hence lower prices would be detected by these tests as self preferences when in fact it is not. And so, I think a couple of, you know, these two points one is sort of, can we qualify the errors on the conditional and observables test, and then thinking about where, what kind of supply side can lead to these trade off that you are capturing. But overall, you know, I'm copying you so thank you.