is that he has been at almost all of the digital economy conferences that we've held in Toulouse since the very first one in January 2001. So that means that he qualifies as a veteran of the series and we're extremely grateful that he keeps coming. So Hal, thank you and you have 15 minutes for your introduction. Thank you, Paul. And thanks for having this at a reasonable time for us West Coasters. I've been getting up at 3 and 330 to talk to some conferences in Europe. Believe me, I think I'd rather have the jet lag.  So watch out for that. How did you discuss? Okay, I want to talk about reputations and recommendations. So I added a little topic. What's the value of information? Well, we all know that helps you make better decisions. What's the value of data? Well, it helps you get better information. And then you can use that information to get better knowledge, better understanding and all of these pieces are part of the famous data pyramid, data to information to knowledge and to understanding. And both reputations and recommendations are important in that. Now, what's the difference? Well, a reputation, a bad reputation as I think implicitly at least a recommendation, don't go to this restaurant or you won't like this movie or whatever. And if I can borrow a little bit from auction theory. Think of the distinction between common value, common value auction, where you've got something that's the same item for everyone, but they could. There's the same value to everyone. They just don't know how valuable that is. Like offshore oil lease work that Bob Wilson did so many years ago. So I think of a common value auction. Well, the same thing with reputation. Everybody agrees on quality, let's say, but they have different noisy views. And so what you want to do is you want to aggregate that information in some way in order to get a single score for the reputation. And then the other distinction would be private value. There you might have different preferences for attributes. So for example, you and I could have the same case in movies or in music or or food or other things and there. They could be common. They could be different. They could be private values. It could be public values, same general ideas, not a contradiction to say the food was of high quality. But I didn't like it because I'm expressing their point about my private value, even though I agree with the public point that the restaurant was a nice restaurant. I just wasn't far into the cuisine. Now, reputations. The first time I ran into this is kind of an interesting story. The FTC sponsored a meeting in 2005 about online auctions. So I gave a paper there, the paper I wrote on position auctions many years ago. But I talked to some of the people at the FTC about why they got into this area. Well, it turns out it was all because of eBay. Back in 2005 and the early 2000s was selling collectibles. They had buyers and sellers. They had all sorts of things. Every now and then a transaction would go wrong. The person we shipped something he didn't ask for or there was some damage on it and so on.  You clicked on that button and you were taken to the FTC website. So all of a sudden one day the FTC started getting thousands of complaints of the form. He said he'd send it to first class and he sent it second class or he said it was in prime condition but there was a scratch on it and so on and so on. And all these things were flowing into the FTC because the eBay decided they just outsourced their whole merchant quality capability. The FTC said we better learn about this. We have to have some policies to deal with this. And of course, what a change from today because today the quality of the reputation is something that's viewed as very important by the websites, by the platforms like Amazon and Yelp and other places. They invest a lot of effort into trying to come up with good quality measures. And the recommendations, this sort of sister topic back in 1996, a computer scientist named Paul Resnick wrote his thesis on collaborative filtering. And the idea of collaborative filtering is I could have a list of all the movies I liked for a score for the movies indicating how much I like them. You could have a similar list. If we had a lot of overlap in the movies we've seen, then it's probably the case that that's predicted for the movies we might want to see. So in fact the collaborative filtering is just a way to automate the sharing of ratings. Find people that are similar to you and their historical preferences and then use that to extrapolate to new choices. You could also add in attributes of the person attributes of the product. But the really nice thing is you don't need that you just need a rank list or a list that indicates this is what I like, you have a list of this is what you like. We compare the two and then offer each other the difference the set difference between those so those things. And then automatically the way these recommendation systems have been built nowadays is to think of it as a matrix completion problem. You have products, you have people, you have a matrix that's partially filled in and what you want to do is you want to fill in the rest of that matrix. And so there are a variety of ways to do this and they work very well. We had one for recipes once and it was great all the vegetarians found each other very quickly. On the other hand, we had one once for jokes and there were a number of jokes. And those were not as good because the joke had this element of surprise to it. So that past behavior wasn't necessarily predictive of the future behavior. Now, in some of these papers that we're going to hear in a few minutes there's a discussion about incentives or at least there's even if it's sometimes a background discussion. Maybe the way to do it is to make ratings more like a recommender system, because the incentives are very good in a recommender system. What do they do they I want to be honest when I reveal my preferences for these products because the quid pro quo is I'm going to get helpful recommendations from you and vice versa. I guess we're pretty well there and maybe if we looked at people who are doing a lot of rating, then if they had similar rating, they were good recommenders, or if they had similar ratings, then they would have a reason to kind of join up. Again, provide honest or accurate ratings in a way that somebody was doing or randomly you're doing it for hire as we'll hear about a few minutes, would not have those same incentives. Now, one of the points that it's useful use worthwhile making about about recommendations and and ratings is that you can use transformation if you find somebody who has just the opposite preferences from you. Because you can do exactly what they don't recommend. In fact, one of the one of the suggestions I've heard is maybe what you should do is you find such a person who's exactly the opposite you then you should marry them. Because then you'll always be able to get good recommendations from each other just by inverting those those preferences. In fact, there's the old nursery rhyme jack Spratt.  And so between them both they let the batter clean. So there was always something to like between the two. So, by the way, I will tell you another one other story about the recommendations and and ratings and I organized the world's first conference on collaborative filtering and the very first session first meeting that was in 1995 I think 95 or 96. The very first question was, what should we call these things obviously collaborative filtering is not a very good name because people have no idea what that means. It turns out specialists find it meaningful. And I said why not when I call a recommender system. People said recommender systems recommender systems that's a good idea. And so now that's been the name of the whole enterprise and of course it's also extremely important in terms of business models for platforms because people want guidance and help they want recommendations. And that's a big part of the business. Now, the papers in the session. Let me just run through them very quickly. I thought they were extremely interesting. Philippa said all said rating inflation is common. And you probably all heard the story about Lake will be gone where all the children above average. It's inflated so much. It's just broken all the laws of arithmetic. One of the problems we had once when I was at the University of Michigan was how to deal with grade inflation because people in other social sciences were we thought inflating their grades of their students and appropriately. And one of the proposals that came out which I think was quite interesting was to publish the students grade, along with their rank. You could say they got an a grade, and only 20% of the people got a is only 10% of the people got a so you could publish the distribution of the score, along with the score itself and I think that was a very helpful idea that can be used more often. And we see that of course when you report class ranking and that kind of thing. So, so that a may or may not mean anything but the top 10% of the class that's meaningful. And because it's doing this ordinal measure. And now laden at all wrote their paper on the incentives to upgrade and I thought that was extremely interesting that the, the fact that apples set the system resets to zero whenever there was an update and so they had that of course ended up discouraging an upgrade if you had a good rating. If you decided to upgrade then you go back down to not having any ratings at all which was a big mess. So, I thought it was really quite an interesting discussion in fact we had exactly the same problem or very similar problem with Android, this Android phones were sold through the carrier in many cases, and the carrier was responsible for the over the but as soon as they updated their help system would go down because it would be overloaded and people try to figure out how this new capability in the phones work or why this menu item and move from here to there etc etc. And we had set up the program so as to discourage these ratings because we made the carriers responsible support. Now that's reasonable because we knew everybody had the phone, but not everybody had Wi Fi at that time. Nowadays everybody has Wi Fi so the upgrades to Android take place generally over the over the Wi Fi rather than over the air and Google has taken on more and more responsibility for managing those those upgrades, but it's very important because upgrades are critical and knowing who should do it and what the impact will be that's a very important question the industry. Next paper so presuppio on fake reviews market. If you think about fake reviews, I think as far as I can tell they're just equivalent to false advertising which is illegal in most places, but it's done by this very fragmented market rather than by competitors or other other companies so you need a system that can deal with this in parallel ways of a company is falsely advertising you want there to be a penalty to that. And if individuals are falsely advertising. Then you want some sort of way to detect and deal with that but who who should police them. That's the question. Should it be the FTC. Well, I think the scale. It's likely to work as that was single regulatory agency that's engaged with this rating Facebook, Google, whatever. Section 230 has this interesting consequence that in fact is somebody is lying on your system. The platform owner is not liable for that line whether it's lying about competitors whether it's lying about politics. So I think it still is a important question of how that can be managed the platform itself has good incentives but they're not, not perfect. But invest a lot of think of Amazon for example who invests really substantial amounts and trying to track down untrusted or palatious reviews. They want to stall when and why do people rate and I think he has a very nice answer the group has a very nice answer on that they are likely to rate when the gap between expectations and actuality or large a model that is a Bayesian they've got a big gap between the prior and the posterior and that model for rating explains various stylized facts this will hear in a few minutes. The last paper by turn close so is on the and his colleagues is about the impact of gender on reviewing behavior. And that's an extremely interesting paper to they looked at cases in. Jack, I'm sorry you're going to have to begin thinking about trapping up. This is my last last episode will be done in a minute, one minute is a hotel manager allowed to respond to reviews. And in some cases I found this very helpful where you look at a hotel. For example, people have an open line you can read what their experiences are every now and then you have a bad experience and when you have a bad experience, the, the manager can step in and respond to it in some way. Now the problem is that can lead to confrontation that you have a debate going back and forth. And the argument in this paper with the finding in this paper I should say is that this has a bigger impact on women's likelihood to engage in this kind of confrontation versus men's. But again, we'll hear about that in a few minutes. So I'm now wrapped up and want to be happy to turn it over to the authors to describe some of these points in more detail. Okay, thank you very much. If you are wondering why I'm taking over from Paul is because it seems that Paul has got connection problems. So the first speaker is Benjamin Leiden from Cornell University. Ben, can you share your screen. Yeah, let me get that she's working wonderful. And so you've got eight minutes. Great. Thank you so much. Well, thank you to the organizers for including me on this program and for how for the great introduction to this session. I'm going to give you a brief overview today of my paper platform design and innovation incentives evidence from the product rating system on Apple's App Store. There's a somewhat longer talk available in a video forum on the conference website and then papers available as well. So the paper, you know, motivated by I think a growing public policy interest and of course a long standing sort of academic interest in how the design of platforms can affect intro platform competitive outcomes. I'm going to ask how the design of Apple's product rating system in particular on the App Store affected product innovation there. The reason I think this is an interesting context to sort of look at this type of question is that for essentially the first decade of this platform. The system was structured in such a way as to penalize updates by higher quality products. Right, so this creates sort of a purpose incentive scheme. And so I'm interested to see whether developers were in fact responsive to it. Leveraging a change in this policy in 2017. What I find is that developers were in fact responsive to these incentives that developers under the initial system updated less frequently. And in fact, this less frequent innovation was probably the result of lower investment or lower product innovation overall, as opposed to sort of delayed and bundled less frequent updates, as I'll talk about in a minute. Okay, so what was the policy that was in place well, you know, for for that first decade roughly, basically the way your rating would be calculated the rating that would show up all across the App Store would be calculated is they would add up all the ratings and reviews and average all reviews from the current version of your app. So when you produce an update you put out a new version, you're going to go from your nice five star review like you are rating like you have for this modern combat game you can see on the right to this no rating sign that you see for enigma two. Okay, so there's essentially a reputational penalty you're paying every time you update your app. And then once you get five or more ratings, then the stars will reappear and it'll be whatever you're getting at that point, but there's this period where you're not going to have any ratings displayed. So this is going to discourage innovation at the high end. It actually might encourage innovation at the low end to the extent that no ratings are better than than one star ratings or something like that. But that won't be the focus for today. So this was famously very unpopular with developers on the platform.  Okay, so that's that's sort of the history of this system. In the paper, I essentially asked three questions. So the first is whether or not the observed relationship between the average rating and the likelihood of updating changes with the policy change. In other words, was there this distortionary effect you can sort of think of the blue line as that the the updating likelihood for a bunch of developers under the initial policy, and after the change do we see a shift back to that sort of optimal red line. Once I find that there was in fact an impact of this initial policy. I'm going to ask how the size of the effect varies with the size of the reputational penalty and app faces so you can imagine. So what I'll show you under sort of question one is that the frequency of innovation was much lower in the initial policy under the initial policy. But you know that could either mean we're sort of throwing out ideas or investing less, or it could mean that I'm just holding and bundling up a bunch of updates. So what I'll show you under sort of question one is that the frequency of innovation was much lower in the initial policy under the initial policy. So we're bundling up a bunch of updates and releasing them in larger packages less frequently. Now even in that case there's sort of a dynamic welfare law so this isn't necessarily some good from a societal perspective, but it would be less bad than if there's just less innovation overall. And so we'll look into that the way we'll sort of address that is to see once the policy changes in 2017, whether sort of small bug fix updates become relatively more frequent, because you no longer face this large reputational penalty for sort of minor revisions to the product. Okay, the empirical strategy lots more details in the 15 minute video version of the stock as well as the paper, but it's basically to estimate a linear probability model to determine whether this ratings updating the relationship changes after the policy change. Okay, I'm going to use a weekly panel of iOS apps in three categories education productivity and utilities. In order to study this question. In order to answer that third question we're going to have to classify the content of app updates as either small or large in some sense I'm going to do this in two ways. One by looking at the version number of these apps. There's sort of this historical system for changing version numbers in certain ways to indicate smaller large updates. This is not always followed it seems to be somewhat falling out of favor with a lot of developers. So I'll also look at text release notes that are written by these developers, describing what they're doing with each update, and I'll classify updates as either large sort of feature adding updates or small bug fixing updates. Okay, so what do I find here to summarize sort of the main results from the paper. The sort of top line result is that the original policy decrease the frequency of product innovation that developers were in fact responsive to this. The storage app in the store is going to increase innovation or product updating by about 8% after the policy change. But of course there's a lot of heterogeneity around this. In particular it becomes really clear in the data that the size of the reputational penalty matters. So first I find no effect of this initial panels, this initial policy on the updating behavior of the most popular apps. Microsoft Word doesn't care what its rating is on the app store, because it's so dominant in the field or in its sub market, whereas my you know very niche note taking app, maybe really dependent on the ratings and that content. And so I'm going to be really responsive to that initial policy. Similarly, I find no effect for apps with the highest arrival rate of reviews so remember the reputational penalties that I go from my nice five star review to that no rating sign until I have five or more ratings when those numbers start showing up again, the stars start showing up again. Some developers are going to get lots of reviews coming in constantly. So this is going to be a very short lived penalty, and those developers aren't very responsive to the initial policy. So for other developers it can take months to get that many reviews or ratings, and so there the penalties going to be very severe, and you're going to see a much larger response from from those developers. Okay, to that third question then of course whether or not this innovation is sort of lost or just delayed. There's suggestive evidence that the original policy did in fact lead to lost product innovation. So, under the version number of approach of classifying updates as sort of small or large. We find no evidence of a relative increase in the small updates after the policy changes. This allows us to reject this theory that developers are holding on to innovations and bundling them into less frequent releases during the initial policy. Using the release notes those text descriptions of these updates. The evidence is a little bit more mixed. But what it appears is happening is in the education category. There's evidence of a relative increase in small updates after the policy change, which means we can't reject either of these either the bundling theory or the lost innovation one. So it's not clear what's going on there right now. But in utilities and productivity my other two categories of apps. There we don't find evidence of an increase in the relative frequency of small updates. And so we can again in those cases reject this theory of sort of bundled delayed innovation in favor of this idea that developers were actually investing less on the platform. Okay, so, you know this was a pretty short talk so I won't just repeat everything I just said but you know I think the big takeaway here is that the structure and the incentives created by this, this rating system had a meaningful effect on on innovation on the platform, which I hope contributes a new empirical evidence to our understanding of both rating systems more generally, but also how, how the design of platforms can have, you know, a real effect on intra platform competitive outcomes which I think is, is of course a growing interest these days with lots of current concerns about dominant platforms and the behavior of platform owners. So thank you for your time. Benjamin that was fantastic timekeeping and a really fascinating presentation. Our next speaker is Conrad Stahl, who is going to talk about when and why do buyers rate in online markets. Conrad the floor is yours. You need to unmute yourself Conrad I think. Okay. Okay, I'm sorry. Yeah, it didn't work. Well, it's it I can hear you now. No problem. Okay, wonderful. Thank you for the organizers as usual and thank you all for the introduction. Let me announce that the longer presentation is somewhat longer presentation is available on the website. So you see the title of this talk. Let me start right away. The motivation is an obvious one. Everyone knows that rating is important in particular in online markets. The key point we want to focus on is that rating is voluntary and every every evidence on this shows that rating is highly non random. In particular, the standard evidence shown is that the rating concentrates on the extremes in the distribution from many positives to a few negatives. The so called J distribution. This gives rise to a number of questions of which we want. We don't answer any everyone but at least a subset of them. The first question is truthfulness of buyer rating. We don't give an answer to this, but the selectivity of rages amongst the buyers is is an interesting question we address. And the selectivity in particular of the individual buyers rating decision. And this this gives rise to the by my standards key question as to whether an unweight rating index that is typically used in in in online's online markets is biased and thus in on informative. Okay, we our contribution we provide a little theoretical model. I think it's a pretty elementary model but it explains a lot by our take. It explains the decision to rate i.e. selectivity at the individual level. The key idea is that the bias utility from rating as how has said is increasing in the intensity of learning from a transaction. And learning is the difference in applies that is involved involving the difference between prior and posterior belief and the key point is how the prior belief is for. Okay. I think, or we think that in spite of selectivity rating aggregates if carefully constructed can be unbiased and thus informative. And the current work this is working progress is on exactly the fine tuning of these rates, which is surprisingly difficult. The empirical results we can be present here are from the eBay raw data from the administrative data I'm sorry the main insights are the following the intensity of rating is decreasing in the number of transactions performed by the typical seller. It's a clear decline in this density. In particular the bias reluctant to leave a negative rating. This is me are reluctant more and more the larger the number of positives if you want that are typically accumulated in the rating record. Now, by inverse when the rating record diminishes by typically by a negative say the first negative. A significant increase in the likelihood to follow up with a negative rating. And this, even if the seller is not that there is no moral hazard. Impa generated on the bay on the level of the seller, I will come back to this point. Okay, now the interesting point is that with our little theoretical model we can rationalize all these results. And most, if not all empirical observations on rating in the literature. Let me quickly walk you through the model. It's on the individual bias rating decision. We consider transactions as being of high versus low quality. The buyer forms a prior belief and that is key here, based on the platform and seller quality that is inferred from the rating index. There is a general platform quality on which I do not elaborate right now. And there is a seller quality which is directly inferred from the rating index that is provided. Okay. Now that that rating index can be pretty general. Rater forms a posterior belief based on her experience that is generated from the transaction when she has purchased. And as we, as I just mentioned, or just emphasized, you know the benefit from rating this is our principle guiding assumption is increasing in the absolute difference between prior and posterior. And the rating then is taking place if the benefit exceeds some just in credit costs. The prediction is quite straightforward. The first prediction is that an increase in the rating index decreases the rating probability and the decrease in the rating index increases the probability that a buyer rates negatively relative to that of rating positively. These are the two predictions we take to the data. But before I go to the data, let me just illustrate what happens here. This is basically the difference between the positive posterior belief and the prior belief as generated on the basis of what I just said. And this is the difference between the negative posterior belief. I mean generated from a negative experience and the prior belief. You see that essentially, you can generate all the results from this little picture. Okay. Let me go to the data just in, I have to save on time. Unfortunately, we have three samples and the guiding sample is the top 5% of the of the sellers. We start, I should say, we start from sellers all starting in a particular time at a particular time and we follow these sellers over time. Okay. So these are these three samples that these are the top 5% sellers we are looking at. And these are sellers that have at least 86 transactions in the first year. This is a balanced panel. So there is no, there is no truncation bias. The rating indices, I guess, are known in the in the scene. The eBay rating indices are the percentage positives feedback score and the so-called DSRs the detailed seller ratings that are differentiated. We use all indices and check the results for robustness. So if you observe the obvious thing is the well-known vast dominance of positive feedback that is typically considered a per se reason for rating bias. I don't think or we don't think that this is not necessarily so. First of all, you have a seller self-selection onto the platform and then you have a platform selection strategy which plays a key role in this and that is probably underappreciated. You're running out of time. Could I ask you to try and wrap up as quickly as possible? This is the first, this is the first empirical result. You see here this decline in the probability of rating with an increasing number of transactions in the horizontal axis. The second result is a bit more involved. We construct, we construct several sub samples of that sample. The key sample we are looking at is that is controlling for seller behavior by looking at transactions before some time at which the first negative arrives and the feedback is given thereafter. And we see when we look at the effect of the first negative then that in this class two, this is exactly the sample I was looking at. You see a significant increase in leaving a negative feedback. So there is a clear path dependence in leaving a negative feedback. That is incidentally concentrating on the young buyers rather than the old buyers that is the experienced buyers. And it is concentrating on the earlier sellers, the young sellers. So let me summarize. I think what I just said, one key point I want to make here. The rating decision is path dependent, which is an interesting point by my take. And the rating index is clearly inefficiently a standard rating index that is on rate is inefficiently aggregating the information, even if the individual ratings are truthful which is assumed here in our, in our exercise. And the work in progress involves unbiased rating index constructions and we give preliminary results but I'm afraid that I'm out of time. Thank you very much. Thank you very much. Comrade. This is a jack I'm taking over. Paul is again cut off. Thank you. The next speaker is. Is that right way to pronounce it. Yeah. Thank you. Okay, so I'm going to share my screen. Doesn't look okay. That's perfect. Okay. So hello everyone. This is a mark. I'm a PhD candidate in marketing at USC. And today I'm going to be presenting a joint work with David and Francesca, which we titled this gender matter. And what we're doing is exploring or trying to investigate the effect of management responses on reviewing behavior. And briefly illustrating what is the context that we're studying, which is online reviews and management responses rate. So you may be already familiar with only reviews, which we know usually consists of a rating, some text, some description to, and at least in some preview platforms, we will also have some information of the author. Now the second component here that you may be less familiar with are these responses from the manager, which in simple words are basically a message that some representative of the business can post, which is in direct response to a specific review. So it's like a one to one message here. And it's also public it's visible everyone else in the platform can see this response. Now, the adoption of this management responses has become quite popular has been gaining important popularity on platforms such as TripAdvisor or Yelp, because business see them as an effective way to manage their own reputation. We know that rating are important for them. Now, we know also from the literature that these management responses may can increase the number of reviews a volume business receive and will also impact the average rating, but in this paper we're trying to understand where is this change coming from in terms of reviewers decisions to write a review, right, who is leaving a review now and who is not leaving a review. Normally, the two research questions which blur here are first two different reviewers react differently to the presence of this management responses, and in particularly we're looking at potential differences between female and male reviewers. And if these differences exist, what are the consequences in terms of their review and behavior. Now, we started blurring this first research question with a survey in which we asked online review platform users, what is the reaction to the presence of management responses. Now we found something interesting significant differences between female and male users, in particular, that when these reviewers have a positive experience and they want to leave a positive review. Female users are more excited about the possibility that now they're talking directly to the manager so they see them as an opportunity to praise or compliment them for the good job. But if the experience was negative, and they want to leave a negative review, there is like female users are also at the same time more concerned that this might create some sort of conflict with the manager or that the manager may discredit what they're saying. Now we found this last concern particularly intriguing, and we wanted to further explore whether is this just a perception driven by gender differences or can we actually find some evidence of this on this platform. Or in other words, whether this concern is partially explained by the way that managers actually address reviewers. Now to explore that we analyze the text of some management responses that we collect from TripAdvisor. Now we look at the differences in with some of the linguistic choices made that managers may depend on whether they address female reviewers or male reviewers. Now we know that especially when responding to reviews, negative reviews from female users. Managers tend to use fewer positive emotion words, but more negative emotion words, more negation words, more anger words with like, for example, saying that the review is a lie or something like that. And they also tend to use more fair personal pronouns. So rather than directly talk to the reviewer like thank you, or using the uver pronoun, they tend to refer it like to the reviewer as she so she did something she said something and so on. Now, this difference may suggest that female reviewers receives some less favorable responses from the managers but we wanted to further formalize this analysis. And for that, we build a text based classifier. So we try to character characterize responses on whether they are contentions or not. And by contentions we mean that the manager is trying to discredit the reviewer, it's being confrontational with the reviewer or maybe responding aggressively. Now this classification is very purely on the text, the content of the message, right? It's a bag of words based classifier, it's a logistic regression, we try many, but the point is that it's really just based on the text, the content. And when we look at who are these reviewers being addressed to, we know that actually responses to female reviewers are more likely to be classified as contentious. Or in other words, seems that female reviewers are more likely to receive this kind of responses. Right, so this far we learned that female reviewers perceive responses as a potential source of conflict, and that that seems to be a well founded conflict that the situations can take place in this online review platforms. So we then wanted to measure or quantify what are the consequences of this in terms of reviewing activity. And to explore this, we collect the entire review history for 5000 hotels and trip advisor. And we use this individual level review data to explore whether the probability that a given review comes from a female user change to change in the presence of management responses. Now, because of time constraints, I'm going to skip the details of the analysis, but it's basically it went up to a difference in difference strategy, in which we exploit the fact that some hotels responds, but some hotels do not respond. So we have some treated our control hotels. And then there is some significant variation and when did they start doing that. So we have some before and after period too. Now, with this analysis we see that after managers start responding to their reviews. It is likely to see that a given negative review comes from a female user. Now, this analysis of course have some concerns of hotels may sell out into treatment so we have some robustness checks with some other treatment variables and we also replicate similar findings in a lab setting. And what it's also quite interesting is that we show that this effect of female users writing fewer negative reviews is stronger for hotels that write more contentious responses. Yeah, so just to write up, we believe that our findings help or contribute a little bit to the understanding and reviewing behavior, this selection into who write reviews, and our findings also have some implications for review platform and for our businesses. Specifically, they may be considered as a red flag right that there is some from from improvement to make sure that there is a fair communication process here. And that especially hotel managers are treating their customers in a fair way. And with that, I think I'm going to close here. Thank you for your time and attention. Thank you very much is Amar, not going to say she did a great job. I'm going to say you did a great job. Lots of positive work. Perfectly to time. So the next speaker on reputation inflation. Thank you very much. Can you see my screen correctly. Yes, yes. So thank you for having me. I wish we could do that in person enjoying southern France and having fantastic conversations, but this will have to do for this year. So I'm going to talk a little bit about one phenomenon in online reputation systems that are being used in online marketplaces. Every time we look at reputation distributions on this marketplaces, we see that they're overwhelmingly positive. Here's an example from eBay. Here's an example from Airbnb. And here's an example from our focal platform. So our focal platform for this study is an online labor market for freelance work. So according to this distribution, things are going really well on the platform. But our first question is, is this reflective of actual very high quality transactions and how could we know. So the first thing that we do is we take advantage of our data set that spans more than 10 years, and we go back in time in 2007, and we take a look at what the average feedback given in each month was over time. So what we find is that average feedback scores have been increasing over time. And up to the point that the average feedback score that's given at the end of our data set is much higher than what was the case initially on our platform. So actually distribution that I applied in the previous slide was just from the last two years of our data set. So feedback scores have not always been overwhelmingly positive. They became over time. And a question here, a natural question is, is this phenomenon taking place only in our platform or is it a little bit more general? So the next thing we did is we tried to collect data from many online marketplaces, and it seemed pretty general in other marketplaces are employing similar reputation systems scores have been increasing over time. And if we want to go a little bit in the non online world, this thing has been happening in other rating systems, and in this case, in GPAs in US schools. So in the 1940s, only 15% of students received ACE. In 2012, this percentage is 45% much higher, and it's probably even higher in 2020. So the question here is, what does this reflect? In the case of colleges, you could reflect, let's say, US professors becoming 300% better, or US students becoming 300% better, or, you know, classroom education technology air conditioning, what have you, becoming much, much better and improving performance. So let's go back to, or it could, of course, reflect the fact that we're just giving out grades more easily for some reason. So let's go back to online marketplaces and see what that what that translates to. So this translates to two sets of potential reasons. First, we could have improvements in platform fundamentals that increase transaction quality. This could be better cohorts of people joining people amassing more experience. Better search matching technology and so on everything that might improve transaction quality. The second set of reasons lower standards giving out feedback more easily is what we call reputation inflation in this paper. The next order of business is to try to disentangle these two sets of potential reasons for this increase that might be taking place at the same time. So the problem here is that we are observing this primary numerical score over time, but we're not observing transaction quality, it's unobservable, and it's affected by many, many things. So what we're doing here to disentangle inflation from improving fundamentals is we're trying to find alternative measures that might be subject to less reputation inflation or no reputation inflation at all. So in this paper we use two alternative scores and the first score is a private feedback measure that the platform started eliciting in combination with public feedback. This private feedback was given after transactions and the platform was not revealing it to everyone. They were just saying the employers that they would get it only to do some internal evaluation. So we have a public feedback measure and a private feedback measure for the exact same transactions. So what we see during the time that both were collected is that for the very same transactions, average public feedback scores increase whereas average private feedback scores increase. So this gives us one way to kind of see this inflation taking place. The second alternative measure that we're using is written text that is given in combination with numerical feedback and we're doing some slightly more sophisticated natural language processing analysis in the paper. But I want to show one piece of evidence that's kind of model-free and I think gets the point through. We see that when employers give the very same phrases in the written text, the feedback that they used to give in 2008 and 2015 greatly differs. So when employers have a good job, they used to give 4.7 stars, 2008, now 2015 they give 4.95 stars. When they used to say terrible, they used to give 1.5 stars in 2008 whereas now they're giving 2.5 stars. So putting all of the estimates together, our most conservative estimate is that at least 50% of the increase we observe in numerical scores is due to reputation inflation. So our next task in the paper is to kind of try to pin down why this is happening. And of course there might be many reasons but we're trying to accurately pin down one of these reasons. And the way to do it is writing down a theoretical model in the paper, but let me give you the intuition behind the theoretical model. So the first thing that this model recognizes is that badly feedback is costly for the rater to give. And raters might dislike giving this bad feedback for many reasons. They might not want to harm the prospects of the rated worker. They might not want to harm their own future prospects because workers will avoid them if they know their harsh raters or they want to avoid costs that have to do with worker retaliation since there's angry emails and so on. But these costs by themselves, they would probably explain only a bias in the feedback, not this trend over time. And to explain this trend over time, the second component we need to alter our model is that what feedback is good and what feedback is bad changes over time. And what this means is pretty simple. If everybody gets a 2.5 out of five stars, then giving somebody four stars is excellent. If everybody gets five stars out of five and giving somebody four stars is bad. So we put this thing in a model. So what feedback is bad depends on the average feedback that's given on the platform at each point in time. And now employers find it costly to assign bad feedback. They incur a cost that's analogous to the cost of giving this bad feedback to the workers. And I might start lying about their experience and start inflating the feedback if the cost of assigning this bad feedback becomes high enough. So this model gives us exactly what we see in the data. And one last component we have in this paper is that we have a direct test for this model from a change in costs to bad feedback. So what happened in our platform, as I told you before, is that they started collecting this private feedback measure. They said they would never reveal it to other users of the platform. And they did some internal tests so it was much more informative, much more predictive of future performance. And of course, what they decided to do ultimately was to start revealing it. So they started revealing this private feedback to everybody in the platform, but they started revealing a batched and anonymized manner in the sense that workers could not see which employer gave which feedback. And its workers private feedback score was updated every five new private feedback ratings that they got. So this kind of controls against fears of retaliation, this kind of batching and anonymization, because the worker cannot trace bad feedback back to a specific employer. But on the other hand, if raiders care about harm, then they will start not giving bad feedback and will start seeing this inflation. So if we go in the series of private feedback scores over time, and we got the time where this private feedback was publicly revealed, we see exactly what this model would predict private feedback scores. I'm afraid you're out of time. Could I ask you to wrap up please? One more slide and I'm done. Thank you very much. This private feedback scores are inflating immediately. So what we see is that much of this increasing feedback scores is due to reputation inflation seems ubiquitous across many platforms and can be driven purely by a desire not to harm rather than fear of retaliation in the terms of raiders. So there is a lot of room here for future research about implications and market design interventions that can counteract this reputation inflation. All right, thank you very much. Thank you very much.


where the governor, Artiku Bagudu, has advocated heavy investment in other sectors of the nation's economy. If much talked about diversification from oil and gas must be a reality. Bagudu said this in a public hearing on the bill for an act to establish the Nigerian Economic Diversification Council, organized by the Senate Committee on National Planning and Economic Affairs, chaired by Senator Ulubumi Aditumi. The governor, who is a former senator, said Nigeria is a blessed nation in all sense of the word, other than that there is need to support different sectors of the country and different constituents to do better. He said that Nigeria is a trillion dollar investment destination that the federal government budget is less than $35 billion. We are a blessed nation in all sense of the word and we need to support different sectors of Nigeria. We need support to support different constituents of Nigeria to do better. And I gave the example that we have done well in petroleum and gas, not because it's the only sector that we have, but that's where we put in the most money. Nigeria is a trillion dollar investment destination. Nigeria, what is our federal budget? Less than $35 billion. Maybe the National Assembly is about to pass the 2022 budget. I'm sure it will be up to $40 billion. What is the budget of Brazil, a country that is similar to Nigeria in terms of population? About $700 billion. So if somebody is taking care of 220 million people with $700 billion, you are taking care of 200 and with less than 10% of that, isn't it a no-brainer that you will not be able to achieve as much? But we have to all come together. We have to borrow more money if that is what is required. Or we have to support all sectors to produce more money so that we can fund all our priorities better. The sponsor of the Bill, Senator Sabi Abdul-Lai, represented Niger North Express, his delight that the public hearing was well attended as stakeholders made very meaningful contributions that will enrich the legislation. Well, for me, first let me say I'm very happy that the public hearing did hold. And I agree with you, it was successful because a lot of stakeholders participated. And we had many of them also making very, very vital inputs. That's what public hearing is all about. So I think that is the way to go. And I'm sure going forward with all these inputs and even more that we confront those who just came to express their support but wish to make further input, I think it will definitely help to come out to the bill that we believe will be in the interest of this country. Well, for me, one of the things that we must take note of is if a bill is good, there is no precedent that will not sign it. As a government, we've been talking about diversifying the Niger economy and this bill seeks to provide a legal framework to ensure that not just this government, successive governments that will come on board will be able to continue from wherever a particular government stops. Click the notification button so you get notified about fresh news updates.


Good morning, good afternoon, good evening, wherever you're handling from, welcome to another edition of the future of OpenShift administration with PM and UXD.  And I'd like to just start our show with a round of introductions. I'm Chris Short, technical marketing manager here at Red Hat, also executive producer of this thing we call OpenShift TV. Ali, why don't you go next in the fun round of intros? Yeah, hey, everybody. Thanks for that show, Chris. So the name's Ali Moverham. I'm the OpenShift PM, console PM. And today I brought a couple of great guests, right? So first off, we're going to have Megan Hall, one of our lead UXD folks that's going to kind of cover anything and everything over the air upgrades. So some really cool stuff. And then after that, we're going to go into some live demos with one of our console architects, Sam Padgett is here today with us and he's going to be our demo keen. So that being said, let me hand this off to Megan. You share my screen. Let me know if you can see in here. Yes, you're good. Awesome. And keep me honest on time.  If I take too long. So as Ali said, I'm an interaction designer, mainly focused on the admin perspective of OpenShift, and sharing today, some of the improvements and enhancements that we've made around the update experience in OCP and the console. So the first part of this, we just want to show like what's landed. Most of what has landed that I'm showing here today was in 46. Didn't jump to what's coming soon. 4849 releases. And then what we're exploring for the future. Yeah, one thing I want to kind of jump in real quick and like give a quick little vision of what we're doing. So like, like Megan from the UXD team, they've been doing an amazing job of like, bringing transparency into the upgrade process. And like, in the future goal, again, this is the future show. So we want to talk about in the future, we want to like build and establish so much trust with our users that we could set up auto updates on the clusters, and that users eventually don't even realize that the upgrades happening. So in order to do that, we got to take incremental steps and showing, you know, kind of what's happening and behind the scenes is, you know, part of that. So back to you, Megan. Thanks, Ali. So we've, we have a group of UXD researchers that have been conducting interviews around all things update related. And so we were able to take action on in 46, some of the feedback that we were hearing, which are things like channels are confusing. Like, it would be great if we could understand better how they work. Things like candidate fast and stable. Users want access to release notes in the console. And more transparency sort of what Ali referenced as like what's happening during the update. And then making things inside of cluster settings with regards to OCM more actionable. So the main areas of focus for 46 as a recap to make it easier to find information on channels and versions, provide recommended update paths, and inform on operator and node progress during an actual update. One thing that people don't know what OCM is is OCM is our cluster manager. That's part of our SaaS offering. So if you go to cloud.rehead.com, you'll get OCM right up there. That's the multi cluster manager piece. So the first part of this new work that landed in 46, focused on guiding users to recommend and update paths and available channels. So what you'll see here on the cluster details page under the cluster settings tab is this new visualization that we've added that I'll talk a little bit more about in a minute. But I want to focus first on some of the like channel help text and linking to documentation that we provided here. So we are trying to surface a little bit more about what channels are and linking out to documentation. So you'll see this new popover that we added here regarding channels. And in that update modal, we also link out to learn more as well. So there's sort of three visualizations that you may find when you navigate to that cluster settings tab. And the first one is where we hope that you find yourself most often, which is up to date. And so what you see is this blue line with a single dot denoting the current version that you're on. And the channel that you're in. So if you see this single line, there are no other available channels and you are up to date and doing great. This second state that you can find yourself in is that there are available updates for the channel that you're currently in. What we do if there are more than one, if there's more than one choice there, we just surface the farthest leap that you can take all the way to the right. And then if there's more in between that you'd want to choose from we service that in a popover. And then the sort of the second or sorry, the last status that you can find yourself in is that there are updates for the current tunnel that you're in. And then this gray line denotes that there's a next available channel. There are limitations to this visualization. So you could be, you know, if you're on four three, we're not showing you the channels for four, five and four, six, because you have to get yourself to that for four channel to continue to make progression through that update. So like Megan said, can't show you everything is kind of overwhelming, right? But we do try to show you like the best possible possible path forward. That's the goal is to like help move people along. Again, there's there's tons of advantages of having the latest stuff, right? You got all the latest security fixes, you got all the latest bells and whistles, you have the most stable build. And it's easiest for us to come in and to resolve issues when you're at the latest, right? So we really are motivated to help you get motivated to get the latest. And just a couple of other small notes here, we're linking now to the release notes for the current version that you're on, so that you can compare your current version and then what's available. And then you can also interact with the versions on that line to link you out to release notes as well. And then in this popover middle, we're linking out so all of the versions that are available for you to choose in between that far this leap are also denoted in that class more with release notes.  Like previously, we didn't have all these convenience leaks to the release notes, you kind of had to go and search for them yourselves. So we thought it was really important to just, you know, bring this information right to people's fingertips, reduce the headache of them having to go search for all the different release notes. And then also, you know, create this visualization of like the best path forward again, right? So it's all about really increasing that transparency and making it easy as possible for users to make smart decisions when upgrading to the cluster. All right, folks, we're back. Apologize for the issues there. Just making sure that we're live everywhere and we can get started again. So we are having just a bang up day here on the channel. I do apologize for all the technical errors and difficulties. It's amazing what happens when you try to just make things more reliable. They break. So yeah, having fun with our own high availability today.  I'm sorry. I'm still sharing my screen. Yes, you are. All right, awesome. So I think where we left off was just describing what our intentions were behind this channel visualization. So in order to provide even more transparency into the upgrade process, what we wanted to do was sort of show an in progress checklist of what's happening during the update. So I'm just going to go through this flow really quickly. Say I am on my current versions 4.3.18. I click that update button because I'm ready to make an update. The version that's selected is the farthest jump in the channel that I'm currently in. So fast 4.3. So I've got <IP_ADDRESS> pre selected. I hit update. And this is when we now have this new in progress checklist that starts to surface the progress of cluster operators, master nodes and worker nodes. And I actually am going to switch to a pre recorded video from YouTube that shows this visualization a little bit better than my static screen. So we've just clicked update and now you can see this update to 4.5.4 in progress. And as soon as these cluster operators start to kick off, what you'll start to see is this progress bar start to load. So 2 of 30, et cetera. Now fast forward through this a little bit. But you'll see you're now going to get feedback every time you go back to this details tab during an upgrade. And I can fast forward to that a little bit too. It's not always the case that these cluster operators finish before master and worker nodes start to progress. But that may happen here in this example. We'll see. This is one of my favorite screens. Like previously there was not this level of transparency. There's no break right like cluster operators versus the master nodes versus the worker nodes. So this is really cool and really amazing that like you can visualize this and get enough information. And then if something goes wrong, like it services it for you. And I think of note the only thing that we've added since these wireframes were created was the ability to add custom machine pools. So if you have like infer nodes that will start a new list underneath the workers and you'll see that progress here as well. Yeah. The only other nuance of this is that at least for 4.6 these worker nodes can still progress once we mark an update is complete. So that's just one thing. It's not surprising here that these worker nodes are still progressing. But we've marked your update status up to date. And soon as those worker nodes have that have reached 8 of 8 in this particular example that will disappear. And I believe in 4.7 or future that those worker nodes will actually be a part of what we validate as a cluster being up to date. So this is I think this is maybe only for 4.6. And there is a little pop over that just tells you a little bit more information of what's happening and not to be surprised to see that progress still occurring. And then the very popular question, what if a failing condition occurs? This is definitely something that the over the air team is continuously trying to work on in terms of the messages that we're surfacing here and how often are playing around with the threshold of the cluster operator actually failing. So what we've added here is a quick link to view conditions. And we're working on improving the messages that we're surfacing there as well to make them more actionable. But the hope here is to provide as much transparency as possible as to what's happening with that failure and if in actuality an action needs to be taken or that that failure will actually resolve itself. And then the last part of all of this that we ended in 4.6 was these new recommendations. So you will there's three new recommendations that were we've added here. One, when a new patch becomes available, when a new minor release becomes available and when a new channel becomes available. These recommendations will surface in that notification drawer and you'll be able to take actions on them there. So I can move now to what's coming soon and sort of what's in the pipeline. We try to cover this really quickly but so we're sort of continuing to try to address this long list of feedback that we are continuing to hear. One is to improve the way that we handle the failing true during an update and the OTA team like I mentioned is working on that. The next big part of this is providing general update information in OCM for users to better understand how OCP releases and updates work. And then there's some small fixes that we made to improve like some channel UX when no channel is available, no channel is selected and when the current version cannot be found as well as providing the ability in the UI to configure air gapped environments in the cluster settings. So Ali, I don't know if you want to provide a little bit more background on the goal behind this release page and the value that this will add into OCM but basically what we're trying to do here is surface the late, the foremost recent versions of OpenShift, their corresponding maintenance support, whether that's full support, maintenance or end of life and the latest version for each of the channels as well for the corresponding OpenShift minor releases as well as some other information like the data was released, etc. And this is just the beginning of this particular unit of work. I think right now we're trying to surface as much information as told like why it's important to keep your clusters up to date and better understand how releases and updates work with OCP and this will become more valuable if we can start to think about how this matters across your current fleet and the state that your current fleet is in. One thing I want to point out or highlight on this screen right here, if you all look at OpenShift 4.6 and you go to the bottom part you see EUS 4.6. I can't remember what the exact acronym stands for but that's essentially our extended support lifecycle. Anyone remember what EUS stands for? Extended users or extended update support? There you go. So that means you get support until the next EUS version that comes out which is going to be 4.10 so you could stay on 4.6 longer if you wanted to and that's to help and enable or customers that aren't able to upgrade as fast as possible aren't as nimbly-bimbly as some others so we have that built in there. Before we go any further I just kind of want to ask anyone in the screen like do you guys have any feedback for us? Any information that we're not sharing with you all that you think is important you'd like to see? Are we sharing too much? We don't want to overwhelm people either. Let us know if you have any feedback on how or what we can improve in this process for all. Yeah the folks are a few seconds behind us as far as liveness and chat so feel free to comment and I can fish those questions back up to the team. All right, back to you guys, let's keep going. I don't know if it's worth saying but so I guess we're focusing on putting this into OCM now because that's the place that's a place where you sort of have a list of all your current clusters and it's the hope there is to be able to apply so these are the latest apprenticeship releases and in the future understand like where are all of my clusters where are they right now and what available updates do they have and how can I get there. These are just some really small improvements that we tried to make in OCP so again we're on the cluster settings details tab. Today we don't do a really great job when a channel isn't selected and so the hope here is to add an alert to surface and like in order to request update recommendations configure a channel and change that like the empty state under channel to say not configured that way we encourage users to sort of set that configuration and receive the recommendations. The second part of this is something that I think we often see which is this version not found and this happens if you are on nightly versions so we would like to change this status underneath the channel like to remove that channel selector and say that no channels are available and that update recommendations aren't available for nightly builds. So just in case people don't know what nightly builds are if you go to openshift.com slash try and you select one of our options right that will take you to assume yeah this is this is different people so openshift.com slash try. I've dropped it in chat for everybody yeah. Yeah so here you could go come in and see all your different options so let's let's just for example take try it in the cloud let's click that one this will actually take you to OCM right or cloud.red.com openshift cluster manager. Now you have all these different options right so if we scroll down a little bit let's say we want to install to AWS ourselves we would select that AWS on the left and then I always recommend people do IPI unless you have some crazy requirements that you can't do IPI but if you look right here and it's subtle right but it's there so right under the macOS drop down we actually have developer preview download free release build too that link is our nightly build so you know every every night we put the latest and greatest there for anyone to come and give it a try so if you scroll down a little bit this will allow you to download and install the nightly build so if you want the bleeding cutting edge this is it you guys can set it up try it out play with it do whatever you want but one of the the caveats of the nightly is I don't believe you could do over there upgrades right hence the hence our warning message of there's no channels available because it's so cutting a bleeding edge we don't we can't guarantee upgrades will work so that's why we just disable it for that make sense and then the last one is I think this does happen I'm not sure how often but perhaps you've like forced an upgrade in the CLI and that current that version that you've upgraded to is not in the channel that you previously selected so here we have what we have today is just version not found and we wanted to provide a little bit more information as to like what's happening so an alert that says the version six out one isn't found and stable for five and then making more explicit messaging and that update modal for the channel that we have actually found your current version and what we're providing in that top down so those are just some small things and then the the last piece of this that we're working on currently is configuring your upstream and being able to edit that in the UI so there's a default configuration from Red Hat or we want to allow the ability to provide a custom update service URL so we're on the cluster settings page a little bit further down underneath that visualization you'll see this upstream configuration field and so we want to make that editable and just provide a modal where you can switch that upstream configuration and it's probably good to know here we should think about adding this to the cluster version details as well because that field is also there so being able to edit that in both places would probably be helpful yeah so so for airgap we give you that ability to set up the Cincinnati is a Cincinnati service so it's called yeah locally behind your firewall so you can set up upgrades that way just in case you're your environment requires that which a lot of a lot of companies and governments and whatever do right so we want to enable that they get the same high-level experience that everybody else does and everything else should perform the same right you'll get the same same UX experience same for the rollout for all of it so this allows you to configure that endpoint to get you to the floor and then the last part of this are just improvements that we're exploring for the future and some of them are follow-ons to the ocp console versus ocm so i can try to run through these really quickly for ocm things that we're trying to explore is just surfacing more information so with the addition of that new releases page we really want to make that valuable to the clusters and that you currently have so your your fleet of clusters so surfacing things like on this overview dashboard the update status what clusters are up to date versus what clusters have updates available and then also surfacing the visualization that we added in the ocp console for channels under the cluster details as well in ocm something that's even even more impactful probably is okay yes you're telling me that i have the ability to update but i may be really far behind so show me how to get there and that's something that i think we're really trying to look at both show me how to get there in the UI or also in the CLI and so that's something that we're definitely looking at as well and these are all very conceptual wireframes so it may not end up looking like this at all i'm going really quickly so stop me only if you want to add anything so go back to the other one real quick this is i need to work backwards i've been told i need some security fix or something that's in this certain version right and i'm at this version let me know the fastest least amount of hops to get me to to this version because i i have requirements or needs of a certain thing right so this is the work backwards mode which is a you know super helpful right like being able to say i want to be on this channel and i want this particular version and this is where i am so show me how to get there um without having to leave the context of of ocm and then ocp these are really small things that we're just sort of surfacing as continuous thoughts one is show me um when i go to update my cluster i want to know why i don't see a particular path to a particular version so being able to surface the missing patches um and surfacing also the reasons why that they were pulled um we've heard a lot of times from customers that four out of their five clusters have migrated um and they go into update to a next and they don't understand why that version made no longer be there um so this is just some thought of surfacing um the numbers and explanations um in the context of the console very early mock-ups and then this is alerting when a channel is reaching end of life so adding a recommendation notification um letting you know um that the channel that you're currently in is reaching end of life and that you should change um that channel and upgrade as soon as possible and then thinking of ways to denote that as a visual UI element as well on the cluster settings page uh next to that visualization thank you go back a screen sir this guy yeah so i just want to like reiterate this one we've had scenarios where we'll discover something right and so at that point we do pull that edge because we're like okay we need to we gotta resolve some issue uh where we're noticing some kind of pattern or something's happening so let's pull that and again like it's all about increasing trust and transparency with our customers so they know what's going on uh so we want to surface to y'all any information that we have with you of why and when and then again try to resolve issues as soon as possible so this is this i think is a really important piece of the work uh we're doing so all right back to me oops and then this is a small thing too um that we're working with some teams on to try to understand how we can surface node versions or the status of a node um during an update so what you'll see here it's probably hard to see but under the status you have nodes that say that they're ready um and there's two conditions they're either ready and waiting to update or they're ready and up to date and so um we would like somehow to surface that level of information um either underneath that ready status with waiting to update or up to date um or somehow servicing the versions that the nodes are on um so you can really uh track and understand the progress of what's happening and just providing again more transparency into that process and i think that's all i had to share um but all that to say is that we continue to track um and here the feedback regarding upgrades and so there's a lot on this list that we haven't even gotten to yet um and um are just continuing to to sort of listen and understand um what will make the biggest impact and um and start to build that trust again do you have a chance of the link to the new access thing or the show the the crazy spider web graph oh i have that somewhere i think so i kind of want to share that find it faster go ahead and drop it and chat or on stream or whatever we were just looking at it yesterday actually yeah yeah so there you go so select some stuff here this is uh this is new we added this to our customer portal which kind of does some of the stuff that we're talking about doing an scp and walk in real quick no update after that one yeah like you can get yourself really now with this tool all right so that now click click the web one is that the one that shows the oh there's the graph at the bottom so behind the scenes this is the what i call the spider web right like this this crazy mess of different every permutation of getting to you know point A to point B and so there's a lot of complexity behind there and we do try to simplify it and give you all the best path forward we don't want you to have to think about the spider web that's a bad experience right but that's what's behind the scenes um and it's super complex as you all could tell um so i think you know uh Megan and team guys have done an excellent job of like really bringing out the essence of the UI and the experience and and simplifying it but also yet providing the transparency necessary um for for our users to to get a feel of what's what's happening behind the scenes so this is really cool and now let's share the URL with everybody as well so this is live today i did i put it in chat oh yeah and i think this is more of what we're trying to think about to surface in the context of the ocm as well um but the cli and then the ui has to get where you need to be and i like to point out that all of this is transparent so long as you filter apps to take advantage of the resiliency of kubernetes or open shift um if you have single pod applications you're going to notice downtime because if it upgrades a node that that pod is running on well your application is down so make sure you have multiple pods spread out across multiple nodes when you're doing updates yeah if you guys build your apps like that then you you should not even notice a hiccup hopefully yeah which is awesome yeah that's the power of kubernetes a great orchestration scheduling tool super exciting megan thank you so much that was that was awesome all right now we're gonna hand it off to uh sam uh aka i like to call him the maestro uh he he uh he always fixes it so thank you sam for all that so we got some cool demos for you now all right thanks i should be sharing yes all right yeah so i i just want to demo quickly uh one other feature um in console that admins can do so the open shift version three console was very developer focused and in version four we still support all those various developer workflows but we also support a number of admin tasks you know megan shared some of those things around cluster settings around upgrading and upgrade status i just wanted to demo uh a how you would set up identity providers in an open shift console you may not be aware that you can do this through the ui it's definitely somebody's possible to do through the cli um and uh if you look at the various various topics for the different identity providers there are a number of steps you need to do if you're using the cli you have to create a secret in a certain namespace you need to go in and work with yaml and update the ol config and there are a number of steps um and it's good you can do this through the cli because you can script it or you can um commit your yaml manifest the source control but if all you want to do is set up an idp it's it's pretty easy to do through the ui so i wanted to share that so this is a four seven nightly cluster um you do see the version not found because it's a nightly ability you can't upgrade from nightly builds as Megan and Ali were talking about earlier um but under cluster settings there's a global configuration tab and here you have all the various config resources for the different cluster components um so you can see here there's one for oauth that's where you configure the integrated oauth server to set up authentication in the cluster so by default when you install all you have is the cube admin identity providers you have one qbat admin user which is essentially root on the cluster and then when you log in you also get this message if you log in it's cube admin saying hey you know your your login is this admin user you should really go and set up identity providers so there's a link here in the banner but you can also get to this later through the cluster settings pages so if i click oauth here it's uh it's a new cluster i don't have any identity providers configured yet um and you can add them here through this drop down sam can you can you go back a page real quick sure just want to show another another thing uh if you click the three dots on the right it's like a little hidden shortcut as well um you get all your options right there also so you can and you can also see the api as well but if you're using the web forms you you don't need to know the api which is nice so i was going to show configuring two different identity providers today so i was going to start with ht password which is a pretty simple one um with ht password you create an ht password file using a command line tool um and then you can for your openshift use that ht ht password file which has all the users in the in the encrypted passwords in it so you say i add identity provider ht password it's really easy there are only two fields there's a name and there's a default preview and this will show up um if you have more than one identity provider this name will actually show up on login when you have to pick which one you want to use to log in so i think i can pick a name like twitch and then the other thing you need is the ht password file so i was going to create one um today from scratch so most uh Linux systems will have the ht password command you it's also available on windows if you have a patch htd server for instance uh so you say ht password minus c to create a new file uh switch ht password and then you tell it uh what user you want to add so i'll i'll add user twitch and font c for a password so type this right yeah silly question but does the file name have to end an ht password it does not it can it's a convention but it doesn't it can be any file um and then you can go through and add additional users here um but i'll just add the one for now so now i've got the ht password file here and then i just go here and i i simply select the file folder twitch ht password and click add and i'm done so that that's pretty easy right um so it does take a moment for this to roll out so i go back to a cluster settings page here and i go to cluster operators i can see the progress um of the authentication operator rolling out the new config to its various replicas so this might take a minute or so but as soon as this completes and the change will be live can we actually go to the logs uh you can add and i'm already done uh yeah so if you were to go over yeah one nice thing is like the all the various cluster operators run as pods on the cluster so you can actually go in and see all of their logs so there's an open shift authentication operator um project um and pretty much all the operators use the same convention so they're always open shift dash operator name so you can go in and see you have one one pod here and you should be able to okay there's a lot of there's a lot of data here um but if something goes wrong you can often troubleshoot by looking um by looking at these log files um but now that i've set up this identity provider hang on me so request from the audience uh authentication is something i would that this is from uh i'm gonna say tony tv uh authentication is something i would like to learn more about creating admins doesn't doesn't have a lot doesn't it doesn't seem to be a lot of walkthroughs on creating admins but it looks easy enough though so as we go through this idp process think of the the cluster admin versus the cluster user if we can gosh so so sam was gonna say maybe we go um so you created a new user right um he's not the new user isn't visible in the cluster yet right like so if you go to user uh management section you won't see the user yet because they haven't logged in correct right that's right yeah so you have to yeah so that happens once a user logged in and then which is a little which is i think a little weird because then you could go add roles and in our back so that they can get cluster admin right yes you can um so you could go and say role bindings and say i want to if you want to make the twitch user cluster admin yeah go into roles and role bindings which is how kubernetes manage is um so it's control the different things and there are a bunch of roles that are out of the box um there is like for instance the cluster admin role that basically makes the user super users you can say cluster admin and you say user and then the subject name is the username here it could also be a service account per group um so let's use a twitch so when i do this this creates the role binding that makes the twitch user a cluster admin so i haven't logged in as twitch there's no twitch user yet um but you're still allowed to create the role binding it's kind of a kubernetes thing where you can create resources in any order and eventually when everything's there things just work right um so as an admin even if my if the user hasn't logged in yet i can grant different authorities to that user um so if i go log out now this is going to kick me back over to this page and it's because i have multiple identity providers so if i only have a single identity provider this gets skipped and you go directly to the log in i still have the kubernetes identity provider which is the default one when you create the cluster and then also the twitch one so let me and uh so sorry no xanth go ahead previously uh on one of our other shows we brought on andrew block and andrew block came in and he showed us how to customize his login page and one of the things that he did was he actually parsed out the cube admin idp right so you can still get to the cube admin idp but it wasn't displayed as one of the options there so if you want to kind of have that cube admin backdoor uh you can but for your normal users that would come to this page uh it would be hidden and there's some cool stuff you could do without the login login because this is customizable so go look at that previous stream yeah i'm not going to wrap it all up i'm going to log in this user twitch i must have typed it uh there we go okay the warden is asking me to save the password i'm going to dismiss that uh there we go okay so now i'm logged in and since i created the role binding you can see that uh let me see i probably created the role binding on let me go back out um oh i i created it in the namespace so i'm only an admin in the authentication operator namespace that didn't create it as a cluster wide role binding um so before before you log out uh one quick thing is you'll notice that blue banner that was at the very top of like hey go set up oh it's gone now right that's and that's the people everyone wondered how to get rid of that blue banner it's you create the idp you log in as a non-cuban user and voila the blue banner is now gone all right same sorry yeah i don't have permission to be written in this namespace any are okay role bindings yeah so i created as a our namespace scope role binding right here instead of cluster scope so let me log out um i'm going to log back in as keybedman um so i can show you setting up an additional identity provider so let me copy the keybedman password from when i installed uh now i have once again logged in as the keybedman user so i'm going to go in and um let's go back to global configuration oauth and then you can see the twitch identity provider i'm going to go ahead and add github as well all right so in order to do this you also need to do some configuration on the github side this is all um detailed in the documentation one thing you need to know is that you need to have a github organizational team setup um because otherwise any github user in the world to log in in your cluster which is probably not what you want so i'm going to go in my github here let's go to settings and if i can find it organization so i set up a set up a demo organization here um that just has me for now um but if you go to settings from here and then i believe it's the developer settings you can register a new application which is what you need to do to set up off with open shift using github so let me do that now and you can give this application any name might open your cluster it asks for a URL so you can make this a um make this your cluster URL or anything you want really we have a description blank for now and then you need a callback URL so what you put in here is is documented in the open shift docs i'm going to copy this so i don't type though anything but essentially you need to have a specific callback URL so that when you've done uh approving open shift from from the github side it will redirect to our ol server and continues along and so this is this URL is is pretty much the same so this part will be whatever your actual cluster is and then this last segment here will be the name of the IDP you create so we're using the github as the name of the IDP so that's what i would put in right here and this is all documented right here in the open shift docs so let me register this application so now i have a client ID i can copy that and then use that as the client ID here my next thing i need is a client secret from github so i don't have any client secrets yet uh you generate a new secret and it's asking me to pulse asking me to my password again and there it is so it generated a new secret i'm going to go ahead and copy that and then put this back over here and then if you're not using github enterprise you don't need a host name or a ca file what you do need is an organization so let me type in organization here which is s project demo is the one i set up so it could be an organization or a team but one of these is required unless you're using github enterprise can you do both use translate to group you think i'm not sure off hand if you can do both you can you can specify more than one you you probably can i think you can so if you specify more than one as long as the user is a member of any of those organizations or team this should be allowed yeah cool actually says cannot be using combination of organizations okay multiple but has to be one or the other so let me add this when we should be done so github shows up here let's go back to cluster settings and check the progress of the authentication operator as it rolls out these changes meanwhile should we add some rback yeah i try to do it right this time let me go back to role bindings so here's what i did wrong let's see can you not create a cluster role binding from here let's do that so one of our regulars uh james dade he's asking if ocp could pull users from ad in advance he's already got groups set up and everything like does he have to have that user login every time or can he create all the things in advance of that user landing in the right place that's a great question i believe that um there is actually a mapping method you can set up when you can figure the identity provider and it has different behavior depending on the mapping right so we can look at that so by default we use claim mapping which uh let me find that configuration so console does have this home explorer where you can see all the various apis and we can drill then to the oauth api here so there's an identity provider map and then looks like it doesn't give you detail information about these that's mapping method yeah oh mapping method yeah mapping method not a lot of detail here so mapping method okay so this is what you want to set if you want different behavior and you can go in and create users manually in the system let's see so typically you use claim and then you just log in as the user today identity provider and openshift automatically creates the user it looks like there are different ways of doing it so you probably want to look up where you can go in and create the users manually and then it will map only to existing users so i have an espadget get a byd i can use lookup mapping method and then it would only let me log in if there's an espadget user on the cluster so if you use this if you use lookup mapping method that's another way where you can set up github as your identity provider without providing an organization if you're not an admin for an organization saying or you don't want to go through those steps so uh this description is so much better than what we have in the UI yeah it's open the bug for that and get all this into the UI because that that's yeah far better yeah so let me go ahead and let me log out i can show you the github login so if i've done this all right let me close some of these tabs here i'll leave this one open okay so now i have another option here i have github so when i click on this it'll go through and it's asking me to go ahead and authorize my openshift cluster to be able to read my identity on github so let me authorize and then it redirects back then i'm logged in so you can see my full name here this is pulled in through my github profile so see i'm pageant there i am i can create projects and use openshift that's awesome this is when i wish i had a ad instance to test with um jpdade let me know if you're still having issues it sounds like you have a ball of issues you're dealing with right now so yeah i dropped the link to the docs and chat any other questions from the audience i'm saying thank you that was awesome thank you i love to buy my salt on that one the claim one is new to me yeah the i don't know how ad treats that like claim assignment because ad isn't quite ill-dapped it's kind of its own beast um i know i've had problems with that in the past but yeah if if you have issues or the docs seem wrong please let me know and we can get that corrected yeah awesome difficult yeah no yeah exactly right like so we want to make sure that we have a all the use cases covered and then b like actual functionality working so yeah if if something's not working let us know we can definitely get it fixed so awesome show uh let's see jpdade says yeah i tried all the mappings i have bigger and different fish to fry right now it works it might not be perfect i will try that on my next cluster okay cool awesome um yeah jpdade's doing a lot of switching over infrastructure and all that fun stuff so yeah so cool great show everybody thank you for thank you megan thank you sam thank you allie as always um great yeah thank you i appreciate that uh it's always great to have y'all on because some folks get to learn how kind of like the sausage is made and sausage is fun um ad is active directory tony um and then there's you know all kinds of things that you can use instead of active directory like github or twitch or navy or other things so cool great show everybody thank you uh appreciate you joining us today the next show isn't for a few hours uh at 3 p.m eastern 20 hundred utc we will be having the one and only getoffs happy hour so um stay tuned for that i have not heard what we're talking about today but i'm sure it will be fun all right everybody stay safe out there and have a great day


Hello everyone. This week we thank Canada's public service employees who work so hard every day for Canadians from coast to coast to coast. They are the people who help make sure families get their Canada child benefit payments on time. The ones who help small businesses find the support they need to grow and thrive and the ones that are there to support Canadians through crises and some of our most difficult moments. The last year was difficult for everyone, but the Canadian public service has once again been at the top of the situation during the pandemic, which is a reminder of the security, health, and the support of the whole Canadian population. Thanks to their dedication, families have received the help they needed to make the money and businesses have received the necessary support to overcome this crisis. The public service has also contributed to the protection of our borders and it ensures that vaccines are distributed to provinces and territories so that the whole of Canadians can be vaccinated by September. Over the past year, they've worked incredibly hard and done everything they could to help all of us. So this week, let's recognize their incredible work and thank them for their service and sacrifice. So, to all the federal employees, thank you for everything. Our country will be eternally recognized. Once again, thank you all so very much.


Dear students, in this topic we shall discuss the kin selection and inclusive fitness. Kin selection is an altruistic evolutionary strategy which favours the reproductive success of an animal's close relatives, even at a cost of animal's own survival and reproduction. Kin selection is an instance of inclusive fitness. It combines the number of offspring produced by an individual himself and the number of offspring it can ensure by sporting other close relatives or siblings. This is an behaviour in which an individual does not try to sacrifice himself for close relatives because he knows that he will die, but if he is alive, he will keep on increasing the population. This is kin selection. Dear students, kin selection has genetic basis. Because fitness of an individual is based on the genes it passes on to the next generation. The fitness also means that if the common genes present in an individual are passed on by his close relatives. Genes' perpetuation will be in both cases, so kin selection is a genetically derived trait. Through kin selection, a gene carried by a particular individual passes on to the next generation through a related individual. Kin selection explains how nature selection acting on relatives of an animal can affect the fitness of an individual. For kin selection to be effective, individuals of a group must be able to recognise or identify their relatives. So kin selection is only in those animals or in those animal groups in which the social structure is quite advanced and the members recognise each other's relatives. Such groups are found in primates and in social insects. Dear students, the altruistic kin selection behaviour is most evident in the sacrifice of parents for their offspring. Usually parents sacrifice for their offspring. This is kin selection. Parents sacrifice of their own reproductive potential actually increases their fitness. Because the reproduction by their kin will maximise the spread of their genes in the population. Even the children have their own genes, so if the children are born, they perpetuate the genes. This means that the potential of the genes will maximise and this is the success of the parents. Dear students, now we shall discuss inclusive fitness. Which is a matter of fitness of a group of members of a species. This is not of an individual, but of a group of individuals. This is not a close relative concept, but a group concept. An organism's inclusive fitness is determined by its personal fitness plus its weighted effects on the fitness of every other individual in the population. This weighted effect is determined by relationship coefficient i.e. the related genes. Inclusive fitness is actually a trait which favour natural selection. Because natural selection acts to maximise the genetic and reproductive fitness of a group i.e. population. Natural selection does not think about individuals. The genes of an individual are maximised in the gene pool. In fact, it selects favourable genes in a population and favour them. This is why natural selection favour inclusive fitness more than kin selection. Inclusive fitness behaviour of an animal is when we see that individuals of a species help those individuals who are not close relatives. So this behaviour is inclusive behaviour.