Hello everyone, my name is Rosario Rojel. I am Professor of Sociology at the Autonomous University of the State of Mexico in Toluca. I'm really glad to be here and share with you this project that we have been working in the past two years. First of all, let me talk about how this project come about. The Weber Project, this is what we have called this website that we have been created in collaboration with a sociology undergraduate students. You can access this web page through this QR code. This course was related to Max Weber Theory. All those who are related with sociology know that this is a very famous theoretical work with a high degree of abstraction. The aim was to keep a purely theoretical course a more practical twist, allowing students to get involved in the co-learning process. We propose to develop an open educational resource in a collaborative way where the students themselves would be responsible for developing the contents of the resource. Another central purpose of the course was to highlight Marianne Schneeckter's contributions to Max Weber's theoretical proposal. She was the Max Weber's wife. Marianne was a key player in the development of Weberian theoretical proposal, but her contribution had been little recognized. We therefore wanted this project to give her the recognition she deserves and has not received. And how did we build this OER? I am in charge of the sociology course as a professor at the university. And therefore, the development of this project, I had the support of my colleague, Brian Rosenblunt, who is a digital humanities librarian at the Kansas University, and with whom I have developed other projects. And also had the support of Alan Colleen, a communication graduate who is interested in digital pedagogy. He acted as connection with the students to solve doubts and support them in the development of the website. We had a goal of having the students learn some basic tools and skills for website building. These are students that didn't have much experience with these tools, sometimes not at all. So we wanted at least to get them familiar with some of these tools. We wanted the website to be sustainable. And for us, that means easy to use and maintenance, easy to update and interactive. In other hand, we had to balance time in class with time devoted to learning these technical skills and building the website. We wanted to develop these skills, but not to take too much time away from the real focus of the class, which was sociological theory. So we ended up deciding on a static website platform. They do not require a database or application to display the website. They are just simple static HTML files that can be stored on any web server. And they are fast and they are secure and they are easy to care for preserve over the time. For the development of the website, we use a very common combination of platforms and tools. GitHub, which was a place to host the files, Jekyll, which is a static excited generator, which we used to create the files and it integrates closely with GitHub. And markdown is how most of the content was created. It is a simple markup language that is both human and machine readable. However, using these three tools has quite a bit of learning curve. So it might seem a big challenge. And the student, they started to work with these three platforms. It was not really easy, but at the end they did it and they did a great job. At the end, what are the contents that the website offer? The homepage contains a great description of what is on the website and allows you to navigate through the seven projects that make up the website. And that they were developed by these students who were organized in teams. Each team developed a project and faced the use of different tools and applications. I needed to say that all these course was made during the pandemic lockdown and because of that, this was a bigger challenge. The first team was in charge of developing a bibliographic selection of books and articles written by Max and Marian Weber or about their theoretical proposal. The bibliography was managed in Sotero and integrated in the webpage through the VIP text tool. Another group was in charge of developing timelines about the life of both authors and their theoretical proposal. To do so, they use the timeline GS tool. Infographics and timelines were used to explain theoretical concepts. The infographics were licensed on their creative comments. Another team was in charge of integrating different web resources related to Weber's theoretical proposal, videos, deducting materials and they even integrate a playlist with music from 1920s when Weber developed this theoretical proposal in the onwards of the students. They said that this is the music you should use in the background to read Weber. The website includes several videos developed by the students where in their own words, they explain some of the concepts of Weberian theory. This is perhaps the most entertaining selection of all. At the end is also a credit section where we include all of the students that collaborate in the creation of this project. It was very important for us to mention them and to recognize all of the great work they did. At the end of the semester, we made a big launch of the website. As I said before, we developed this web patient discourse while we were under the pandemic lockdown. Then all the classes were online. At the end of the semester, we could meet each other on person and that was really, really great. In the words of students, it is important to create a web patients about authors to promote learning, exercise new strategies and approach new technological tools. It also serves to make some topics with high level of abstraction easier and more dynamic. And there are different voices of them, but I don't have time to share. At the end, I wanted to remark some of the challenge and opportunities of this project. First of all, the students need a computer to work on most of these tools and some of them didn't have access to a computer at home or they had to share it with other family members. And the campus was still closed because of the pandemic. So some of them couldn't use the computers at the campus. So when we were creating the project, we made sure to organize the class in different teams and that at least one person in each team have access to computers so they could upload all the contents to GitHub. And another challenge was that we needed to keep constant communication with the students because they were experiencing issues constantly while uploading the content or they were still scared to observe the contents and they will worry that they could break the website if they forgot a comma or if they pushed the wrong button, even thought of course that it wouldn't happen because of that we need a close communication. In this case, the support of a recent graduate students like Alan Colleen who inspired confidence in the students was essential. And if we talk about the opportunities of these kind of projects, I can say that creating an open educational resource is as a practical assignment for theoretical courses is a great experience. Both for students and lecturers, we believe that we have to improve this part trying to involve instructors in these kind of exercises. The lack of sociology and open educational resources in Spanish is a key challenge because it is needed to say that there are a lot of open educational resources but most of them are in English and not all of our students in Latin America speak English and because of that they are not close to this. So these kind of resources are important for them because we think it is really important for students to develop their own resources and in Spanish for their own colleagues. At the end, I would like to highlight the importance of collaborative work where educators are not the only ones who create educational content and who are in charge of teaching. It is important to have the support of digital librarians of students from different levels in areas of knowledge from different countries and speaking different languages but above all, playing to build and know how together we, where we all learn and together we build knowledge. Thank you very much.


That reminds me of the ERC interview, by the way, like 10 minutes ago, then shut up, right, sorry. So I'm gonna talk today, thanks first of all to the organizers for inviting me to a very interesting meeting and I think compared to a lot of the really interesting, sophisticated stuff that we've seen in the previous days. This will look comparatively simple-minded, but maybe still useful in some way. But I'm gonna start with a start with a warning and Munahiro, I hope you forgive me for taking your quotes. I think this quote's due to you because I looked it up on the internet, I couldn't find it anywhere else. Theory minus experiment equals mythology. Anyone who knows me re- Sorry? You translated it. Anyway, anyone who knows me reasonably well will know that this probably describes my approach to science. Because I do theory and not really experiment and I think it has the status of mythology. Sorry, guilty as charged, but maybe maybe I rewrite it though, if I'm allowed, if you forgive me, and I say theory minus experiment equals prediction and that means a prediction is an experiment waiting to happen. So maybe I'll have some predictions today that will be of interest to maybe experimental people out there. So I'm gonna make some predictions relating to, well, I'm gonna discuss optimal decision-making by individuals. So using optimality theory and optimal decision theory for groups, both small and large. But then I'm going to try and bridge the two. And if you haven't been to Venice, this is the Rialto and if you have the chance to visit while you're in this part of the world, please make sure you do. So I like building bridges. Maybe this talk will achieve that. So I'm gonna start with discussing the wisdom of the crowd, this very old idea that some people have alluded to in their talks already. And if for anyone who doesn't know the story, it goes back to the days of country fairs in which we still have in the UK, in England, and the kind of the popular pastime of guess the weight of the animal. And Francis Galton took, as you'll probably know, a load of estimates of the weight of an ox. And did some analysis of them and got a result that showed that the median of all the estimates of the crowd was it within 1% of the true value of the weight of the ox. So with, you know, crowds together, uninformed individuals, sorry, relatively poorly informed individuals, combining their information, even if they're not aware that their information is being combined, can do better than individuals themselves, lone individuals. I show the actual paper for two reasons. One to show how easy it was to get away with, you know, to get a nature paper a hundred years ago, one page and one graph. Also, I was just looking at this the other, well, last night, and I thought I'd highlight the first, the opening sentences that Galton wrote. In these democratic days any investigation into the trustworthiness and peculiarities of popular judgment is of interest. Well, very true words for our times. Okay, well, that's people. I'm going to talk about animal groups and I kind of did a lot of the thinking that led to this first project, which is basically a review project. When I was wandering around the Kalahari following meerkats or rather following meerkat followers. So the people who go around logging the data. And a meerkat group or any other animal group that forages collectively, for example, has a problem about how to avoid predation. And meerkats solve that by having certain individuals taking a role shown here in this picture known as raised guarding, where they basically stand on their hind legs on whatever they can find in the environment. In this case, a dreedory bush here. And they look around and they pay attention and they got to ask themselves continuously a question. Is there a predator present in which case I should warn the rest of the group or is there no predator present in which case I can leave them to carry on foraging? And we heard earlier in the week about, you know, vocalizations by meerkats. Various things they do when they're cooperatively foraging and one of the vocalizations is an alarm call. So I'll come to how we can solve that problem optimally in a second at the individual level. But before I do I'll talk about another kind of piece of law in collective behavior, which explains why groups are effective, more effective at making decisions than individuals, typically, and that's known as the condorsay jury theorem. And it's just a very simple mathematical argument. So like Galton was data driven, but this is more of a mathematical first principles argument for why groups making binary choices should be more accurate than their constituent individuals. And it's based on the binomial distribution, very simple piece of maths. But it makes some assumptions. It assumes that the opinions of the individuals are independent and individuals have equal decision accuracy to each other. And it also assumes that there's only or it's been claimed to show that there's only a benefit from group decision making when the accuracy of individuals exceeds one half. And another thing is that this indicates a function here indicates that only majority decisions are considered. So basically whatever the majority of individuals in the group decide that's taken to be the group decision. So what I'm going to do in this talk is actually question a couple of these assumptions and what happens when you relax them and whether things look any more interesting. But before I do so let's just give you a simple numerical sketch of what happens. So here on the x-axis we have group size increasing and on the y-axis probability that the majority decision is correct. And you can see it increases from 0.6 because all the individuals in this in this simulation have accuracy of 0.6 and it asymptotes towards one. So large groups make the correct choice with high probability basically. So I mentioned there's a couple of assumptions. The first one I'm going to relax is that individuals have equal decision accuracy. So this section of the talk is all review. There'll be some new stuff after which I'll highlight. But if we relax this assumption or if we recognize something from the biology it's that individuals have variable quality information in the real world. So real individuals have different decision accuracies for a variety of reasons such as individual differences in ability but also very likely access to different qualities of information used to spatial positioning like stood on top of a bush versus foraging in the mud. And in individual decision making individual behavior should change according to information quality. And this has been shown experimentally. So here for example is work by Adam Kepix and colleagues on choice behavior by rats faced with an ambiguous stimulus. And what Adam found was that rats where there's a where a stimulus predicts a reward but you can manipulate the reliability of that stimulus. Rats if the rats presented with an ambiguous stimulus they'll wait longer before giving up for a reward that they think they should be getting. So basically they're getting a kind of binary stimulus reward present versus absent ambiguous stimuli they wait longer to receive the reward because basically they're not very confident in the level of their information. And actually in neural firing rate in orbit of frontal cortex predicts the willingness to wait for reward so there's neural correlates of confidence found in the rat brain and it's reasonable to think in many other animals. The other thing to know is that the theory of confidence has also been incorporated into collective decision making theory and as I said this is review so I'm not the only person to notice this. I came at this from working with people in machine learning where ensemble theory shows how to combine multiple weak classifiers like neural networks via voting schemes. So that the result gives superior performance to a single classifier in other words the wisdom of the crowd for machine learning. But it also shows exactly how individual classifiers individual votes should be weighted by accuracy. And one way you can derive this is basically to if you assume that decisions are kind of all or nothing there's like a zero loss zero one loss function. You can take an exponential upper bound on that and do some calculus to minimize the error you get from adding each additional votes. And what you find is that individual votes should be weighted according to the log of the odds ratio the log of the individual decision makers accuracy over their error rate. This isn't new by the way as I said this is all in review so other people have noticed the same weighting scheme for example for example Albert Cowell and colleagues have applied it to learning models. Max Wolfen colleagues including Jens who's here have applied it to optimizing medical panel decisions so you know this is known it's out there. What I'm doing is just reviewing the state of the art what we know. But this shows what happens relative to Condorcet when we actually include confidence weighting. So the lower curve we have standard Condorcet but now we have variable individuals so we have a very small number of high quality individuals whose accuracy is 0.75. There's only three of them and then we have 97 other individuals who are comparatively poor decision makers and then we increase on the X axis their accuracy from one half. So they're always you know they have no information up to 0.75 so they're as well informed as the minority we have. And what we see is that the Condorcet curve goes like this but with confidence weighting we get benefits for very small group sizes. We get substantial increases in group accuracy because basically the weighting scheme means that unconfident individuals who are poor at making decisions don't contribute hardly anything to the group decision leaving it to those who are most competent to drive the group decision. So as you'd expect these are just numerical simulations but they just show you where there are benefits from confidence weighting what kind of groups. And these are just two examples for different values of the group the minority of voters accuracy so 0.6 versus 0.75 and as you'd expect intuitively we can just confirm through simulation. The situations where you get the biggest gains for confidence weighting over just simple majority Condorcet voting are when there's reasonably high standard deviation of accuracy within the group. So there is variable quality of decision making ability in the group and also the groups are relatively small. So the red regions are the regions where we have the biggest improvement over Condorcet's group decision making accuracy. So if you're in a small group of variable individuals pays to weight your decisions by confidence. So as I said this is all in review people have noticed this noted this kind of idea in human collective decision making where of course there's very great potential for communication of things like subjective confidence. So Barami and colleagues have done these kind of opinion pooling experiments where you actually have a negotiation phase before a group decision is arrived at. And as you'd expect when you allow information pooling the decisions of the pairs of experimental subjects increase remarkably in accuracy. But are there simpler mechanisms for determining individual confidence and combining them than verbal negotiation in human groups? Well how I'm going to tackle this is by looking at how very low level early sensory decisions are made in the brain. So this is psychophysics and we already saw this in Dan's talk this random dot field. If you have a random dot field so you have a stochastic stimulus you have some signal in there and you have some noise in the stimulus. And you want to make a decision as effectively as you possibly can given the information the stimulus contains. Well it turns out the optimal solution to this in terms of the statistically optimal solution the one that optimally compromises between the expected speed of the decision and the expected accuracy of the decision basically reduces down to what's known as the drift diffusion model. So this is very elementary physics of course. Basically you describe the decision process as a particle moving along an evidence line towards decision thresholds for the two alternatives in this case rightwards versus leftward motion. There's a constant tendency to move towards the correct decision boundary and the magnitude of that is proportional to the stimulus signal. But there's also the Brownian motion component the white noise in that particles motion and that's the standard deviation of that or the variance of that is proportional to the noise in the stimulus. So then what I'm going to just show you is how people so that's a kind of optimal decision making mechanism and there are neural circuits that can implement that in a variety of or approximated in a variety of neuro biologically plausible ways. Well I want to talk to you about his work by Mike Shadlin showing up here and his colleague Keany on how you can get from the drift diffusion model to actually estimating subjective confidence. Because Adam Kepex with the rats odors had a kind of observation of confidence in neural correlates but here we can go back to first principles actually. And what they show is that actually if you have if you're making a decision using a drift diffusion process but there's some uncertainty about quality of the information you've presented with what that means is you don't know about the signal to noise ratio. Yeah so you might be looking at a good stimulus or you might be looking at a bad stimulus of course you don't know a priori so you need to be able to infer that somehow from your decision making process. Well if you plot some quantity in fact it's x it's the position or this is the probability mass function of where the decision variable is where the particle is over time with time going along the x axis. Zero in the middle so that's zero evidence positive evidence in the top half negative evidence in the bottom half. If you plot some quantity that evolves or the probability of some quantity that evolves over time you get something that looks like this. And if you work under let's say what's known as the interrogation protocol so you say at some point in time you say okay what's your decision but also how confident are you in it. What's going to happen is this for one particular trial your your evidence is going to move around randomly but with drift towards the correct boundary. But when you're then asked to declare your decision well first you'd say based on the sign what your decision was but you could also work out your confidence in that decision because. Depending on where you actually moved to that's information about what the quality of the stimulus what the quality of the information you actually integrating was and that looks a little bit messy perhaps but if we just take a slice through and say okay for some decision variable value. How does our confidence evolve over time actually this can have a very simple functional form so it actually is you could approximate it nicely with piecewise linear your confidence starts reasonably. Well not your confidence a quantity starts reasonably high tails off linearly and then and then flattens out what is this quantity that we're plotting this is actually the log odds ratio. So that's exactly what we were looking for in optimal group decision making as well. So I find this quite nice because this is a this is one of the bridges I'd like to find between individually optimal decision making and collectively optimal decision making individuals individually optimal decision makers can compute reasonably easy easily the quantity they need to optimally combine their votes with those of their group mates. So this leads to just a few simple predictions first of all animals should account for decision reliability when they're signaling to or integrating signals from group mates. You have to pay attention to possible conflicts of interest so actually who should do the weighting varies according to the relatedness in the group in highly related groups you can expect signalers to want to reliably signal their confidence. But in unrelated groups there's the risk of manipulation for example if you think about collective foraging there's the risk of making a false alarm call so everyone runs away and you can go in and get the food which you actually even see between species in the Kalahari never mind within species. So there should be but there should be selected process at some level within the group to actually attend to or signal this kind of information this kind of confidence information. And from the link with individually optimal decision making theory animals could use hesitancy as a cue for inferring confidence in their own or others decisions because under the drift diffusion process if you have poor quality information it takes you a long time to reach a decision threshold. So if an animal or a human is taking a long time to reach a decision that's actually good evidence that they have access to poor quality information and you should down wait any vote they do eventually reach. Okay so that's all the that's just that's the review stuff it's from a review article we wrote last year as you just saw and it was just really trying to link things together but nothing new. So let's try and do something let's try and look at something new now by relaxing the other main assumption of the condo safe jury theorem which is that the benefit from group decision making only arises when individuals on average make fewer mistakes than they make correct classifications. The accuracy of individuals is greater than one half this seems to come out because if I read it the condo safe plot with accuracy less than one half then actually group as you increase the group size accuracy approaches zero instead of one. So it seems that there is a kind of lower bound on the individual accuracy you should you should allow group voting to occur at but this neglects something rather important which is signal detection theory. And here is green of green and sweats for anyone who knows about signal detection theory and signal detection theory recognizes that accuracy is not a one dimensional quality as it's treated in the condo safe theorem. And that's because there's two kinds of mistakes you can make in a binary choice. So signal detection theory in its simplest form basically assumes you have like a scale a scalar random variable so some continuous quality which comes from one of two random distributions shown here and really you're trying to say when something is there or is not there. So if you say some that something is there when it is there it's called a hit or a true positive if you say and if you don't say it's there when it is there it's a miss or a false negative. On the other hand if you say something is there when it's not like a predator for example then it's a false alarm but if you say something isn't there when it really isn't and it's a correct reject. And what signal detection theory does is realize that actually there are probably in fact the exception rather than the rule that the errors sorry the cost of these different errors are the same as each other. What you really expect is highly asymmetric errors so signal detection theory kind of grew out of looking for bombers during the early days of radar so it's highly asymmetric case. If you raise a false alarm the cost from that of you know scrambling a fighter squadron to intercept is different from the cost of missing the bomber that comes in and drops the bomb. Also there are different prior probabilities of the two different states of the world as well right. Most of the time there is no bomber so you can say your prior is that there isn't a bomber there but occasionally there is. What signal detection theory does for you is actually to then enable you to calculate how you should set the threshold on this random variable that optimally the optimizes your expected loss or your expected gain from these classification decisions taking account of the different error costs and the prior probability. And this is done through the ROC curve or receiver operating characteristic curve so basically the decision problem so how well separated these two distributions are defines the difficulty of your decision. And then that gives you as you move your threshold it gives you a set of possible values you can at you can achieve as a decision maker compromising your true positive rate against your false positive rate. So when the decision is hard you'll be very close to the diagonal when the decision is easy you'll be up near the top left hand corner. Then your problem as a decision maker is to choose your threshold that actually gives you a realized pair of these values a realized true positive rate a realized false positive rate. And the values you actually should realize depend on these costs and prior probabilities. So here's one example which might correspond to some asymmetric costs or some asymmetric prior on the state of the world. But then shifting those costs or shifting those priors you would actually be moving the optimal decision point and thus the realized true positive rate and false positive rate through through the ROC curve. So again this is individually optimal decision making theory you know it's been around for 50 plus years. And so Max Wolf and colleagues including Jens who's in the audience as I said have already noticed that true positive rates and false positive rates need to be accounted for differently. And I proposed how you could simultaneously improve true positive and false positive rates towards one which is the best possible outcome through quorum sensing. So very simple but effective idea now four years old at least in our literature. And the idea is this if you don't follow it read the paper but in positive states of the world the group accuracy will converge to the accuracy in positive states of the world. In negative world states the group accuracy will converge to the accuracy in negative states of the world. So if you just let everyone choose and then select a quorum threshold between those two a proportional quorum threshold between those two accuracies. Then according to which side of the group that the number of which side of the quorum the number of votes is you can make your classification at the group level. And so you'll simultaneously optimize both true positive rate and false positive rates. You'll make you'll become in other words a perfect decision maker who never makes mistakes under either possible state of the world. And you can always do this if true positive rate is greater than false positive rate because then you can choose the quorum threshold to be between them. Between those two rates for the quorum to be set. And actually this is enforced by signal detection theory. Although I won't I need not explain well I'll explain now why briefly. The reason true positive rate must exceed false positive rate is because if you're down here this is where true positive rate is below false positive rate in the dark grey triangle. You're a systematically bad decision maker in both states of the world. So all you need to do is reverse your predictions all the time and you flip from that triangle to this to this to this triangle in the top left. And so you simultaneously improve both true positive and false positive rate. So that's good because it means individually optimal decision makers or groups of them should always be able to use the quorum trick to simultaneously optimize true positive and false positive rate at the group level. So again that's all existing stuff so here's the new stuff the exciting stuff which we're about to submit. At least I'm excited about it. All right so true positive rate less than false positive rate impossible so we don't care about this region down here we'll never see optimal decision makers there. In the white region condos a predictions are correct which you can take as being equivalent to using a simple majority is optimal in that it will always improve group decisions. So when true positive rate and false positive rate lead you into this region of the ROC curve condos a works correctly and majority voting is optimal. But that leaves a brief aside so now we have to relate true positive rate and false positive rate to the accuracy that condos a actually concerned concerns and of course it's pretty straightforward to do that. It's just expected accuracy which is accuracy in the positive states of the world weighted by the probability of being in that state of the world and then the same for accuracy in the negative states of the world. So the accuracy for the condos a calculation is just the expected accuracy given your prior probabilities and your accuracies in the two states of the world. What this means is there are two regions of the ROC space where condos a makes incorrect predictions and simple majority voting is suboptimal. I'm going to take you through now a number of errors and when they occur. So when is condos a and simple majority voting wrong there's a number of ways in which condos a can be wrong and these all correspond to cases where majority voting is suboptimal. The first error I call it error 1 a is the condos a predicts group accuracy approaches one but simple majority group groups do not. When does this occur well this is a delineation of some of the space of possible decision problems where this occurs so in the gray region this is where this error occurs and the space works like this along the X axis we have the prior probability of the positive states of the world. So this is the decision ecology if you like and along the Y axis we have the ratio of the costs of false positives versus false negatives. The ratio of those two quantities so it tells you basically how costly are the two errors the two kinds of errors relative to each other. This is on a linear scale along here but a logarithmic scale here. So actually we can see quite a lot of the space of possible decision problems condos a makes this error and a simple majority vote goes wrong. Let me show you how it goes wrong so here's an example in in this example when you're applying a majority decision making rule the group accuracy doesn't converge to one but it actually converges to the prior probability. So the blue lines are simulated our averages of simulations of con of majority groups condos a predicts they should be approaching one because the expected accuracy is above one half but they actually converge on the prior probability of the positive states of the world. They do however there is how they're still an improvement relative to the individuals who all have this accuracy of just have just above point eight. Okay so condos a makes a bad prediction majority voting doesn't work how can we fix it well the answer is of course to apply them the quorum rule and just choose the quorum value appropriately. And when we do that we can see that the groups the group does indeed approach accuracy one. Let's look at another error that's very closely related to the first which is in fact it's a refinement of the first error condos a predicts group accuracy approaches one. But simple majority groups do worse than individuals. So again the expected accuracy is still above a half so condos a predicts as we increase group size we should be approaching one. But we can find situations where although the group accuracy doesn't go to zero it does go to a value lower than the accuracies of the individuals within the group. And again we can fix that by applying the quorum rule from Max and colleagues and setting the quorum appropriately and we rescue the situation and the group accuracy approaches one again as we would hope. The final error is that condos a predicts the group accuracy approaches zero because the expected accuracy is below one half but simple majority groups that doesn't happen to them. So here's an example of that and although the group accuracy does decrease it doesn't go down to zero it actually approaches the frequency of one of the states of the world. Again we can fix that by applying the quorum trick and again we can rescue the group decision making and have it approach zero as group size increases. So why is this interesting? Well I think it's interesting because if you ask yourself when is condos a making incorrect predictions maybe that seems a bit esoteric and a fairly self referential exercise perhaps it's not relevant for the real world. But it actually turns out to be equivalent to asking the question of when simple majority voting is the right thing to do. And if we ask that question we just take the union of all the sets I've shown you then this white region here is when condos say predictions are correct and when majority voting and or when majority voting is the right thing to do. So we actually see that majority voting is only the right thing to do in a very narrow range of possible decision scenarios. And that's interesting because a lot of people have made claims over the years about the majority rule being very robust or even optimal and what this analysis shows is actually it's really only for a very special set of cases. Well the analysis also shows us is that when we're above this region on the top left that's when we should use a sub majority quorum. And when we're in the decision region below right we should use a super majority quorum and I'll decode those in a little bit because what we're doing here is relating quorum usage quorum characteristics to decision ecology. So here are the predictions quorums should be a robust feature of decision making even in single shot decisions without conflicts of interest. So quorum thresholds have been proposed for kind of ongoing decision making processes to manage the speed accuracy trade off and in single shot decisions quorums have been proposed to manage conflicts of interest within the group. But actually even if you have a group of individuals making a single shot decision where they're all agreed on what the best outcome would be even if they have different information on what the best outcome is they should still use quorums very frequently. And in particular if the negative state of the world is rarer and or false negatives are relatively expensive compared to false positives we should be finding super majority quorums in use. And the converse conversely we should find sub majority quorums being used whenever the positive state of the world is rarer and or false positives are relatively expensive. And of course then for a particular decision scenario with a real organism you just have to map positive and negative onto different states of your environment for something that the organism cares about. Just a proposal I think maybe bacterial quorum sensing is well studied so perhaps that would be a good system to look at a while. Also be very interested in talking to anyone who works on vertebrates and so forth who think they might be able to test some of these ideas out. So that's really it but I'll just give brief advert for what we're doing next because that's all relating to animal behavior. But what we're currently now looking at is these were very simple models but can we embed the rules we derive from the simple models in more complex environments. So for example embed them in heterogeneous networks and then see how well they work. And here's one of our algorithms based on confidence sharing converging in a spatial network over time. And we're also trying to translate the insights from the decision theory into the design of artificial engineered systems artificial engineered decentralized systems. So we're using kilobot swarms and so this is just Giovanni who's given a talk already is in my group and is working on these aspects as we speak almost. And this is all part of a larger project funded by the ERC called Distributed Optimal Decision Making Algorithms where we're really trying to find we're trying to establish the two way bridge between biology and decision theory and engineering. So maybe it's not a two way bridge maybe it's a three way bridge or a four way bridge but it seems like it's being fairly productive so far. Okay thanks for your attention. Any question?


Hi, this is Allison Sheridan of the No Silicast podcast, hosted at podfeed.com, a technology geek podcast with an ever-so-slight Apple bias. Today is Sunday, December 4th, 2022, and this is show number 917. This week our guest on Chit Chat Across the Pod is Nick of Nick's HomePod Repair. You might remember the article about Steve's amazing experience with Nick repairing our big girl HomePod, I don't know, like a month or so ago. Now it wasn't just that some guy repaired our HomePod, it was how he did it and how he broadcast live video of the repair as it was happening that made it even more interesting. I wanted to know more about how Nick got into doing this and how he creates his videos, so I asked him to come on to talk about it. Now I expected to have a fun, nerdy conversation, and that definitely happened. But what I didn't expect was to find a very kind, thoughtful person who just exudes good karma. Whether you care about HomePod repairs or not, I am sure you'll find this a delightful conversation. I've been getting some amazing, I'm still using it stories from people and it is killing me to wait until Christmas to read them to you, but I must stay strong. These stories will help me to have time to play with my grandkids so they're very valuable to me. If you haven't yet sent yours in, I hope you'll write up a couple of paragraphs about a piece of tech, software, hardware, something that's still bringing you joy after many, many years. Send me an email with the title, I'm still using it, so that it sorts into the mailbox I made just for these wonderful stories. Thank you so much to everybody who sent them in, and I hope more of you will continue to write these stories because they're getting a lot of traction and people really enjoyed the ones that I played over Thanksgiving. I have to say, I feel like Steve and I have made fire, but it's because we've done something probably 85% of you have already done. We finally cut the cord and canceled our cable TV service from our ISP Frontier. I want to chat about it because there's a few interesting points I didn't appreciate before we went through the process. We were paying for Frontier, I'm sorry, we were paying Frontier for Fios Internet, a landline telephone, I know, we're old, and paying them for TV. Outside of Frontier, we also subscribed to the major TV streaming services including Hulu, Paramount Plus for Star Trek, Amazon Netflix, HBO Max, and probably a few more I can't even remember. Oh yeah, Apple TV Plus too, I forgot about that one. Anyway, while my TV watching is pretty much only the streaming services, Steve likes to watch the news along with a few documentary shows here and there. We've been loyal Tevo subscribers for ages to record our shows. We had a primary Tevo plus a smattering of Tevo mini sprinkled about the house, so all shows were available in every room with a TV. Even though the Tevo user interface hadn't seen much change in a long time, it was reliable and it made sense to us. When people first started talking about cutting the cord, they talked about using an antenna. This sounded cool because the major network channels actually have less compression than if you use a cable or Fios in my case for the signal, so you get a higher quality picture. We have a two-story house with a clay-tow roof that's very fragile, so there was no way we were going up a 30-foot ladder to put up a rooftop antenna. I bought one of the recommended ones to mount on the walls inside your house and we tried it out. It lasted, I think, maybe three days before Steve was ready to throw it out the window. While we could get a good signal, it would somehow lose the signal and we'd have to fiddle with it. Fiddly was not exactly what we were looking for. Over the last few years, watching the Cord Killers podcast with Tom Merritt and Brian Brushwood over at CordKillers.com, I've learned a lot about how to watch TV without cable and over the years I've made suggestions to Steve, but he was never interested. When our friend Pat Nengler tried a couple of these services out and she told us about them, he still wasn't ready to try it out. But then something did happen. Our buddy Ron, who's been on the show a few times, you've heard from him, he comes over every Friday night and one week he told us he was cutting the cord. This was interesting because he also had Frontier Fios, TV and a landline phone service. He said he saved an enormous amount of money switching to YouTube TV and that Frontier actually helped him to do it. I was dubious that it would work for us and I didn't even push Steve to consider it. But then Ron got a new TV and he asked Steve to come over to help him mount it. When they were done getting it set up, Ron showed Steve YouTube TV. When Steve came home, he said he was ready to give it a try. I was shocked. Ron had demonstrated for him how easy it is to record shows and that he could record as many simultaneous shows as he could currently using Tevo. He showed him how to find the channels and it all worked as expected. It was different from Tevo but not bad different. I wasn't sold yet because I didn't believe it would actually save us that much money. Ron was convincing but he had not spent the time I had every darn year negotiating with Frontier when they tried to raise our bill and I would fight them every time until they agreed to bring it back down. Some years I actually finished the negotiations paying less than when they started trying to raise my rates. I had negotiated my bill with Frontier down to $155 per month for the three services while Ron was paying close to $200 per month. I called Frontier to chat about what my savings might be. The first thing I found out was that in less than a month my clever negotiation from the last time I called was just about to run out and our bill was going to go up to $190 a month. Yikes! That was as high as Ron was paying. So with that as my starting point my new friend at Frontier immediately told me that she could give me a $10 per month discount coupon for the first year of YouTube TV if I quit paying Frontier for TV service. So YouTube TV is normally $65 per month but I would be paying $55 per month for that one year. Now I have no explanation for why they were helping me. Why are they paying me to leave? I know I was an annoying customer because I called them every time they tried to raise our rate but they were still making bank on us. I'm also sure they get some sort of deal from Alphabet for converting us to YouTube TV but you know the math just doesn't work out. As Ron had explained to me the reason cancelling TV service saves so much money is that virtually all of those weird annoying fees disappear. Maybe you don't have this kind of nonsense in your country but we were paying a VoIP administrative fee and a sports broadcast fee and a cable card fee. The one that really bothered me was that sports fee since I've watched virtually zero sports. Another item in our inexplicable discount category is that they gave me a $10 per month loyalty credit for the next year. And that's right for leaving the TV service they paid me $10 per month since my new rate for Fios was going to be higher than what new customers get they also gave me a $10 per month new subscriber discount for the following year. So in case that's becoming confusing Frontier saved me $30 per month on buying YouTube TV and paying Frontier for Fios. When I canceled my TV service my bill went from $190 per month down to $46 per month. I told the woman on the phone that I was glad she explained those two credits and that they were only for the next year. I said that'll keep me from calling and hollering at you in a year when the price goes up.  I thought that was really interesting she actually encouraged me to call back and find out what I could get. So there was one more item in the savings column. I was able to cancel our Tivo subscription which saves us another $150 per year. So the grand total savings for the first year will be, drum roll please, $1,217 for a year. We were paying over $1,000 extra a year for television. That doesn't even count all of the streaming services we pay for. That was amazing. Anyway, the financial benefit takes my breath away but something else happened that sweetened the deal even further. Ron told me to ask for this but I can't believe they gave it to me. We had 100 megabits per second symmetrical Fios internet when I started the conversation but when I cancelled our TV service saving all of that money they upgraded us to 500 megabits per second symmetrical for free. Like no technician had come out and worked with us for hours to get it working but they didn't charge us anything. So the bottom line is that we saved $1,200 per year, we got our internet speed increased by a factor of five and we still get to watch all of the same TV we were paying for before. We've also simplified our environment. We watch YouTube TV through our Apple TVs that were already in place. We have them one at every single TV which means we were able to get rid of a Tivo edge, a Tivo mini Vox and three Tivo minis. Steve's been selling the Tivos on eBay as we speak and one of the edges he got, he already got $85 for it so that's even more savings. Now Ron also pointed out that those Tivos use a lot of energy but I haven't been able to quantify the savings there yet. The only complaint we have with YouTube TV is that it's much harder to precisely scroll in a show to skip commercials using the Apple TV remote. There also seems to be an issue restarting YouTube TV if it was the last app we ran on the Apple TV. We have to restart the app sometimes three times before it'll start up. These are small annoyances and a small price to pay for $1,200 per year savings and a 5X increase in internet speeds. Now I'm sure you're all smiling knowingly at us because you've been saving this money for years but like I said we feel like we have made fire. A show from the Northwoods is a beast at learning new things and implementing the things she learns. When she met me in real life she bought one of my older MacBook Pros because she wanted to learn the Mac and potentially move over to Mac OS leaving Windows behind. Shortly after that she decided to start a podcast and I offered a wee bit of help. I suggested a few app stirrer. I suggested Hindenburg as a multi-track editor, Audio Hijack for recording different sources and Offonic to level my audio or level her audio I should say and get it to the loudness standards for podcasting. Approximately 8 minutes later she had abandoned Windows, bought a modern M1 Mac, mastered all of the apps I suggested and created her podcast, start with small steps with 160 episodes already recorded. Now when I say she mastered the apps I suggested I'm not kidding. She actually watched training videos, she read the manuals and she is now regularly helping me with these apps even though I've been using them for years when I run into problems. She also keeps up with what's new on the apps she uses. Jill's diligence and constantly staying up to date and being so knowledgeable will now bring a definite benefit to you. Jill tip me off that the web service Offonic that processes our mutual audio files had a new feature. George Holdsman the developer implemented a speech recognition engine using an open source tool called Whisper by OpenAI. What this means to you is that I'm going to be able to provide transcripts of all of the pod feed podcasts. Now the alert amongst you are thinking, wait a minute, doesn't she script the NoCillicast with full-fledged, beautifully crafted blog posts? While that's true, chitchat across the pond light normally has no text version at all. And even on the NoCillicast you might find benefit to hearing Bart's explanation on security bits beyond just the articles to which he links in the show notes. Deep Dives are written out fully but you know there's a lot more content available in the audio. Not only will this be a benefit for those with hearing impairments but what if you did hear an episode but you wanted to reference something you heard or quickly refresh your memory. You could search for a word or a phrase in the transcripts and you'd be able to find it. Now the process to create the transcripts is incredibly trivial and easy through Offonic. The service works on the concept of presets. I have one for the NoCillicast, one for programming by stealth and one for chitchat across the pond light. The processing is mostly the same between those three presets but the three presets add the correct logo to the file, add the metadata like the artist me and the podcast name and then it securely transfers the MP3 file to Libsyn which is the service that serves all of my audio files. To produce a show I select the preset, point it at my audio recording on my computer and Offonic does all of the rest. When it's done the MP3 file is ready for you to download with your podcatcher and I can download a copy for my own archives. Now adding transcripts to a preset is just as easy. In addition to the MP3 output file I can choose three different output file formats. Subtitle which is a VTT file, transcript which is HTML and speech which is a JSON file. Now the only output file type I think I'll be using is the HTML transcript unless someone can explain to me something nifty I can do with the VTT subtitle or JSON speech files. Offonic is fairly quick to transcode the high quality M4A file into an MP3 but transcription takes a bit longer. I haven't timed it since I've only done it once or twice but it seems to be I don't know maybe anywhere from 20 minutes to a half hour maybe an hour I've really got to keep track of that but I did one today that went pretty quickly but the one I did last time seemed to take a lot longer probably a longer show. Anyway the main thing is that it does take a little bit longer than doing just the audio so I might have to wait a little bit to publish the podcast until the transcript is ready. Now I love the download interface of Offonic because it shows me what the waveform looked like before it leveled the audio. If I've been in a conversation with Bart or another guest it is astonishing how different the two waveforms look from before and after. I can also play the audio from this interface. Once the transcription and MP3 files are ready there's a download button right next to them in that audio player interface. I realized as soon as the transcription was available that it would be very tedious to deal with these files on my own so it would be in my best interest to automate what I did with them right from the beginning. Now I use Hazel from NoodleSoft to automate file management and I knew it would be the perfect tool to help me with the transcriptions. I was already using Hazel to deal with the MP3 file downloads from Offonic. Hazel watches my downloads folder and if it sees any file starting with the NC underscore prefix and with an extension of .mp3 it automatically removes them from the downloads folder and puts them into a dedicated folder I have called NC Audio 2022. Hazel also watches that folder and when the files have been sitting there for two weeks it moves them over to my Synology for Cold Storage archive. Now what I needed for the transcripts was going to be a bit more advanced but not that much harder. I set up Hazel to again watch the downloads folder and this time to look for files starting with NC underscore and then ending with .hdml. That should uniquely identify the transcripts files. I set up a second rule for Hazel to look for the chitchat across the pond transcripts. When Hazel finds a match I told her to move these files to a folder on my Mac called Transcripts All Shows. Now for the tricky bit. Hazel can also connect to remote servers and use secure file transfer protocol, SFTP, to log in and push the files up to my server. So I created a directory on podfeed.com at the top level called Transcripts and I told Hazel to watch that transcripts all shows folder on my Mac. Anytime it finds a new one it pushes those new files up to podfeed.com slash transcripts. Now I created a diagram, of course I did, to try and illustrate all of the automation I just described. Hopefully it'll clarify what I'm doing. Now this was only the first part of my automation. You may have noticed that the no-cellicast show notes are very standardized. That's because I use TextExpander to create them. It automatically creates the introduction including the date for the show and it creates the sections for the articles, how to support the show, and the outro. This automation needed to now include the link to the transcript of the show. One of the cool features of TextExpander is that you can embed snippets inside of other snippets. That allows me to create a standalone snippet that links to the transcription and then use it in different ways for the different shows. The chitchat across the pod show notes don't have as much of a rigid structure because they're often just a short paragraph with a link to play the show. If it's programming by stealth it'll also include a link to Bart's show notes. With its standalone snippet for the transcripts I can then include it just like that in the chitchat notes but I can also embed it in the more complex snippet for the no-cellicast shows. TextExpander's embedded snippets or nested snippets I guess it's another way to call them is super powerful and it makes automation so much easier. In theory it also makes me less error prone as well. I say in theory because much of these podcast related snippets rely on me executing them on the correct day. The files you download whether the audio files for your pod catcher or the transcript are all date based. The names are date based. So I've instructed TextExpander to automatically create the episode names using the format of year month date. Now on rare occasions I don't post chitchat across the pond on the same day I record it and basically Anarchy ensues as a result. I use the same date format snippet to name the audio files so I can end up with the blog post and the podcast episode and the transcript will all have the wrong date. On other rare occasions I start the no-cellicast show notes before Sunday and I have to manually go in and change all of the data info there too. Now both of these are surmountable problems for those rare occasions when I do things on an off day but the automation works all the rest of the time. Now the bottom line is that the pod feed podcast shows will now be even more accessible with full transcripts for the hearing impaired and those transcripts might benefit others. I have no intention of editing these transcript files but Offonic does offer an editor where you can listen and edit at the same time. If a volunteer wanted to take on this task I might consider that but for now you'll get whatever whisper by openai gives us. I may also change slightly where the files end up on my server but if I do move them be assured that I'll have a redirect so you can always go to podfeed.com slash transcripts slash the episode named on html. Now I'm working on a way that you'll be able to just look at that top level directory so you could scan what's available but I don't have that working quite yet. Please let me know if you find value in the transcripts whether it's because you aren't able to listen you choose only to read or you want it as a reference to find something you heard on a podcast. Even if only one person gets value from this it's basically free for me to create it through Auphonic and with my automations by Hazel and TextExpander it won't be any more work than clicking that download button in Auphonic. By the time you hear this the transcript of my conversation with Nick about HomePod Repair will already be available linked in the blog post for that episode of Chichat Across the Pond. How cool is that? I am delighted to announce that we have a new hero today and his name is John Endel. He's our hero because he went to podfeed.com slash PayPal and he donated money to help support the shows. So you remember all those tools I just finished mentioning that helped make the show work like Auphonic and TextExpander and Hazel and Hindenburg? None of those applications are free so every bit helps to keep the show going. Thank you John for being our hero this week. I've mentioned quite a few times that I use the awesome blogging tool for the Mac, MarsEdit from Red Sweater Software. Every blog post you read has come from MarsEdit. MarsEdit lets me write in the plain text language Markdown or I can write in HTML or I can mix and match the languages when needed. For example it's much easier to create a bulleted list in Markdown with a simple asterisk on each line than it is to use opening and closing LI tags in HTML. But sometimes I prefer the formatting of links in HTML. MarsEdit lets me use the right language for the right job without fighting with me. Now another reason I love MarsEdit for blogging is how easy it is to add images to my posts and to format and size them exactly the way I want. I can drag them into the text editor and the upload utility opens. Within there I can use medium markup templates that I've created. One of my templates allows me to specify that I want an image uploaded into a figure that is right justified with a 10 pixel margin on the left and a caption underneath. If I'm feeling frisky I might use my left justified or even center justified template. MarsEdit prompts me to create a post slug which is the text you see after podfee.com. It lets me use posts or put posts into categories which is what allows you to see the blog posts or just the security bits posts or all of the chitchat across the pond posts. I also add tags using MarsEdit but I'm pretty sure the way I've built my interface to the site doesn't actually allow you to use the tags for anything but I still tag away as though they do something. When I'm done writing up a blog post I can send the post to my website as a draft and do final updates on the site like adding the featured image. When I'm done and I publish I can refresh MarsEdit and the final published version syncs back down from the web to my Mac and MarsEdit. It's a delightful tool and I love using it. There's only one thing I wanted to do that it doesn't do. I wanted to run on iPadOS but it doesn't and even though I constantly beg the developer Daniel Jalkett to write the iPadOS app he has not done it for me yet. Now if I've got a fabulous writing tool on the Mac why do I need it on my iPad too? Because I find it delightfully relaxing to write on my 12.9 inch iPad Pro with its magic keyboard. I'm less distracted and it's a great device when I'm sitting in bed sipping my morning coffee. I also prefer to use the iPad while watching TV or writing in the car on long road trips. I spend a lot of time in my studio working with my big display in my comfy giant chair but I need to change a venue after a while. I might want to sit on the back deck with the cats and Tesla playing outside on the grass or maybe just sit in the kitchen so I can see outside just a different place. But I don't always want to write on my iPad. I want to flip back and forth between the iPad and Mac. While it would be ideal to have MarsEdit on iPadOS the next best thing would be a cross-platform markdown editor for writing my rough drafts. I don't need to do the final edit there I'd be happy enough copying the markdown from the text editor when the first draft is done and pasting it into MarsEdit on the Mac. A few years ago I went on a hunt for a tool that would let me write my blog post rough drafts on the iPad and Mac and sync happily between them. My requirements were it had to sync between the Mac and iPad so I could easily write in either place depending on my mood. Number two was it had a support markdown and if possible a mixed environment with HTML. Finally number three and it's just as important as one and two for me to use any tool on the iPad to write long form it simply had to have support for text expander. I gotta tell you life is too short to type everything out like an animal. According to my weekly text expander report I normally save around 45 minutes per week using text expander. That's an average of 39 hours per year so think about that's like a week of work vacation. So yeah text expander is a requirement and I've already just finished telling you all the reasons I love text expander. So there's a surprising number of apps that support markdown for text editing on the iPad but adding in that text expander requirement really narrowed down the playing field. I wish Apple would just implement text expander in iPadOS already. And when I did my search a few years ago I settled on Ulysses because it was on both platforms and it supported text expander. It was in set up for the Mac so it was an easy pick to just try it out. Finding it adequate I bought into their subscription model so I could use it on my Mac and iPad. After I got into using the Ulysses they changed their pricing model. To use a cross-platform on all of my devices is now $40 per year or $6 per month. That's super expensive but it's not insignificant. Now Ulysses has nice themes and it has a mode where you can focus on just a paragraph at a time. It also has a typewriter mode that's delightful. The line you're currently typing on is always centered on the screen. For some reason I don't keep it that way but I'm always delighted when I give it a play. Ulysses allows you to sort notes into folders with a simple drag and drop and this is how my brain works so I really like that. It has metrics in the sidebar to tell you how many words you've written and how long it will take someone to read it to themselves or read it out loud. Now that out loud reading part helps me out a lot when I'm figuring out how long the blog post will take me to read. Now there's two things that have been really getting on my nerves with Ulysses over the past few years. These two paper cuts have been grading on me and why I decided recently to ask for help looking for an alternative to Ulysses. I struggle with links in Ulysses. Like most Markdown editors it lets you hit Command-K to produce a pop-up window where you can enter the text you want people to see and a second field for the link itself. It will create under the hood the standard Markdown link format. This format puts the text in square brackets and the link in round brackets. Now I say under the hood because as soon as you create the link it puts Ulysses puts the text and link in this ugly box where you can't see the plain text format for the links. It's annoying as heck to try to edit because my cursor either won't go inside the box or it is inside the box and I can't type text to the right outside the box. I'm always struggling with getting my cursor in the right spot. It's kind of hard to explain but it irritates me all the time. Again, it's a paper cut but it's all the time. Now as I mentioned I sometimes also want to use HTML links like I do with MarsEdit. It seems Ulysses seems to get very confused by these HTML links. It'll italicize part of it for example and sometimes everything that comes after that. The markdown export also seems to get confused by these HTML links. In the export it'll escape out the angle brackets in the HTML which makes them useless when it's put into a real blog post. It's so aggravating I have to go back and fix any links I created this way. In the same vein Ulysses gets very confused by programming code I may embed into a post. It will escape out certain characters even when I've got them in the little back ticks like I'm supposed to so when I export the text and markdown I have to peer very closely at every character to see where it might have borked it up. As you know I like to put images into my blog posts. My process is to take screenshots with CleanShotX while I'm writing an article. I save them to a folder and I name them exactly what I want the alt text to be for those images. By doing that it speeds up my process because when I pull them into the final product in MarsEdit the tool automatically takes the title as the alt text. Does make my titles long but who cares? I get the alt text for free and I can tell my images apart and find her. I still want to pull all my images into the blog post using MarsEdit during the final edit but I need a good way to put that placeholder into the markdown text editor I use. Ulysses does let you drag in images but it puts them in as dee-dee-diny little thumbnails that don't help me see what the image is. It also doesn't show me the markdown that it would include in the image title. My work around I should say has been to type the image name onto a separate line near where I want to put the image offset it by three equal sign so hopefully I notice it when I go into the final edit in MarsEdit. It was a hacky way to do it but I had no choice. I put the call out to our Slack community giving them my requirements for Ulysses replacement and I got some swell suggestions one of which became my new text editor. Let's briefly pass through those apps I didn't choose before I talk about the one I really like. Marty Gensius suggested Craft from Luky Labs. Craft is a beautifully designed freemium app that has a different take on notes. It uses blocks so you see your notes in a rectangular layout like kind of like playing cards on a page. It emphasizes design elements like embedded images. There's a lot of capability in the free app and if you want more storage and collaboration you can subscribe for five dollars a month. While Craft is a cross-platform markdown editor and has text expander support I didn't actually give this a good college try because it was too fancy for this particular work. I might give it a try later to meet some of my other note-taking requirements but I actually want something plain with few bells and whistles. If a beautiful design in a note-taking app is something you're looking for check out Craft from Craft.do. Now Allen suggested I take a look at Obsidian. He said he really likes it. Grumpy, also known as Mike Price said David Sparks of Mac Power users goes bananas about it and that Federico Vetticci is a fan of it as well. David is a dear friend and a brilliant technology guy so that sounded like a good recommendation. Sometimes he goes a bit overboard on the nerdiness of his integrations but you know I respect that in a person. Obsidian has a plugin architecture that allows the functionality to be extended. I like that in an app. In order to use text expander you would have to install a plugin via their Obsidian hub. Rather see it built in but I assume that it works just as well. I went to Obsidian.md to check it out and I was immediately stuck. While Obsidian works on a very reasonable freemium model I wasn't able to check out the cross-platform integration without paying $10 per month for the iPad app. That's kind of a lot of money compared to the $40 per year I was paying for Ulysses. Now Marty also suggested trying drafts which will do markdown and supports text expander. I tried drafts years ago and the basic design didn't suit me. It's more for crafting short drafts that you would then export as a tweet or a tutor or a micro.blog post. Drafts may have changed but when I tried it every time you open the app you were facing a blank page. That makes sense for the use case I just described and for which drafts is designed but I want a sidebar with folders and notes nicely filed away and to be my opening page. I didn't test drafts this time around. The winner for me turns out to be a delightful cross-platform app called bear from bear.app also suggested by Marty. Berry is incredibly simple and elegant. It is of course cross-platform and it works with markdown and text expander. It's the most reasonably priced of everything I tried. The subscription price is only $1.50 US per month or $15 per year. You could use a lot of the features of bear without paying a dime but you know at $1.50 a month it seems an easy buy to support the developers and get a bit more capability in return. Sinking between iOS and macOS is essential and I have a confession to make. I don't know how bear does it. I know it works and I know it's super fast. I clocked it in once at 8 seconds but I don't remember configuring it and I can't find any settings to enable syncing. The only thing I can find is a toggle in the app to disable syncing on a specific device. I'm guessing it's via iCloud sync and it just magically works because if so I guess if I can't remember how I did it we can safely say that the sync is effortless. Before I get into the functionality of the app I want to say that I love the way bear handles links. If I want the link to be on some text I can copy the URL first select the text and hit command K. This pops open a little window already pre-populated with both the text and the link so I can review it and I can either hit save if I want to keep my hands on the keyboard I can hit enter to go through the options to save. When the link is created it shows the text in red in square brackets and then a simple chain icon inside round brackets. There's no need to see that long URL glopping up my text, right? If I click on the chain link icon it allows me to edit that text link combo again that I talked about but if I click on the text for the link it opens it in my browser. And bear treats HTML links like first class citizens too. The HTML doesn't get escaped in any way the URL itself is formatted in red and it's a live link so you can test it to make sure it's correct. It's absolutely the best of all worlds for linking. I felt my stress level go down 50% using bear over Ulysses just because of this. And so far I haven't run into any weird escaping problems in bear when I type in code inside of back ticks. Now I tested dragging in images from my desktop into bear while writing on my Mac and images come in nice and big just like I wanted. When I export to Markdown the images don't come along as Markdown because Markdown is just plain text but the names of the images show up in the Markdown. This means that when I get over to text edit I can see the name of each image where it goes in the blog post. No longer do I have to type in the three equal sign on a separate line to grab my attention for where the images go. It's another time saver. I can even take screenshots on the iPad and drop them into bear and they look great. I can hear Bart right now losing his mind because he prefers to add images at the end so he doesn't lose his train of thought. We joke around often about how I can juggle multiple thoughts without dropping many of them and I think that's evident my preferred workflow. If I don't drop it in images while I'm writing I find that I either forget what screenshot I wanted to drop in or I get lazy later and I don't go back in and recreate them. Now bear doesn't use traditional folders to organize notes. Instead the app uses the concept of tags. I'm still a bit clumsy at using this way to organize my information but I'm getting better at it. You can create a hierarchy of tags that create what look like folders and you can drag and drop notes into one of these nested tag I'll call them folders once they exist. When you're starting from scratch to add a note to a tag you actually type the tag right into the body of the note. You type a hashtag followed by a word or words and it'll put a little pill shape around it so it knows it so you know it's a tag. Now this new tag will show up in the left sidebar. Using tags lets you keep notes in multiple locations which could be handy. If you want to create nested tags you add the upper level tag as before but then you add a slash followed by the sub tag and enclose it in a second hashtag. Once you have these tags and nested tags created you can start new notes within the appropriate nested tag in the left sidebar and that newly created note will automatically be tagged. When I first started using bear I found this whole tagging thing really annoying but I keep discovering new ways to make it easier and I'm kind of getting the hang of it. When I exported my first blog post to markdown and pasted it into Mars at it I realized I'd have to delete these tags so they didn't appear in the blog post but then I found a toggle in bear's preferences where you can have the tags removed on export. Perfect solution. Now there's only one problem with this tagging system and it's that you can't use the hashtag symbol in any of your text because it'll turn into a tag. I ran into this with some links that had hashtags as in page anchors which are shown with a hashtag. The suggested workaround is to wrap anything with a hashtag in it as a code block to make sure it doesn't become a tag. That's fine in bear but it would get back to Mars at it it'd be formatted as mono space text which may or may not look okay. I used it in some of the articles here this week and it looks a little funny but it's okay. Now on the Mac version of bear you can use command F to search within a note just like any normal app. You get the classic little window with a button to add a replace option if you desire but the iPadOS version of bear is oddly missing the ability to search within a note. Command F does nothing. So I went searching the bear website to figure out how do you search within a note? Turns out you can't. You can only search from the column of notes and it finds all notes with your search term. While that might be useful you still can't find all instances of a word or a phrase within a single note. I searched for the word draft because I wanted to change it and it correctly found this note that I've written here but it only showed a tiny preview where I found one instance of the word draft. When I selected the note it didn't highlight the word in my document so I didn't know where the word was nor did it allow me to find other instances of the word. If I wasn't so delighted with the markdown on link support and bearer I think I might have given up on this app when I found this big missing feature. I sincerely hope someone tells me oh Allison you just missed it it's here here's how you do it please I really like that but I don't think that's going to happen because the bearer documentation doesn't mention it so I don't think I'm wrong but again I hope I am. Now this is going to sound like I'm describing an app I don't like but I'm going to tell you about another missing feature. Bearer doesn't have any kind of built-in viewer for your rendered markdown. Now on the Mac the app supports integration with Brett Terpstra's marked app which is a markdown viewing application. Mark 2 is $14 in the Mac app store or you can buy it directly from Brett at mark2app.com and it's available also as a part of setup. I already have setup and I already bought mark 2 so on my Mac it's easy enough to keep it launched when I'm writing in bear so I can make sure everything looks right. But on iPadOS there's no equivalent of marked and I think that's an Apple imposed limitation. The bearer developers could have included a viewer but they apparently chose not to. This isn't a showstopper and not being able to check how things work does keep me more focused but I kind of still wish it had that capability. Now I don't know how important metrics are to you but I do need to keep situational awareness of how long my blog posts are to make sure Steven Getz won't yell at me for the show being too short. Maybe you have a certain number of words or characters you're allowed for an article you're writing for someone else or maybe you're working on your great novel and you want to keep track of how many words you've written. I'm both the Mac and iPadOS versions of Bearer. There's a little eye icon in the upper right that tells you how many words, characters and paragraphs you've written and approximately how long it will take to read out loud. Below those metrics are icons that you compress to allow you to export to Markdown, PDF, HTML, Word, JPEG or Rich Text Format with a single tap. On iPadOS even with an external keyboard you get a set of icons that allow you to do some standard Markdown formatting such as adding headings, a line separator also known as a horizontal rule, bold, italics, underline strike through, adding links, bulleted and numbered lists. You can also add tags with the hashtag icon and even enter a drawing mode or insert an image from the camera or image gallery. On the Mac you get a few more options like code and code blocks along with highlighting and attaching files but it doesn't have the drawing mode. Changing themes is always fun and both versions have several built in. Oddly there isn't much flexibility in choosing the font. I really like the red graphite theme that uses the Avenir Next font. It's a good thing I found a font I like because you only get to choose between six different fonts within Bear. I love the distraction free mode with both sidebars collapsed. Those sidebars contain the list of tags and the list of notes within the current tag. Bear allows you to link from one note to another within the note. I don't have a lot of use for this option but I gave it a go while working on the next I'm Still Using It segment for the Christmas week. I created a separate note for each of the seven new entries that we have so far. I put them all in a nested tag called I'm Still Using It all lowercase because that's how Bear does it. Then I created a note called I'm Still Using It part 2 and I pasted in links to each of the short notes from the contributors. From the main note I can click on any of the note links and they open immediately. This is kind of a contrived example but I wanted to test it out in case it's a value to you. The bottom line is that I really enjoy writing with Bear. It's $25 per year, less expensive than Ulysses and now I don't cringe when I know I need to enter a link. In fact, you'll notice the show notes this week have a lot of links. I was just putting links in right and left about everything I talked about so you can find those things if you want to follow those links. I was able to export all of my notes from Ulysses and import them into Bear with no trouble at all. They're waiting for me in an auto-generated, untagged folder but I might just leave them be. The good news is that I finished my analysis three days before my annual Ulysses subscription was due. Big thanks to Marty, Al, and Grumpy, Mike Price, for helping me find a great alternative that actually saved me money. Hey, did you know that a transcript of this very podcast is available? I've put a link in the show notes and you'll find that at the end of every episode it'll just say transcript and then it says view an unedited transcript of this podcast auto-generated by Alphonic Speech Recognition using Whisper by Opa and AI and a link to look at that transcription. So I hope you guys appreciate it but that is going to wind us up for this week. Did you know you can email me at allisonatpodfeed.com anytime you like? In fact, that's how you should send in your I'm still using it suggestions. We want to read those. We want to hear about the things you're using that have lasted the test of time. You can also ask me a question, send a suggestion, just send it on over. You can follow me on Twitter at podfeed but I'm not as active there as I am on mastodon at podfeedatchaos.social. Of course, there's a link in the show notes. If you want to join in the fun of the conversation you can join our Slack community. Where is it? Everything good starts with podfeed.com. So it's at podfeed.com slash slack. You can talk to me and all of the other lovely no-silla castaways. You can support the shows at podfeed.com slash Patreon or with a one-time donation at podfeed.com slash PayPal like John did this week. And if you want to join in the fun of the live show head on over to podfeed.com slash live on Sunday nights at 5 p.m. Pacific time and join the friendly and enthusiastic no-silla castaways. Thanks for listening and stay subscribed.