Today is Michael, the floor is yours. All right, thank you. So hopefully you can see my screen. Yes, so what I'd like to talk about today is a line of research that we've been kind of pursuing in our group for the last couple of years. And we're interested in mathematical modeling of biological systems from the scales of individual proteins all the way through populations. And in all these cases, we're interested in particular in the kind of stochastic nature of the dynamics. And can we apply ideas from statistical physics? And in the last couple of years, in particular, can we actually have some measure of control of these types of stochastic processes? So what I'll describe today is essentially two stories, one of which is kind of controlling on the kind of larger scale, so entire populations of organisms. And the other is control on the level of individual proteins. So kind of biochemical networks. So it kind of motivates the first of these stories. We all know the phenomena of antibiotic resistance, right? This is basically Darwinian evolution happening at the level of diseases where you have individual, for example, variants developing which are resistant to drugs and can have terrible effects on patients like in spread and actually lead to so-called superbugs, right? These multiple antibiotic resistance diseases. And this also happens intriguingly, though it's a little bit less well known to the wider public for cancer therapies. In fact, it's estimated that the majority of cancer deaths are due to the fact that eventually, for example, certain targeted therapies cease to work because you end up with variants that are resistant to that therapy. So this is a major problem in medicine and people in the last couple of years are trying to essentially figure out ways of mitigating this problem. You can't turn it off completely. You can't make Darwinian evolution cease, but perhaps there are ways of steering evolution in directions that are, you know, make the disease more treatable. Like if you know there's a particular variant that you have a particular drug that works for that variant really, really well, perhaps if you can take a mixed population and steer it towards a population with that particular variant, then you have a methodology of treatment. But that kind of begs the question of how do you do this steering in the first place? So we need to develop methods to kind of fundamentally understand how do you take a stochastic population, stochastic system, and control it and to arrive at a given destination in a finite time, right? Because we can't do this kind of quasi-statically because we need to do this on time levels of actual treatments. So if you have a population of various genetic variants, and these of course could be, I'm gonna focus on genetic variants here, but you can imagine this could also be equally applicable to things like epigenetic changes. How do you steer it? Well, there are several different processes going on in this population. Cells, let's focus on single-celled organisms. These cells are dividing at certain rates. Those rates, the fitnesses may depend on your environmental conditions. So it may depend on the concentration of a given drug and different variants might have different fitnesses under different drugs.  They also can mutate, right? So you have a different, a population of these various types, and essentially from the perspective of control, your control knobs are your fitnesses, which will be dependent on some control parameters. We're gonna focus on drug concentrations. This could be a single drug, which is the simplest case. It could be a cocktail of different drugs, but it could be a various other things as well, nutrients, environmental parameters. Anything that basically can affect the fitness of your individual variants. So how do you describe such a system? Well, we're gonna, throughout this, we're gonna use actually ratios of these fitnesses. So we're gonna choose one type to be kind of the reference type. We'll call it the wild type, and we'll have a ratio of the fitnesses to that wild type fitness minus one. We'll call that a selection coefficient. And what you end up doing eventually is building a mathematical model, which is quite familiar, and we've seen it in many of the talks in this conference, which is essentially a Fokker-Planck description of the system. So you have, if you have M types, you're gonna define an M minus one dimensional vector. This is just a, each component of the vector is the fraction of that type in the population. The last fraction, everything has to sum to one. So the last fraction, you don't need to keep track of that's automatically known from normalization. And you end up with a multi-dimensional Fokker-Planck. And this equation essentially has two major components. There is this kind of a velocity term or what we would call in statmate the drift term, which consists of two contributions. One comes from mutations. So there's a mutation matrix that describes possible transitions between the different types. It's kind of like essentially a transition matrix in a Markov system. And the other part is essentially the role of fitness. So this is where these selection coefficients as a J come into play. Each of them might depend on the time varying, for example, drug concentration. And then what translates selection coefficient into your velocity is this matrix G, which is given here. And that matrix is also proportional to your diffusion matrix. And essentially captures the nature, the kind of randomness of the evolutionary process. So if you have finite populations, if your total population size N is quite small, you're going to have fairly large amount of randomness just because of finite population sampling from generation to generation. And that's your underlying model. And the question is, okay, you have some given system, you know the types, you know the fitnesses, you know how the fitnesses depend on drugs, you're going to get some kind of dynamics, your probability distributions will change in time, but can we now shepherd those distributions over a chosen trajectory? And in particular, because as I'll explain in a couple of slides, you're going to be thinking about a methodology known as counter-diabatic driving, where we're trying to shepherd over trajectories that are equilibrium trajectories. We can imagine that associated with every single parameter, let's say drug concentration, is going to be a given equilibrium distribution of our genotypes. So this would be, this is for example, in this cartoon, a three type system. So you're basically living on a two dimensional probability simplex, and the distributions are just basically distributions on the simplex. So as we change drug, for example, where we go from this distribution on the right, this distribution on the left, we can also characterize it by the mean genotype frequencies in each case. And so we have this kind of trajectory and probabilities space. And the question is, can we actually force the system to follow a particular trajectory? And that trajectory might be chosen for medical purposes, but here it's just kind of a generic problem of can we force a system onto a trajectory? We're going to focus initially on this trajectory, just being a sequence of equilibrium distributions, but I'll show later that we can generalize these methods to kind of arbitrary distributions as well, that are not necessarily equilibrium ones. And what we're going to do is we're going to take advantage of the close parallels which the Fokker-Planck equation has with the Schrodinger equation, right? So there's been a series of really nice approaches developed in the context of quantum computing, in particular quantum adiabatic computation, where you're interested in controlling quantum states. And I'll describe how we can do this analogously for classical systems, but in the quantum case you're often, you're interested in, you started a given, let's say you started a very simply prepared ground state and you do some kind of manipulation of your system. So you're, for example, changing local magnetic fields and you want to prepare some quite complicated final state which is the ground state under your final configuration of the fields. And we know from the quantum adiabatic theorem that if you do this change infinitesimally slowly, you will end up at your desired target state. But at any finite speed of doing this, there's a chance that you're going to end up going to an excited state or you're going to a superposition of excited and ground states. So there's a price to pay. You're not going to be necessarily at your desired state. So in the early 2000s a variety of researchers including Barry, Demerplak and Rice developed a kind of approach called counterdiabatic driving where what you're essentially doing is adding an additional perturbation to your Hamiltonian in order to force the system to be in the ground state of your original system at all times. Even if you're doing this at finite speeds, which is really nice from a computational perspective you want to arrive at your answer in finite time. And in our case, when we're manipulating these classical biological states we also want to get at the desired end goal in a finite time. So how does the analogy work? Well, in the quantum case we had the target structure was basically a sequence of ground states for each field configuration. Here we're going to be thinking about a sequence of equilibrium genotype distributions. And the control protocol there was perhaps maybe often it's a very complicated perturbation potentially non-local. Here we're going to hope that our control protocol is somehow implementable in the system and it's just going to essentially going to be a modified drug concentration over time. That will allow us to basically hit this target trajectory. And the general prescription of doing this is what we showed in this first paper. Now, let me give you a concrete example. So this is going to be a, this was a system of yeast that was actually developed to study anti-malarial drug resistance. So what they did is they put a malarial DHFR gene into the yeast and there are 16 possible variants of this gene, which you can think of as you're going to enumerate them as kind of bit strings. So the wild type gene where there are no mutations is just 0000 because there are four possible places where you can mutate. And then if you have a mutation or location you just put a one there, right? So you have 16 possible alternative, different mutants going from 0000 all the way through 1111. Each of these individual mutants under a particular anti-malarial drug called permethamine has a certain growth rate which you can actually measure experimentally. So each one of these curves is for one of these mutants the growth rate, or we can think of this in our model terms as fitness as a function of drug concentration. And you can see it's a quite complicated curve, right? Things that are fit under low drug are not necessarily fit under high drug. And then associated with every single, if you fix the concentration your system will go into an equilibrium evolutionary state. Right, so this for example here, this Tesseract that I'm showing here the circles are proportional to the population and this would be an equilibrium state under a very low drug concentration. And you can imagine what's gonna happen now if I start a low drug concentration and suddenly push it out. So if you were to do this numerically so you'd choose a certain drug dosage we start from low, we go up to some high value. What you would see in the system is which is showing the solid curves here, a change in the various genotype frequencies as a function of time. And what I'm showing in the dashed lines are the corresponding equilibrium concentrations or frequencies at each individual time step. And you can see as typical of the system if I drive it out of equilibrium it's going to lag behind the instantaneous equilibrium concentrations and dashed lines and not exactly how it eventually if I wait long enough it will reach those instantaneous equilibrium ones but in between and intermediate times there's going to be this discrepancy between the two systems. So, and here so this is at a small time you can see this transition happening over here where the differences in colors here represents the differences between actual and equilibrium. And so there's this kind of period here where things are not precisely following the track that we wanted to. So the question is, can we get it to follow that track and can we get to do this for all 15 genotype frequencies here? The answer it turns out, there's a closed form solution which is, we were quite surprised by that this particular solution works in the large population limit of fast mutations. And you can express essentially the perturbed selection coefficients in terms of your original ones plus a function of time where these X bars here are the mean genotype frequencies for your equilibrium distributions as a function of your original protocol. So then the question is in terms of implementation is finding the drug protocol that corresponds to these perturbed selection coefficients. And in this system, and we tried a couple of other similar systems, it shows that you can do this quite well. So for example, that drug protocol for this particular system looks like now this red curve here, it has a spike in the middle. You cut it off at a particular maximum dosage because there are certain limits physiological limits to what kind of dosages you can give to a system. For these are all numerical simulations but we wanna keep it as realistic as possible. And you can see that with this now modified drug protocol you're following your instantaneous equilibrium curves quite well. And I've shown only the top four genotypes but this actually holds true for all 15. So you can actually implement these things at least at a numerical level in these types of evolutionary systems. And what's also really nice about this system, this is all so far theoretical is that this particular yeast system is something that is actually has been developed in the experimental context. So we have a collaborator, Kerry Samarad at Arizona State who's working to directly implement these same kind of protocols. And we can actually see if you can, these numerically predicted modified dosages will they actually work as well in the experimental context. So where this is kind of ongoing work as part of this collaboration. So everything that I described so far as kind of focused on evolution but these ideas are fairly general. So they don't necessarily any stochastic biological system is potentially amenable to these types of counterabatic control ideas. And so the second story that I'm gonna tell you is gonna be kind of focusing on the other scale of things. So looking at biochemical reaction networks. And here rather than fitnesses what we're changing essentially are concentrations of chemical species and those concentrations influence transition rates in some kind of reaction network system. This is a in this particular system I'll explain a little bit later is essentially a system of for example chaperones binding to proteins. But in general what we have as control knobs now are these external chemical concentrations and our control targets are gonna be can we shepherd the system through a set of probably distribution of system states and as a sequence of these probability distributions. Now here we're gonna kind of broaden our scope a little bit. So we focus primarily in the evolutionary case on just shepherding through equilibrium concentrations but what if we now are basically allow non-equilibrium trajectories as well. And what if in some cases we also are not interested in controlling every single state in the system what if you only wanna focus on a particular subset of states and only control that subset. So we can then distinguish between local control we're only controlling some of the states versus global control. And we can also think about whether these trajectories necessarily have to be instantaneous stationary distributions. And it turns out that for this biochemical network system for the discrete Markov approach you can find solutions to all of these individual problems. But let's start with the traditional problem which is basically global control where your targets are instantaneous stationary targets. So what you're doing, I'm not gonna go into the complete technical details here but what you're effectively doing is you start with a Markov model for your system you have transition matrices describing your rates they depend on some kind of external parameters lambda and you have an underlying master equation that describes the system and then you have some target probability trajectory vector this is what you want the system to behave at in a particular time duration. And for the traditional problem the catalytic problem this target probability trajectory is essentially a right eigenvector of your transition matrix because it's a stationary trajectory. And what you're essentially looking for is a modified transition matrix that where this row is now a solution to your master equation. So in effect what you're doing is you're inverting the master equation normally you start with omega and you try to find P here we have the row and we're trying to find an omega tilde that does this driving and it turns out that using some of the kind of standard tools that have been applied to this Markovian system so graph theory ideas you can come up with a general graphical algorithm to completely enumerate all possible solutions to this inverse problem. And in fact what's really interesting at least to us maybe it's in hindsight it's obvious but we were a little bit surprised when we first saw it is that overall this is you can have infinitely many such solutions and many of them can be actually physically realizable and in particular for graphs with loops generically you're going to have infinitely many solutions. Let me just give you an example of this in a very simple system that's well characterized. So this is a three state biochemical network of a repressor and a core repressor binding to a gene and essentially turning it off. This is from this experimental study where we know all of the individual transition rates and how things depend on concentrations. You have three concentrations in the system that of the repressor, the core repressor and their complex when they bind together and you can set up a basic Markovian description where let's say you change concentrations of these things over time you have some associated equilibrium probabilities trajectory that you want to now have the target of the system and you're interested in, okay well if I just directly apply this and I directly solve the mass equation I'm going to get this characteristic lag I'm not going to follow the equilibrium trajectory but now I can add a perturbation to the concentrations to basically get it exactly on target. This is one solution, for example this is another solution both of these concentration profiles give you exactly the same probability behavior and in fact there's an infinite family of such profiles that can be enumerated using our approach and you can actually begin to do interesting things begin to ask questions like among all these individual concentration profiles that all hit the same target sequence which is the one that minimizes entropy production at every single time step. And so you can find an optimal this is entropy production as a function of time you can find the trajectory that optimizes that and so you have for example this concentration set of concentration profiles is optimal from that sense and you can imagine other types of objective functions that you want to optimize among this entire family of possible control solutions and that's something that we're also actively looking at what are the characteristics of these types of optimal solutions but this is just so far everything here is now just equilibrium targets what if we wanted to make this completely general? So the most general version that you can imagine here would be to say, okay I don't necessarily want things to be in equilibrium or instantaneous equilibrium at all time steps and I'm only interested in maybe just a subset of these individual states. So let's say there are NT of these states that I want to control and so I specify the functions row one through row NT of those states that I'm interested in controlling and the other ones the other probabilities can be arbitrary we'll call those PI and let's say in this problem maybe there are only a subset of the individual edges in the network that I can actually control because maybe I only have a finite number of chemical species that influence these edges. So if we look at the network of in this case it's a probability current network and in dark red are the ones for example that can be controlled and light reds are the ones that you have no influence over. So you have this finite subset of edges that are controllable and then the dark blue states are the ones that you have particular functions that you want those states to obey. So for a given problem like this there it turns out to be a solution it's a somewhat more complicated than just the standard kind of abatic solution but what's nice there's a fairly easy result that actually tells you if a solution is possible at all. And this is what we call kind of a local control criterion and in order to figure this out what you do is essentially a construct target subgraphs. So for every single one of your states that you're interested in controlling you look at the subgraph of other states that are connected via controllable edges and you enumerate those subgraphs. So you construct all those subgraphs or your given network and the criterion states that local control is possible if every single such target subgraph has at least one non-target state. And one particular consequence of this is for example if your number of edges that you are that you are available for control is smaller than the number of states that you are trying to control that's gonna be impossible. But this is essentially a nice criterion where even without doing it at calculations you can immediately just read off whether local control is possible. So this for example, this is another example in this case it would not be possible because this target state that you're interested in controlling the target subgraph does has no non-target states in it. Okay, so let me try to illustrate this with a particular biological example to show you that in some sense that it can actually help us rationalize some of the things that nature may be doing not just ways in which experimentals can go and control biological systems but perhaps ways in which nature regulates itself. And this is the example of chaperone-assisted protein folded. So proteins tend to misfold with some small probability but this could be actually a major problems for cells if these misfolded proteins end up aggregating. And this tendency is actually exacerbated when you go to higher and higher temperatures. So what you need essentially is a way to solve to basically fix this problem. And this is the one way of doing this is essentially through chaperone proteins. What these proteins do is basically bind to misfolded proteins and catalyze their unfolding perhaps into an intermediate state, perhaps into a native state but essentially allows you another chance to fold correctly. What's interesting about this is that it's a natural non-equilibrium stationary state that develops from here because this catalysis that typically involves some kind of energy usage for example, ATP hydrolysis oftentimes is actually several ATP at once that complete a single cycle. And so you can actually study the non-equilibrium stationary distributions that arise from these types of chaperone networks. The other interesting aspect of them is that under optimal growth conditions there's just enough chaperones typically to deal with the normal level of misfolded proteins. So it's kind of like you're building a hospital and you just have enough capacity for the typical patient load. But suddenly if the cell goes into a new condition for example, you go into a high temperature environment you go into a heat shock you have a lot more misfolded proteins you have a lot more wounded patients you need to basically dynamically and very quickly build up your hospital so up regulate your concentration of chaperones. And this is what happens experimentally if you look at this is examples from two different organisms from yeast and E. coli you essentially in the minutes after the start of your heat shock. So this is a yeast for example subjected to 39 degree heat shock. This is a proxy for the number of chaperones these are the expressions of genes associated with chaperone proteins and you can see in some cases almost a two fold increase in the expression of those chaperone genes after the heat shock. And you also see this typical word increases very rapidly in the beginning and then kind of levels off which we'll return to a little bit later for E. coli you have similar kind of a similar story this black curve here is the expression of a chaperone gene. And then for E. coli additionally you also have this transient peak in ATP concentration which again I'll return to a little bit earlier. It's not a universal property of these chaperone systems but E. coli does show this also transient increase in ATP. So can we take a look at these and experimental kind of observations can we rationalize them in the language of this local control theory? So if we build up a kind of simple model of what's going on so this is kind of a two loop model for these chaperones in the simplest case you essentially have in this network only a single edge that you can actually control. And that's essentially the edge going from misfolded to chaperone because that's dependent on the chaperone concentration. So if you have such an edge what can you do with this type of system? So you write it as a four state Markov model and it turns out that with this one controllable edge if your target state is the probability of a misfolded protein. So you want to let's say reduce that probability very quickly. So at the beginning of heat shock it may be very large and you want to get it down to something very small and follow it very sharp. For example, sigmoidal downturn then with that single controllable edge you can actually do this. So you can actually fulfill local control here of that target state and you can do that but you can't control precisely any other things. So for example, I wanted to control native state probability with that single edge I couldn't do that. So in this type of with these types of control knobs you can get rid of misfolded proteins can't necessarily arbitrarily quickly make an increase in the rise in the probability of native proteins. What about for the coli case? Let's say you have control of both chaperone concentration you can up-regulate that and you can also change ATP concentration. Well, that gives you actually now two controllable edges and now you can have actually two target states. So you can control both the misfolded probability and the probability of the misfolded binding to the chaperone. Why would you wanna do that? Well, in the previous case because you didn't control this probability number two you actually get a transient accumulation of chaperone on the misfolded proteins. But let's say you wanna reuse these as quickly as possible. So you wanna get rid of that transient accumulation. Well, now if you have the second control knob you can actually get rid of that hump. So you can actually control both states one and two directly. So it allows you basically to reuse to recycle these chaperones as quickly as possible in this process. So this is not a direct quantitative one-to-one mapping to these networks because the underlying networks are a bit more complicated than a simple example but it does illustrate that some of these ideas in some sense in a qualitative way could help us understand how nature actually regulates itself. So again, like I said, these ideas are fairly general. So we're working on ways to extend it to things like developmental biology to ecology. But where I wanted to kind of leave off at is the kind of the challenge going forward in trying to kind of translate these kind of control ideas from kind of quantum context into the classical one. Everything I described to you so far really depends on a detailed information about fitnesses in evolutionary case or about the underlying Markovian networks in a biochemical case. What can we do in the absence of detailed fitness information because oftentimes it's actually very laborious. Like the experiments to set up and actually measure all of these individual growth rates versus concentration, this is actually a fairly time consuming series of experiments. And this is just for possible mutations. It becomes combinatorially really hard to do this for much more complicated systems. So one potential solution would be to instead of trying to control the entire distribution of genotypes or system states, perhaps focus on means of those kind of map. In the quantum context, this is coming like macro state versus micro state control. So you're trying to, you have less control of the system but perhaps it's more easy to implement. And the other thing that we're looking at is using techniques from machine learning, for example, reinforcement learning, things like that to dynamically learn underlying fitness models that will enable this type of control, right? When we have imperfect information. So we basically may start with a system, we give it small perturbations, small changes in drug. And from the response of the system to those drug changes, learn its fitness landscape and then try to then control the system from that point. With that, I thank my collaborators on this project and I'm happy to take questions. Okay, thank you very, very much, Michael. If there's anybody else with questions, if you could please raise your hand. In the meantime, we have two in the chat. The very first one was from Megan Engel. Megan, if you're there, do you want to unmute and videoize yourself, whatever the word would be and ask your questions? Sure, yeah, it was just a little thing. You may have mentioned it. Thanks for the talk, by the way. This was so cool. Yeah, so you might have mentioned it, but what was the motivation, kind of medically, biologically, for trying to maintain those yeast gene? I think it was yeast. I can't remember. The one where there were 16 different mutations. What's the motivation for wanting to maintain them in their equilibrium distribution? Because it seemed like at the very end, they got there anyway, whether or not they lagged. Yeah. So like, why? Yeah, why? So there are two kind of motivations. So one is, yeah, you're right. If the target is in equilibrium state, ultimately, then it's the question of getting to that target quicker, right? So that's the first motivation, is getting to, you know, equilibrating the systems much more quickly so you get to the final distribution that you're interested in more quickly. But why maintain equilibrium in the middle, right? Maybe you can find there are easier solutions where you go off the equilibrium manifold at intermediate times. There, that's actually a totally valid question. One of the advantages of doing these things, mimicking equilibrium is if there's any interruption in your protocol, right? Something, you know, treatment is interrupted in some way. If you are at any intermediate point in an equilibrium distribution, you're guaranteed to stay there. So in some sense, it's robust to interruptions. You can return to the system later on and continue manipulating it. But the general question is, I mean, this is something that we're interested in. There are whole sets of techniques in quantum control of fast forward techniques where you don't necessarily maintain equilibrium in the middle. And those in principle should also be doable for the evolutionary case. We can definitely do them with our approach with the biochemical networks. So there we don't necessarily have to stay in equilibrium at all. So, John, I think you've got a related question. Yeah, I was gonna say, I mean you've at least partially or maybe mostly answered the question, but it was that you wouldn't necessarily have to specify in advance what the trajectory is in between. Like if you wanted to get to some final state and you start from an initial state, you could say, well, I just wanna get there in some time. And maybe I put some constraints on where the trajectories are allowed to go in general, but I don't try to specify. And that can give you more freedom.  So you presumably do even better than your optimum, I guess. Yeah, and that's one of the interesting questions that we have about these systems is, is the relationship of these types of approaches to the more traditional kind of control theory approaches. You know, in generically, how far are these counter-davag solutions from generic optimal solutions for optimizing a particular objective function? That's something that we're also looking into in terms of follow-up papers. But it's, yeah, these are all interesting questions. At some level, we started with the lowest hanging fruit because when the trajectory is specified and when it's in this incident equilibrium, you can actually have these really nice closed-form solutions for the control protocol, even in these very high-dimensional cases. And so that kind of excited us as a kind of a starting point. Yeah, thanks. So we have three raised hands and the sequence, I believe, was started first, then Jonathan and then Gabrielle. Okay, thanks for the talk, Mike. Let's see if my camera operates. So just kind of a small question. Is there any particular cost to trying to control things too fast? Do you have some trade-offs? Maybe you have to dissipate more if you try to change things fast or if you go beyond some internal time scales, you can't really do it. Yes, yeah, there are both trade-offs and limits. We've seen these in individual systems. If you look at individual biochemical networks, generically speaking, for example, entry production, there's gonna be a trade-off. The faster you try to do things, the more entry production is gonna be involved. The more you'll be forced to be further and further away from detailed balance. So we don't yet have a kind of a completely generic formulation of this. So what we would love to do is something like a thermodynamic speed limit type formulation for these evolutionary systems. And we think that there are already several different approaches that people have used these for costful systems. We think many of those can translate into the evolutionary case, but we have yet to fully formulate that. But you're totally right, there will be trade-offs. So in terms of like, for example, one of the most interesting ones medically would be drug doses. So oftentimes you look at what they call area under the curve. So you're basically integrating a drug concentration over the duration of the protocol. And let's say you want to go faster, presumably you're going to have to have larger overall drug dosages. And there's gonna be a fundamental limit there because you're not allowed to, for example, give Delta function drug dosages to patients, generally found upon. And there will also be limits, as you said, fundamentally because certain rates may be not controllable and may basically set kind of be rate limiting steps overall in your system. All of these things in terms of the general formulation, I think are really interesting questions. Thank you. Jonathan, you're up now. Hi, Michael, thank you so much for the great talk. Near the end, you were saying one of the big drawbacks is just the need to know a lot of the actual rates of the underlying model you want to deal with. And sometimes that's just very hard to get. Is there any thoughts on doing any sort of like entropic inference, like maximum entry methods where you have just some information about the whole model but you don't have all the information and then so then you just try to use sort of maximum entropy to fill in the information that you don't have to the best of your ability? I think, yeah, that certainly sounds, I mean, we haven't specifically looked at that approach but it certainly sounds like it should be, it's one thing that that's a possible solution. Essentially any, we haven't figured out for these specific problems here, which exact, I mean, there's so many different possible, there's so many approaches people have developed from kind of purely black box machine learning through something a little bit more informed like these kind of maximum entry approaches. We haven't yet figured out what's the most practically useful or successful one of these, but there's something that we're working on. Thank you very much. Okay, and then to finish off today's, Vost, you're up, Gabriel. Yes, thank you for this nice talk. You mentioned at the end that you're planning to use a machine learning technique for learning. Could you give more details about that? Yeah, so you can imagine, you can imagine this almost like playing a game, right? So let's say, and this is actually gonna be quite relevant, at this level, this is all of course still pure theory, but let's imagine some kind of blue sky future where you are trying to do this kind of individually cater a therapy to a patient. So patient walks in your door, that patient, even if you know the average behavior of a population in terms of that particular disease, each individual patient has its own environment and will have his or her own kind of underlying fitness landscape. Now, you're not gonna go and measure these growth rates of various disease variants in the patient's environment. That's impossible, right? All you can do is essentially give the patient a pill and then maybe through biopsy or some kind of like over time have individual data points where you're measuring the response of the patient to that pill. So a biopsy may give you essentially a distribution of possible types.  From that single time step, you make the best, you kind of try to infer the best model. And that might be like a previous question through some kind of like parsimony in terms of like entry maximization. Or you can imagine just giving this to like a machine learning, like a reinforcement learning algorithm just the way like machines can learn how to play go or chess without even knowing the rules beforehand. Essentially, the machine is learning the rules which are the fitness landscape as it goes along. So for a single time step, maybe it builds a very bad model and now you give a, and you then perturb it again. You increase maybe the dosage or you switch drug. And now you have two time points that you measured. And so gradually you build a better and better model that the machine is able to infer more and more about the landscape. And then that hopefully does that fast enough to get a good control algorithm going to get you the target before bad things happen. For example, patient ties. So that would be in some sense the kind of the rough picture of what this would look like. But it's an interesting problem because it's not like teaching like a self-driving car where you're allowed to basically run a thousand different trials and the car goes off the cliff, right? This is learning, it's like one shot learning where you have to succeed for this patient. You can't, you don't have an ensemble of a thousand patients to work with where you can kill off half of them. So it's an interesting kind of learning problem. Okay, well, thank you everybody. If anybody for any of today's talks has more questions that occur to them then you can get email addresses of people from agreeing with Tsar, seconding his sentiment. And I'll see everybody tomorrow then I guess. Bye.


a great many things, including Alaska. Most people, if they thought of Alaska at all, thought of it as a cold rugged wasteland of little value except for its gold, fur, and fisheries. Now suddenly it seemed to have considerable additional value, both to us and to the Japanese. And its strategic position was not comforting. From Alaska, the Aleutian Islands stretched out invitingly toward a shrewd and daring enemy. The great Japanese air and naval base at Paramushiro was only 750 miles from Attu. Attu was only 1200 miles from the mainland of Alaska. And Japs in Alaska would be a direct threat to the west coast of America and also to the interior. Alaska's long and broken coastline was weakly defended. Our bases were few and far between. Only means of supplying them was by sea or by air. There was no overland connection across the wilderness of northwestern Canada, not even a trail. If our shipping lanes could be interrupted, Alaska might fall. The situation called for immediate action. The Canadian government had already carved out a series of five airports between Edmonton, Alberta, and Whitehorse, Yukon territory. And there were other fields in Alaska between the Yukon border and Fairbanks. With Canada's consent, the United States War Department decided to build a military highway from Rails End at Dawson Creek, British Columbia, to Fairbanks, Alaska to link up and supply these airfields and to provide emergency access to Alaska for troops and materiel. This highway would extend roughly 1500 miles about the distance from Washington to Denver. The frontier town of Dawson Creek saw the arrival of the 1st Army Engineer Troops in March 1942. Here they set up a camp. Enlisted men were trained in the special problems of maintenance and operation of tractors in the north. Workouts in sub-zero weather over the surrounding hills soon produced skilled cat skinners. Before the arrival of additional engineer troops, these first troops moved out over a trail broken by the Canadian Department of Transport to Fort Nelson, where they established a base camp and began working northwest. Other regiments entered the Yukon through Skagway and established a base at Whitehorse. They began working both northwest and southeast. Still, other regiments entered Alaska at Valdez and moved up to a port in the interior. From here, they began working toward Fairbanks and toward the Yukon border. In all, there were seven engineer regiments, or about 10,000 men. Their immediate job was to break through a pioneer road, a road without frills, but a road which could be used to supply the airfields and to construct a permanent road later. Both did their full share. They cut around mountain sides and across valleys. They corduroyed and filled over swampy ground. They slid it in the mud of thawing ground frosts over it over Brooks and small streams. They built bridges over mountain torrents and full-fletched rivers. On the 20th of November, 1942, eight months after the first troops plunged into the northern bush, the pioneer road from Dawson Creek to Fairbanks was officially opened. The freight rolled through, better than they expected. In fact, considering its length, more than 1,500 miles, the difficulties of terrain and climate, and the remarkably short time of construction, it was one of the wonders of the modern world. The terminus, founded 40 years ago as a gold mining camp, was already a modern little American city in the heart of the Alaskan wilderness. Of course, the highway was still largely a winter road, usable in some sections only when the ground was frozen. Its bridges were temporary, and many of them would be washed out by the ice and floods in the spring. Beams were sometimes covered by ice, and logs had to be laid across them, and cemented in place by water to keep the trucks from breaking through. And there were other hazards. Active springs built up mounds of ice that had to be fought constantly. Mushroomed over the road, it had to be cleared, even at 40 below, all in a rut or ditch where ice was forming. And while the driver went for health, it would be frozen fast. At many points, the grades were too steep, especially when they became icy. It was impossible to keep the road safe at all times and places. Even on the best sections, if drivers relaxed for a moment, they might find themselves in a ditch, pulling up and down the highway, dragging trucks out of trouble whenever they found them. Full stations, established at 100-mile intervals, were manned by quartermaster troops who checked over and refueled each truck as it came through. Troops had to repair and service their equipment as best they could. But repair facilities were badly overtaxed. Both spare parts and mechanics were scarce. Many trucks were deadline for weeks at a time. At the start, troops working on the highway had only tents for living quarters. By the beginning of 1943, however, most of them were living in quonset huts. Sometimes for added warmth, they were banked with sawdust. They came in sections and could be erected in a few hours. Following closely behind the soldiers who built the Pioneer Road, came civilian contractors to turn it into a permanent all-weather road. They established their own camps and repair facilities at vantage points along the route. Road administration surveyors to relocate and improve the alignment of the road, with power shovels to widen the road and cut down grades, which were opened all up and down the highway. There were bulldozers as well as trucks to build up the road. There were bulldozers to spread and pack the gravel. There's to dig drainage ditches. There were motor graders to smooth the surface. There were still structural workers and carpenters to build permanent bridges, replacing the temporary ones of the engineers. Range new patterns and steel rose against the cold northern sky for telephone crews to put in a line stretching the entire length of the highway. The signal corps directed the work and its men did the pioneering. The Japs, although they had been entrenched in the illusion since June of 1942, were unable to interfere with coast-wise shifting to Alaska, supposed to increase the volume of supplies moving in through Skagway, plus Krondyke during the Gold Rush of 97 and 98, when gamblers and desperadoes like Sophie Smith held sway. Then it became a ghost town, surviving only as a small port and railroad terminal, attracting a few hundred tourists each summer. It came to life again as a transfer point for troops and supplies for the Alaska Highway. The ease were improved and enlarged. The fork was extended. The inside passage from Prince Rupert in British Columbia could be unloaded quickly and easily. This could be beached and unloaded at low time. It moved across the mountains on the narrow-gauge White Pass and Yukon Railway. This winding railway is one of the steepest in the world. It follows closely the famous trail of 98, on which many a man lost his life climbing its icy ledges on his way to the Klondike. To make sure the railway would continue to operate during the emergency, the army leased it and installed a railroad operating battalion to run it. All winter long, in spite of cold and snow and treacherous rock slides, these trainmen in uniform kept supplies and equipment rolling steadily 110 miles into Whitehorse. As an example of the railroad troops' devotion to duty, one engineer was awarded the soldiers medal for risking his life to save a train from destruction. Whitehorse, at the head of navigation on the Yukon River, 40 years ago in its gold rush heyday, boasted a population of 20,000 people, which later dwindled to a few hundred, unfamiliar voices of soldiers and truck drivers and catskinners. Through the spring and summer, the road building continued. By now, there were 81 contractors and about 14,000 men scattered along the highway. Crews worked in two shifts of sometimes 10 and sometimes 11 hours each. Specifications called for a 26 foot road bed with 20 to 22 feet of surfacing with gravel or crushed stones through the highway itself, an important supplement was being built. The Haynes Cut-Off. Running from Haynes in the Alaska Panhandle, 160 miles north to a point on the highway, 95 miles west of Whitehorse, the Cut-Off was designed to relieve the strain on the White Pass and Yukon Railway. It was begun in the spring of 1943, with engineer troops pioneering the road south from the Alaska highway. At the same time, civilian contractors started north from Chilcoute Barracks, an early military post which lay just outside the village of Haynes. For the first 42 miles, an old motor road was improved, widened, and in places relocated. Way to the Alaska Yukon border, this road wound through spruce and poplar forests, over cold mountain streams, and through a sleepy Indian village. Here the contractors started from scratch, clearing timber and surfacing. On reaching the pioneer road of the engineers, the civilian crews continued along, improving and widening it all the way to the main highway. When the Haynes Cut-Off was finished in the fall of 1943, the Alaska highway had an all-weather connection with tide water. As the highway and the Cut-Off were nearing completion, so were the emergency flight strips being built by the Canadian Department of Transport and the United States Army engineers, tween airports to provide a safe landing surface for pilots who suddenly ran into trouble. In addition, the engineers were improving the air ports themselves, and ways and aprons were added. These airports were enlarged, and the flight strips were built not only to speed American planes into Alaska, but also to service red-starred fighters and bombers being ferried on land least direct to Russia. At the same time, most of the 86 major bridges on the highway were being finished. The largest of these was the Peace River bridge, spanning one of the great rivers of the north. Near the southern end of the highway, the bridge is 2,275 feet long with two suspension towers reaching 200 feet high and a concrete deck 24 feet wide. The bridge was officially opened in August 1943. Action workers came from various sections of the highway. There were officers and officials representing both the United States and Canada. There were speeches. There was a ribbon to be cut. But the crowd didn't need to be told of the importance of the occasion. The towering bridge itself was ample evidence of the new day dawning in the north. Now the lower half of the highway was finished. In all seasons, trucks from Dawson Creek could travel straight through to Whitehorse. At intervals of about 100 miles, there were relay stations where the trucks were checked over and refueled from railhead at Dawson Creek or brought up the inside passage in tankers to Skagway. But with the completion of the Cannell project, with its refinery and network of pipelines, the Alaska highway had its own fueling system. Oil from Norman Wells was refined at Whitehorse and gasoline was pumped through supplementary pipelines to all points on the highway from Watson Lake to Fairbanks. By October 1943, there was only one gap left in the highway. It was just east of the Alaska-Uconn border. On the 13th of October, the remaining gap was only a few hundred feet for two crews working toward each other. In this section, there was permanent ground frost. The ground stayed frozen all summer long down to a depth of 50 or 60 feet. If the surface cover of moss and vegetation were disturbed, the ground would quickly thaw and become a quagmire. So the contractors used a road-building technique peculiar to the north. Rock fill and gravel were spilled out on the ground and carefully spread over the surface. In this way, insulation was added to that already provided by nature, and the frost was permanently locked in. When the end was in sight, the truck drivers and catskinners, the foremen and superintendents, felt the growing excitement. They were witnessing the climax of 20 long months of toil by 25,000 men. It was six o'clock. The sun was down. Their blades touched. The Alaska highway was finished. A handshake in the gloom of an autumn evening marked the end. This is a good road. As good as any gravel road in the world. Better in alignment and grades than any road of comparable length on this continent. Food surface provides a safe, all-weather route for trucks and cars from the transportation network of Southern Canada to the interior of Alaska. Through the 1600 miles of integrated canal pipelines, fuel to supply the road, the airports and the flight strips, are fully available. Now we can press home the attack. This is the wedge which has pried open the last great frontier of America. The key which has unlocked the treasure chest of Alaska and the Canadian Northwest.


When organizations enter into strategic planning, they conduct different types of relevant research and analysis. Marketers often talk about summarizing and concluding their research and analysis in a SWOT and a TAUSE. Before we start, let us list the learning goals for this video. Firstly, the goal is to establish a connection and overview when in the process with the SWOT and the TAUSE be conducted. Secondly, the goal is to gain an understanding of what a SWOT and a TAUSE is, why marketers use it and how we could go about conducting a useful SWOT and TAUSE. So let us begin. So what is the connection? When in the process with the SWOT and the TAUSE be conducted? Let us establish an overview. Once having established a problem or an opportunity that we wish to explore, we would do some research. We might wish to conduct both secondary and primary research. We would consider conducting qualitative and or quantitative research and use different survey types. We would check and evaluate the quality of our research data by critically applying the terms validity and reliability. The gathered data might be used in a process of conducting different analysis. Internal analysis, which could include a look at the company accounts, current business model, core competencies and others. And external analysis, such as PEST, Porter's Five Forces, competitor analysis and others. And ultimately, we would summarize all our research in a SWOT. This can be followed by a TAUSE in order to identify several strategic options to choose between. This will enable us to make informed strategic decisions about which direction to take. The SWOT and the TAUSE is what we will address with this video. Let us use an example. This café is located in a large city close to a busy high street. It's called the French Café and it specializes in serving high quality cakes and desserts. The French Café is troubled by the fact that less and less customers visit the café and sales are decreasing as a result. The owner of the French Café has carried out some research and analysis in order to find out why the customers are looking elsewhere for their cakes and coffee. He is hoping that his research can help him make decisions about what to do to turn things around. Here is a short summary of the types of research and analysis the French Café have carried out. They have spoken to customers and suppliers. They have analyzed their financial accounts and their online presence and interactions in detail. And they have also investigated their competitors. During the process they have collected vast amounts of data, both qualitative, quantitative, primary and secondary. But how do they organize and summarize all this information? They need an overview. That clearly shows what information is relevant, what is good and what is bad. And they need to distinguish between the information that describe their own internal situation and which describe the external environment, the world around them. This is where the SWAT comes in handy. The SWAT is like a large bucket where all the conducted research and analysis is collected. However, the information is not just thrown in at random. Instead it is neatly organized and presented in order to provide the overview that we were looking for. Firstly, we will collect and present all the internal information that we find relevant. We will divide them into what we believe to be strengths of the French Café and what we believe to be their weaknesses. Let us highlight just a few of these findings. Let us start with the strengths. The analysis showed financial stability and the confectioners French and highly experienced which brings great authenticity to the café. Some of the weaknesses revealed by the research were a high turnover of staff which causes excessive time spent on hiring and training and the building is quite run down which shows. Secondly, we collect and present all the external information that we find relevant. We will divide this into what can be considered as opportunities. These are external developments that we might want to take advantage of. And threats. These are things that could have a negative impact on us. Some of the opportunities discovered are Food tourism. An increased number of tourists actively seek out high quality food experiences. And a general increase in disposable income. Some of the threats revealed are An increased focus on healthy living. This could be a threat considering the high fat and sugar content in the French Café's products. And locally, competition has increased considerably with many new restaurants and cafes opening up. Many more strengths and weaknesses, opportunities and threats should be included in the SWAT. But for the purpose of this video, we will deal with just these few to serve as an example. Now let us make the move from the SWAT to the TAUSE. The purpose of the TAUSE is to come up with possible strategic options based on the findings in the SWAT. We start by bringing in the SWAT and placing the four areas like this. Then we create four squares in the middle. These will serve as our framework for creating strategic options. Now let us see what strategic options our strengths and opportunities allow us to create. One option could be to create an offering and campaign targeted directly at food tourists. Our strength in having a highly skilled and experienced confectioner allows us to capitalize on this recent trend. Now let us see what strategic options our strengths and threats could generate. One option could be to intensify promotional activities. Our strong financial situation should provide us with the resources needed to face the threat of increasing competition. Let us move on to see what strategic options our weaknesses and opportunities could create. We could reorganize and invest in staff well-being in order to create a more consistent and positive cafe experience for our customers. If we are to take advantage of the general increase in disposable income and trends in our favor, then we will need to minimize our weakness of the poor staff retention rates. Finally, let us look at what options our weaknesses and the threats might suggest. We could carry out much needed repairs on the building or even consider moving location. If we are to face the increase in competition and appear attractive to our customers, then it could be necessary to minimize this weakness. Many more strategic options could be generated in a house, but for the purpose of this video we have created just a few to serve as an example. So, the SWAT is a useful tool to conclude, organize and summarize our conducted research and analysis. It provides us with a good overview of what our findings mean to the French cafe. And the TAWS is a useful tool for taking the next step of generating strategic options, possible actions we can take based on our findings. We have now established a connection by showing when the SWAT and TAWS are used in a research analysis and strategic planning process. And by using an example, we have illustrated what a SWAT and a TAWS is, why they are useful tools, and ultimately we have shown how we can go about conducting a useful SWAT and TAWS. If you want to learn more about research analysis and strategic planning or about other marketing related subjects, then I suggest that you watch additional videos on my channel. To further support and substantiate your learning, I recommend that you read Market Research and Statistics by Forbes and others, as well as Principles and Practices of Marketing by Jobba and Ilis Cedwig, and if you are able to read Danish, International Marketing by Rolighed Andersen and others. My name is Tina Wade. Thank you for watching.


Hey folks, Jesse here at Mountain View Dodge. Beside me today I have a 2019 3500 Tradesman White Dually. This here is your worker truck. This is the one that you want to haul everything. This one here has a 6.7 liter with the ascent transmission. Inside you can seat cloth with the bent seat. Still has power windows and power locks. Has all the creature comforts that'll get the work done that a Ram is known for, folks. This here is the truck. A spray-in bed liner with our dual wheels with our mud flaps that come with it. We still have our backup camera, a 4 and a 7 pin with our Class 4 receiver. This truck here, folks, will tow anything. You're about 30,000 pounds in your towing capacity. As you can see inside, still has room. Gets the job done. Gets everybody there to work making you money. So this truck here will look great in your yard. Come on down to Mountain View Dodge and see us. My name's Jesse.


We're back. We're live. I'm Jay Fidel, 10 o'clock block on a Thursday, December 17th already. Whoa, we're really getting on there. Seems like the year only began a minute ago. Interesting how that works. And the legislative session only began like a minute ago, except it was a dud. Sorry. Talking tax with Tom. Tom Yamachika. Hi Tom, welcome to the show. Hi Jay, Grant. I'm glad to be back. Interesting. You're talking about legislative session because that's what we're going to talk about today. What I hear so far is that we may see a repeat of 2020 and that it's going to be an abbreviated session. What is abbreviated meantime? Like they'll only consider a fraction of the bills they normally do. Yeah, what I've heard is they're going to have all the hearings remote, whatever hearings there are, they'll all be by remote, which means a different paradigm and maybe you can't get as much done as you say. But one of the things on the agenda is dealing with a very walloping large deficit which should not be happening. We break the constitutional provision, like all the time, we're at 300 million over so many years in the past 10. And now we have this prospective deficit of 1.4 1.5. And this is only one thing that presents itself and that's a tax increase. So you must be hearing about that. Of course, you know, at first the talk with Governor Ege was all about furloughing and reducing the cost to government. Okay, so the plan was that he was going to make everybody take two days a month as furlough days without pay, and which would save around 10%. And then on top of that, he would ask each department to basically lock their budget between 10% 20% depending on the department. And so that that was kind of the narrative for a while. And then of course, the unions get in the picture. And they say, you can't do this, because we have a collective bargaining agreement and, you know, we have collective bargaining laws that protect the collective bargaining agreement, and we have a contract and you can't break it. Are they right? Well, there are a lot of nuances to it this time. Because when one of the things that Governor Ege did when he made his emergency proclamations is he wiped out the entire collective bargaining law. Can you do that? That's a matter of dispute. So the, the, the unions took him to task for he said you can't. And you really didn't intend to do any, you know, citing correspondence between high level state officials in the union. And so they brought it before the Hawaii Labor Relations Board sometime in July. And the Hawaii Labor Relations Board in an oral ruling. It's not written down as far as I can tell. They said, Well, yeah, the governor didn't really mean to suspend the law in its entirety, although that's what the proclamation says. So, you know, we are going to keep going with the case and we're not going to proceed on the assumption that the, that the law under which we're operating has been scotched. Okay. Okay. But there are a couple of problems with that. Number one is that apparently cooler heads prevailed and in a September ruling. The Hawaii, Hawaii Labor Relations Board itself walked that position back a little bit, and they said, Well, okay. In the Hawaii Labor Relations Board, we make it we interpret and make decisions on the labor relations laws unfair labor practices that kind. Okay. We don't have enough pay grade to consider what the effect of the governor's proclamations are. So, we are going to proceed. And we're not going to make any decisions on whether the law is suspended or not, which is, which is kind of interesting because they can't proceed unless there's a lot. Right. So, they are proceeding to say they can't proceed, they can proceed. And inherently they're they're upholding the law because otherwise it wouldn't be able to proceed. So, so, so implicitly they're saying it's, it's, it's valid. Yeah. Okay. So there's not, there's not a lot of courage in that. Well, not only that right about it. Yeah. Not only that. But if you look at the emergency powers law itself. You know, chapter 127 of the Hawaii revised statutes. There's a section that says even courts are prohibited from declaring emergency proclamations invalid, unless there is a three judge panel in the circuit court that is convened to consider such a question. And they say it's about. And it provides, you know, for expedition of court business and all that kind of stuff. So yeah, this is this an important case. And we want to make sure that gets hurt and decide very quickly so you know, expedited in all possible ways, etc. etc. Those were the statutes. But I don't think anybody. This year has convened a three judge court panel. And, I mean, certainly, the Royal Labor Relations Board is not the three judge court. So they really don't have the authority to say, well, we're not going to follow this. They have the authority to kick it up to a three judge panel. I don't know what the procedure is inside of the Labor Relations Board, but I suppose at one point either party, either the government or the union could take it to circuit court and get that kind of. You know, if they either A, where were the process or B, wanted to do it. Now, I have heard in the news media of, you know, a single judge. I think this is on the big island. Saying that the the governor's proclamations are valid. And I suppose that because the judge isn't saying they're invalid, you know, the judge can certainly, you know, profess that opinion. And the case, not involving Labor Relations, but basically involving somebody who said, well, you know, emergency proclamations are only good for 60 days. That's the law says.   And that tells me about this and footnote to my, my comment that was a thing in the paper about. I guess it was the bars. Yeah, the bars in Honolulu, who have been closed most of the time this year. Went to federal court for an order from the magistrate Leslie Kobayashi. And I think that that that that be set aside and it be permitted to open the bars. Bars are notoriously dangerous for COVID. So you say, hmm, you know, does anybody get it that we are in a pandemic, does anybody get it that our economy is in the in the tank. Does anybody get it that that the legislature doesn't have the money to provide basic services.  That's it's a rhetorical question time. Yeah, no, I certainly don't think they do. Matter of fact, when the, you know, when the governor was looking at, you know, what departments to cut 10% with what departments to cut 20%. It looks like, you know, the 10% is being saved for the, you know, the large and already bloated departments like education, health, human services. Okay, the 20%, they're going to foist upon like the department of taxation.  If the money doesn't come in, how is that going to help your situation. That happens on the federal level to you know, they've been defunding exercises with the IRS for decades. Everybody wonders why, you know, they had trouble collecting.  Yeah, right. Last on the list. Yeah, we have to learn how to do priorities, you know, Yeah, it's right after defund the police, I guess, right. It's off the list. Exactly. Okay. But so, so all of this turmoil is happening with the, with the furlough situation. So, so then the legislative leaders are taking a look at it and they're, and they're saying and I'm not making this up. There is a, with Senate President Koichi on this, it came out just a few days ago. And, and the way G's, you know, there is the prospect of lengthy litigation over whether these furloughs can be implemented. I mean, certain certainly there was under governmental angles. Right. It's not in big trouble with furloughs, big political problem, and ultimately was in court wasn't it. Yeah, well, of course it was. But, but she did it. And so, so so the legislative leaders are looking at the prospect of litigation around this. And they're saying, well, geez, we're to come up with another plan. Revenue tax, you and me. Hang on to your wallets because there will be there, there will be tax proposals. There will be tax proposals. Well, it's, you know, it's like exhaustion of remedies, you'd think that they would take dramatic steps to cut the state budget. Step number one, but they can't get there because people don't recognize the exigencies of the situation. And so we wind up, you know, laying it on the taxpayer. Yeah. Somebody, some, some people think that the collective bargaining agreement is the law in the left, the Supreme Law. In the state, maybe it is who knows. Political. That's what it is really. It's a political situation. Who are the governor, who are the governor of the legislative leaders going to pay attention to. Okay. Is it the unions who are in their face all the time? Or is it the business community who is in their face, maybe sometimes. Well, maybe it's a question of degree, but I mean, I can't believe that the collective bargaining agreement is a law no matter what kind of emergency we have. There must be some kind of exception to that. Must be. For example, suppose it's not a $1.5 billion deficit. Suppose it's a $2.5 or 3.5 or $5 billion deficit. You know, there's got to be a point where we treat it as an emergency. Suppose it's. But give me my meal break first, give me my mat break first. Exactly. Exactly. I mean, I really, it's not a credit to the people who negotiated those agreements in the first place. They should have seen the possible possibility of emergencies.  I suppose you can say there must be a force measure in there. Isn't this a force measure. As far as I'm concerned, this is a force measure, just the same as the storm. So I don't know, I don't know why people take the position that the agreement is binding no matter what happens, no matter what. That's not being flexible. It's not good public policy.  And let's see what happens. Meanwhile, as you say, there will be bills for a tax increase. Yeah, and tend to make things worse. Civil beat that a good an interview with the governor. And asked him point blank, you know, will there be tax increases. And you know, kind of hemmed and hard and said, Well, you know, there's a couple of things that I'm thinking about myself just to make things work out. Oh, so he's not not only not ruling it out. But he's bringing up the possibility of submitting something yourself. So let's talk about that. What kind of tax increase are we talking about? I mean, if it's a 10%, let's say a 10% tax increase on income tax, what does that look like? How, how much of a bath of cold water would that be? How would it affect the tax paying public? Well, here's what I think. I mean, I think that, you know, and I've talked about this on the show before. There are only two tax types that are big enough to move the needle in any appreciable. One is general excise. The second is individual income tax. So, you know, the first line that the lawmakers are probably going to say is, Oh, let's tax the rich. We're already taxing with 13%. Which is, I think, the second highest rate. I think only California is higher than we are. Well, when you say tax the rich and all that, these days, if you look at the income tax, it's a function of income. A lot of people aren't earning any income. So it's kind of a skewed result. Sure. Theoretically, if you have income, you're going to be taxed with an increase in the income tax. Oh, no, it's a question that you basically finesse by giving you good news and bad news. The good news is we're only going to tax the rich. Bad news that you're rich. Wait, wait, what about all the poor people who are not making anything at all, who are out of work? The taxing, an income tax increase is not going to have any effect on them. It's not going to be able to collect. They know that. You can't take blood from a stone. They know that. So they're not planning on getting any more from those guys anyway. Yeah, there's nothing there.  That's Tom, you know, that's regressive. So it's a penalty on board people. What else can you do? So, but, you know, back in the income tax space, you got income, you can pay tax because most of the people who are there don't have income. They can't pay tax. So, so those of us who haven't, we're rich. Anybody with income is rich these days. That's right. Well, so. Okay, so I assume he puts in a bill for that, or someone else does puts in a bill for say I'm making this up at 10% income tax increase. What's the pushback and who's pushing. You know, people like our organization will push back. If there are some business groups that are, you know, immediately affected they may push back. But it's, it's unlikely, I think that you will run into resistance from, you know, the unions, or ERP or other of the other of the groups that traditionally are organized and show up at the Capitol. Well, you can't get there from here. What I mean is, so if we have this deficit and we need money in the state to provide essential services and all that, and we're not getting any money from the federal government or at least not now, then you have to do this. I mean, that's a pretty good defense on the attack against an increase. You have to do this. What's the choice? There's no choice. What would you like to do? You'd like to see the state collapse. You know, you need the state to have money.  Probably that's where the waste is. I mean, you look at, you know, there have been several studies on, you know, what happens when you have a very large organization and, you know, the larger the organization is, the more chance you have to get stuff slipped in under the cracks, right? Because it's a bigger beast and you have no idea where all the tentacles are. You look back to the first point, we should be saving money like crazy. And the government didn't go far enough and there's a lot of fat in the state budget and it's time to pare it down. On the same idea, though, just having say a rate increase in the income tax is really not surgical. If you want to do surgery, that's probably better, you know, nuanced amendments, rather than some gross across the board kind of amendment to encourage or discourage activities you want to encourage or discourage, you know, implement policy. I think tax is always an implementation of policy, whether you intend it or not. And so it ought to be done carefully. But the wound is too big for surgery. You need something, you need something gross to come up with the kind of numbers we need. Okay.  Not big enough. Interesting. So, again, you know, the big, I mean, if you really want to bring money in the door. You basically have to do some kind of rate increase for income or GE. I don't even know if the, you know, the tax, the rich mantra is going to work. I think what you would have to do is lower the thresholds at which, like the, you know, the higher 1112 and 13% rates kick in. That's, it's at 400, 500, 600,000. That would have to be, I think, drop much lower. So, you will be money that way. Yeah. You will be rich. It's all relative. Well, I mean, what about these big projects to, I mean, I'm thinking of rail, I know that's a city project, but, you know, as billions are going into that and billions will continue to go into that. There's a lot of momentum on moving that ahead and finding. We've got contracts in place.  And, you know, what about real property tax increases in the various counties. Those are the rich guys they, they own the owners would, you know, of course they would pass it on to the tenants and so forth. But, but what about that? The counties are in dire straits also. And there's talk of having property increases. Well, of course there are. Yeah, I mean they would, I think they would have to do that. Because, you know, up to, up to, up to now we've only been talking about the state we haven't been talking about the counties. The counties have basically one, one source of revenue and that's property tax. The second source of revenue, which was like a sharing of the transient accommodations tax that got ripped up in May. When, when the governor through one of, you know, one of the emergency proclamations think that they've been 16 so far said we're going to stop sharing revenue with the counties. Yeah, talk about, talk about the rule of law. What happened there. What excuse that statute. I, that was really kind of a maneuver wasn't it. I mean, he can't do that, can he. Well, he did. Okay. It doesn't sound like the rule of law to me. And, and, you know, none of the counties have come forward. And what they what they should have been doing is, you know, send some of the legal vehicles into the court saying, you know, give me a three judge panel.  None. None, or at least that's the argument. Grab. Yeah, grab. It's a money grab. And of course this table come back to you. We need the money. Well, sort of the counties. Yeah. The counties are, you know, shouldering a lot of the burden of COVID. And they need money to operate provide essential services. You know, fire, police, roads, sewers. Let me let me go to another point that we should catch before we're done here today. Because there's been talk about allowing DHHL to do gambling on its properties. And of course it's, I don't want to say slippery slope but that's sort of a pathway to having gambling in general and it's something seductive. In a time of crisis about having gambling it means the state could raise money by, you know, various types of gambling activities. And people would like that would support it. For one reason or another. There are also lots of negative things about gambling and it, you know, over over the last oh well since statehood, every governor has opposed gambling. And it hasn't really got its foot in the door although Lord knows there have been lobbyists here from time to time trying really hard to get gambling through. Yeah, what about gambling now. One of the things that that, you know, we all have to be worried about when it comes to gambling is, you know, once the, once the genius out of the bottle, you really can't put it back. Okay, and not that you can really limit it to DHHL, like if DHHL has this law passed that says gambling is allowed on DHHL property. There's federal law applying to the Indian tribes that basically say they can come in here, set up shop, pretty much anywhere they want, and they can run casinos. Why? Because the state is allowing gambling period. The only defense against that is if the state doesn't allow gambling. And I think only two states qualify, which is us and Utah. Are you saying that Indian organizations from the mainland could find a way to come here if DHHL allows gambling? Yes. Wow. That is original slippery slope. That is big deal. Yeah, and you can't keep them out. Right. We have the US Constitution, which gives the right to travel. So, and they're allowed the same privileges and immunities of any of us who live in Hohoi and have lived in Hohoi since, you know, the beginning. All of a sudden we have a lot of gambling. All of a sudden we'll have a lot of gambling. Yeah, and I think that's one of the reasons why the political leaders are being so cautious about that, because they are, they have been advised of this consequence. Nevertheless, it may happen because a lot of gambling means a lot of revenue to the state, right? It's taxable, isn't it? Even from Indian tribes, right? Yes. So if the state wanted to raise Ukubucks fairly quickly, they would say, okay, boys and girls, it's time to do gambling. You can do it. You can do it on cruise ships to extent there are cruise ships. You can do it in casinos. And you can do it on DHHL or Indian lands before you know it. You know who would oppose that? The people who support their junkets to Las Vegas. The Las Vegas community wants those junkets to come to them and they would oppose it. But I think at the end of the day, if the state is in dire straits and arguably it is, the gambling is a, at least from a fiscal point of view, it's a solution, isn't it? Sure.   Here in Hawaii, we have no experience whatsoever with gambling. So how are you going to make sure that it's being done correctly and that the United States is receiving a fair amount of it? Well, I don't think we have confidence in our regulators. That particular principle shows up, you know, whenever we address a new issue, a new risk. And I think it's a valid point. Our regulators are, they're just not up to it. It's a big question anyway as to whether they're up to dealing with gambling interests from out of state who would come here real pros and get into the legislature, learn how to sidle up to the legislators, sidle up to the regulators. And before you know it, there's nobody stopping them, nobody regulating them. That's a real possibility here in Hawaii. Of course it is. Well, do you think, do you think that in fact, Tom, to close here, do you think there will be a tax increase this year? All things considered, even the vaccine. What I can assure you is that tax increase proposals will be made. That will be considered. Whether or to what extent it'll pass. That is, I think, a more open question. I think that, you know, people who are invested in the outcome of it need to make their views known. They need to let the legislative people know what they're thinking. Because if you don't, you do know that the unions and the others supporting tax increases will be there and they will be making their presence felt. And if you don't do the same, you know, kind of deserve what you get. Yeah, I can't complain later. Okay, the next question is what prospect do you think gambling has in this 2021 session? I think a lot of leaders like Senator Kowich here are still flatly opposed to it. But who knows. We have a big revenue book. I think we should always have something humorous in our shows. Tom, that's why my final question is, it travels along the line of humor. My question is, so what do you think the prospects are that there'll be a tax reduction this year. I'm going to freeze over first. Okay, there is humor in that. Tom Yamachika, president of tax foundation away. Thank you so much for helping us understand what was likely or not likely to happen in this year's session on taxes.


My name is Patrick Derde and I am a Togaf 9.2 and I can make a 3.0 certified enterprise architect. In 2009 I started Envision Specialized in Data Driven Enterprise Architecture and in 2017 Envision joined Bion, which is the banking industry architecture network and we were going to lead the development of the Bion Banking Business Object Model. This is actually a reference model that enables banks to develop their own proprietary information architecture landscape as a tailored version of the reference model. The Bion reference architecture provides a comprehensive and globally accepted service landscape for the financial industry. An archimate representation of the Bion reference architecture provides a lot of value for the financial industry as Bion can be easily implemented in any EA tool. At the moment all Bion publications are in an informal notation or in the UML notation as class diagrams. However at conferences and in our working groups we are starting to introduce the use of archimate modeling language and as a result for enterprise architects who are familiar with the archimate language benefits of bringing the archimate modeling notation to the banking and financial industry are at least a better understanding of the Bion concepts that are used. Also there is a better insight in the coherence of the Bion concepts that are referenced with as a result a lower degree of ambiguity and when we are developing the Bion content there is a lot of time saving because we have no discussions anymore about the meaning of the concepts and on top of that because of archimate is a language which has an interchange format all the tools the enterprise architecture tools that are used can use the archimate model. The aim of the open group executable standard principle is to abstract from the internal behaviors of structural components by defining the services they offer and this matches perfectly well the Bion service oriented architecture approach. The principle building blocks of the Bion industry architecture network service landscape consists of a service domain and a service domain is often talked about as a structural component that offers services. Bion specifies the architecture building blocks but it is up to the member banks and the application service providers to create solutions that are conformant to the Bion architecture. Within Bion we are convinced that by using archimate the Bion banking industry reference architecture model will be an interoperable and executable standard for our members. The value is in the common language but also in the common logic that is expressed by the Bion Meta model according to the Archimate Meta model. This makes it possible to exchange information and information services between Bion and its members as if there are no boundaries and this is the real value of Archimate.