Well, I only see me actually, but that's but I can look in the mirror anytime. I don't see Angie, I don't see JB.
The box, see JB, but I don't see Angie.
She's three minutes 123, or three minutes late, and I'm on time. Uh, hi from Tokyo, Japan, Konichiwa. You're
in, you you're in, aha, Tokyo. Actually,
I'm not in Tokyo. I am in izmo in Shimane Prefecture for business, potential business customer like for business meeting, I met a business developer that is building a luxury hotel and spa with onsen, and they want me to be a Chief Design Officer to build the hotel and the spa. So, yeah, I'm here for that.
Well, that sounds like a delightful project.
My business partner here, she's Japanese, and she has a foundation called Arts and Peace Foundation, so she's interested to train next generation of Japanese children and teenagers for peace and art in this complex of hotel that will also have a school called School Number eight, because the eighth number is the infinite and it's the It's a very important number here in this place. Next Sunday, we're gonna go to a kami festival. Once a year, 8 million gods are invited to this place. 8 million spirits. Yes, well, shintoist, a Shinto ceremony, which celebrates all the spirits of all the planets. So it's when the spiritual meets the political and the citizen of this world. So I mean conjunction point here. So I
shall be very interested in your conversation, whichever God you talk to.
Okay, okay, I would be happy to to zoom with you afterwards to All right. And to show you some pictures of my japan, japan trip I shared with Nila. I went to some sacred site in in Wakayama, in Koyasan to meet one of the famous alchemic from Alchemist from the middle age in Japan, called Kukai. Is very important Buddhist Alchemist, and his spirit is still in the forest over there. And it connects also to lots of energy spots in the planet, like my business partner, she went to Sedona in the US, for example. And we have other energy spots in Japan, usually close to volcanic activity, places that are also linked with water, with some properties where people, they go in the onsen, so today's body, like in SLN, where you've been many times, where people would take this bath as well, in order to put their mind and body so but I would be happy to to chat more with you about that, of course, Yeah,
I, I don't usually. I don't think I've ever been to a place where I could just have a conversation with one of 8 million gods, right?
I will, I will send you a link if you're interested, because it's really nice. Oh, I see now that Angie aI had just connected. So because Angie, she will connect in one minute, but she has an AI called Other, other AI that is recording all our zoom and then synthesize and give her things. So if you look in the participant list, we are now three people, and it's me, you, and Angie's AI. So okay, maybe I should, yeah, monitor what I say, but it's okay.
It transcribes right away, or That's right. Oh, after
the meeting is over, she receives an email with the the transcription and synthetics and the keywords. She's very efficient person, super efficient. Wow,
there are times when I'd like to have that. Other times, well, not did I ever send you my article on the million diagram project?
Maybe I'm sorry, no problem, Angie, hi. We are chatting Hi, hi, to see you. Good morning or afternoon for you, it's morning for me here in Japan, and I guess I don't know, it's afternoon for for PT, and night, evening, maybe for you, something like this.
Well, good day, good day. Actually, it's
Thursday for me. It's still so I'm in the future. Angie, just to tell you, I have a business meeting I need to live in 20 minutes sharp, but you are Feel free, of course, to continue the meeting without me, but I wanted to be present for first part as showing my respect to Bob and to you, and also listen to the first part of things. Odor was already connected. So I guess I'm gonna also, if you can share the transcript of the meeting, I would be happy to have part I missed as well. So now floor is yours, Angie, so
I so I won't have to take any notes. I close my book,
My daughter, I will share the honor after the meeting. And yeah, so separate in a separate meeting. JB and Bob each helped me to have a kind of a develop a kernel model to reason about how startups or organization allocate their resource into the operation side or the market side. And Bob and Bob thankfully reach out to me asking what is next, which prompted me to share the progress, and I shared with JB. I talked with Charlie, and he was happy to collaborate on the conference submission. So I was thinking after today's meeting, he asked me to share the updated abstract for our submission, so I need some help in that aspect.
Okay, concrete, yeah,
yeah, sure. So let me just briefly walk you through the model that I have and I had
before. JB, just to give a comment. So Charles fine, or Charlie is Angie, a PhD advisor, and he's an operation research professor since the 90s at MIT Sloan Business School, where there is a traditional school, or like research labs about operation research since the 60s that basically were associated with system dynamics research, basically people like von Forrester were teaching in Sloan in this and Charlie met von Forrester and so on. And so it's just this kind of evolution of people now in Sloan. This is why I thought it could be so interesting for you, Bob and Angie to meet, because it's people you gravitating around, and you have your own stance on that, Angie as well. So I'm happy for it. Bob. I
have one footnote I knew for a while, Dennis meadows, oh, for a while I had, I had the second largest collection of simulation games in the world and Dennis, and when I sold it to a company in Japan, Dennis came in his red pickup truck and picked up the whole collection from my office and took it off. Enough of that. But anyway, I've some connection with that lab.
Thanks for sharing. Bob. So I think the work here is somewhere between Bayesian and evolution and JB and I had discussion last time about how to understand the adaptation, co adaptation of agent and environment from a agent's perspective. So this is one attempt for it, and the product in this page is mostly based on Bayesian agent. So you can see what I mean by Bayesian is they have some belief about the rate of rate, ROI return per investment, and they are learning depending on what environment they are in. So the easiest way to think about is, if you I think this graph, so I observe that in a digital product area versus industry versus physical industry, there seems to be different ratio of time invested in the marketing versus the operations. And this model tries to explain there is that is like different behaviors observed in different environments is not irrational. It is because that environment has higher return of investment in so in digital it is better to invest more like proportional to the underlying ground truth of that environment. So, yeah, long story short, what this model I built is trying to explain is investment as a vector. And by vector, I mean it has both direction. So this is what direction would be about, and also the speed, the magnitude. And what determines the magnitude is how fast, like thinker versus doer, and in most cases, the faster clock speed industry, like it, has more execution because the delayed cost time, expected utility, utility gain per time if you are delayed in the fast moving industry is much larger, so it is logical to act more compared to think more. So that's what I have right now, and what I wanted your input was this part. So since, like, considering JB would be leaving soon, I would start from the second one. But like so far, am I doing a good job? Am I? Am I moving too fast?
Crystal clear for me. Oh, okay,
thanks. Are you okay? Bob, too,
I think you're muted.
You're muted. Bob,
oh, the only question I have is distinguishing between what I think of as an entrepreneur and what I think of as an investor. And do you make a difference between those folks? Because I've been I've been mostly the entrepreneur struggling with new ideas and new implement, you know, implementations, rather than as an investor,
I see, yes, I think that's,
I don't know what that has, I haven't read any of the literature on this or anything like that, so, but that's only a distinction that sits back in the back of my mind. Go ahead. Go ahead with you.
That's a very important point, because the underlying assumption here is we are not doing the capitalization operations, meaning there is, like, if I were to make an analogy, there is limited amount of oxygen, and I have to decide whether I should, like explore more or somehow grow my muscle more.
So growing muscle, okay, so, so you're the entrepreneur, I'll take it. Then you're the entrepreneur, and when the investor comes in to ask you how things are going, you say, things are going fine.
Yeah, you have to persuade them that the chosen market has promising, like future, and also, I have competitive advantage to capture the value in that growing market. So that's like whole another operations. That's a second module for my research. But for now, I would focus on the first this two, between the two which I should do. And topic JB and I was chatting about was the title is like parallel evolution versus sequential Bayesian and we were last time we discussed that I had some hypothesis that parallel search is better when the testing cost is low, comparing to A pivoting cost later, and also uncertainty level is high, and when the market side and operation side is highly, actually low, correlated. So the question for me to JB today was given the model that I built, could we try to connect the parameters with what I explained as vectors, direction and speed? So it's
easy for me to understand, because I worked with you many times. But maybe you could give an example, a concrete example, to Bob, so you could understand the difference between parallel and sequential search. What you mean by search for that? Yes,
yes. Um, so for instance, like Tesla, when they were looking for the market, they can look for luxury sedan and luxury sports car at the same time. So that's parallel, but some people say that it's better to just have a very like sharp focus on one market that you think would work well, but if you have a lot of uncertainty and you cannot always control the market, so my kind of hypothesis, or how I operate, is test two market And then choose one of them in the end, feel free to add Jamie.
So either, either you are parallel, so you have, like the low class sedan and very high speed one. So this would be parallel, or you just choose one, but you will iterate. So you are you will have many iteration and instances as a sequence compared to as a parallel process. Maybe. Do you have at hand the NASA graph we used also with this, because I'm
trying to pull that up. Yeah.
This is serial. And peril
was the there was a NASA one that was nice also.
Or this one, this one Exactly,
yeah. So here you see it's for, I think it's for software development, but it applies the same. So we lost you exactly. So here we could have, like, some sequence, and then at some time we could have test with multiple prototypes, and then we can have a sequence again. So in concept development or software development or car development, you can iterate on one thing you do, and you do it better and you improve. Or you can test like AB testing like two products or ABCD, and you put all of them on market, and you see the one that is competing. So it gives you some kind of evolutionary approach where the one that fits the most most of the market might win, compared to making one bet on one thing, and if the bet is not a good one, you lose everything. So it's again, how do you allocate resources in a finite environment? And I did my PhD about evolutionary psychology and taking concept from that, and with Angie. Now we are trying to use some of these concepts and operationalize them in terms of the mathematics, the optimization. So we can apply this model to entrepreneur related to their resource operations that are limited, choice of market, choice of VC, which VC should I choose? And so on and so on. Because engineer entrepreneurs, they have to make choices all the time, and they search for their next future strategic move. This is the search in parallel search or sequential search. What are the parameters for entrepreneur to choose their next step, being this or that, and this is what we try to model together. I hope it's it's understandable. We are still making it, but it's basically we are comparing two very simple things, either you iterate on something or you do many things at once, which is most of the environment of entrepreneur Angie,
yeah, I think we can move on now. So you asked me the question of how I can help you and we can help in the discussion today to connect the first thing with the vectors, which is direction and rate, or like pace or speed, compared to this, I need a bit more time to answer this question. I read some papers in the evolutionary space which is very similar, where you have a rate of evolution and you have also a vector, and vector could be growth of a population, or could be specific traits that they're going to evolve, for example, and then rate is the speed of that. Stephen Jay Gould, we mentioned Dennis meadows, but Stephen Jay Gould is also a very interesting person for simulation. He has this theory of punctuated equilibrium, punctuated equilibria, where he showed that evolution is linear, is not linear. Sorry, it's, it's, it's
punctuated equilibrium. That's right, it goes up and then for a long time, and then up quick. Yeah,
but it's not, it's not just a linear function. It's like it goes up and sometime for a very long time, let's say, 1 million year, nothing is happening, and then it goes up again. So yeah, it was one of the first to create some kind of a function, like a mathematical modeling of this very complex evolutionary systems. And since then, in exaptation, we have other model, and one of them is more parallel. So I will try to look at this model, look at the mathematization of them, and then try to see how they correlate with your vector based approach. Because I think being in a multi dimensional space an hyperspace, like, let's say, a vectorial space, and have a vector which is a direction and this rate, which would be, I guess, some kind of a coefficient on this vector, which is a parameter of the vector, makes a lot of sense, because basically, it's like a walk in an hyperspace. And then I understand how you, as a Bayesian or as a computer scientist, you can use this concretely and put it in something operational. Is that correct? What you're looking for?
Yes, and I was looking for an example where, like, imagine I am an animal, and I wanted to train, be trained with Alliance, meaning like this is analogy of a fast clock speed industry, where the competition is really scarce. So I trained really well, and I somehow migrated to a more slow moving environment so that I can be the like the strongest predator in that ecosystem. So this can have a very straightforward analogy in startup as well, because if you are trained in software industry, your decision is really fast and it's very efficient, and with that, like brain and body, like if you move to the hardware industry, I'm not saying hardware industry is very easy, but perhaps there might be some benefit, or like, in terms of data, if you are if you want some time series forecasting, that's actually how I did, and I need some data to train my model. So I first started in the industry, like an ad tech industry, where you can get a lot of data and then train that, and then move use the same model to an industry that has slow data so it has higher predictive accuracy. So I think I need some example in evolution around this for pivoting. Does that make sense?
Makes lots of sense. I put a comment about disruption. There's this idea of disruption. So if you are a data centric company, and then you go to a field which is not database, you will become the lion amongst the zebras or the ship, I guess. So makes sense, good.
So, yeah, I mind flipped your time. So like you may ask, how it relates with what we were discussing and the second one was parallel, but I put in the third one when we had the software and hardware, or software or hardware that has two different direction and a speed, then you can imagine how the allocation would change if the environment changes, and how the agent can adapt to the new environment. So I put like for the format for all the three questions I wanted to like chat with you today, I put question and my answer to it, so I would be really happy to continue, either through mail or another Zoom meeting, to continue discussing this
is that good for me? Because I'm gonna fly off now. Thank you three questions, thank you, yes, and I'm really happy you continue the discussion without me, and I'm eager to see to read what you're going to talk about. It's a representation problem that Angie is trying to model, and it's an active representation, not just to describe the past, but to describe situations, and then try to not only simulate, but also test out some hypothesis. And I think this is where we could learn the most from you, Bob, because at many times I talked with you and you, you said that you had your own criticism, or your own stance on system dynamics, simulation and so on. And we would love to learn what is this particular savoir faire, or know how you have that gives you an edge to towards this things that, for the moment, for us, represents the state of the art, and we would love to extend our knowledge of that with you. So with that being said, Angie, great work. Questions are very clear. I'm looking forward to think about them, because it's very complex question. And Bob wishing you a great day and see you guys soon. Good
to see you. Good to see you. Bye, bye. Hi, Bob,
again. Okay. First of all, have you looked at the extensive literature on predator and prey in in in biology and populate in population Dyna biology,
I wouldn't say I read a lot, but I have made a model in a hard co Bayesian way, and try to calibrate based on the real data. So I am aware of what the model implies, and
you're aware that that the over time the predator and prey goes up and down and up and down. Yes, that's the main you know, they're mostly interested in the that kind of length of time. But it does seem to be that that's not what you're interested in.
I'm more interested in agents adapting to the environment, how to make a concrete model that, oh, everyone can see agents are learning from like depending on where you are in the supply chain, tier three versus tier one, or hardware versus software industry, I'm trying to model the brain of the entrepreneur. Does that make sense? Because, yeah, it's more about belief dynamics, rather than the in predator. It's more about the physical things, so the birth rate and death rate. But this is more on a belief level that, oh, if I invest $1 in the operation side, if you are in a green physical industry, then the rate of return is 1.5 times larger for when you invest in operations compared to a marketing
Well, that's a different model, as far as I can understand, than a predator and prey going up and down and up and down, yes, and so, and therefore it confuses me. I'm still just trying to, you know, I assume it will confuse other people. If you use that as an example,
I see, I see, so I think that's where the prey predator, where the competition comes in is the speed of the vector. And
no, no, you're, then you jump way up to some kind of vector thing, which isn't what predators and preys are doing in their life, in their action, they're, they're competing with each other. You're, you're you're marketing or operations are not competing with each other. So it's a different situation.
I have one question. I'm just curious. What gave you the impression that I am interested in prey and Predator model.
You talk about evolution and you talk about predator and prey. That's why. That's what gives me the impression
I don't know much about the evolution, but is evolution like kind of I don't for me the reason, right? Yeah,
go ahead. I was going to say, look at the transcript. You got to have a transcript. Look at the transcript and see whether the words evolution and predator and prey appear.
Yes. Okay, so my point, you have that.
So go ahead. You know, I this is an, I think, important to yes
for me, when I say evolution, it's more about the growth. So I haven't so I think there's two parts in here. When I say evolution, I'm thinking more in Charlie's framework of the organization founder goes through the nail scale sales changes and they evolve. So that's perhaps where the evolutionary concepts comes from. And I remember saying that there is some kind of a train with a lion, and you become a new predator in the like another environment. And I use that example to illustrate that changing the environment is also the choice for a startup. So that's, I thought was related to what JB and I was discussing. Oh, startup, yeah, sorry to come so
it's getting a new skill. It's getting a skill and using that skill in some other environment.
Exactly, exactly, yes, okay, thanks for asking, and you
would like to know what the skill is between in investing in operations or marketing, is there some sort of formula or something like that?
Yeah, yeah, yeah. So I,
I personally, I, you know, I hope you can convince me differently, but right now, I hope you can, but right now, it doesn't seem to me that, while you know, it seems to me that, yes, an entrepreneur with a certain amount of money, which he gets a he gets it, not usually all at once, but along the along the line, along the way, makes, makes choices, investment as as the in the company's capabilities and status at that time, at a particular state changes, and then the company goes on and changes the environment, which then is the occasion, or maybe the occasion for another set of decisions. So there isn't so that it isn't a big decision at the beginning, and at least you know that that's the way it looks to me. Look to Me now maybe there are big decisions like that in some in some situations, clearly you gotta, you gotta make a product. If you don't make your product, you haven't got anything to market. Yes,
yes.
So you know, how does that fit into your model? In other words, you gotta, you know, you have a as well as I understand it. You got some idea of a model. You got some idea of a product. You don't have a product, do you? But maybe, maybe I missed it. I don't know.
I think I am scoping more on the scaling phase of a startup, meaning that they have a clear value proposition of what they want that satisfies customers and employees and the investors. So it's like, kind of, they are ramping up the production. So I think they would have some product
they they are. So you will, you gotta, you gotta tell me, you know, if I'm gonna, if I'm going to evaluate your your paper, I'm going to say, did they have the product, or didn't they have the product? You know, they didn't have the product. You know, some, some software, as I understand it, large language systems took some somewhere. Even after Hinton and others, still took another bunch of years, for three to five years to make until, until open. Ai, two years ago, came out with their product, yes, yes, yes. They didn't just think of it that day and the next day, they had a product. They had to make it, and they had, they had to spend, you know, I don't know sometimes was it weeks? Was it months? Training it and probably several of those circa situations, because the training didn't go well, yeah, yeah, and so forth. So, you know. So how does that fit into I don't get how that fits into your model.
So when you say fit in, like, if I say, Oh, they have some clear product like Tesla road star, and they are making a decision of, should we expand the market from luxury sports to luxury sedan, or should we invest more in the production side and add more factory in China, not only in California? That's the decision that we are okay. We have a
so you already so you need to state that. As you say, I didn't understand that. We already have a product. It works. People buy it. Yes, thanks. Yeah, it's a product that could be better. And the question is, shall we make it better? Exactly? Is that right?
Yeah, scaling up is defined as in parallel, you expand the market and also your production and delivery capability. So yeah, we are making it better. That's what we are doing. You
uh, well, then the question comes, how do you find out? How do you find that out,
that being, how
do you find out, what kind of process you know, well, what kind of what are your sales? That's what. First of all, you got a product. All right, you got a product. And are people buying it? Yes. Okay, hmm. And you want, you want to decide then whether you've got, and you got a pot of money here, and you want to decide whether to to spend that money on more marketing of some kind, or to make the product better, or maybe, or maybe both of difference
that that was in our last chat we you asked me to come up with three prediction questions. So I had the EV manufacturing, and another column was the ad tech. So that somehow gave me the idea, yeah, oh, when I was making that, the physical industry and digital industry had some interesting differences. And like, I tried to summarize them in this table again. So yeah, so I thought perhaps these are the three prediction questions that we discussed last time about, like, how to do the expected cost reduction. And, yeah. So long story short, while I was making this, I realized that a lot of questions raised in the physical part is a little tilted toward the cost saving, because it's kind of however good market they have. If you don't produce the EV, then you don't you cannot capture the value, whereas software, it's a little straightforward to come up with a product like the developer can just spend one night, not not always. That's straightforward, but comparing to, compared to EV, where you need to actually factory labor and all the machines, it's here. People's attention is more focused on the market side and in the hardware industry, people's attention is more focused on the cost reduce saving side. Does that somehow?
Does that give you two different models?
No, no, no. What I was explaining was with the same model I can describe how agent in like so this was this graph where agent was the same, prior started imagine a twin. Twin was the same. Belief system was dropped into one as a EV and one as a ad tech, and as they do the decisions and interpret the signal, they learn their prior is evolves as they interact with the environment. So later, a person who dropped, who was dropped into the EV now learned that, oh, in this environment, it's more beneficial for me to allocate like seven to three to the operations and marketing, whereas in the ad tech industry, this agent that started from the same prior now learned though, like three to seven might be the optimal ratio. So this is, like, what I call as An evolution or adaptation. Oh, I
Well, I
so you hope to have a model that here I am, here I am an entrepreneur, and you hope to to and I've got a product, and it's been selling, and and I'm, I've got a new investor, the investor is going to put X million into the company, and possibly, but has asked, you know, what are you know, what are you going to spend this on? Right? Yes. Are you going to spend it on making the product better, or are you just going to keep doing the same, pretty much the same product? Or are you going to spend it on set? You know, it's really sending whatever marketing and sales involved, yeah, so you already have, I would, you know, as the investor, I would be asking, well, how you know, how big a a potential sale is there? You know? How many you know over the next five years? How big, how big a market do you think you have, and what is the competition? And and so forth. But that doesn't seem to be in your any in your model.
So that is not in the model, but abstract it in a way that the rate of return captures all that, um, the
rate of return, the rate of return of in the past of some other products,
on average, like the future profit, is minus cost plus the revenue. So if I invest $1 in the marketing, then I somehow suspect I'm expecting this to have much higher leverage on the revenue increase.
And why should I believe that?
Because this model tries to explain that different allocation of resources in different environments. There is a reason for that. So that's why the title is recovering rationality of ventures adaptation.
I'm asking, what the reason I've got a real product. I've got a real product in a real market. And I'm asking, What do you you know? Why am I? Why am I spending this on this or that?
So I don't know whether this is the answer to your question, but the signal, the frequency of the signal, is different, meaning, I think that's this row. So the cost signal versus revenue signal in the manufacturing side, like you have much more chance to get informative information on a cost side, but for a revenue side, it's a little slower and harder to get, and the feedback speed is much slower, like 33 months horizon for the q2 whereas in the software, the digital part, the cost is very low clarity, because they're like after all, if you are doing ad tech, there is not much difference between the code that you're using, but depending on what market you choose, you can have a very clear signal and the feedback is very fast. So perhaps these are some factors I think contributes to the rate of return.
So you have, I haven't. I haven't seen the model, have I you haven't? Yeah, well, that's that. Maybe that may be the difficulty that I'm having.
This is just the model that I have. Like, this is the agent part, and the environment part is it has some underlying PC and PR in it.
Okay? And I can't read that stuff, so I don't, you know that's not what I can do. I can. I probably can't help you there, unless you can, unless you can say it in English.
So like just simple, simple three steps. This is an agent. All inside agents had, and there is an environment module and Agent start with some belief of the this PC is the rate of return, and sigma is the processing speed related variable, meaning, if you if the speed is very fast, sigma is very low, and if the speed the feedback is very like clock speed of the industry, like digital, is much faster, so they would have lower sigma, whereas the hardware would have much higher sigma, because there is a lag. And so that's where the PC from, the belief of rate of return in cost side and the revenue side affects the predictive predicted cost and predicted revenue and your next action to invest, whether in marketing or the operations, is computed in a art max of additional profit, which is a function of minus cost plus revenue. And at each time point you can invest only one of them. So this is just how each step agent updates its belief and makes their decision of investment. So that's that's as simple as that.
And
the diverging plot I showed you previously was a result of this learning happening after 20 time steps so and does the action selection allow me to to place proportions of the fund of whatever funds I'm investing in either cost reduction or revenue growth.
Are you asking, does the amount of investment affects the action? No,
whether I can proportionally choose at at some future time intervals, whatever time intervals are involved here, the next quarter, the next quarter, the next quarter, I can, I can, I can say to my Vice President for Operations, you get this much, and my vice president for marketing, you get this much. Is that the sort of thing you can do as a as a result of your action selection.
So the way to do that is, if your time unit is one quarter, and you want to allocate 30 to operations and 70 to market hitting, the way you do that is divide that into 100 times that or 10 times steps, and choose 100
or 10 times what steps
to say if you allocate three to seven, then instead of modeling as one quarter, you can divide this into 10 and assign operations, operations, operation and marketing, marketing, marketing. I think that's kind of how we can get around. But in here, this is the constraint. I can only choose one of them.
But you see how i What, what I experience doing here is trying to translate how you're describing a decision into how it feels to be an entrepreneur in this quarter and next quarter, and, you know, with a with a new potential investment and so forth. And maybe, maybe I'm completely wrong on, you know, to try to do that, am I
I'm not sure.
Ah, so in these three steps, do they take? Do you do this only once at the very beginning, or do you do it every quarter or every year or every two years? Or what
that is dependent on, like what you choose, but in here, I did it 30 times. So if one step is one month, then this is 30 months. But if one like one time step is one year, then this is 30 year. Hmm,
yeah. Well, you know, I can see how, I mean, I can't see how 30 years would be of any use at all. I can, I can see how trying to to think about the investment decisions between production and and marketing are important. Are important. Who would be important? And they would depend on beliefs that I, the, you know I, or the the management team that that I'm a part of, purport assign any capital that we have to it, and we would, you know, depend, depending on the nature of the product and the nature of the market, which are really, you know, they're, they're not even if you even if you say the software market or the automobile market, are not just all The same. So depending on a whole lot of of detail, we would be making decisions about, you know, what to what to spend the capital that we have on for the next quarter or the next year, depending on where our decision process is yes and if And and then you think with your system that you either you can, or I, as the entrepreneur, can assign a some kind of a value, a prior value, to what the proportion of spend, you know, we're going to have to spend it on, you know, given that we already have the product, we're going to The proportion is going to be zero, to, to, to production, and 100% to marketing, or some other proportion, right? And I'm going to have, and that's going to depend on my, on my belief system, which you're going to, which has some sort of, not, you know, some sort of get, you know, my some sort of belief, and I go in as an entrepreneur, and I believe that with this product in this market, my
production, I should spend zero on production and 100% on marketing. That's my belief. Then what happens then with your model, to me, when you're you're a consultant, you're helping me make this decision, and you have this model that will help me, and it's in the software business,
I'm a little careful about the decision that that question, because as much as I usually like prescriptive model, This model school is not prescriptive, but more descriptive.
It's, it's either prescriptive or descriptive. Let's, let's, let's not have it more or less,
I think it is descriptive. It is explaining why software people invest more in market. They're more customer pool and technology or the hardware Space X, people are more tech push. That's what this model explains you.
And perhaps one more thing and it can explain is, I didn't touch a lot on this part, but hardware people tend to have longer time. Step is what it can also Explain as well.
So models attempt to allow you descriptive. Models allow you to attempt to describe a set of behaviors that have happened in the past. In the past,
predictive models, which you're apparently also you think that you're you think that your descriptive model will, in some sense, be predictive,
based on the assumption that this descriptive makes sense, because I assume people are rational, rational, and that they are making the optimal decision at each time step. And as long as people keep being rational, the same thing will happen. So that's where the descriptive becomes predictive in my model. Okay,
that's right. Very good. I
i really then the question of descriptive model becomes, are you describing any one particular situation, or are you attempting to describe some boundary set instead of entrepreneurs or companies,
I think there is some variance between the like type of industry they're in, and the supply chain chain position, so there are some boundary sets.
Um, okay, oh. Well,
what's the meaning of the question that you just asked? Just out of curiosity?
Well, you know, a theme of our conversation, at least from my standpoint, has been
the ability of i
The use, well, it's really the usefulness of of, you know, this general model in an in actual situations. I mean that that's, you know, I, in my own experience, we're always making, you know, we're always making decisions about a little bit, you know, spending a little bit more on, on whatever. And it's not only production and marketing, but staffing and expansion and decisions of about, you know where we're going to go two years from now and so forth. But that you know that aside, I
I, you know, I keep putting myself in a role, in a role playing situation, in a role playing situation as as the entrepreneur, yes, that's perfect. And I put you in a position of consultant or expert, expert, or something like that, and you're coming and telling me you can help my decision making. Is that what you're trying to do?
I don't think so. Huh? But perhaps this can tell you, like, how you should do because, like, the way that I came up with this model is based on observing how I do the decision making. So it's more like educational. Hey, if you want to be rational, observe the signal around you and try to update your prior on, like, if you invest $1 here, like, how did that pan out? And try to record it and be more rational about that. That's, I think, the moves I can help the real entrepreneur, because not many people are doing that, because it's first of all, there are so many things to keep track of for the entrepreneurs, but, yeah, I think that would be helpful.
Oh, so you're in some sense, trying to build on the ability to describe this. This situation, more from the standpoint of mate of let's have, let's, let's see if we can, we can make, let's say, Well, how do I want to say this? We can improve our description and understanding of decision making. That's what that's what your project is about. Yes, and then, if we could, if, if it's to improve it, then you have the question of comparison, better better than what?
Better than the people who don't keep track of this data and where it can be most helpful this thing is when you need to pivot from hardware industry to software industry, for instance, and knowing that the two industry has different underlying like ground truth of the signal. If you know this, then you can somehow try to rejuvenate your prior, like the prior that you build on so far. You just try to abandon this prior and then start a new learning, a new whereas, if you don't know that, you would just keep on kind of having a very sticky learning based on the environment that is not relevant to where you're currently at,
it seems To me that you're not the what you just described and named the prior is a very complex concept. It's not just a number. It's not just a number. I know in Bayesian, you start with a number, but I don't know much more about Bayesian than that, but if you're changing, when you just talk to me about this, you change your prior. It's from one industry to the next. What is that prior that you're changing?
Interesting question. So the way Bayesian would model that is adding one more layer that has like physical environment, it's one and non physical environment is zero. So you are adding hyper prior underneath what you already have. So you can keep adding more layers underneath you
have, for me, you're gonna, you know, for this to go further, you're gonna have to describe the prior that you change, because you can, clearly, you can do, you know, human beings are very good to abstract, to make up a concept in their in their minds, that has a little bit of weight and maybe has some fuzzy boundaries, and think that somebody else can understand what that is.
That is a great comment. Thanks for that. Yeah, I will prepare next time about how changing the environment can be modeled as a hyper priors. So,
well, oh, that's, that's also abstract you, but you're, you're shifting. You were shifting from a prior, whatever that is, from soft, from a hardware to a software, or the vice versa. And I want to know what that high, what that prior is, because it's not just a number, yes, and you have to be able to describe or define your prior there.
Yeah, it has, like, alpha and beta. They are the parameters. But I am adding one more like industries digitalness in here. And given this industry is the digital like, what are my alpha and beta? But now that I change this industry from digital to physical, then I need to redo my parameter estimation of alpha and beta. You know, hardware industry,
so still is a number. It's still a number. Yes? Is it?
Yes? I It is a number. It's a number that that somehow you've gotten from history,
yes, yes, yes, like a memory.
So an important thing about the number that you've gotten from history is, how good is that number?
Exactly, yeah, yeah, yeah. So that's why I'm sorry. Go on. Sorry, good,
well.
Well, as you can see, it would be helpful in our next conversation for you to spell, you know, to write out in English, what you know, what you're showing me right here.
Okay, you mean elaborate more on what this means, right?
Let me show you something.
Okay, thank you, Bob.
Let's see. Where do I have it? No, here it is, and I will send you this. So one of the, one of the last projects I did, um, it was pretty complex project. Let's see. Oh, here I've got it on my screen now, and let me share my screen. Here we go. That's one share. All right, can you see it? It's, it should say, whole free scenario modeling process.
This was a project for the European Union. By by the way, I think all of this is on on my website, under the poll, about there this and about four or five other downloads, PDFs, so you get the whole, the whole thing. But here, here's what we here's what the modeling process was. We described three different alternative scenarios for the European Union which does not have very much energy or material supplies. It's a very small The European Union is a very small area, and it just doesn't they import practically everything, including a lot of food so and they wanted to figure out they paid this a whole team of us. There were seven or eight or 10 of us on the team, including some very good simulation modelers. And we created three different scenarios. Here they are, global cooperation, Europe goes ahead, and civil society leads again. This is, you know, I'm sorry, it's I should have sent this ahead if it was relevant. And all of those model ideas were put into a stimulation model, a very big one, and the simulations were then run again, also against the reference scenario, a sort of keep doing what you're doing now, basically, and then the output for the model was this, I'll give you just a chance of I
so this is not a prediction, right? It's a what, what it is not a prediction, right? I'll put I'm a little surprised that yes or not, yes,
yes, yes, it's a prediction. Yes, yeah, I'll show you. I can show you what that one looks like. I
uh, well, first I'll show you what one scenario looks like. Here's um, Europe goes ahead. That's, that's what the scenario looks like. It has to do with resource governance, power mobility, buildings, industry, land and global developments. And the time frame is over 10, the 2010s the 2020s the 2030s and 40s, and then the outcome, the numerical outcome of the modeling process, against what the targets were for the project. So project. Now there are two other models, like two other scenarios like this. Oh, I should probably show you what the vision for the whole project is. And just a
quick question from the first scenario of our ability, yeah. Like, do you draw from the 2050 goal and then reverse engineer this?
Yes. We started with the goal and, and, and then worked on three different sets of assumptions, three different sets of assumptions, for Europe goes ahead, civil society leads and so forth. So here, here's the here's the overall project. Start at the top. Europe wants to improve its quality of life. That's why they're paying us, right, all right. And you know, in all of these areas, this is nothing other than we'd like to have we like to have something better in our homes. We like to have better solidarity between countries, blah, blah, blah, then, however, in the real world, let me I'm zooming in a little bit here so you can read it. I in the real world, we're actually dangerously exceeding planetary boundaries and approaching a whole bunch of others. Well, they took that seriously. So what does Europe do when you're facing that well, and what do you have? What are you doing when there's all of this resource depletion and global independence? Well, this requires enabling the economy to grow while total resource use decreases. That's the problem for Europe and for the rest of the world also, by the way, but, but we were just working for Europe, and so Europe has to have new targets and those that was what was on the far right hand side of those scenarios. The change these were these resource efficiency targets. Were the goals. They're the goals.
You see, you are trying to explain how the goals were elicited by we need the Europe needs to increase their quality of life, but physical resources are constrained, so we need to do some goals so that we ramp down the use of resources while making the quality increase, right. Yeah,
which implies certain characteristics for a resource efficient future.
I'm very, like, surprised by all the sequences are. This is travel syntax. How you do the sequencing? Like, realize through, or
like, Well, we had to think through, you know, they said, you know, the project was, what do we do? So then our team had to think through, you know, what, what we had to do all the way, you know, the way through. And I think some, maybe something like this could be useful to you. Then, yeah, then we created those scenarios. And now I'm gonna really overload you, but I will send this.
Yeah, yeah, thanks, because, if you think about it, the last realize through part is the action in my modeling, right? Yeah, and all Yeah, and all that is like predictive revenue and cost. Well, this is amazing.
So here, these are the numerical outcomes of the simulation. And these are, again, interesting.
Well, let's see this over. Wish we had bigger screens, bigger bigger screens so you can see that again, you've got resource governance, power, mobility, buildings, industry, and then the outcomes that were found from the simulation for for the business as usual, one and the reference one, and then for global cooperation, the same thing, the same targets, Europe goes ahead and civil society, those are the main scenarios.
So are the three. Europe goes ahead and civil society leads and the first one mutually exclusive.
They're, yes, they're, they're different 40 year futures based on different sets of assumptions, which are then articulated in each of these areas, like power and mobility and buildings and so forth. And then you get the outcomes
of that maybe apply in my situation, as the reference is you always allocate same amount to operations and marketing and like, the first is allocate more on the marketing, and the second is allocating more on the operations, and how it depends out depending on the industry.
Yeah, it could be, yeah could be. Let me, I close down. Let's see, how do I get out of entire screen. No, how do I get out of share?
Share? Maybe I can share and I can rescue you.
Okay, let's see it's I should be able to just double click on it, but find your project. Oh, here's, yeah, okay. No, I don't want that. I don't want to share that. I want Sure so
it doesn't
or I can, yeah, oh,
did that do it? Yes, yes, yes, okay, good. Oh, yes, yes, there you are.
Yeah. Try to make the diagram. But is there any tool that you specifically use, like, it's very high resolution? Well,
you see, that's, you know, that's, that's how we prepared this for the European Commission to make, to begin to make their decisions, you know, which is 27 countries, people from 27 different countries had to get together in a room and talk about this and decide and so to, you know, it had to be communicated as as well as possible, which was partially, you know, partially my role, but I was also involved in figuring out, first what to do, and then figuring out the scenarios with other health, you know, it's always a team effort in that, yeah.
My question, just to be clear, was, did you use PowerPoint to build that?
No, I use Illustrator. I see although, although I understand, there is some other new kind of free software you can get, because Adobe charges. You have something every month, forever,
Keynote, but I hope I can get away with, like, more simpler version than yours. Yeah.
So that's what I use anyway, but simply because I can, then, well, I I can articulate it, but also it's a vector oriented one, so that I can take my little icons and take them apart and put new things in them and so forth, pretty easily. And I've got 10,000 of them now. 10,000 my own icons, yes, wow. I've been, well, I've been doing this for 20 years,
and you have like, 300 arrows, right?
Yes. Well, if you, you know, if you start collecting them, which I didn't do it all at once. You just, you know, I just collected them as we made them. And I charged a little, you know, in in making the kind of things I was making there, as you could see, there were, oh, something like 100 150 icons in all of those different, visualizations. And if I had to charge a client to make every one of those my my price would be too high, and they wouldn't they wouldn't want me to work for them. So it's, it's handy to have 10,000
I hope I can bring the truck like the nella meadows and get all the 10,000 icons in front
of your home. So, all right, yeah. So I think that that the
one that started the top and led down through the thinking process would be something that would be very helpful for you to create, even if you don't necessarily need to do it all visually, but to actually do it very carefully, all the way out and and it would be you would find, if I think we I haven't looked at it Recently, because we did that, finished it in 2016 but I think that we also included at the bottom a set of the sets of assumptions that we made for each scenario, and which also then implied what we excluded also which, are, which are, which is important in any kind of making of a model.
I will start by simple things. First main how the perception of the situation for the founders is like the belief representing the bill, and then, based on that, how they do the predictions, and perhaps I can add in some assumption that they only have, like 10,000 cash for the next one year, how they can allocate and in what time step and in what time units they need to make a decision. So that would be the action. So have space full of increasing the profit by two folds in the next one year. What are some monthly actions they need to do?
Yes, so got it very good. Well, I will send you those, and I hope they're helpful to you. Thank you. Bob, all right, and whenever you want to talk next, let us know. JB, it was very helpful to have. JB, present,
yes, thank you. I've been excited so much about today's meeting. And I'm sorry, I'm like,
