Sort of a classical move

My editorial

I continue nailing down details in the business plan I am preparing for the BeFund project. BeFund is the name I gave to that combination of an investment fund with a research centre specialized in behavioural experiments regarding the interaction of human beings with smart technologies, mostly those connected to the environment of smart cities. I have already passed in review an outline of the concept, now I am developing on the details. I like approaching details as a sequence, or timeline. Details happen, and figuring them out consists very largely in assessing how and when they happen. This is a powerful lesson, which I have mostly learnt through sport: if I can figure out, precisely, any type of achievement as a sequence of measurable steps, I can do it.

How it is supposed to start? This is one of those funny moments, when I am asking myself when is it the right moment. I see a few occurrences, which can kick the project off into action. I mean, I see them in my mind. No, I think I am still sane. So, the first initial event I see is the equipping and the opening of the research lab properly spoken. The second one consists in starting the first BeFund’s own research project, supposed to attract the first customers, i.e. the first startups. The third one is the institutional incorporation of BeFund, including looking for investors.

Good, these are the initial actions. Now, what are the milestones of the project, i.e. its measurable goals? Breaking even is a must, of course. Reaching the capital size and structure of a medium-sized investment fund is another one. I can represent the basic chain of strategic stages in my project:

The trick, now, consists in deconstructing those milestones, and those transitions in between. Right now, I have in mind two particulars: the general strategy as for reaching the breakeven point, and the outline of that initial research project to kick the BeFund project off into action with. As for the first one, I mean the dilemma between strategically tolerating the fact of running at a loss, on the one hand, versus imposing a reasonable deadline for reaching and passing the breakeven point, on the other hand. Running that sort of intentionally non-profitable business, with the net loss being transformed, via some sort of debt-based financial instruments, into capital shares, is technically feasible. I saw a few businesses like that, for example Proton Power Systems or Solar World in Germany. The former seems to be chronically unprofitable, and yet keeps running and kicking. The latter had just some temporary episodes of running at a loss. Technically, both cases prove it is workable to go into a financial loss and survive. Still, my own observation of those two cases suggests that strange things happen when you strategically tolerate losing money. For example, as in the case of Solar World, one day (a year ago, to be exact) you have a sub-website for investor’s relations, with all the nice financial reports there, and another day (now, I mean), there is no more investor’s relations site. Just as if some level of loss forced the company and its shareholders to go into deals that just look bad as press release.

Thus, I think I prefer to weave my business plan around the assumption that BeFund’s normal state will consist un running at a profit. Transition 1, in that visualisation above, should be reasonably swift, like no more than 2 years, possibly even shorter, by leveraging the launching with a public research grant. I have an idea for the initial research project to run at BeFund. In lots of my research, for example in my book entitled “Capitalism and Political Power”, I keep turning around that idea that the density of population is a fundamental factor of anything that happens in any social group. The behavioural experiment I have in mind would aim for discovering, how do the users of smart technologies – possibly the technologies peculiar to smart cities – react to changes in the density of population. I imagine a group of people placed in an environment, where they can experience various densities of population. Now, every word in this short description is important. I said those people will experience various densities of population, and not that there necessarily will be different densities of population.

How can I simulate, experimentally, various densities of homo sapiens per square kilometre? Well, here comes both the trick and the unknown of that experiment. I assume that we essentially experience the density of population as the frequency and/or intensity of social interactions. When I see more people in the street than one month ago, I experience greater a density of population. If more people come to me and want to talk, similarly I experience greater a density of population. I assume that if I participate to a meeting every day, in my work, I experience something like greater a density of population as compared to just one meeting a week. And so comes the unknown, namely how exactly do we recognize the actual density of population in the place where we live? Is my assumption 100% correct? Can I simulate perfectly a given density of population simply as a certain intensity of social interactions? Technically, this assumption holds. Still, as a social scientist, I know that technically, given the positive and statistically significant correlation, the more firemen come to put out a fire, the greater that fire grows. Technically is not the same as actually.

I think about starting the experiment by simply placing a population of participants in an environment that simulates various densities of population in a city (check ‘My individual square of land, 9 meters on 9’ ). I start from simulating a social outset corresponding to roughly 3000 people per square km, and I progressively ramp it up. Checkpoint #1: Define the set of experiential variables that simulate a given density of population. In that outset, participants in the experimental population have access to a range of prototyped technologies. They are being observed in their interaction with those technologies. Checkpoint #2: Defined the set of prototyped technologies to include in the experiment. Checkpoint #3: Define the behavioural variables to observe in those users. The design of the experimental outset is completed with the presence of engineers, who are an experimental sample in themselves: they are being observed in their engineering behaviour as they receive information about the users’ behaviour. Checkpoint #4: define the behavioural variables to observe in the engineers.

Now, my checkpoints, I mean those I named in the preceding paragraph. The word ‘checkpoint’ came spontaneously to my mind, as something both not quite defined yet, and, in the same time, needing definition. The set of experiential variables, corresponding to the density of population, is probably the trickiest of the four checkpoints I named. There is whole scientific universe of theories as for how do we actually experience the social structure we are living in. Apparently, our brain stem reacts even to small changes in our actual hierarchical position in the society, and, in a broader sense, logical statements we make about social reality around us are looped with very basic neurological processes in us. When I say basic, it pertains to things like the respective levels of serotonin and adrenalin.

Anyway, in a first approach, I am distinguishing two types of variables: strictly interpersonal, on the one hand, and abstract logical on the other hand. Under the heading of strictly interpersonal ones, I mean, for example: a) the number of different people I meet per day and per hour b) the number of different people I am dealing with simultaneously (strictly simultaneously, like in a public place) and c) the number of conversations with distinct people I have per day. At the abstractly logical level, my experimental subjects can be provided with information about how many people currently stay in the same place (district, block of flats, office space), as well as about the numerically changing density of population.

My checkpoint #2, namely the range of technologies being tested in behavioural interaction with the users, I remain quite open. I pragmatically assume that regardless the amount of funding my project can receive at this stage it would be a good thing to invite start up teams to join the fun right from the beginning. Logically, these start up teams would propose to include in the experiment their technologies, the ones they will be currently developing. As for checkpoints #3 and #4, I gave sort of an angle of approach in Une boucle de rétroaction qui reviendra relativement pas cher as well as in There are many ways of having fun with that experiment.

Good, so I have an outline of the experimental research I want to open up with in my BeFund project. I know that the cost of equipping the behavioural laboratory will be between €200 000 and €500 000, with kind of a sibling value in terms of circulating capital to cover the current expenses until it breaks even. The best I could hit with public funding would be the total of those two, thus between €400 000 and €1000 000. From that bit of benchmarking I did, mostly with Foresight Group , I assume that my temporary final goal, i.e. the capital size and structure, would be an investment fund with 30 – 50 companies in its portfolio, and with some €150 millions in the balance sheet.

As I am rummaging a bit in all those details regarding the details of a business plan, I made some sort of a classical move: a calculation of prospective revenues from the rental of the experimental lab on an hourly basis. This type of prediction is like frying a nice and juicy steak in a culinary contest: a nice demonstration of basic skills. In a rough outline: I have 30*16 = 480 rentable hours a month in the lab, and I can attract customers, who would like to rent the lab per hour, following two distinct progressions, namely the normal one, as in the Rogers’ model (see Robertson 1967 ), or a sharper one, with a long launching phase, according to the Poisson progression (see Poisson 1827 ). I generally assume that my average customer will take 5 years, thus 60 months, to get acquainted with the possibility to rent the lab per hour I am pricing one hour of lab rental at €200. In table 1, below, I present those two alternative calculations. I label the normal progression as the ‘OPTIMISTIC SCENARIO’ and the Poisson-based one as the ‘PESSIMISTIC SCENARIO’.

Table 1 – Alternative scenarios of revenue from the rental of experimental lab

By the way, out of pure vanity, I am pushing the link to an article I authored, published recently with Impakter.com. I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?