Rythm Diversity

When I worked as a starting programmer at what was then called the Center for Data Analysis of the Social Faculty of the University of Utrecht in 1969, there were still no Internet and no search engines or Social Media at all. Collecting data was very time-consuming and cumbersome and required either access to archives that were often carefully shielded from outsiders’ gazes, or measuring things, possibly organizing surveys. The availability of computers brought the performance of calculations on large data sets within reach. This phenomenon pushed the empirical research of the social sciences towards statistical methods that called for rectangular data files (tables) with research objects as rules and, for each object, individualized values for the variables (columns).

This approach is fruitful for examining situations and linear relationships, but problematic for examining composite social processes that are dynamic, including those recreated in COVID games. I am not arguing that statistical research into linear relationships is of no use. Just that what I can do with it is not enough for the research with COVID games of which we all saw the @realtruth versions to be complex, adaptive and reacting erratically under different geopolitical conditions. As a consequence I will open up to systematic errors when I assume otherwise. Like if I (this is a metaphor) were to apply the results of an Arrow-Debreu-based theoretical model in an economic reality where there are no convex, but do exist opposing individual preferences (such as those that switch regarding the COVID-measures).

My solution is to use linear models where possible (for situations, for processes that work towards equilibrium) but to look for other methods where this is not possible.

Fig. 1 Durkheim’s heuristic in the Netherlands

The question then is: where is that? What causes these non-linear problems? I am looking for this in Fig. 1 once again to hold on to Durkheim’s heuristics (and my somewhat idiosyncratic addition to Mary Douglas’s elaboration). The thought is simple again. In fig. 1, I gave eight unnamed institutions a place. They are responsible for 8 COVID-related measures. The institutions involved are: (1) health care, (2) the citizens, (3) the citizens and the test streets, (4) the citizens (no supervision of this is organized in the Netherlands), (5) the government and the enforcement agencies, (6) customs, (7) government and parliament and again (8) citizens.

All of them, health care, citizens, test streets, government, enforcement agencies, customs and parliament have their own rhythms for their daily work, for adaptations to customs and rules, for reproduction or renewal, and for transition and communication. Together they form a network that shapes the pandemic.

As long as that network does not function like a watch in laboratory conditions, I cannot trust that it will be at the time that I expect.

It may be that parts of the network function like such watches, but I don’t know or can’t read all the parts that matter or what time they show or how fast they are running or how they talk to each other.

That is a bit like the problem that those who forecast the weather are facing. Maybe I too can improve something by measuring the state of the pandemic on a daily basis and predicting on the basis of COVID-game-NL and COVID-game-CN how it will fare on in the coming week / weeks.

That is the method I am working towards.

I’m not there yet by a long shot. There are for instance still a few clocks to add to the collection that I now have. The virus and its mutants are also on board, as are the vaccines, then the EU and the rest of the world’s geopolitical actors.