When we want to organize, we look at the problem and then try to find a solution by changing the system. This is based on a mechanical view where we assume that actions on a system can change the output of the system in the way we predict. An example is the discussion on the business cycle by the economists Hayek and Keynes. Both assumed that the propriety actions on the system would result in the wanted changes in the local economy. Another example is the idea of classes in a society where one assumes that specific actions from the government like laws for inclusion can change the structure of classes in a society.
The results are normally not as been predicted by the scientists. On a macro level one could assume that there is a stable system, but as soon one zooms in the system appears changing continuously. Physicists recognized this problem long ago; originally, they were also thinking in a mechanical way using Newtonian physics. Material was described by them as systems of atoms, and they assumed that those atoms could not be broken up. Until they started to investigate the structure of the atoms and found that it was much more complex. Physics had to change from an interaction model to a field model. From thinking in stable systems to continuously changing fields. From predicting discrete results to calculating probabilities.
Using a field model changes how and what we measure. The field is infinite, and one measures only the part of the field which includes the measurer; secondly the act of measuring has an influence on what is measured. So, when one, for instance, wants to measure the norms, rules and values in a network, one must know that one is measuring in only a part of the total structure of networks and that one is part of what is measured.
System thinking has led to silo thinking; where sociologists and anthropologists measure the group behavior regarding human and social capital, economists measure the group behavior regarding financial capital. In a field model one cannot separate these types of capital, they are related. The norms, rules and values used for the decisions made by human objects are based on the properties of the human objects in that field, the network they are in. And they are different depending on the network. It is different in a family network from a country. When someone lives in an Islamic family in a European country the norms, rules and values in that family are different from the norms, rules, and values in the country, although they do not have to conflict with each other. The norms, rules and values of young students are different from the norms, rules, and values of elderly people.
Those norms, rules and values are difficult to measure because they are often implicit. Only when we make them explicit in, for instance, laws we can use them when organizing. Regarding financial capital, different institutes have described norms, rules, and values and made them explicit in laws and regulations. This started for human capital last century because of the international developments in the labor market, which required standards to describe jobs. The research area of social network analysis has recently led to a start in describing the norms, rules, and values in networks regarding the relations between human objects in the networks. The result was a description of the social capital properties of a human object in a network based on trust in the social graph.
So how can we organize, optimize the capital, in such a complex environment, a field consisting of layers of networks described by financial, human, and social capital? How can governance, the use of the norms, rules, and values in a network be developed and implemented? When describing the norms, rules and values in human capital, the same problem was detected by scientists developing models for knowledge management. Knowledge, skills and experience or autonomy are often not described, made explicit. But to fulfil a vacancy one needs to have a measurement of these properties. This has led to the definition of the four phases in the exchange of human capital: (1) explicating, using explicit values for the level of knowledge, skill and authority, (2) combining, using different sets of information containing values for knowledge, skills or autonomy, (3) internalizing, using the values for knowledge, skills and autonomy in the organization and (4) socializing, constructing the values of knowledge, skills and autonomy. We propose to use those four phases in the exchange of norms, rules, and values as well. In that case the first phase is the socialization, the creative social construction process. In that process a network is built based on a goal and the available capital. The distribution of available capital will be optimized using the exchange of capital. During this process the norms, rules and values become clear and are accepted by the members of the team in the network. The second phase will be to make them explicit. In most cases this is done by making procedures or protocols, how a member of a team, the network, must act in certain situations. The third phase is the combination of different sets of norms, rules, and values, when the new network becomes part of a bigger structure. There will be influence from outside the network to the network. This can be in the form of regulations when the team wants to operate in a certain country, in the form of capital by getting financial capital or acquiring human capital or even start creating social capital by doing marketing and sales in a certain country. This will be different for different teams or networks. Starting a company as an entrepreneur is different from moving to another country as a family.
The fourth phase, internalization is often forgotten. One of the reasons why startups of elderly people have a better probability of success is that they can explain why a certain set of norms, rules and values is used in the startup and how it relates to the set used by the entrepreneur in his own life. Internalization, the process of teaching others how to cope with certain influences on the network, is necessary for success.
The conclusion is that we must cope with a complex environment with different levels of networking and different sets of norms, rules and values. This makes organizing complex and almost impossible when the sets of norms, rules and values being used in the exchange of financial, human, or social capital are only implicit available.
Especially when one wants to make use of self-learning AI agents to support the exchange of capital the sets of norms, rules and values being used for the exchange should be available explicit. The self-learning AI-agent needs that information during, for instance the execution of a smart contract, to determine whether the intended exchange is according to the norms, rules, and values. The use of the self-learning AI-agents will lead to a new type of blockchain linked to the network involved containing the norms, rules and value (symbolic) data.
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