History can be divided in pre-classicism, classicism, modernism, and postmodernism. Pre-classicism is a collection of knowledge accumulated over hundreds of thousands of years until Greece, classicism over few thousand years since Greece, modernism over a century or so since 18th century, and postmodernism over few decades in the second half of 20th century. Classicism, modernism, and postmodernism removed some knowledge from previous periods, causing a drastic contemporary reduction in knowledge and increase in risk and values.

Metamodernism, coming after postmodernism, aims to restore the lost knowledge from each period making it available for contemporary use. This is enriching knowledge from each of the periods making it safer and more valuable. Modernism and postmodernism especially had little time to develop knowledge yet have great power and are therefore very unsafe. Their risks and quality would be lowered by adding pre-classical and classical knowledge.

The main purpose of modernism is to reach perfect objective efficiency. All major contemporary artificial intelligence (AI) projects are modernist.

In all major projects AI is used to replace human decision making by reducing it drastically to the most efficient decision making parameters. Remaining parameters benefit the centralized power which designed it, maintaining the main functions of the decision while removing parameters which provided benefits to the members of the system influenced by the decisions. Removed parameters were based on empathy and other human behaviors.

Example is credit score rating which included a human-to-human interviews, but is now completely automated by AI. Human-to-human interviews still included, to some extent, higher level functions such as empathy, wisdom, compassion, relationship. Once automated credit score rating has been reduced to only a fraction of parameters a human used. Such reduction in the decision making process increased the efficiency of the process in the short term.

Mechanical automations applied, for example, in factories, copied and added to human processes increasing efficiency and benefiting all members of the system. However, AI does not copy the whole decision making process, but actually reduces it drastically by removing higher level human thinking. Such thinking was developed in humans as a safety precaution and a relationship element. Once such thinking is removed, such decision making processes drastically loose value and potentially cause damage.

This is a problem metamodernism and openness should address. Metamodernism as a design method should allow AI designers to fully understand the decision making processes – it their entirety. Such understanding would allow them to prevent or minimize damage by pure modern design.

Openness applied to AI design should be used as a continuous improvement method which minimizes further damage once AI starts engaging.

Knowledge can be defined as an ontology. Language is the dominant ontology management method in contemporary systems. It is more effective than imagery and other methods because contemporary systems depend highly on abstract yet precise ontologies. Therefore ontology, knowledge, and language are one in this article and will be presented by the “ontology”.

If a goal of a system is to increase collective intelligence and consciousness, system changes should be observed through ontology changes. Ontology changes between traditionalism, modernism, and postmodernism show what kind of language is needed for metamodernism and openness to increase collective intelligence and consciousness.

Traditionalism had a stable ontology. Modernism rapidly expanded the traditional ontology by adding to it and rarely changing it. Postmodernism is mostly focused on changing traditional and modern ontology, adding little to both. Postmodernism depends on closed centralized power to create these ontology changes.

Metamodernism and openness should direct ontology development towards following goals:
– Postmodernism expands instead of changes traditional and modern ontologies,
– Ontology changes are more open and less centralized,
– Adoption of non-binary (oscillating) ontologies.

If ontology development is directed towards these goals a system will experience increase collective intelligence and consciousness growth, and will avoid reductive conflict caused by closed and centralized ontology changes.