Part of ‣.

In this essay, I try to convey a set of related ideas that emerged in my mind upon reading the following papers:

• "Dream of Being: Solving AI Alignment Problems with Active Inference Models of Agency and Socioemotional Value Learning." (Safron, Sheikhbahaee et al. 2022)
• "Regulative development as a model for origin of life and artificial life studies." (Fields & Levin 2022)
• "A free energy principle for generic quantum systems." (Fields et al. 2022)
• "Technological approach to mind everywhere: an experimentally-grounded framework for understanding diverse bodies and minds.” (Levin 2022)
• “Minimal physicalism as a scale-free substrate for cognition and consciousness.” (Fields et al. 2021)

And listening to this conversation between Michael Levin and Joscha Bach: "The Global Mind, Collective Intelligence, Agency, and Morphogenesis”.

I don’t try to confine this essay to a distillation of the ideas and theories in these resources, nor explicitly distinguish in the below between the ideas of others and my additions to them — I just present my overall view. Many errors could be due to my misinterpretations of original papers.

Minds everywhere: radically gradualistic and empirical view on cognition and agency

Life is organised in multiscale competency architectures (MCA, ‣): every level of life organisation “knows what to do” (Levin 2022).

Levin calls for a radically gradualistic understanding of cognitive capacities, such as agency, learning, decision-making in light of some preferences, consciousness (i. e., panpsychism), and persuadability. In other words, every level of competency in MCAs can be called an “intelligence”, and an “agent”.

Persuadability is an interesting cognitive capacity, introduced by Levin in the paper:

Reproduced Figure 2 from Levin 2022.

Another aspect of Levin’s approach which goes hand-in-hand with gradualism is empiricism. Levin argues that philosophical debates about the nature of agency and intelligence, which tend to produce discrete classifications of systems as either agentic (or intelligent) or not, are mostly futile.

The correct level of agency with which to treat any system must be determined by experiments that reveal which kind of model and strategy provides the most efficient predictive and control capability over the system. In this engineering (understand, modify, build)-centered view, the optimal position of a system on the spectrum of agency is determined empirically, based on which kind of model affords the most efficient way of prediction and control.

Gradualism and empiricism in understanding agency is compatible with definitions of agency in Free Energy Principle (FEP) in both classical (Friston 2019) and quantum (Fields et al. 2022) formulations. In these frameworks, agent is an extremely generic concept, applying to virtually everything we can think of: