Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the gro...

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Autor principal:	Jesse Hoey
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	free energy uncertainty POMDP active inference emotion affect control theory Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/9e41807a82454486bc5ec2a0328bcc81
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id	oai:doaj.org-article:9e41807a82454486bc5ec2a0328bcc81
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spelling	oai:doaj.org-article:9e41807a82454486bc5ec2a0328bcc812021-11-25T17:29:11ZEquality and Freedom as Uncertainty in Groups10.3390/e231113841099-4300https://doaj.org/article/9e41807a82454486bc5ec2a0328bcc812021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1384https://doaj.org/toc/1099-4300In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.Jesse HoeyMDPI AGarticlefree energyuncertaintyPOMDPactive inferenceemotionaffect control theoryScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1384, p 1384 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	free energy uncertainty POMDP active inference emotion affect control theory Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	free energy uncertainty POMDP active inference emotion affect control theory Science Q Astrophysics QB460-466 Physics QC1-999 Jesse Hoey Equality and Freedom as Uncertainty in Groups
description	In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.
format	article
author	Jesse Hoey
author_facet	Jesse Hoey
author_sort	Jesse Hoey
title	Equality and Freedom as Uncertainty in Groups
title_short	Equality and Freedom as Uncertainty in Groups
title_full	Equality and Freedom as Uncertainty in Groups
title_fullStr	Equality and Freedom as Uncertainty in Groups
title_full_unstemmed	Equality and Freedom as Uncertainty in Groups
title_sort	equality and freedom as uncertainty in groups
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9e41807a82454486bc5ec2a0328bcc81
work_keys_str_mv	AT jessehoey equalityandfreedomasuncertaintyingroups
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Equality and Freedom as Uncertainty in Groups

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