Choosing a Markov blanket

Parr, Thomas

doi:10.1017/s0140525x19002632

Cited by 9 publications

(4 citation statements)

References 130 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simultaneously, sociality, as implemented via belief sharing (which is necessary to the degree that we are to be predictable to one another) limits variety or entropy, and amounts to an accuracy constraint. In such a system, the sharing of beliefs broadens the evidence base available to each agent (I learn as much about the world by listening to others as by direct observation), but with built-in constraints on both conformity and eccentricity of belief (radical or unusual beliefs may emerge, but they cannot by definition be the norm in the population)-as agents both "infer together" (as part of a larger model) and "infer each other" (as each constitutes part of the external environment for the others) (Parr, 2020).…”

Section: Comparison To Current State-of-the-art Approachesmentioning

confidence: 99%

Designing ecosystems of intelligence from first principles

Friston,

Ramstead,

Kiefer

et al. 2024

Collective Intelligence

View full text Add to dashboard Cite

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

show abstract

Section: Comparison To Current State-of-the-art Approachesmentioning

confidence: 99%

Designing ecosystems of intelligence from first principles

Friston,

Ramstead,

Kiefer

et al. 2024

Collective Intelligence

View full text Add to dashboard Cite

show abstract

“…Unfortunately, Friston blankets have been applied almost as liberally as their statistical counterparts, including to individual neurons (Palacios, Isomura, Parr, & Friston, 2019), body substructures such as the brain (Seth & Friston, 2016), and eyes as well as larger organisms . This plurality of blankets is acknowledged by Parr (2020) and celebrated by as evidence for the ubiquity of the free-energy principle (FEP). We contend that this flexibility in what is cast as internal, external, sensory, or active states, is dangerously confused; it gives the false impression that the theory can recruit causal concepts, for example, Markov blankets, without committing to the full implications of a causal model-based understanding of perception and action.…”

Section: Bruineberg Et Al Argue For a Crucial Distinction Between Inf...mentioning

confidence: 99%

The Emperor's New Markov Blankets

et al. 2021

View full text Add to dashboard Cite

The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov Blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between ‘Pearl blankets’ to refer to the original epistemic use of Markov blankets and ‘Friston blankets’ to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programs: ‘inference with a model’ and ‘inference within a model’. Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone.

show abstract

“…Markov blankets are ascribed to real-world systems in order to model the conditional independence between internal states and external states [50][51][52]: for example, conditional independence between a production process and the environment in which it is carried out. Markov blankets are not real features of real-world systems.…”

Section: Markov Blanketsmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

View full text Add to dashboard Cite

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

show abstract

Choosing a Markov blanket

Cited by 9 publications

References 130 publications

Designing ecosystems of intelligence from first principles

Designing ecosystems of intelligence from first principles

The Emperor's New Markov Blankets

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Contact Info

Product

Resources

About