Variational Free Energy and Economics Optimizing With Biases and Bounded Rationality

Henriksen, Morten

doi:10.3389/fpsyg.2020.549187

Cited by 4 publications

(3 citation statements)

References 59 publications

(75 reference statements)

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“…Already, active inference appears in deep learning [72] and economic models [73]. In coming years, it will likely be used more widely in artificial intelligence and the social sciences.…”

Section: Active Inferencementioning

confidence: 99%

Science-Driven Societal Transformation, Part III: Design

Boik

2021

Sustainability

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Climate change, biodiversity loss, and other major social and environmental problems pose severe risks. Progress has been inadequate and scientists, global policy experts, and the general public increasingly conclude that transformational change is needed across all sectors of society in order to improve and maintain social and ecological wellbeing. At least two paths to transformation are conceivable: (1) reform of and innovation within existing societal systems (e.g., economic, legal, and governance systems); and (2) the de novo development of and migration to new and improved societal systems. This paper is the final in a three-part series of concept papers that together outline a novel science-driven research and development program aimed at the second path. It summarizes literature to build a narrative on the topic of de novo design of societal systems. The purpose is to raise issues, suggest design possibilities, and highlight directions and questions that could be explored in the context of this or any R&D program aimed at new system design. This paper does not present original research, but rather provides a synthesis of selected ideas from the literature. Following other papers in the series, a society is viewed as a superorganism and its societal systems as a cognitive architecture. Accordingly, a central goal of design is to improve the collective cognitive capacity of a society, rendering it more capable of achieving and sustainably maintaining vitality. Topics of attention, communication, self-identity, power, and influence are discussed in relation to societal cognition and system design. A prototypical societal system is described, and some design considerations are highlighted.

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“…Already, active inference appears in deep learning [72] and economic models [73]. In coming years, it will likely be used more widely in artificial intelligence and the social sciences.…”

Section: Active Inferencementioning

confidence: 99%

Science-Driven Societal Transformation, Part III: Design

Boik

2021

Sustainability

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show abstract

“…The free energy approach implies a radical reformulation of economic agency since the concept of "utility" would be discarded as a general medium of optimization and substituted by free energy minimization, or, even more generally, safeguarding the low entropic states of the human AA, in the KW sense [67]. This is close to the view of the so-called "old institutionalists", such as Thorstein Veblen, that human behaviour must be analysed in terms of "habits", and not in terms of rational choice [68].…”

Section: Consequences For Theorizing the Economic Agent: The Case Of Neuroeconomicsmentioning

confidence: 99%

The Natural Philosophy of Economic Information: Autonomous Agents and Physiosemiosis

Herrmann‐Pillath

2021

Entropy

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Information is a core concept in modern economics, yet its definition and empirical specification is elusive. One reason is the intellectual grip of the Shannon paradigm which marginalizes semantic information. However, a precise concept of economic information must be based on a theory of semantics, since what counts economically is the meaning, function and use of information. This paper introduces a new principled approach to information that adopts the paradigm of biosemiotics, rooted in the philosophy of Charles S. Peirce and builds on recent developments of the thermodynamics of information. Information processing by autonomous agents, defined as autopoietic heat engines, is conceived as physiosemiosis operating according to fundamental thermodynamic principles of information processing, as elucidated in recent work by Kolchinsky and Wolpert (KW). I plug the KW approach into a basic conceptual model of physiosemiosis and present an evolutionary interpretation. This approach has far-reaching implications for economics, such as suggesting an evolutionary view of the economic agent, choice and behavior, which is informed by applications of statistical thermodynamics on the brain.

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“…In psychology, they have been used to ground a computational account of neuropsychological syndromes [ 3 ] and to develop emotional recognition devices, which allow resolving uncertainty about emotional states by interaction and learning [ 4 ]. In economics, the free energy principle has been exploited to reformulate the agents’ optimization process in terms of their beliefs [ 5 ]. Variational approaches have been employed to explain niche construction, based on the free energy principle [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

The Free Energy Principle for Perception and Action: A Deep Learning Perspective

Mazzaglia

Verbelen

Çatal

et al. 2022

Entropy

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The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i.e., they minimize their free energy. Under this principle, biological agents learn a generative model of the world and plan actions in the future that will maintain the agent in an homeostatic state that satisfies its preferences. This framework lends itself to being realized in silico, as it comprehends important aspects that make it computationally affordable, such as variational inference and amortized planning. In this work, we investigate the tool of deep learning to design and realize artificial agents based on active inference, presenting a deep-learning oriented presentation of the free energy principle, surveying works that are relevant in both machine learning and active inference areas, and discussing the design choices that are involved in the implementation process. This manuscript probes newer perspectives for the active inference framework, grounding its theoretical aspects into more pragmatic affairs, offering a practical guide to active inference newcomers and a starting point for deep learning practitioners that would like to investigate implementations of the free energy principle.

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Variational Free Energy and Economics Optimizing With Biases and Bounded Rationality

Cited by 4 publications

References 59 publications

Science-Driven Societal Transformation, Part III: Design

Science-Driven Societal Transformation, Part III: Design

The Natural Philosophy of Economic Information: Autonomous Agents and Physiosemiosis

The Free Energy Principle for Perception and Action: A Deep Learning Perspective

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