A Theory of Physically Embodied and Causally Effective Agency

Causality is fundamental to agency. Intelligent agents learn about causal relationships by interacting with their environments and use their causal knowledge to choose actions intended to bring about desired outcomes. This paper considers a causal question that is central to the very meaning of agen...

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Bibliographic Details
Main Author: Kathryn Blackmond Laskey
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Information
Subjects:
Online Access:http://www.mdpi.com/2078-2489/9/10/249
Description
Summary:Causality is fundamental to agency. Intelligent agents learn about causal relationships by interacting with their environments and use their causal knowledge to choose actions intended to bring about desired outcomes. This paper considers a causal question that is central to the very meaning of agency, that of how a physically embodied agent effects intentional action in the world. The prevailing assumption is that both biological and computer agents are automatons whose decisions are determined by the physical processes operating in their information processing apparatus. As an alternative hypothesis, this paper presents a mathematical model of causally efficacious agency. The model is based on Stapp’s theory of efficacious choice in physically embodied agents. Stapp’s theory builds on a realistic interpretation of von Neumann’s mathematical formalization of quantum theory. Because it is consistent with the well-established precepts of quantum theory, Stapp’s theory has been dismissed as metaphysical and unfalsifiable. However, if taken seriously as a model of efficacious choice in biological agents, the theory does have empirically testable implications. This paper formulates Stapp’s theory as an interventionist causal theory in which interventions are ascribed to agents and can have macroscopically distinguishable effects in the world. Empirically testable implications of the theory are discussed and a path toward scientific evaluation is proposed. Implications for artificial intelligence are considered.
ISSN:2078-2489