Consumer buying decisions can be seen as a form of human reasoning based on preference. There are two main schools of thought about preference. While mentalism maintains that preference reflects a person’s true mental state, behaviorism is the view in which preference is a mathematical construct.
According to behavioral scientists, it is people’s actions, not words, that determine their preferences. Economists reinforce this behavioral preference of consumers through Revealed Preference Theory (RPT), also known as Consumer Theory.
Because logic is involved in preference, it is useful to generalize RPT to artificial intelligence (AI), which is currently dictated by mentality.
With this preconceived thinking, Professor Van Nam Huynh of the Japan Advanced Institute of Science and Technology (JAIST) and Assistant Professor Nguyen Doi Hung of Sirindhorn International Institute of Technology, Thammasat University in Thailand, recently generalized consumer theory to the logic of artificial intelligence using dialectic – a type of reasoning which draws inspiration from the process of people exchanging arguments to reach conclusions in everyday life.
In an article published in International Journal of Approximate InferenceThe researchers provided the theoretical basis and analytical tools for the practical applications of the arguments in the analysis of consumer mentality and behaviour.
Highlighting the novelty of their work, Professor Huynh says, “This paper bridges two lines of research: argument-based reasoning and behavioral economics. In particular, it explores the relationships between economic rationality and the semantics of argument, between consumer preference and AI agent preference, and between consumer purchasing behavior and rational behavior to an artificial intelligence agent.”
In this work, the researchers’ contributions are threefold. They first developed the Revealed Preference Arguments (RPA) framework. The researchers argued that current frameworks are governed by an opposing rational interpretation of preference. Thus, they reconstruct and arguably unify two major approaches to RPT, demonstrating that consumer analyzes based on RPT, including examinations of various rationalities of consumer behavior and extrapolation of such behaviors, can be interpreted as computational tasks in RPA.
Next, the researchers successfully combined mental and behavioral to present the framework of the Integrated Preference Argument (IPA). They prove that RPA is just a special case of IPA with only “disclosed” preference. This result is particularly important as preference-based discussion frameworks are presented as IPA frameworks with only ‘declared’ preference.
Finally, the researchers developed comprehensive IPA algorithms, rigorously proving their accuracy and termination to a general class of IPA frameworks. The researchers successfully implemented the algorithms in Prolog – a logical programming language associated with artificial intelligence and computational linguistics – and obtained an IPA inference engine. Next, they tested the tool developed to effectively analyze RPT-based consumer behavior.
In sum, this work makes remarkable progress in a largely unexplored area in the economics of consumer behaviour. “Not only does this paper provide a theoretical and algorithmic foundation, but also development tools for argumentative applications of behavioral economics in analyzes of consumer behavior such as examinations of rationality, retrieving consumer preferences, and behavior extrapolation,” notes Prof. Hoen.
Hung Nguyen Duy et al, The Integrated Preference Argument and Applications in Analyzes of Consumer Behavior, International Journal of Approximate Inference (2023). DOI: 10.1016/j.ijar.2023.108938
Provided by the Japan Advanced Institute of Science and Technology
the quote: Developing New Tools for Argumentation Applications in Behavioral Economics (2023, May 22) Retrieved May 22, 2023 from https://phys.org/news/2023-05-tools-applications-argumentation-behavioral-economics.html
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