Developing novel tools for applications of argumentation to behavioral economics

Ishikawa, Japan -- Consumer purchasing decisions can be considered as a form of preference-based human reasoning. There are two major schools of thought on preference. While mentalism asserts that preference reflects the true mental state of a person, behaviorism is the view in which preference is considered as a mathematical construct. According to behaviorists, people‘s actions and not words are decisive in determining their preferences. Economists foster this behavioral preference of consumers with Revealed Preference Theory (RPT), also known as consumer theory.

As reasoning is involved in preference, it is instructive to generalize RPT to artificial intelligence (AI), currently dictated by mentalism. With this forethought, Professor Van-Nam Huynh from Japan Advanced Institute of Science and Technology (JAIST) and Assistant Professor Nguyen Duy Hung from Sirindhorn International Institute of Technology, Thammasat University, Thailand, have recently generalized consumer theory to AI reasoning using argumentation – a kind of reasoning that draws inspiration from the process of people exchanging arguments to reach conclusions in daily life.

In an article made available online in the International Journal of Approximate Reasoning on 10^th May 2023, the researchers present the theoretical foundation and analytical tools for practical applications of argumentation in analyzing consumer mentalism and behavior. Prof. Huynh highlights the novelty of their work, “This paper bridges two lines of research: AI argument-based reasoning and behavioral economics. In particular, it explores the relationships between economic rationalities and argumentation semantics, between consumer’s preference and AI agent's preference, and between consumer’s purchasing behavior and AI agent’s reasoning behavior.”

In this work, the contributions of the researchers are three-fold. They first developed a Revealed Preference Argumentation (RPA) framework. The researchers argued that the existing frameworks are governed by the opposing mentalistic interpretation of preference. Thus, they re-constructed and unified two main approaches to RPT in terms of argumentation, demonstrating that RPT-based consumer analyses, including different rationality checks of a consumer behavior and extrapolations of such behaviors, can be interpreted as computational tasks in RPA.

Following that, the researchers successfully integrated mentalism and behaviorism to present an Integrated Preference Argumentation (IPA) framework. They established that RPA is just a special case of IPA with only ‘revealed’ preference. This finding is particularly important as existing preference-based argumentation frameworks are presented as IPA frameworks with only ‘stated’ preference.

Finally, the researchers developed comprehensive IPA algorithms, rigorously establishing their accuracy and termination for a general class of IPA frameworks. The researchers successfully implemented the algorithms in Prolog – a logic programming language associated with AI and computational linguistics – and obtained an IPA reasoning engine. Subsequently, they tested the developed tool to effectively analyze RPT-based consumer behavior.

In summary, this work makes remarkable inroads to a largely unexplored area in consumer behavioral economics. “This paper not only provides a theoretical and algorithmic foundation but also development tools for applications of argumentation to behavioral economics in consumer's behavior analyses such as rationality checks, consumer's preference recoveries, and behavior extrapolations”, notes an optimistic Prof. Huynh.

Here’s hoping for a variety of opportunities for future research in this field and novel applications of argumentation in behavioral economics!

 

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Reference

Title of original paper:	Integrated preference argumentation and applications in consumer behaviour analyses
Authors:	Nguyen Duy Hung, Van-Nam Huynh
Journal:	International Journal of Approximate Reasoning
DOI:	10.1016/j.ijar.2023.108938

 

                                           

About Japan Advanced Institute of Science and Technology, Japan

Founded in 1990 in Ishikawa prefecture, the Japan Advanced Institute of Science and Technology (JAIST) was the first independent national graduate school in Japan. Now, after 30 years of steady progress, JAIST has become one of Japan’s top-ranking universities. JAIST counts with multiple satellite campuses and strives to foster capable leaders with a state-of-the-art education system where diversity is key; about 40% of its alumni are international students. The university has a unique style of graduate education based on a carefully designed coursework-oriented curriculum to ensure that its students have a solid foundation on which to carry out cutting-edge research. JAIST also works closely both with local and overseas communities by promoting industry–academia collaborative research.  

 

About Professor Van-Nam Huynh from Japan Advanced Institute of Science and Technology, Japan

Dr. Van-Nam Huynh is a professor at the Japan Advanced Institute of Science and Technology (JAIST). He is also a part-time lecturer at the University of Tsukuba in Japan. He completed his bachelor’s degree in Mathematics from the University of Qui-Nhon in Vietnam and Ph.D. in Mathematics from the Institute of Information Technology at the Vietnam Academy of Science and Technology. He is a respected researcher in his field and a member of IEEE since 2007. He has over 230 publications with more than 1800 citations for his research.

 

Funding information

Nguyen Duy Hung is supported by Thammasat University Research Fund (Contract No. TUFT 54/2565); Van-Nam Huynh is supported by the U.S. Office of Naval Research Global (ONRG, Grant No. N62909-19-1-2031).

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