THECOG 2022 - Transforms In Behavioral And Affective Computing (Revisited)

Document Type

Conference Proceeding

Source of Publication

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Publication Date

10-17-2022

Abstract

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.

Publisher

ACM

First Page

5163

Last Page

5164

Disciplines

Computer Sciences

Keywords

Behavioral economics, Affective state estimation, Computational affective models, Perceived loss, Data-driven strategy recommendation

Indexed in Scopus

no

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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