THECOG - Transforms in Behavioral and Affective Computing

Document Type

Conference Proceeding

Source of Publication

International Conference on Information and Knowledge Management, Proceedings

Publication Date

10-26-2021

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.

ISBN

9781450384469

Publisher

ACM

First Page

4876

Last Page

4877

Disciplines

Computer Sciences

Keywords

affective state estimation, behavioral economics, computational affective models, data-driven strategy recommendation, perceived loss

Scopus ID

85119178414

Indexed in Scopus

yes

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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