Data protection psychology using game theory
- URL: http://arxiv.org/abs/2402.07905v1
- Date: Wed, 3 Jan 2024 13:07:30 GMT
- Title: Data protection psychology using game theory
- Authors: Mike Nkongolo, Jahrad Sewnath,
- Abstract summary: The research aims to explore how individuals perceive and interact with data protection practices.
The study employs a game theoretical approach to investigate the psychological factors that influence individuals' awareness and comprehension of data protection measures.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The research aims to explore how individuals perceive and interact with data protection practices in an era of increasing reliance on technology and the widespread availability of personal data. The study employs a game theoretical approach to investigate the psychological factors that influence individuals' awareness and comprehension of data protection measures. This involves using strategies, moves, rewards, and observations within the game to gain comprehensive insights into these psychological factors. Through the analysis of player strategies and moves within the game, the research identifies several psychological factors that impact awareness of data protection. These factors include levels of knowledge, attitudes, perceived risks, and individual differences among participants. The findings highlight the intricate nature of human cognition and behavior concerning data protection, offering insights crucial for developing effective awareness games and educational initiatives in this domain.
Related papers
- The Impact of Human Aspects on the Interactions Between Software Developers and End-Users in Software Engineering: A Systematic Literature Review [10.307654003138401]
We present a systematic review of studies on human aspects affecting developer-user interactions.
We identified various human aspects affecting developer-user interactions in 46 studies.
Our findings suggest the importance of leveraging positive effects and addressing negative effects in developer-user interactions.
arXiv Detail & Related papers (2024-05-08T03:38:36Z) - SoK (or SoLK?): On the Quantitative Study of Sociodemographic Factors and Computer Security Behaviors [31.18834611268347]
We survey existing scholarship on sociodemographics and secure behavior.
We then conduct a focused literature review of 47 papers to synthesize what is currently known and identify open questions for future research.
By incorporating contemporary social and critical theories, we establish guidelines for future studies of sociodemographic factors and security behaviors.
We present a case study to demonstrate our guidelines in action, at-scale, that conduct a measurement study of the relationships between sociodemographics and de-identified, aggregated log data of security and privacy behaviors among 16,829 users on Facebook across 16 countries.
arXiv Detail & Related papers (2024-04-15T23:56:03Z) - PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
Psychological measurement is essential for mental health, self-understanding, and personal development.
PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
arXiv Detail & Related papers (2024-02-19T18:00:30Z) - Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection [4.439102809224707]
Mental health conditions necessitate efficient monitoring to mitigate their adverse impacts on life quality.
Existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications.
We introduce a differential private federated transfer learning framework for mental health monitoring to enhance data privacy and enrich data sufficiency.
arXiv Detail & Related papers (2024-02-16T18:00:04Z) - Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable.
Existing susceptibility studies heavily rely on self-reported beliefs.
We propose a computational approach to model users' latent susceptibility levels.
arXiv Detail & Related papers (2023-11-16T07:22:56Z) - Virtual Harassment, Real Understanding: Using a Serious Game and
Bayesian Networks to Study Cyberbullying [0.9246281666115259]
This study explores an innovative approach, employing a serious game as a non-intrusive tool for data collection and education.
Preliminary pilot studies with the serious game show promising results, surpassing the informative capacity of traditional demographic and psychological questionnaires.
arXiv Detail & Related papers (2023-09-15T14:30:28Z) - Influence of External Information on Large Language Models Mirrors
Social Cognitive Patterns [51.622612759892775]
Social cognitive theory explains how people learn and acquire knowledge through observing others.
Recent years have witnessed the rapid development of large language models (LLMs)
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
arXiv Detail & Related papers (2023-05-08T16:10:18Z) - Affective Idiosyncratic Responses to Music [63.969810774018775]
We develop methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform.
We test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses.
arXiv Detail & Related papers (2022-10-17T19:57:46Z) - The world seems different in a social context: a neural network analysis
of human experimental data [57.729312306803955]
We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals.
An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
arXiv Detail & Related papers (2022-03-03T17:19:12Z) - Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
This study focuses on providing a novel learning mechanism based on a rivalry social impact.
Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.
arXiv Detail & Related papers (2020-11-02T21:54:18Z) - SensAI+Expanse Emotional Valence Prediction Studies with Cognition and
Memory Integration [0.0]
This work contributes with an artificial intelligent agent able to assist on cognitive science studies.
The developed artificial agent system (SensAI+Expanse) includes machine learning algorithms, empathetic algorithms, and memory.
Results of the present study show evidence of significant emotional behaviour differences between some age ranges and gender combinations.
arXiv Detail & Related papers (2020-01-03T18:17:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.