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Bayesian Social Subgraph Generative Models: Social Network Twins using Belief Networks and Ego Behavior Models

2023 | HICSS | Citations: 0
Authors: Naidu, N.; El-Gayar, O.

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is t ... Expand

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is that a subgraph is independent of lower order subgraphs in a sparse network. This is not entirely true especially for nonsparse networks. Additionally, the generated networks lack the typical properties of a social network because of an assumption of random growth for nodes and edges. Finally, there is no concept for explicit ego choice or bias when connecting to dyadic or triadic relationships. We develop a novel graph generative model referred to as the Bayesian Social Subgraph Generative Model (BASSUGM). We ground the BASSUGM in a proposed sociological model and leverage Bayesian tools like belief networks. We introduce novel concepts like the networks' macro theme when combines with an ego's individuality realizes the ego's intent. We also demonstrate how the social network twin generated with BASSUGM outperforms SUGM for non-sparse, small, social, networks. Collapse

Semantic filters: probability theory

Topics: social network logistics management telephone

Methods: cluster analysis Bayesian network experiment hierarchical linear modeling computational algorithm

Theories: probability theory graph theory
Removing order effects from human-classified datasets: A machine learning method to improve decision making systems
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2023 | Decision Support Systems | Citations: 0
Authors: Romanov, Dmitry; Molokanov, Valentin; Kazantsev, Nikolai; Jha, Ashish Kumar

Abstract: Although recent developments in Artificial Intelligence (AI) and machine learnin ... Expand

Abstract: Although recent developments in Artificial Intelligence (AI) and machine learning (ML) aim to enhance the fairness and transparency of decision-making systems, research has found that neural networks (or other similar AI techniques) are still effected by human cognitive biases due to the training datasets. In this study, we focus on order effects, i.e., when the input of information impacts human perception and the decisions resulting from this information. We propose the Order Effect Removal Method (OERM) for handling the order effect which leads to bias and for helping organizations remove these biases from their training datasets and, therefore, from auto mated decision-making systems. Using design science principles to theoretically create, test, and validate the method, we can eliminate the order bias even in basic classification systems. Furthermore, the method can be applied in a multidisciplinary context, where an AI-based algorithm substitutes for manual work. Collapse

Semantic filters: probability theory

Topics: decision making artificial intelligence big data cloud computing enterprise resource planning

Methods: naïve bayes design science artificial neural network classification method kernel theory

Theories: probability theory
Context-Aware Marketing Attribution Based on Survival Analysis

2023 | International Conference on Business Informatics | Citations: 0
Authors: Züllig, Kilian; Napirata, Stefan; Zimmermann, Steffen

Abstract: Companies increasingly invest in digital marketing channels to promote their pr ... Expand

Abstract: Companies increasingly invest in digital marketing channels to promote their products and services. While the expenditures for each marketing channel are known, the contribution of marketing channels to a successful conversion, and therefore the value they generate, is unknown, but highly relevant for strategic decision-making. In this paper, we develop a novel, context-aware additive hazard marketing attribution (CAHMA) model based on survival analysis to address this problem. In addition to channel-specific, time-decaying effects of marketing on the users' conversion rate, we control for the effects of contextual features, such as the device or country from which users interact with marketing channels. Based on a prototypical implementation, we demonstrate the model's applicability and evaluate it on real-world data from the industry. We find that CAHMA outperforms other models in terms of accuracy while offering unique interpretability of the results and hence, providing deep insights for practitioners into the effects of marketing. Collapse

Semantic filters: probability theory

Topics: marketing management iOS Android mobile system MapReduce

Methods: machine learning survival analysis hazard function design artifact Markov chain Monte Carlo

Theories: probability theory
A Probabilistic Perspective of Human-Machine Interaction
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2022 | HICSS | Citations: 0
Authors: Canan, Mustafa; Demir, Mustafa; Kovacic, Samual

Abstract: Human-machine interaction (HMI) has become an essential part of the daily routin ... Expand

Abstract: Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-theart Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The humanhuman interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decisionmaking than merely using classical probability theory (CPT). In this paper, we examine the HMI from a QPT perspective. Applying QPT to studying HMI for decision-making shows improvement in understanding the decision process when interacting with machines because it provides insights into the mental uncertainty of a human that is not apparent in CPT. Collapse

Semantic filters: probability theory

Topics: web page decision making search engine human computer interaction usability

Methods: case study experiment

Theories: probability theory decision theory
Cognitive automation
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2022 | Electronic Markets | Citations: 6
Authors: Engel, Christian; Ebel, Philipp; Leimeister, Jan Marco

Abstract: Facilitated by AI technology, the phenomenon of cognitive automation extends the ... Expand

Abstract: Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization. We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research. Collapse

Semantic filters: probability theory

Topics: artificial intelligence process automation system robotic process automation database system decision making

Methods: business process modeling machine learning structured literature research literature study deep learning

Theories: probability theory
Discovering Primary Medical Procedures and their Associations with Other Procedures in HCUP Data
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2022 | Information Systems Frontiers | Citations: 0
Authors: Mardini, Mamoun T.; Raś, Zbigniew W.

Abstract: In recent years, healthcare spending has risen and become a burden on government ... Expand

Abstract: In recent years, healthcare spending has risen and become a burden on governments especially in the US. The selection of the primary medical procedure by physicians is the first step in the patient treatment process and is considered to be one of the main causes for hospital readmissions if it is not done correctly. In this paper, we propose a system that can identify with high accuracy the primary medical procedure for a newly admitted patient. We propose three approaches to anticipate which medical procedure should be primary. Additionally, we propose the procedure graph, which shows all possible paths that a new patient may undertake during the course of treatment. Finally, we extract the possible associations between the primary procedure and other procedures in the same hospital visit. The results show the ability of our proposed system to identify which procedure should be primary and extract its associations with other procedures. Collapse

Semantic filters: probability theory

Topics: recommender system personalization usability database system

Methods: computational algorithm cluster analysis experiment

Theories: probability theory
Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information
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2022 | Group Decision and Negotiation | Citations: 0
Authors: Tan, Xiao; Zhu, Jianjun; Wu, Tong

Abstract: We present a consensus improvement mechanism based on prospect theory and quantu ... Expand

Abstract: We present a consensus improvement mechanism based on prospect theory and quantum probability theory (QPT) that enables the manifestation of irrational and uncertain behaviors of decision makers (DMs) in linguistic distribution group decision making. In this framework, the DMs pursue the possibility of working with different partial agreements on prospect values. Considering that the reference information should be comprehensive and accurate as it guides information modification and affects consensus efficiency, objective and subjective information is integrated to obtain the information. Several studies have verified that the interference effect will occur when the brain beliefs flow towards the different decision classification paths. To address this problem, QPT is introduced into the information integration and the optimized value of the interference term can be acquired by the designed multi-objective programming model based on the maximum individual utility. Finally, as the reference point changes during the preference adjustment process, a dynamic reference point-oriented consensus model is constructed to obtain the optimized modification. A case study is performed on the emergency plan for the selection of designated hospitals, and comparative analyses are performed to demonstrate the feasibility and advantages of the proposed model. Several important insights are offered to simulate the most likely possibility of consciousness flowing into different decision classifications for DMs and moderators. Collapse

Semantic filters: probability theory

Topics: decision making user attitude

Methods: multi objective optimization trade-off analysis method optimization model cross sectional research computational algorithm

Theories: decision theory behavioral theory prospect theory probability theory
A simulation-based risk interdependency network model for project risk assessment
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2021 | Decision Support Systems | Citations: 0
Authors: Guan, Li; Abbasi, Alireza; Ryan, Michael J.

Abstract: Project risks are mostly considered to be independent in risk management, ignori ... Expand

Abstract: Project risks are mostly considered to be independent in risk management, ignoring interdependencies among them, which can lead to inappropriate risk assessment and reduced efficacy in risk treatment. This study in troduces a new Monte Carlo simulation-based risk interdependency network model to support decision makers in assessing project risks and planning risk treatment actions more effectively. The Interpretive Structural Modeling method is integrated into this model to develop a hierarchical project risk interdependency network based on identified risks and their cause-effect relationships. The Monte Carlo method is used to model the stochastic behavior of risk occurrence and to generate numerous possible risk scenarios through simulation. To evaluate single risks and overall project risk level while considering risk interdependencies, five major risk indicators are therefore proposed, namely simulated occurrence probability of a risk, simulated local and global influence of a risk, as well as total risk loss and total risk propagation loss of a project. An additional sensitivity analysis is also included to examine the effects of input uncertainties of this model on risk assessment results. Moreover, two case studies are provided to demonstrate the application and effectiveness of the proposed model. The findings accentuate the significance of considering risk interdependencies in project risk assessment and validate the model's robustness and feasibility in risk management of projects with complex interrelated risks. Collapse

Semantic filters: probability theory

Topics: decision support system risk management procurement management logistics management artificial intelligence

Methods: simulation sensitivity analysis case study Monte Carlo simulation descriptive statistic

Theories: probability theory
Event-Driven Assessment of Currency of Wiki Articles: A Novel Probability-Based Metric

2021 | International Conference on Information Systems | Citations: 0
Authors: Klier, Mathias; Moestue, Lars; Obermeier, Andreas; Widmann, Torben

Abstract: Wikis provide an easy and efficient way to share and aggregate knowledge. The c ... Expand

Abstract: Wikis provide an easy and efficient way to share and aggregate knowledge. The currency of wikis is of enormous importance since user satisfaction is correlated to the information contained being up-to-date. However, although the automated assessment of currency of wiki articles is an important issue in science and practice alike, existing approaches cannot cope with currency-relevant events as the underlying reasons for outdated articles. To address this issue, we propose a probability-based metric for currency of wiki articles accounting for currency-relevant events. Our event-driven metric reflects the probability that a wiki article is still up-to-date in the sense that no currency-relevant event changed the state of the corresponding real-world entity. We demonstrate the practical applicability and effectiveness of the metric in a real-world setting by analyzing articles from the English Wikipedia. The results show that the provided metric values are reliable and discriminate well between current and outdated articles. Collapse

Semantic filters: probability theory

Topics: wiki Wikipedia data quality knowledge base website

Methods: literature sample statistical hypothesis test descriptive statistic literature study anomaly detection

Theories: probability theory
The role you play, the life you have: Donor retention in online charitable crowdfunding platform
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2021 | Decision Support Systems | Citations: 1
Authors: Xiao, Shengsheng; Yue, Qing

Abstract: Crowdfunding was first used by individuals and entrepreneurs to collect small-si ... Expand

Abstract: Crowdfunding was first used by individuals and entrepreneurs to collect small-sized investments from crowds to support for-profit ventures, but now it is being touted as a valuable alternative to raise money for non-profit causes. Similar to various online settings, a key challenge for online charitable crowdfunding platform is the problem of donor retention. In this research, we disentangle donor retention behavior and build up a structural model to jointly examine donors’ donation and latent attrition. By incorporating donation relationship and ac tion related covariates into the model, we illustrate the drivers of donor retention and quantitatively examine their influence on individual donor’s contribution and attrition activity. After calibrating the model with lon gitudinal donation transaction data from a leading charitable crowdfunding platform which enables teachers to request materials and resources for their classrooms, we find that (1) Teacher-donors (people who can be both donation makers and fundraisers) usually exhibit higher donation rate and lower attrition rate than normal donors on the platform; (2) Compared with site-donors (donors directly acquired through website visit), donors acquired through teacher referral usually have lower contribution and attrition rates; (3) The provided “charity gift card” and “donation matching offer” prosocial marketing programs on the platform seem to be a double-edged sword to donor retention. They have positive impact on donors’ contribution rate, at the same time, they significantly increase donors’ attrition rate; (4) Donors’ initial contribution amount to the platform, successful donation result and “Thank-You” feedback from fundraisers can significantly decrease their attrition rate. Our results provide insights on new donor acquisition and donor relationship management in online charitable crowdfunding market. Collapse

Semantic filters: probability theory

Topics: crowdsourcing crowdfunding marketing management website

Methods: robustness check longitudinal research statistical hypothesis test conceptual modelling linear regression analysis

Theories: probability theory

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Found 47 articles

Bayesian Social Subgraph Generative Models: Social Network Twins using Belief Networks and Ego Behavior Models

Removing order effects from human-classified datasets: A machine learning method to improve decision making systems

Context-Aware Marketing Attribution Based on Survival Analysis

A Probabilistic Perspective of Human-Machine Interaction

Cognitive automation

Discovering Primary Medical Procedures and their Associations with Other Procedures in HCUP Data

Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information

A simulation-based risk interdependency network model for project risk assessment

Event-Driven Assessment of Currency of Wiki Articles: A Novel Probability-Based Metric

The role you play, the life you have: Donor retention in online charitable crowdfunding platform