The Influence of Trust Score on Cooperative Behavior

Ignat, Claudia-Lavinia; Dang, Quang-Vinh; Shalin, Valerie L.

doi:10.1145/3329250

Cited by 13 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, we can provide the explanation if we reject or accept a particular network flow, make it easier for engineers to debug the system. By explaining the behavior of the system, we can as well gain the trust of users on the model (Ignat et al , 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The core issue is, machine learning algorithms usually are treated as a black box (Ribeiro et al , 2016), despite many attempts to understand the decisions of these algorithms recently (Roscher et al , 2020). Several studies suggested that lacking understanding in how a system works will lead to the lack of trust from users on the system (Ignat et al , 2019). Furthermore, if we do not understand why does the system make a particular decision, we will be limited in debugging the system, especially when the prediction is wrong.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the performance of the intrusion detection systems by the machine learning explainability

Dang

2021

IJWIS

Self Cite

View full text Add to dashboard Cite

Purpose This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models. Design/methodology/approach The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection. Findings The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance. Originality/value The authors draw our conclusions based on their own research and experimental works.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the performance of the intrusion detection systems by the machine learning explainability

Dang

2021

IJWIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, we will introduce different constraints into the model, for instance the maximum loss one can resist while using a model. Another approach is to integrate simulated behavior of users in non-cooperative or cooperative markets [5,16].…”

Section: Discussionmentioning

confidence: 99%

Reinforcement Learning in Stock Trading

Dang

2019

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

Using machine learning techniques in financial markets, particularly in stock trading, attracts a lot of attention from both academia and practitioners in recent years. Researchers have studied different supervised and unsupervised learning techniques to either predict stock price movement or make decisions in the market. In this paper we study the usage of reinforcement learning techniques in stock trading. We evaluate the approach on real-world stock dataset. We compare the deep reinforcement learning approach with state-of-the-art supervised deep learning prediction in real-world data. Given the nature of the market where the true parameters will never be revealed, we believe that the reinforcement learning has a lot of potential in decision-making for stock trading.

show abstract

“…The MATG, first published by Collins 1 in his master’s thesis, is a game of strategic interaction combining features of two commonly used games, the multi-arm bandit game ( Robbins, 1952 ) and the trust game ( Berg et al, 1995 ). The trust game was first used by Berg et al (1995) to examine behavior in one-shot interactions and later used by other researchers to study behavior in repeated interactions ( Dubois et al, 2012 ), group interactions ( Ignat et al, 2019 – control condition), and over various periods of time ( Strachan et al, 2020 ). The multi-arm bandit game ( Robbins, 1952 ) has been used in various psychological studies to explore exploration-exploitation behavior in both animals and humans (e.g., Cohen et al, 2007 ).…”

Section: Empirical Study Methodsmentioning

confidence: 99%

Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model

Collins

Juvina

2021

Front. Psychol.

View full text Add to dashboard Cite

The literature on trust seems to have reached a consensus that appropriately calibrated trust in humans or machines is highly desirable; miscalibrated (i.e., over- or under-) trust has been thought to only have negative consequences (i.e., over-reliance or under-utilization). While not invalidating the general idea of trust calibration, a published computational cognitive model of trust in strategic interaction predicts that some local and temporary violations of the trust calibration principle are critical for sustained success in strategic situations characterized by interdependence and uncertainty (e.g., trust game, prisoner’s dilemma, and Hawk-dove). This paper presents empirical and computational modeling work aimed at testing the predictions of under- and over-trust in an extension of the trust game, the multi-arm trust game, that captures some important characteristics of real-world interpersonal and human-machine interactions, such as the ability to choose when and with whom to interact among multiple agents. As predicted by our previous model, we found that, under conditions of increased trust necessity, participants actively reconstructed their trust-investment portfolios by discounting their trust in their previously trusted counterparts and attempting to develop trust with the counterparts that they previously distrusted. We argue that studying these exceptions of the principle of trust calibration might be critical for understanding long-term trust calibration in dynamic environments.

show abstract

The Influence of Trust Score on Cooperative Behavior

Cited by 13 publications

References 46 publications

Improving the performance of the intrusion detection systems by the machine learning explainability

Improving the performance of the intrusion detection systems by the machine learning explainability

Reinforcement Learning in Stock Trading

Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model

Contact Info

Product

Resources

About