Collaborative Human Decision Making With Heterogeneous Agents

Geng, Baocheng; Cheng, Xiancheng; Brahma, Swastik; Kellen, David; Varshney, Pramod K.

doi:10.1109/tcss.2021.3098975

Cited by 9 publications

(7 citation statements)

References 41 publications

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“…This article examines and supports the use of consensus techniques based on the PIS learning process using numerical examples and simulated testing sets. Although research on signal processing and information fusion in this area is still in its infancy, there is a substantial body of work in cognitive psychology that models human decision-making Geng et al (2022). Researchers look at an issue that includes local decisions made by humans, a fusion center, and distributed detection (FC).…”

Section: Detailed Review Of Existing Decision Recommendation Modelsmentioning

confidence: 99%

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Gonnade,

Ridhorkarb

2024

Multidiscip. Rev.

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Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for functionality-specific use. Therefore, they tend to validate multiple process models, which increase deployment time, cost and complexity levels. To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. Fuzzy logic, analytical hierarchical processing (AHP), the technique for order performance by similarity to ideal solution (TOPSIS), and their variants are highly useful for the recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models and functionality-specific models for their deployments. To further assist in the model selection process, this paper compares the reviewed models in terms of their computational complexity, recommendation efficiency, delay needed for recommendation, scalability and contextual accuracy. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This paper also evaluates a novel decision recommendation rank metric (DRRM), which combines these parameters, to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing the performance of their decision recommendations under real-time scenarios.

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Section: Detailed Review Of Existing Decision Recommendation Modelsmentioning

confidence: 99%

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Gonnade,

Ridhorkarb

2024

Multidiscip. Rev.

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“…The authors in [1], [2] showed that adding human inputs may or may not improve the overall performance of humansensor networks, and they derived the conditions under which performance is improved. Furthermore, collaborative decision-making in multi-agent systems was investigated when the rationality of participating humans is modeled using prospect theory [6]- [9]. To a large extent, the literature on human-machine collaborative networks has not considered the distributed nature and the openness of wireless networks in which the physical sensors deployed in the network are low-cost, insecure, and vulnerable to various attacks, e.g., jamming [16], wiretap, spoofing [16]- [19] and Byzantine attacks [20]- [25].…”

Section: Introductionmentioning

confidence: 99%

Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks

Chen¹,

Geng²,

Han³

et al. 2023

Preprint

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This paper proposes a belief-updating scheme in a human-machine collaborative decision-making network to combat Byzantine attacks. A hierarchical framework is used to realize the network where local decisions from physical sensors act as reference decisions to improve the quality of human sensor decisions. During the decisionmaking process, the belief that each physical sensor is malicious is updated. The case when humans have side information available is investigated, and its impact is analyzed. Simulation results substantiate that the proposed scheme can significantly improve the quality of human sensor decisions, even when most physical sensors are malicious. Moreover, the performance of the proposed method does not necessarily depend on the knowledge of the actual fraction of malicious physical sensors. Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced.

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“…However, many classical results in decentralized detection assume that agents’ observations are independent, conditioned on the underlying hypothesis. This assumption fails to hold in many of these recent applications, such as human decision-making [ 5 ], sensor networks with correlated observations [ 6 ], and quorum sensing in microbial communities [ 7 ]. Unfortunately, the problem of decentralized detection with correlated observations is NP-Hard [ 8 ], and many of the classical results are not applicable in this case (for examples, see [ 9 , 10 , 11 ]).…”

Section: Introductionmentioning

confidence: 99%

Joint Detection and Communication over Type-Sensitive Networks

Shaska,

Mitra

2023

Entropy

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Due to the difficulty of decentralized inference with conditional dependent observations, and motivated by large-scale heterogeneous networks, we formulate a framework for decentralized detection with coupled observations. Each agent has a state, and the empirical distribution of all agents’ states or the type of network dictates the individual agents’ behavior. In particular, agents’ observations depend on both the underlying hypothesis as well as the empirical distribution of the agents’ states. Hence, our framework captures a high degree of coupling, in that an individual agent’s behavior depends on both the underlying hypothesis and the behavior of all other agents in the network. Considering this framework, the method of types, and a series of equicontinuity arguments, we derive the error exponent for the case in which all agents are identical and show that this error exponent depends on only a single empirical distribution. The analysis is extended to the multi-class case, and numerical results with state-dependent agent signaling and state-dependent channels highlight the utility of the proposed framework for analysis of highly coupled environments.

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Collaborative Human Decision Making With Heterogeneous Agents

Cited by 9 publications

References 41 publications

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks

Joint Detection and Communication over Type-Sensitive Networks

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