Influence Diagrams Research Articles

Strategy Graphs for Influence Diagrams

An influence diagram is a graphical model of a Bayesian decision problem that is solved by finding a strategy that maximizes expected utility. When an influence diagram is solved by variable elimination or a related dynamic programming algorithm, it is traditional to represent a strategy as a sequence of policies, one for each decision variable, where a policy maps the relevant history for a decision to an action. We propose an alternative representation of a strategy as a graph, called a strategy graph, and show how to modify a variable elimination algorithm so that it constructs a strategy graph. We consider both a classic variable elimination algorithm for influence diagrams and a recent extension of this algorithm that has more relaxed constraints on elimination order that allow improved performance. We consider the advantages of representing a strategy as a graph and, in particular, how to simplify a strategy graph so that it is easier to interpret and analyze.

A Novel Social Network Group Decision-Making Method in a Quantum Framework

Social networks (SNs) have become popular as a medium for disseminating information and connecting like-minded people. They play a central role in decision-making by correlating the behaviors and preferences of connected agents. However, it is difficult to identify social influence effects in decision-making. In this article, we propose a framework of how to describe the uncertain nature of the social network group decision-making (SN-GDM) process. Social networks analysis (SNA) and quantum probability theory (QPT) are combined to construct a decision framework considering superposition and interference effects in SN-GDM scenarios. For the first time, we divide interference effects into symmetry and asymmetry. We construct an influence diagram, which is a quantum-like Bayesian network (QLBN), to model group decisions with interactions. We identify symmetry interference terms from Shapley value and asymmetry interference terms from trust value, respectively. The probability of an alternative is calculated through quantum probability theory in our influence diagram. The combination of QLBN model and social network could gain an understanding of how the group preferences evolve within SN-GDM scenarios, and provide new insights into SNA. Finally, an overall comparative analysis is performed with traditional SNA and other quantum decision models.

Industry 5.0 and the Circular Economy: Utilizing LCA with Intelligent Products

While the move towards Industry 4.0 has motivated a re-evaluation of how a manufacturing organization should operate in light of the availability of a new generation of digital production equipment, the new emphasis is on human worker inclusion to provide decision making activities or physical actions (at decision nodes) within an otherwise automated process flow; termed by some authors as Industry 5.0 and seen as related to the earlier Japanese Society 5.0 concept (seeking to address wider social and environmental problems with the latest developments in digital system, artificial Intelligence and automation solutions). As motivated by the EU the Industry 5.0 paradigm can be seen as a movement to address infrastructural resilience, employee and environmental concerns in industrial settings. This is coupled with a greater awareness of environmental issues, especially those related to Carbon output at production and throughout manufactured products lifecycle. This paper proposes the concept of dynamic Life Cycle Assessment (LCA), enabled by the functionality possible with intelligent products. A particular focus of this paper is that of human in the loop assisted decision making for end-of-life disassembly of products and the role intelligent products can perform in achieving sustainable reuse of components and materials. It is concluded by this research that intelligent products must provide auditable data to support the achievement of net zero carbon and circular economy goals. The role of the human in moving towards net zero production, through the increased understanding and arbitration powers over information and decisions, is paramount; this opportunity is further enabled through the use of intelligent products.

From conceptual model to conceptual framework: A sustainable business framework for community water supply businesses

Businesses must do more than safeguard their financial interests to survive in today’s market. Sustainability, or long-term viability, requires consideration of society, the environment, and the global community. However, community-based businesses cannot use some of the current components of the sustainable business framework because they were created for large corporations. Both quantitative and qualitative approaches are used in this study. First, quantitative methods were used to develop a conceptual model for the organization’s current needs using the PPT, the TOE, and an Expectation Confirmation Theory (ECT). A simple random sampling method was used to gather the data, with a sample size of 14 villages. Using a five-point Likert scale, the researcher gathered data from 2,584 households and collected 627 valid responses. After that, descriptive statistics were used to describe the data (frequency distributions, percentages, averages, medians, and standard deviations), and PLS-SEM was used to investigate the interactions between variables and launch the conceptual model using partial least squares (PLS) path modeling. First and foremost, qualitative through Interactive Qualitative Analysis (IQA). There are two ways to create a congested SID and an uncluttered SID: the Affinity Relationship Table (ART), the mapping of the Inter-Relationship Diagram (IRD), and the system influence diagram. According to the study, sustainable community water supply businesses are the primary driver. The conceptual framework presented in this paper is consistent with the results of the combination of quantitative and qualitative methods and the current constraints placed on community water supply businesses to thrive.

Abstract 14830: Comparative Cost-Effectiveness of Cardioprotective Glucose-Lowering Therapies for Type 2 Diabetes in a Middle-Income Country: A Bayesian Network Model

Introduction: Unprecedented economic and social burden has expanded with the steady increase in Type 2 Diabetes (T2D) prevalence, particularly in low- and middle-income countries. Hypothesis: We hypothesize that novel anti-diabetic therapies such as SGLT2i or GLP1-RA would figure as more cost-effective among T2D subjects. Methods: Electronic databases were searched between August and April 2021. Inclusion criteria were: randomized clinical trials with T2D; articles published in English; double-blind, phase 3 or 4 trials; ≥100 patients per arm; follow-up >24 weeks; and the pre-specified endpoints: death from any cause, major adverse cardiovascular events, and hospitalization for heart failure. Two prospective cohorts of T2D patients were used to feed the Markov Influence Diagram transition probabilities. Bayesian Networks and Markov Influence Diagram were used to estimate the incremental cost-effectiveness ratios (ICER) per disease-adjusted life years [DALYs] averted. The perspective of the Brazilian public health system was considered for costs and values were converted to international dollars (Int$). Results: We selected a total of 157 trials generating a sample size of 684,389 patient-years. Modeling was based on a primary and secondary prevention cohort of 851 and 1,337 patients, respectively. SGLT2i [HR:0.81 (95%CI: 0.69; 0.96)], GLP-1RA [HR:0.79 (95%CI: 0.67; 0.94)], and pioglitazone [HR:0.73 (95%CI: 0.59; 0.91)] reduced the relative risk of non-fatal cardiovascular events. In primary prevention, pioglitazone resulted in 0.2339 DALYs averted, with ICER of Int$7,082 (95%CI: 4,521; 10,770) per DALY averted, when compared to standard care. SGLT2i and GLP-1RA also increased the effectiveness (0.261 and 0.259 DALYs averted, respectively) but with higher ICERs [Int$12,061 (95%CI: 7,227; 18,121) and Int$29,119 (95% CI:23,811; 35,367) per DALY averted, respectively]. In secondary prevention, the three classes were cost-effective at a maximum willingness to pay threshold of Int$26,700. In both scenarios, however, pioglitazone had the highest probability of being cost-effective. Conclusions: In a middle-income country, pioglitazone presented a higher probability of being cost-effective followed by SGLT2i and then GLP-1RA.

Timing and necessity of staging imaging in clinical stage II cutaneous melanoma: Cost-effectiveness and clinical decision analysis

BackgroundPreoperative imaging in clinical stage II melanoma is not indicated per National Comprehensive Cancer Network (NCCN) guidelines but remains common in clinical practice. MethodsPatients presenting with cutaneous clinical stage II melanoma from 2007 to 2019 were retrospectively reviewed. A clinical decision analysis with cost data was designed to understand ideal practice patterns in managing stage II melanoma, with pre-versus selective post-operative imaging as the initial decision node. ResultsThere were 277 subjects included, and 143 underwent preoperative imaging (49.5%). This changed management (i.e. no surgery) in one patient (0.4%). Overall, 16 patients had additional findings on imaging (5.8%). Upfront surgery with selective postoperative imaging was a more cost-effective strategy than routine performance of preoperative imaging, with savings of $1677 per patient. ConclusionPreoperative imaging is a low yield, costly approach for patients with clinical stage II melanoma with minimal impact on the decision to proceed with surgical management.

A Risk Treatment Strategy Model for Oil Pipeline Accidents Based on a Bayesian Decision Network Model.

Risk treatment is an effective way to reduce the risk of oil pipeline accidents. Many risk analysis and treatment strategies and models have been established based on the event tree method, bow-tie method, Bayesian network method, and other methods. Considering the characteristics of the current models, a risk treatment strategy model for oil pipeline accidents based on Bayesian decision network (BDNs) is proposed in this paper. First, the quantitative analysis method used in the Event-Evolution-Bayesian model (EEB model) is used for risk analysis. Second, the consequence weights and initial event likelihoods are added to the risk analysis model, and the integrated risk is obtained. Third, the risk treatment strategy model is established to achieve acceptable risk with optimal resources. The risk treatment options are added to the Bayesian network (BN) risk analysis model as the decision nodes and utility nodes. In this approach, the BN risk analysis model can be transformed into a risk treatment model based on BDNs. Compared to other models, this model can not only identify the risk factors comprehensively and illustrate the incident evolution process clearly, but also can support diverse risk treatment strategies for specific cases, such as to reduce the integrated risk to meet acceptable criterion or to balance the benefit and cost of an initiative. Furthermore, the risk treatment strategy can be updated as the risk context changes.

Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model

SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.

Distributed Information Exchange With Low Latency for Decision Making in Vehicular Fog Computing

Traditional decision making in a vehicle network includes uploading vehicle sensing data to faraway cloud platforms and then returning correlated results to the vehicles. The data have features of large quantity and high redundancy, which causes high communication latency and vehicle applications to deteriorate. Vehicular fog computing (VFC) is a new network paradigm that uses local fog nodes for decision making. However, how to achieve distributed information exchange with low latency is a challenging issue because the connectivity of the vehicle network is low due to vehicle mobility. In this article, a distributed information exchange scheme with low latency in VFC is proposed. First, considering the frequent changes in vehicle positions and the randomness in driving routes, public transportation facilities with a wider driving range such as buses and taxis are used as fog nodes to increase the probability of uploading data. Then, the fog nodes should dynamically adjust the data sampling frequency according to the time-space correlation of the data to ensure that only nonredundant data are received. To minimize the interruption latency caused by accidents during an exchange, the fog nodes evaluate and predict connection states among them and their neighboring vehicles when establishing exchanges. If a fog node finds that a vehicle cannot complete information exchange because the vehicle may move outside its communication range in a future period, it will recalculate an optimized relay route for the vehicle by using mixed integer programming. Theoretical analysis and simulation results show that compared with the existing work, the proposed scheme can completely exchange all vehicle data with lower latency.

Teaching Probabilistic Graphical Models with OpenMarkov

OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC.

Using decision analysis to achieve the desired image of the future

The article is devoted to the methodology and features of the scientific direction of decision analysis, which make it essential for the state strategy development in order to achieve the desired image of the future. The history of decision analysis is studied, the basics of the scientific direction are considered: systems analysis, decision theory, probabilistic analysis and cognitive psychology. Organizations that actively use the methodology and most popular computer programs for the practical implementation of decision analysis methods are represented. The domestic contribution to the decision analysis is shown. A promising sub-direction which allows strategy development in conditions of conflicting objectives (multicriteria decision analysis) is presented. The modern methodology of decision analysis is considered: the system of values and objectives of the organization, decision context, identification and evaluation of the mathematical model elements, graphical representation of the model using influence diagrams and decision trees, the procedure of mathematical modeling, optimal strategy determination based on the criterion of maximizing/minimizing the objective function value and the criterion of risk profile dominance and sensitivity analysis methods: one-way and two-way sensitivity analysis, probability sensitivity analysis and strategy dominance analysis. The article concludes that the scientific direction of decision analysis has a number of advantages that make it suitable for use in the field of state strategy development.

Predicting Plasmodium falciparum infection status in blood using a multiplexed bead-based antigen detection assay and machine learning approaches.

Plasmodium blood-stage infections can be identified by assaying for protein products expressed by the parasites. While the binary result of an antigen test is sufficient for a clinical result, greater nuance can be gathered for malaria infection status based on quantitative and sensitive detection of Plasmodium antigens and machine learning analytical approaches. Three independent malaria studies performed in Angola and Haiti enrolled persons at health facilities and collected a blood sample. Presence and parasite density of P. falciparum infection was determined by microscopy for a study in Angola in 2015 (n = 193), by qRT-PCR for a 2016 study in Angola (n = 208), and by qPCR for a 2012-2013 Haiti study (n = 425). All samples also had bead-based detection and quantification of three Plasmodium antigens: pAldolase, pLDH, and HRP2. Decision trees and principal component analysis (PCA) were conducted in attempt to categorize P. falciparum parasitemia density status based on continuous antigen concentrations. Conditional inference trees were trained using the known P. falciparum infection status and corresponding antigen concentrations, and PCR infection status was predicted with accuracies ranging from 73-96%, while level of parasite density was predicted with accuracies ranging from 59-72%. Multiple decision nodes were created for both pAldolase and HRP2 antigens. For all datasets, dichotomous infectious status was more accurately predicted when compared to categorization of different levels of parasite densities. PCA was able to account for a high level of variance (>80%), and distinct clustering was found in both dichotomous and categorical infection status. This pilot study offers a proof-of-principle of the utility of machine learning approaches to assess P. falciparum infection status based on continuous concentrations of multiple Plasmodium antigens.

Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details

Dynamic Bayesian network (DBN) models are widely used for structural risk analysis because of their powerful parameter learning ability and capability of simplifying the problem by parsing it using nodes and arcs. However, these models update the reliability after inspection and maintenance (I&M) in a manner different than that specified by the aircraft structural integrity program (ASIP). In this study, a DBN model is developed to correctly represent the ASIP method. The model updates the crack length distribution after I&M based on the nondestructive testing (NDT) reliability and repair crack length distribution. The nodes for inequality and equality data are explicitly represented in the DBN, which correspond to the crack length vs. signal amplitude and noise characteristics of the NDT system. The proposed model overcomes drawbacks of existing models—initial overestimation and final underestimation—by appropriately considering the repair crack length distribution. Specifically, a decision node is used, which records the fraction of the crack length distribution removed after inspection. Furthermore, a method for constructing conditional probability tables is presented. The proposed model is applied to the in-service fatigue problem for a J85 engine compressor rotor blade. The findings demonstrate that the proposed model can be used in a wide range of applications.

Negotiating an Illicit Economy in the Time of COVID-19: Selling and Buying Dilemmas in the Lives of People Who Use Drugs in Scotland.

The impact of COVID-19 itself and societal responses to it have affected people who use drugs and the illicit drug economy. This paper is part of a project investigating the health impacts of COVID-19 related control measures on people who use drugs in Scotland. It examines their roles and decisions as economically situated actors. It does this within a moral economy perspective that places economic decisions and calculations within a context of the network of social obligations and moral decisions. The paper uses a mixed methods approach, reporting on a drug trend survey and in-depth interviews with people who use drugs. It finds they were affected by restrictions in the drug consumption context and changes in the supply context, both in terms of what was supplied and changes in the relationship between sellers and buyers. Face to face selling became more fraught. Participants in more economically precarious circumstances were faced with dilemmas about whether to move into drug selling. The double impact of loss of income and reduced access to support networks were particularly difficult for them. Despite the perception that the pandemic had increased the power of sellers in relation to their customers, many full-time sellers were reported to be keeping their prices stable in order to maintain their relationships with customers, instead extending credit or adulterating their products. The effect of spatial controls on movement during the pandemic also meant that the digital divide became more apparent. People with good access to digital markets and easy drug delivery through apps were in a better position to manage disruption to drug sales contexts. We make recommendations in relation to how policy can respond to the interests of people who use drugs in a pandemic.

Behavior Trees for Smart Robots Practical Guidelines for Robot Software Development

Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.

Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios

The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja’s simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.

Fuzzy Ranking Algorithm with Seagull Optimization-Based Decision Tree for Short-Term/Long-Term Rainfall Prediction

An exact rainfall prediction is a major challenge for agriculture subordinate nations for evaluating the productivity of crop, utilization of water resources and preplanning of water assets. Besides, because of different climate nature, rainfall prediction system cannot execute well for short-term and long-term rainfall prediction. Thus, to enhance the accuracy of short-term and long-term rainfall prediction, hybrid machine learning techniques are used in this approach. At first, we present fuzzy ranking algorithm to select the optimal subset of features. Using the selected features, short-term and long-term rainfalls are predicted by presenting optimized Decision Tree (DT). The decision node or upper level of the DT is chosen optimally using seagull optimization algorithm (SOA). Results of the article prove that the proposed rainfall prediction model obtains better accuracy than the existing prediction models.

Integrated framework studying contribution of information system to firm performance

The main purpose of this paper is to study the correlation between information system (IS) success and firm performance based on two evaluation models already construct. The first model allowed to define the criteria and sub-criteria for evaluating the firm performance and the second model consisted of evaluating the IS success. Our contribution is to formalize a decision-making process based on the criteria of the two models as well as the weights generated by the implementation of the analytic hierarchy process (AHP) method to construct the influence diagrams that will allow us to trace the causal links between the two models. This approach has been implemented in three sectors chosen according to their use of information systems. The results obtained confirmed that the evaluation models are sectorial and therefore even the influence diagrams, hence the difficulty of studying the contribution of the IS success in achieving the firm performance with a general and generic approach.

Power of Heuristics to Improve Health Information Technology System Design

Abstract Background Clinical decision-making can be prone to error if health system design does not match expert clinicians' higher cognitive skills. There is a gap in understanding the need for the importance of heuristics in clinical decision-making. The heuristic approach can provide cognitive support in designing intuitive health information systems for complex cases. Objective We explored complex decision-making by infectious diseases (ID) clinicians focusing on fast and frugal heuristics. We hypothesized that ID clinicians use simple heuristics to understand complex cases using their experience. Methods The study utilized cognitive task analysis and heuristics-based decision modeling. We conducted cognitive interviews and provided clinicians with a fast-and-frugal tree algorithm to convert complex information into simple decision algorithms. We conducted a critical decision method–based analysis to generate if–then logic sentences from the transcript. We conducted a thematic analysis of heuristics and calculated the average time to complete and the number of crucial information in the decision nodes. Results A total of 27 if–then logic heuristics sentences were generated from analyzing the data. The average time to construct the fast-and-frugal trees was 1.65 ± 0.37 minutes, and the average number of crucial pieces of information clinicians focused on was 5.4 ± 3.1. Conclusion Clinicians use shortcut mental models to reduce complex cases into simple mental model algorithms. The innovative use of artificial intelligence could allow clinical decision support systems to focus on creative and intuitive interface design matching the higher cognitive skills of expert clinicians.

A Complete Criterion for Value of Information in Soluble Influence Diagrams

Influence diagrams have recently been used to analyse the safety and fairness properties of AI systems. A key building block for this analysis is a graphical criterion for value of information (VoI). This paper establishes the first complete graphical criterion for VoI in influence diagrams with multiple decisions. Along the way, we establish two techniques for proving properties of multi-decision influence diagrams: ID homomorphisms are structure-preserving transformations of influence diagrams, while a Tree of Systems is a collection of paths that captures how information and control can flow in an influence diagram.