Influence Diagrams Research Articles

A binary decision diagram lifted domain for analyzing program families

Many software systems today are highly configurable. They can produce a potentially large variety of related programs (variants) by selecting suitable configuration options (features) at compile time. Recently, specialized variability-aware (lifted, family-based) static analyses based on abstract interpretation have been developed. They allow analyzing all variants of a program family (or, any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. In effect, they produce precise analysis results for all individual variants. The elements of the underlying lifted analysis domain represent tuples (i.e. disjunction of properties), which maintain one property from an existing single-program analysis domain per variant. Nevertheless, explicit property enumeration in tuples, one by one for all variants, immediately yields to combinatorial explosion given that the number of variants can grow exponentially with the number of features. Therefore, such lifted analyses may be too costly or even infeasible for program families with a large number of variants. In this work, we propose a more efficient lifted static analysis of program families with Boolean features, where sharing is explicitly possible between analysis elements corresponding to different variants. This is achieved by giving a symbolic representation of the lifted analysis domain, which can efficiently handle disjunctive properties in program families. The elements of the new lifted domain are binary decision diagrams, where decision nodes are labeled with Boolean features and leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the abstract (property) domain for leaf nodes. To illustrate the potential of this representation, we have implemented a lifted static analyzer that uses a combination of forward and backward analyses for inferring numerical invariants and necessary preconditions of C program families. It uses APRON and BDDAPRON libraries for implementing the new lifted analysis domain. The APRON library, used for the leaves, is a widely accepted API for numerical abstract domains (e.g. polyhedra, octagons, intervals), while the BDDAPRON is an extension of APRON which adds the power domain of Boolean formulae and any APRON domain. An empirical evaluation on C benchmarks taken from SV-COMP and BusyBox indicates that our binary decision diagram-based approach is effective and outperforms the baseline tuple-based approach.

Risk Assessment and Management Workflow—An Example of the Southwest Regional Partnership

This paper summarizes the risk assessment and management workflow developed and applied to the Southwest Regional Partnership on Carbon Sequestration (SWP) Phase III Demonstration Project. The risk assessment and management workflow consists of six primary tasks, including management planning, identification, qualitative analysis, quantitative analysis, response planning, and monitoring. Within the workflow, the SWP assembled and iteratively updated a risk registry that identifies risks for all major activities of the project. Risk elements were ranked with respect to the potential impact to the project and the likelihood of occurrence. Both qualitative and quantitative risk analyses were performed. To graphically depict the interactions among risk elements and help building risk scenarios, process influence diagrams were used to represent the interactions. The SWP employed quantitative methods of risk analysis including Response Surface Method (RSM), Polynomial Chaos Expansion (PCE), and the National Risk Assessment Partnership (NRAP) toolset. The SWP also developed risk response planning and performed risk control and monitoring to prevent the risks from affecting the project and ensure the effectiveness of risk management. As part of risk control and monitoring, existing and new risks have been tracked and the response plan was subsequently evaluated. Findings and lessons learned from the SWP’s risk assessment and management efforts will provide valuable information for other commercial geological CO2 storage projects.

An integrated approach to testing and assessment of high aspect ratio nanomaterials and its application for grouping based on a common mesothelioma hazard

Here we describe the development of an Integrated Approach to Testing and Assessment (IATA) to support the grouping of different types (nanoforms; NFs) of High Aspect Ratio Nanomaterials (HARNs), based on their potential to cause mesothelioma. Hazards posed by the inhalation of HARNs are of particular concern as they exhibit physical characteristics similar to pathogenic asbestos fibres. The approach for grouping HARNs presented here is part of a framework to provide guidance and tools to group similar NFs and aims to reduce the need to assess toxicity on a case-by-case basis. The approach to grouping is hypothesis-driven, in which the hypothesis is based on scientific evidence linking critical physicochemical descriptors for NFs to defined fate/toxicokinetic and hazard outcomes. The HARN IATA prompts users to address relevant questions (at decision nodes; DNs) regarding the morphology, biopersistence and inflammatory potential of the HARNs under investigation to provide the necessary evidence to accept or reject the grouping hypothesis. Each DN in the IATA is addressed in a tiered manner, using data from simple in vitro or in silico methods in the lowest tier or from in vivo approaches in the highest tier. For these proposed methods we provide justification for the critical descriptors and thresholds that allow grouping decisions to be made. Application of the IATA allows the user to selectively identify HARNs which may pose a mesothelioma hazard, as demonstrated through a literature-based case study. By promoting the use of alternative, non-rodent approaches such as in silico modelling, in vitro and cell-free tests in the initial tiers, the IATA testing strategy streamlines information gathering at all stages of innovation through to regulatory risk assessment while reducing the ethical, time and economic burden of testing.

The Fatal Conceit of Foreign Intervention: Evidence from the Afghanistan Papers

The fatal conceit of foreign intervention refers to the limitations faced by governments using discretionary power to address perceived problems in foreign societies. Drawing on evidence from the “Afghanistan Papers”—a collection of internal government documents compiled by the Special Inspector General for the Afghanistan Reconstruction (SIGAR) and released in December 2019—we demonstrate how the failure of the U.S.-led intervention in Afghanistan to meet the stated objectives illustrates the fatal conceit of foreign intervention. We explore the limitations faced by foreign government interveners in three stages, reflecting distinct decision nodes within a means-ends framework in which the policies relating to foreign intervention are formulated and implemented.

Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity.

ObjectiveThe interpretation and clinical application of guidelines can be challenging and time-consuming, which may result in noncompliance to guidelines. The aim of this study was to convert the Dutch guideline for colorectal cancer (CRC) into decision trees and subsequently implement decision trees in an online decision support environment to facilitate guideline application.MethodsThe recommendations of the Dutch CRC guidelines (published in 2014) were translated into decision trees consisting of decision nodes, branches and leaves that represent data items, data item values and recommendations, respectively. Decision trees were discussed with experts in the field and published as interactive open access decision support software (available at www.oncoguide.nl). Decision tree validation and a concordance analysis were performed using consecutive reports (January 2016–January 2017) from CRC multidisciplinary tumour boards (MTBs) at Amsterdam University Medical Centers, location AMC.ResultsIn total, we developed 34 decision trees driven by 101 decision nodes based on the guideline recommendations. Decision trees represented recommendations for diagnostics (n = 1), staging (n = 10), primary treatment (colon: n = 1, rectum: n = 5, colorectal: n = 9), pathology (n = 4) and follow-up (n = 3) and included one overview decision tree for optimal navigation. We identified several guideline information gaps and areas of inconclusive evidence. A total of 158 patients’ MTB reports were eligible for decision tree validation and resulted in treatment recommendations in 80% of cases. The concordance rate between decision tree treatment recommendations and MTB advices was 81%. Decision trees reported in 22 out of 24 non-concordant cases (92%) that no guideline recommendation was available.ConclusionsWe successfully converted the Dutch CRC guideline into decision trees and identified several information gaps and areas of inconclusive evidence, the latter being the main cause of the observed disagreement between decision tree recommendations and MTB advices. Decision trees may contribute to future strategies to optimize quality of care for CRC patients.

Assessing the fluvial system resilience of the river Bacchiglione to point sources of pollution in Northeast Italy: a novel Water Resilience Index (WRI) approach

Modelling and evaluating the resilience of environmental systems has recently raised significant interest among both practitioners and researchers. However, it has not yet been used to measure the absorption and recovery capacities of a river subject to varying levels of pollution due to natural and anthropic sources of contamination within the basin. Fast worldwide population growth and climate change are contributing to an increased degradation status in surface water bodies and to a decreased efficiency of their natural self-purification processes. Decision-makers are, therefore, more and more encouraged to implement alternative management strategies focussed on improving the system resilience to current and future perturbations. To this end, a novel Water Resilience Index (WRI), based on different quality parameters, was developed, and it is here proposed to estimate the ability of the river Bacchiglione, located in Northeast Italy, absorb continuous and unpredictable changes due to potential effects of point sources of pollution, that is, urban and industrial wastewater, and still maintain its vital functions. This new index is integrated in a mathematical model, which represents the river as an influence diagram where the nodes are the gauged stations and the arcs are the fluvial reaches among the stations, to identify the river reaches in need of resilience improvement. In addition, in order to simplify the analytical procedure and lower the costs and times of the monitoring activities, a principal component analysis is also used, as it is able to reduce the number of the water quality parameters to be collected from the sampling stations, distributed along the main river, and thus to calculate a minimum WRI. The good agreement between the results obtained by both the original and minimum WRI shows the effectiveness of the proposed methodology. This approach could be applied to all basins with the same issues, and not just in the Italian case study here analysed, as it might be a valid tool to plan interventions and mitigation actions, protecting the resource from pollution risks and achieving environmental quality and Sustainable Development Goals both in the water bodies and their surrounding territories. In addition, this strategy could be integrated in the existing models supporting local decision-makers and administrators, aiming at increasing the resilience of urban and rural areas to pollution phenomena and facilitating the development of effective policies to reduce the impacts of global change on water quality.

Optimal firefighting to prevent domino effects: Methodologies based on dynamic influence diagram and mathematical programming

Fire is one of the most costly accidents in process plants due to the inflicted damage and the required firefighting resources. If the firefighting resources are sufficient, firefighting will include the suppression and cooling of all the burning units and exposed units, respectively. However, when the resources are inadequate, optimal firefighting strategies to answer “which burning units to suppress first and which exposed units to cool first?” would be essential to delay the fire spread until more resources become available.The present study demonstrates the application of two decision support techniques to optimal firefighting under uncertainty and limited resources: (i) Dynamic influence diagram, as an extension of dynamic Bayesian network, and (ii) mathematical programming. Both techniques are illustrated to be effective in identifying optimal firefighting strategies. However, unlike the dynamic influence diagram, the mathematical programming is demonstrated not to suffer from an exponential growth of decision alternatives, making it a more efficient technique in the case of large process plants and complicated fire spread scenarios.

Teaching conceptual models in engineering research with mathematical model approaches

In the field of social sciense generally uses a conceptual model as an initial hypothesis of research. But in the field of technology and engineering, objects of study or research can be modeled in a more appropriate form. This research proposes the application of mathematical modeling approaches in the process of designing conceptual models. Using this approach it is hoped that researchers and students will understand more accurately the interrelationships between variables so that appropriate solutions can be proposed. The diagram that we recommend are influence diagram with the enhancement using stock and flow diagram, and causal loop diagram. The modelling process can be supported by System Dynamics Software.

Hybrid Dec-POMDP/PID Guidance System for Formation Flight of Multiple UAVs

This paper proposes a guidance system with a new hybrid architecture for multiple fixed-wing unmanned aerial vehicles (UAVs) formation flight. The proposed architecture is hybrid in the sense of combining two control policies found in guidance systems: the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) policy and the well-known PID controller. The Dec-POMDP policy part enables every UAV to avoid collision with any other UAV from the formation. The PID part is activated for regions far from the collision risk zone and is implemented in such a way that each UAV is completely controlled by only its own data, independent of its neighbors. The major novelty of the proposed system, compared to other hybrid approaches, is its decentralized nature which favors overcoming the usual problems that come with centralization such as the high dependence on a central decision node, a leader, or a ground station. Inherited from both parts, the overall system performance is robust in noisy and uncertain environments. Simulations were extensively performed to show the effectiveness of the proposed guidance system in distinct scenarios.

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

The performance of the Prognostics and Health Management (PHM) depends both on the functioning of the measurement acquisition system and on the actual state of the system being monitored. The dependencies between these systems must be considered when developing optimal inspection and maintenance strategies. This paper presents a methodology to support the definition maintenance strategies for PHM-equipped industrial systems. The methodology employs influence diagrams when seeking to maximize the expected utility of system operation. The optimization problem is solved by mixed-integer linear programming, subject to budget and technical constraints. Chance constraints can be also included, for instance to curtail risks based on measures such as the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of system operation. The viability of the methodology is demonstrated by optimizing the inspection and maintenance strategy for a gas turbine equipped with PHM solution. The computation of the Value of Perfect Information (VoPI) provides additional insights on maintenance management.

Predicting War Outcomes Based on a Fuzzy Influence Diagram

Predicting or deducing war outcomes is the basis of military strategy formulation. Considering the high information uncertainty and complex influencing factors of modern wars, we applied a fuzzy influence diagram (FID) to realize the macroscopic prediction of war outcomes. In particular, we identified multidimensional factors that influence war outcomes in a relatively comprehensive and practical manner, thereby obtaining a relatively complete FID containing 24 nodes, with the value node “combat outcome” as the core. Subsequently, we performed a simulation analysis for a hypothetical war scenario to demonstrate the specific application and applicability of the proposed FID model. This study expands the prediction methods of war outcomes, and demonstrates that an FID can be feasibly applied in an uncertain battlefield environment to realize the prediction and risk assessment of war outcomes. Moreover, this approach can be used to analyze prewar decision making. The findings highlight that combat participants should attempt to further improve their military technology and combat effectiveness to gain combat advantage.

Balance Dynamic Clustering Analysis and Consensus Reaching Process With Consensus Evolution Networks in Large-Scale Group Decision Making

Large-scale group decision-making (LSGDM) solution is usually based on the clustering analysis process (CAP) and consensus reaching process (CRP). However, CAP and CRP can be contradictory since CAP is performed based on the differences between potentially small groups and CRP is conducted to improve the overall similarity of a large group. To balance CAP and CRP, a dynamic clustering analysis process (DCAP) based on consensus evolution networks is proposed. A clustering algorithm proposed based on community detection method can be used to handle the diverse network structures with dynamic consensus thresholds. The clustering validity based on the intracluster consensus levels in subgroups and the intercluster consensus level among subgroups is evaluated. Then, the DCAP after each feedback adjustment round in CRP is reanalyzed. In such a way, effective clustering can also be found after a satisfying consensus is reached. Finally, a case study shows the availability of this approach and comparative analyses are provided to highlight the advantages from both theoretical and numerical perspectives.

Roles of the Default Mode and Multiple-Demand Networks in Naturalistic versus Symbolic Decisions.

The default mode network (DMN) is often associated with representing semantic, social, and situational content of contexts and episodes. The DMN may therefore be important for contextual decision-making, through representing situational constraints and simulating common courses of events. Most decision-making paradigms, however, use symbolic stimuli and instead implicate cognitive control regions, such as the multiple demand (MD) system. This fMRI study aimed to contrast the brain mechanisms underlying decision-making based on rich naturalistic contexts or symbolic cues. While performing an ongoing task, 40 human participants (25 female) responded to different sounds. For one sound, the stimulus-response mapping was fixed; responses for the other sounds depended on the visual context: either lifelike scenes or letter symbols, varying across participants. Despite minimal behavioral differences between the groups, posterior DMN regions showed increased activity during context-dependent decision-making using the naturalistic scenes only, compared with symbolic cues. More anterior temporal and frontal DMN regions showed a different pattern, with sensitivity to the need for contextual control, but not to the type of context. Furthermore, in the scenes group, widespread DMN regions showed stronger representation of not just the context but also the sound whose significance it modulated. In comparison, the MD system showed strong univariate activity for every decision, but, intriguingly, somewhat reduced activity in the case of a scene-based but demanding context-dependent decision. Depending on context, we suggest, either DMN or MD regions may play a prominent role in selection and control of appropriate behavior.SIGNIFICANCE STATEMENT Contextual knowledge is widely believed to be important for guiding real-world goal-directed behavior. Much remains to be understood, however, regarding the underlying brain mechanisms. Using a novel paradigm to contrast decisions based on richly meaningful naturalistic scenes with decisions based on symbolic cues, we find that both multiple demand regions and default mode regions may contribute to the cognitive control of behavior. Rich semantic context enhances representation not just of the context itself, but also of the contents of the decision that it controls. Dependence of a decision on naturalistic context can also reverse the common pattern of multiple demand regions responding more, and default mode regions responding less, to more difficult decisions.

Probabilistic Model-Based Malaria Disease Recognition System

In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network (BN) model to predict the occurrences of malaria disease. The proposed BN model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table (CPT) also becomes more complex.

Consensus reaching with the externality effect of social network for three-way group decisions

Three-way group decisions provide an efficient method to settle complex and high risk decision-making problems. To obtain reasonable decision results that satisfy different backgrounds and knowledge of decision makers, it is necessary to design a proper consensus reaching process (CRP) for loss functions of decision-theoretic rough sets (DTRSs). Unlike existing researches, this paper not only extends the group relationship among decision makers to the social network, but also considers the externality of social trust network in group decision making. In light of this idea, we design a new CRP with the externality of social network for three-way group decisions. In the CRP, the adjustment of a decision maker who is persuaded by the moderator can influence other decision makers to accordingly adjust evaluations. Thus, by using the linkage externality influence among decision makers, we establish a two-stage mixed 0–1 linear optimization consensus model for the determination of loss functions of DTRSs. Then, based on Bayesian decision procedure, we construct a complete decision procedure for three-way group decisions with social network. Finally, we apply our proposed method to assess desert locust invasion areas and verify its validity.

Computing Stackelberg Equilibrium with Memory in Sequential Games

Stackelberg equilibrium is a solution concept that describes optimal strategies to commit: Player 1 (the leader) first commits to a strategy that is publicly announced, then Player 2 (the follower) plays a best response to the leader's commitment. We study the problem of computing Stackelberg equilibria in sequential games with finite and indefinite horizons, when players can play history-dependent strategies. Using the alternate formulation called strategies with memory, we establish that strategy profiles with polynomial memory size can be described efficiently. We prove that there exist a polynomial time algorithm which computes the Strong Stackelberg Equilibrium in sequential games defined on directed acyclic graphs, where the strategies depend only on the memory states from a set which is linear in the size of the graph. We extend this result to games on general directed graphs which may contain cycles. We also analyze the setting for approximate version of Strong Stackelberg Equilibrium in the games with chance nodes.

Structuring an Ontology for Mathematical Modelling Wastewater Flow through Porous Media

Single or multi-layer infiltration rate models describe water penetration into soil porous zone at a variety of comparable complexity levels. Commonly, those models are indicating a route leading from general to more specific case depending on the mathematical structure and the simulated ongoing phenomenon. For the purpose of wastewater flow quantification through porous media, an algorithmic procedure was developed which includes certain interconnected activity stages and decision nodes. Furthermore, a schematic cross shaped representation of the infiltration rate models’ ontology is presented within a route, leading from general to more specific models and evaluation criteria are introduced to determine highest ranking and thus the best model amid those placed on the horizontal axis of the cross shaped scheme, via a simple Multi-Criteria Analysis Decision Making.

Nadya Bair, The Decisive Network. Magnum Photos and the Postwar Image Market

Nadya Bair, The Decisive Network. Magnum Photos and the Postwar Image Market

ETAS: An Efficient Trust Assessment Scheme for BANs

A wireless body area network (WBAN) is a wireless network of wearable computing devices and intelligent physiological sensors. The intelligent physiological sensors collect and process sensitive data from the patient body. The security, reliability, and trustworthiness of sensitive data collected and processed by intelligent physiological sensors are critical due to its unique application domain. Moreover, consistent and reliable data gathering along with its transmission also plays a vital role in WBANs. Trust management (in BANs) has been found as a useful tool to improve cooperation among sensor nodes, security as well as reliability. The paper recommends a novel and efficient, lightweight trust assessment scheme (ETAS) suitable for health application domains and does not rely purely on any encryption technique. The primary purpose is to develop an exciting comprehensive, novel trust estimation framework for BANs to enhance reliability, dependability, security by isolating compromised (hotspot) nodes with great resource efficiency. ETAS incorporates several exclusive (unique) features like efficient trust evaluator, secure and attack resistant, along with competent trust aggregator function to achieve comprehensive trust score. The trust evaluator function is a multi-trust (communication trust, data trust, and energy trust) strategy to deal with severe internal security threats such as badmouthing attack, ballot-stuffing attack, sybil attack, traitor attack, etc. with less resource consumption. Moreover, ETAS incorporates both the success rate and misbehavior component during trust evaluation. The success rate record the number of successful/unsuccessful interactions among sensor nodes in terms of packet send/receive. The misbehavior component keeps records of current and past misbehavior of sensor nodes for effective decision making. Moreover, ETAS focus on the frequency of interaction among biomedical sensor nodes within a specified period to analyze their behavior for efficient trust decisions. Furthermore, ETAS incorporates temperature, data trust as well as the trust score of biomedical sensors to identify hotspot nodes in BSN. ETAS offers full flexibility to adjust the trust threshold, trust domain, reward, and punishment term according to system and application requirements. ETAS's efficiency is validated through several outcomes (MATLAB R2019a) along with theoretical analysis in terms of energy consumption, attack detection, mitigation, trust computation cost, and packet delivery ratio.

A bi-objective production-distribution problem in a supply chain network under grey flexible conditions

One of the main topics discussed in a supply chain is the production-distribution problem. Producing and distributing the products plays a key role in reducing the costs of the chain. To design a supply chain, a network of efficient management and production-distribution decisions is essential. Accordingly, providing an appropriate mathematical model for such problems can be helpful in designing and managing supply chain networks. Mathematical formulations must be drawn close to the real world due to the importance of supply chain networks. This makes those formulations more complicated. In this study, a novel multi-objective formulation is devised for the production-distribution problem of a supply chain that consists of several suppliers, manufacturers, distributors, and different customers. Also, a Mixed Integer Linear Programming (MILP) mathematical model is proposed for designing a multi-objective and multi-period supply chain network. In addition, grey flexible linear programming (GFLP) is done for a multi-objective production-distribution problem in a supply chain network. The network is designed for the first time to cope with the uncertain nature of costs, demands, and capacity parameters. In this regard, due to the NP-hardness and complexity of problems and the necessity of using meta-heuristic algorithms, NSGA-II and Fast PGA algorithm are applied and compared in terms of several criteria that emphasize the quality and diversity of the solutions.