Defensible Decisions Research Articles

The critical thinking approach to tactical development in team sports: a review of the work of Jean Francis Gréhaigne

ABSTRACT Background: Critical thinking underlies tactical ability, and in the sporting context, it is known as reflective thinking that is used to make reasonable and defensible decisions in movement tasks. Critical thinking and sports tactics can then be described as analytical processes that involve solving situations in a reflective way. It is through experiences and discussions around experiences that more meaningful learning is generated (Godbout and Gréhaigne 2022). If we want to develop the tactical ability of players and, therefore, their decision-making ability, we should try to improve their critical thinking skills. Purpose: The purpose of this study was to review French Professor Jean Francis (J.F.) Gréhaigne's research on tactical development in team sports from a critical thinking perspective to enhance player decision-making. Gréhaigne, a professor in the Department of Sports Science and Physical Activity at the Institute for Teacher Education of Franche-Comté in Besancon, France, has dedicated his research career to identifying the elements associated with the teaching and pedagogy of team sports. His work is particularly noteworthy in that it proposes that critical thinking underlies tactical ability; it means that before a motor action, there must be an analysis of the situation, and this analysis must be explicit for the current moment. Method: A reflexive thematic analysis was conducted using electronic databases of research articles published before September 2021. The databases utilized were SCOPUS and Google Scholar. The search did not target any exclusive journal but focused on articles where Professor J.F. Gréhaigne was the lead or co-author, specifically related to tactical development and critical thinking in team sports. Although the search was primarily conducted in English, French works were also considered due to the author’s linguistic capabilities and the relevance of Professor Gréhaigne's contributions. A total of twenty articles were included in the documentary analysis. Results: According to the contributions of Professor J.F. Gréhaigne, it is possible to recognize that the proposals based on tactical development in team sports arose from the need, mainly, to identify a pedagogical didactic process that sought to develop analytical abilities in line with the reality of the game. Recognizing the game as a dynamic, chaotic, and uncertain system reveals that the way in which its development should be approached should be far from rigid pedagogical structures. Conclusions: Critical thinking is always essential for team sports players during the game. Pedagogical proposals should focus on developing players’ critical awareness and analytical capacity so that they can solve spontaneous situations on their own. This study is the first reflexive thematic analysis of the work developed by Professor J.F. Gréhaigne related to critical thinking and tactics in team sports. The findings of this study provide significant information and directions for the future development of tactical skills in team sports.

Saudi Arabia's Foreign Policy Under MBS: Transition from Defensive Realism to Offensive Realism (2016-2023)

This study seeks to elucidate the underlying factors driving Saudi Arabia’s shift from defense realism to offensive realism, focusing on MBS’s authoritarian rule and structural reforms aimed at minimizing bureaucratic hurdles in security and defense decision-making. The research examines the kingdom’s transition from conservative foreign policies to a more activist stance, illustrated by its support for Palestine and its adoption of non-conventional approaches to address emerging challenges. Adopting the John Scott document analysis model and utilizing secondary data, this study delves into the nuances of Saudi Arabia’s foreign policy shift under MBS’s leadership. It examines how his leadership style, combined with Vision 2030 goals and objectives, reflects a post-ideologically motivated transition that seeks to assert power and influence in the Middle East. The research explores the potential implications of this shift on regional security, including the Yemen civil war, balance of power realignment, and Saudi Arabia’s relationships with Western partners.

Catch the Cyber Thief: A Multi-Dimensional Asymmetric Network Attack–Defense Game

This paper presents a novel multi-dimensional asymmetric game model for network attack–defense decision-making, called “Catch the Cyber Thief”. The model is built upon the concept of partially observable stochastic games (POSG) and is designed to systematically incorporate multi-dimensional asymmetry into network attack–defense problems. The attack agent is called a “thief” who wants to control a key host by exploring the unfamiliar network environment, and the defense agent is called a “police” who needs to catch the opponent before its goal is accomplished. The results indicate that the asymmetry of network attack and defense is not only influenced by attack and defense strategies but also by spatio-temporal factors such as the attacker’s initial position, network topology, and defense lag time. In addition, we have found that there may exist the “attack rhythm,” which makes “how to maintain a good attack rhythm” and “how to generate a robust defense strategy against different attackers” worth exploring. Compared with existing attack–defense game models, our game model can better generate a direct mapping relationship with real elements, enabling us to understand network attack and defense interactions better, recognize security risks, and design defense strategies that can directly serve real-world decision-making.

Machine learning embedded hybrid MCDM model to mitigate decision uncertainty in transport safety planning for OAS countries

Providing defensible decisions is a prerequisite for methodologies of multi-criteria decision-making (MCDM) activities, and this is especially true for socio-economic analysis in public sector. This study proposes an all-in-one MCDM model with machine learning algorithms. The model integrates the method based on the removal effects of criteria (MEREC), combined compromise solution (CoCoSo), and density-based spatial clustering of applications with noise (DBSCAN), i.e., MEREC–CoCoSo–DBSCAN. In particular, the uniform manifold approximation and projection (UMAP) is implanted in DBSCAN to reduce the data dimensionality, and the k-nearest neighbors (KNN) algorithm is embedded to determine the inflection points (ɛ) and minPts in the data. This counters the inherent model failure of DBSCAN in dealing with high-dimensional data and eliminates the requirement for manual intervention in the model procedure, thereby fully avoiding potential human error and automating the computing process. A case study on benchmarking transport safety systems for member countries of the Organization of American States (OAS) demonstrates the reliability, adaptability, and efficiency of the proposed model. It moreover reflects its feasibility in resolving real-life socio-economic issues by offering valuable insights and potential solutions in economic investment and funding allocation in regard to transport safety strategy. Overall, this study provides government officials, managers, and policymakers with a valuable tool for handling MCDM activities in socio-economic development with considerable practicality and credibility.

Network defense decision-making based on deep reinforcement learning and dynamic game theory

Network defense decision-making based on deep reinforcement learning and dynamic game theory

Using Wargaming to Model Cyber Defense Decision-Making: Observation-Based Research in Locked Shields

Defensive Cyber Operations (DCO) in complex environments, such as cyber wargames, require in-depth cybersecurity knowledge and the ability to make quick decisions. In a typical DCO, execution rarely follows a pre-planned path because of extensive adversary influence, challenging an already complex decision-making environment. Decision-making models have been extensively studied from perspectives of military operations and business management, but they are not sufficiently researched in the context of cyber. This paper responds to this need by examining the decision-making models of DCO leaders in a live-fire wargame environment. This study was conducted by observing leaders of cyber operations during the world's largest live-fire cyber exercise, NATO Locked Shield 2023. In this exercise, the blue teams plan and execute their defensive cyber operation in a realistic operational environment, while the red team conducts attacks against the defended environment. The large-scale, wargaming-style environment of Locked Shield is one of the best environments for modelling DCO decision-making models; in this exercise, the DCO is broad and multi-faceted, a perspective which cannot be achieved in a typical capture-the-flag competition or a single security incident. DCO leaders must be able to manage two distinct decision-making processes with different sets of required skills to be successful in the mission. While the primary process relates to the execution and evolution of the pre-designed plan with traditional operational leadership skills, the secondary process deals with unplanned and deliberately caused cyber-related events that require a deep understanding of cybersecurity. In this respect, the main contribution of this research is the constructed decision-making model of the DCO leader. This model is based on observations collected and presented in the context of multiple well-known decision-making frameworks. This model can be further used to train future DCO leaders and assess artificial intelligence's usability to support and automate decision-making in such operations.

Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation-based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution-based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.

The reliability of the College of Intensive Care Medicine of Australia and New Zealand “Hot Case” examination

BackgroundHigh stakes examinations used to credential trainees for independent specialist practice should be evaluated periodically to ensure defensible decisions are made. This study aims to quantify the College of Intensive Care Medicine of Australia and New Zealand (CICM) Hot Case reliability coefficient and evaluate contributions to variance from candidates, cases and examiners.MethodsThis retrospective, de-identified analysis of CICM examination data used descriptive statistics and generalisability theory to evaluate the reliability of the Hot Case examination component. Decision studies were used to project generalisability coefficients for alternate examination designs.ResultsExamination results from 2019 to 2022 included 592 Hot Cases, totalling 1184 individual examiner scores. The mean examiner Hot Case score was 5.17 (standard deviation 1.65). The correlation between candidates’ two Hot Case scores was low (0.30). The overall reliability coefficient for the Hot Case component consisting of two cases observed by two separate pairs of examiners was 0.42. Sources of variance included candidate proficiency (25%), case difficulty and case specificity (63.4%), examiner stringency (3.5%) and other error (8.2%). To achieve a reliability coefficient of > 0.8 a candidate would need to perform 11 Hot Cases observed by two examiners.ConclusionThe reliability coefficient for the Hot Case component of the CICM second part examination is below the generally accepted value for a high stakes examination. Modifications to case selection and introduction of a clear scoring rubric to mitigate the effects of variation in case difficulty may be helpful. Increasing the number of cases and overall assessment time appears to be the best way to increase the overall reliability. Further research is required to assess the combined reliability of the Hot Case and viva components.

Global trends in applying decision science in mangrove restoration: are we missing some dimensions?

Mangrove restoration involves complex decision-making processes and can often lack adequate stakeholder engagement and empowerment to inform effective restoration outcomes. Decision science has the potential to overcome such challenges by facilitating structured approaches to make informed, participatory, and defensible decisions through the use of decision-support frameworks and tools. In this paper, we conduct a bibliometric and scoping review of peer-reviewed scientific literature to identify how decision science has been applied to advise mangrove restoration efforts and the extent to which social, economic, and ecological variables have been used to inform these studies. We find a steady increase in the number of mangrove restoration studies that employ decision science from 1990 to 2022, with an annual growth rate of 4.4%. We identified over 300 decision support tools used in mangrove restoration literature. Spatial data was the most documented tool to support mangrove studies. We also find that most papers focused on ecological variables (85.5%), while social (27.2%) and economic (15.1%) variables received less attention. Studies that used decision support frameworks were also sparse, where the most featured framework was Systematic Conservation Planning. The number of studies explicitly incorporating social and economic variables was limited, and there was also a scarcity of studies incorporating variables associated with governance. Regardless of the dominance of ecological variables, this review highlights a shift towards incorporating social dimensions into decision tools, emphasizing concepts like vulnerability to climate change, ecosystem services, and social benefits. Collaboration among countries, scientists, and practitioners is crucial to operationalise a socio-ecological framework within decision science. Enhancing the utility of scientific research for practitioners remains a critical goal in addressing the challenges faced by mangrove restoration projects globally.

A method of network attack-defense game and collaborative defense decision-making based on hierarchical multi-agent reinforcement learning

Faced with the challenges of security strategy design brought about by the complexity of attack behavior and the dynamism of network structure, dynamic hierarchical intelligent defense methods have shown their effectiveness. However, in complex network environments, their application requires a higher level of coordination mechanisms. Therefore, this paper proposes a hierarchical multi-agent reinforcement learning network attack and defense game and cooperative defense decision-making method, which autonomously and efficiently completes the formulation of defense strategies and defense behavior responses. Firstly, we construct a Stackelberg hypergame model of cyberspace conflicts, and under the condition of information loss, characterize the multi-layer dynamic defense coordination response mechanism. Secondly, By utilizing a hierarchical multi-agent reinforcement learning method as the driving force for game evolution, we sequentially solve the Nash equilibrium of the game, and form a dynamic autonomous defense strategy. Finally, we construct a hierarchical multi-agent reinforcement learning framework, which decouples the defense decision problem, reduces the dimension of the defense action space and the exploration difficulty of the strategy space, and learns coordinated defense strategies more efficiently. We used the CybORG (Cyber Operations Research Gym) environment for simulation. We compared and analyzed the autonomously generated cyber defense strategies with other related works, verifying the superior coordination performance of our method in defense strategy generation and control.

Deriving optimal defensive resource deployment of complex system-of-systems for risk response considering cascading effect

As disruptions happen with increasing frequency, over-deployment or insufficient deployment can lead to inefficiency or defensive failure for security risk management. Therefore, it is necessary to deploy appropriate defensive resources so as to defend a complex system-of-systems (SoS). Component heterogeneity and cascade effect must be considered in the high-risk environment. To that end, a novel security defensive deployment decision model is proposed in this research considering cooperation and non-cooperation. We formulate a maximum utility model to figure out the optimal defensive deployment of component in the case of independent defence. In addition, the complex SoS’s optimal defensive behaviour is calculated under all components work together to fulfil the demand and complete the mission. We also compare two scenarios and draw that the utility of coordinated defence is greater than that of independent defence in a SoS. The proposed model is employed in a weapon equipment SoS, elaborating how the SoS operators should operate against disruptive events.

The Concept of Total Defense Options of the Republic of Serbia

With the current National Security Strategy, the Republic of Serbia has adopted the total defense concept to respond to the proclaimed military neutrality. The decision in question, among other things, affects the direction of the overall defense capability of the state and society as a whole. There are several fundamentally important issues in which a choice must be made. One of the essential questions is which tasks are best performed under the auspices of the Armed Forces and which should be assigned to civilian actors. In other words, a clear division of responsibilities between the civilian and Armed Forces involved in total defense is necessary. On the other hand, the Republic of Serbia has a negative legacy that dates back to the conflict on the territory of the former state, as well as certain decisions that contradict the decision to develop a total defense and confident decisions that have a direct impact on the adopted concept of national defense. The paper presents the possible content of the total defense of the Republic of Serbia through the analysis of the concept on the examples of individual countries. In this connection, it should be noted that the Defense Strategy of the Republic of Serbia had to expand the content of the total defense concept. This is necessary not only for a more complete understanding but also for the fact that the provisions of the Strategy are further operationalized through documents of lower generality.

Effective End-of-Life Management of Assets in Organization by Snowflake Integration

To preserve device stability and security in today's industrial environment, vulnerabilities must be found and fixed. Asset managers face issues in managing End-of-Life (EOL) statuses, which are subject to constant change, and in traversing many data sources. It is challenging to have uniform view into important data without a centralized dashboard. We suggest putting in place a scraping method to collect data from various sources in order to overcome these issues. After the EOL data has been thoroughly verified and updated, the results will be shown on a consolidated, scalable, and dependable dashboard that easily links into the current systems. Advanced data slicing and dicing techniques will be used, utilizing Snowflake's capabilities, to handle enormous datasets efficiently. This all-encompassing strategy seeks to improve asset management procedures, strengthen device security, and offer useful information for defensible decision-making

Generation of security system defense strategies based on evolutionary game theory

The physical protection systems of Nuclear Power Plant are utilized to safeguard targets against intrude by attacker. As the methods employed by attackers to intrude Nuclear Power Plant become increasingly complex and diverse, there is an urgent need to identify optimal defense strategies to interrupt adversary intrusions. This paper focuses on studying the defense of security personnel against adversary intrusions and utilizes an evolutionary game approach to select the optimal defense decisions for physical protection systems. Under the assumption of bounded rationality for both the attacker and defender, the paper constructs replication dynamic equations for attack and defense strategies, investigating the process of strategy selection and the stability of evolution. Finally, a minimal model is proposed to validate the feasibility of utilizing the evolutionary game model for defense strategy selection.

A novel threat assessment method for maritime defense integrating improved IT2FBWM, prospect theory and VIKOR under hybrid fuzzy environment

The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers’ psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism.

“We Don’t Want to Talk About It”: Building Trust for Difficult Decisions

Clinical ethicists must be attuned to nonverbal cues and the deeper issues they raise and, if needed, be willing and agile to change course from discussing goals of care to establishing trust. We present a case study where a family and care team disagree about whether a lack of a do-not-resuscitate and do-not-intubate (DNR/DNI) order for a patient with a terminal prognosis is leading to medically justifiable and ethically defensible decisions. An ethicist called a family meeting to discuss the order but determined, after reading the family’s nonverbal cures, that establishing trust was first needed in order to have that discussion, and that such trust could be built by not discussing the order.

A realist synthesis of prospective entrustment decision making by entrustment or clinical competency committees.

The real-world mechanisms underlying prospective entrustment decision making (PEDM) by entrustment or clinical competency committees (E/CCCs) are poorly understood. To advance understanding in this area, the authors conducted a realist synthesis of the published literature to address the following research question: In E/CCC efforts to make defensible prospective entrustment decisions (PEDs), what works, for whom, under what circumstances and why? Realist work seeks to understand the contexts (C), mechanisms (M) and outcomes (O) that explain how and why things work (or do not). In the authors' study, contexts included individual E/CCC members, E/CCC structures and processes, and training programmes. The outcome (i.e. desired outcome) was a PED. Mechanisms were a substantial focus of the analysis and informed the core findings. To define a final corpus of 52 included papers, the authors searched four databases, screened all results from those searches and performed a full-text review of a subset of screened papers. Data extraction focused on developing context-mechanism-outcome configurations from the papers, which were used to create a theory for how PEDM leads to PEDs. PEDM is often driven by default (non-deliberate) decision making rather than a deliberate process of deciding whether a trainee should be entrusted or not. When defaulting, some E/CCCs find red flags that sometimes lead to being more deliberate with decision making. E/CCCs that seek to be deliberate describe PEDM that can be effortful (when data are insufficient or incongruent) or effortless (when data are robust and tell a congruent story about a trainee). Both information about trainee trustworthiness and the sufficiency of data about trainee performance influence PEDM. Several moderators influence what is considered to be sufficient data, how trustworthiness data are viewed and how PEDM is carried out. These include perceived consequences and associated risks, E/CCC member trust propensity, E/CCC member personal knowledge of and experience with trainees and E/CCC structures and processes. PEDM is rarely deliberate but should be. Data about trainee trustworthiness are foundational to making PEDs. Bias, equity and fairness are nearly absent from the papers in this synthesis, and future efforts must seek to advance understanding and practice regarding the roles of bias, equity and fairness in PEDM.

Constitutional and legal status of the National Security and Defense Council of Ukraine

The article clarifies the constitutional and legal status of the National Security and Defense Council of Ukraine, its nature, its place in the system of state authorities and the specifics of granting separate powers and the problems that arise in the practice of their implementation. It was concluded that in the language of armed aggression against Ukraine and encroachment on its territorial integrity by the ruscist regime, the discussion of issues related to the national security and defense of Ukraine acquires special importance. The National Security and Defense Council of Ukraine, which acts as a coordinating body for national security and defense under the President of Ukraine (Article 107 of the Constitution of Ukraine), becomes a very important body of public authority in wartime conditions. According to the Constitution, it coordinates and controls the activities of executive authorities in the field of national security and defense. We have two models (from the constitutional law of foreign states). The body could be purely advisory (Poland, Turkey, Kosovo, Estonia), or would be empowered to make a number of important defense and security decisions (Montenegro). In the Ukrainian case, use the word "coordination", which is unlikely to be purely of a recommendatory nature. However, in this case, the issue of granting some powers to the National Security Council, and not, for example, to the Cabinet of Ministers, should be well-founded and truly related purely to issues of defense and national security. The doctrine of the limitation of the President's powers by the Constitution itself is built into the constitutional design for a reason (Article 106 of the Constitution). Despite the rather broad and evaluative formulation of the President's powers regarding leadership in the field of national security, individual law enforcement by imposing sanctions, assigning the status of an oligarch, etc., can hardly be characterized exactly as "leadership". Their application is more related to the implementation of state policy in this area. That is why, perhaps, one should think about granting the relevant powers to the Cabinet of Ministers of Ukraine. After all, the very nature of the Cabinet consists in ensuring the implementation of state policy. In addition, Art. 116 of the Constitution (clause 10) directly allows the Cabinet of Ministers to be empowered by laws.

A hierarchical model-based framework for evaluating probabilities of area-wide freedom from lymphatic filariasis infection based on sentinel site surveillance data

The design of population surveys to substantiate the elimination of disease transmission across large implementation units (IUs) has become important as many parasite control efforts approach their final stages. This is especially true for the global program to eliminate lymphatic filariasis (LF), which has successfully reduced infection prevalence in many endemic countries, such that the focus has shifted to how best to determine that the area-wide elimination of this macroparasitic disease has been achieved. The WHO has recommended a two-stage lot quality assurance sampling (LQAS) framework based on sampling children from selected clusters within an IU, called the Transmission Assessment Survey (TAS), for supporting such decision-making, but questions have emerged regarding the reliability of this strategy for assessing if LF transmission is broken effectively everywhere within an area. In this study, we develop and describe an alternative probabilistic framework that combines infection status information from longitudinal parasitological surveys of whole communities carried out in sentinel sites, imperfect diagnostic tests, and locally-applicable extinction thresholds predicted by transmission models, to overcome the problems associated with TAS. We applied the framework to LF infection and intervention data from the country of Malawi, and demonstrated how our hierarchical coupled model-sentinel site survey tool can be used to estimate the probability that LF transmission has occurred at the individual survey, village, and countrywide scales. We also further demonstrated how the framework can be used in conjunction with zonal or areal design prevalences to estimate the number of sentinel sites and durations of interventions required to acquire sufficiently high confidence that an area is free from infection. Our results indicate that the application of the spatially driven model-data freedom-from-infection tool developed here to follow up data from high-risk sentinel sites in a region may offer a highly cost-effective framework for guiding the making of high-fiducial and defensible area-wide LF intervention stopping decisions.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy, discrete defense actions, and delayed defense feedback in the face of network attacks, ignoring the influence of the external environment on defense decisions, thus resulting in poor defense effectiveness. Therefore, this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent, designs the network structure of the intelligent agent attack and defense game, and depicts the attack and defense game process of cloud boundary network; constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment, and portrays the interaction process between intelligent agent and environment; establishes the reward mechanism based on the attack and defense gain, and encourage intelligent agents to learn more effective defense strategies. the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics, interaction lag, and control dispersion in the defense decision process of cloud boundary networks, and improve the autonomy and continuity of defense decisions.