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Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectives by shortcutting their reward signal? This question impacts how far RL can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we study when an RL agent has an instrumental goal to tamper with its reward process, and describe design principles that prevent instrumental goals for two different types of reward tampering (reward function tampering and RF-input tampering). Combined, the design principles can prevent reward tampering from being an instrumental goal. The analysis benefits from causal influence diagrams to provide intuitive yet precise formalizations.

Heart rate variability (HRV) corresponds to the adaptation of the heart to any stimulus. In fact, among the pathologies affecting HRV the most, there are the cardiovascular diseases and depressive disorders, which are associated with high medical cost in Western societies. Consequently, HRV is now widely used as an index of health. In order to better understand how this adaptation takes place, it is necessary to examine which factors directly influence HRV, whether they have a physiological or environmental origin. The primary objective of this research is therefore to conduct a literature review in order to get a comprehensive overview of the subject. The system of these factors affecting HRV can be divided into the following five categories: physiological and pathological factors, environmental factors, lifestyle factors, non-modifiable factors and effects. The direct interrelationships between these factors and HRV can be regrouped into an influence diagram. This diagram can therefore serve as a basis to improve daily clinical practice as well as help design even more precise research protocols.

An influence diagram is a kind of graphical model that can represent both the probabilistic relationship between variables and can easy to make decisions. It can make full use of Bayesian Network and Decisiton Tree. Influence Diagram should be modify to inprove the effiency to express relationships among variables. Extensional model of Influence Diagrams is introduced in this paper to express the new kind influence diagrams. And this kind of new model can be applied in the area of supply chain management.

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.

Socially assistive robots represent a promising tool in assistive contexts for improving people’s quality of life and well-being through social, emotional, cognitive, and physical support. However, the effectiveness of interactions heavily relies on the robots’ ability to adapt to the needs of the assisted individuals and to offer support proactively, before it is explicitly requested. Previous work has primarily focused on defining the actions the robot should perform, rather than considering when to act and how confident it should be in a given situation. To address this gap, this paper introduces a new data-driven framework that involves a learning pipeline, consisting of two phases, with the ultimate goal of training an algorithm based on Influence Diagrams. The proposed assistance scenario involves a sequential memory game, where the robot autonomously learns what assistance to provide when to intervene, and with what confidence to take control. The results from a user study showed that the proactive behaviour of the robot had a positive impact on the users’ game performance. Users obtained higher scores, made fewer mistakes, and requested less assistance from the robot. The study also highlighted the robot’s ability to provide assistance tailored to users’ specific needs and anticipate their requests.

Conservation practice requires an understanding of complex social‐ecological processes of a system and the different meanings and values that people attach to them. Mental models research offers a suite of methods that can be used to reveal these understandings and how they might affect conservation outcomes. Mental models are representations in people's minds of how parts of the world work. We seek to demonstrate their value to conservation and assist practitioners and researchers in navigating the choices of methods available to elicit them. We begin by explaining some of the dominant applications of mental models in conservation: revealing individual assumptions about a system, developing a stakeholder‐based model of the system, and creating a shared pathway to conservation. We then provide a framework to “walk through” the stepwise decisions in mental models research, with a focus on diagram‐based methods. Finally, we discuss some of the limitations of mental models research and application that are important to consider. This work extends the use of mental models research in improving our ability to understand social‐ecological systems, creating a powerful set of tools to inform and shape conservation initiatives.

BackgroundOrganisational leadership (OL) as a construct faces challenges in being universally defined, especially in South Africa, because of its contextual diverse nature (historically, politically, socially and culturally). This is amplified by the limited availability of contextual (South African) leadership research, and an overreliance on international and universal leadership models.AimThis study aimed to conceptualise OL from an emic perspective.SettingThe study was based on the South African workforce.MethodAn exploratory qualitative design was employed. Interactive qualitative analysis (IQA), a systematic qualitative research methodology, was used. The inductive nature of IQA, combined with deductive analysis techniques (axial and theoretical coding), facilitated the identification of the elements of OL, as well as its inter-relational nature. Based on the participants’ lived experiences, this is established from a pragmatist and social constructivism perspective.ResultsThe outcome of the study is a system influence diagram (SID), indicating the relationship between the various elements of OL. The primary driver was the leader’s emotional awareness, with the secondary drivers including leadership style, characteristics, culture, communication and vision. These drivers resulted in secondary outcomes of leader support and team dynamics, ultimately culminating in the primary outcome of delivering strategy.ConclusionOrganisational leadership has relational, emotional and rational elements that should be navigated to reach the primary outcome, namely organisational success through strategy implementation.ContributionConceptualising OL is valuable as it advances our understanding, highlights the social and cultural dynamics that influence leadership effectiveness, and offers a foundation for future research and leadership development.

Systems models of the Food–Water–Energy (FWE) nexus face a conceptual difficulty: the systematic integration of local stakeholder perspectives into a coherent framework for analysis. We present a novel procedure to co-produce and systematize the real-life complexity of stakeholder knowledge and forge it into a clear-cut set of challenges. These are clustered into the Pressure–State–Response (PSIR) framework, which ultimately guides the development of a conceptual systems model closely attuned to the needs of local stakeholders. We apply this approach to the case of the emerging megacity Pune and the Bhima basin in India. Through stakeholder workshops, involving 75 resource users and experts, we identified 22 individual challenges. They include exogenous pressures, such as climate change and urbanization, and endogenous pressures, such as agricultural groundwater over-abstraction and land use change. These pressures alter the Bhima basin’s system state, characterized by inefficient water and energy supply systems and regional scarcity. The consequent impacts on society encompass the inadequate provision with food, water, and energy and livelihood challenges for farmers in the basin. An evaluation of policy responses within the conceptual systems model shows the complex cause–effect interactions between nexus subsystems. One single response action, such as the promotion of solar farming, can affect multiple challenges. The resulting concise picture of the regional FWE system serves resource users, policymakers, and researchers to evaluate long-term policies within the context of the urban FWE system. While the presented results are specific to the case study, the approach can be transferred to any other FWE nexus system.

The presence of artificial reefs (ARs) in the south of Portugal that were deployed a few decades ago and the corroboration of fishing patterns and other activities related to the use of these habitats have not been followed. It is important to note that monitoring the use of ARs was difficult in the past but is currently facilitated by the application of non-intrusive tools. In the present study, an approach is developed where, based on monitoring data from fishing and non-fishing boats, influence diagrams (IDs) are constructed to provide some evidence on fisheries or other use patterns and consequent AR effectiveness as coastal tools. These IDs allow us to infer various usefulness scenarios, namely catches, which are tangible, and satisfaction, which is intangible, and overall assessment of ARs and nearby areas in terms of human activities. After calibrating the Bayesian ID based on monitoring evidence, the obtained model was evaluated for several scenarios. In the base case, which assumes the occurrence of more fishing than recreation (assuming 3:1, respectively), the obtained utility is 18.64% (catches) and 31.96% (satisfaction). Of the scenarios run, the one that obtained the best results in the utility nodes together was the second one. The use of these tailored tools and approaches seems to be of fundamental importance for the adequate management of coastal infrastructures, particularly with regard to the inference of fishing resources and their sustainable use. An adequate interpretation based on the use of these tools implies being able to safeguard the ecological balance and economic sustainability of the communities operating in these areas.

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.