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In this article we consider an aggregate loss model with dependent losses. The losses occurrence process is governed by a two-state Markovian arrival process (MAP2), a Markov renewal process process that allows for (1) correlated inter-losses times, (2) non-exponentially distributed inter-losses times and, (3) overdisperse losses counts. Some quantities of interest to measure persistence in the loss occurrence process are obtained. Given a real operational risk database, the aggregate loss model is estimated by fitting separately the inter-losses times and severities. The MAP2 is estimated via direct maximization of the likelihood function, and severities are modeled by the heavy-tailed, double-Pareto Lognormal distribution. In comparison with the fit provided by the Poisson process, the results point out that taking into account the dependence and overdispersion in the inter-losses times distribution leads to higher capital charges.

This work studies a parabolic-ODE PDE's system which describes the evolution of the physical capital "$k$" and technological progress "$A$", using a meshless in one and two dimensional bounded domain with regular boundary. The well-known Solow model is extended by considering the spatial diffusion of both capital anf technology. Moreover, we study the case in which no spatial diffusion of the technology progress occurs. For such models, we propound schemes based on the Generalized Finite Difference method and proof the convergence of the numerical solution to the continuous one. Several examples show the dynamics of the model for a wide range of parameters. These examples illustrate the accuary of the numerical method.

In the realm of cost-sharing mechanisms, the vulnerability to Sybil strategies, where agents can create fake identities to manipulate outcomes, has not yet been studied. In this paper, we delve into the intricacies of different cost-sharing mechanisms proposed in the literature highlighting its non Sybil-resistance nature. Furthermore, we prove that under mild conditions, a Sybil-proof cost-sharing mechanism for public excludable goods is at least $(n/2+1)-$approximate. This finding reveals an exponential increase in the worst-case social cost in environments where agents are restricted from using Sybil strategies. To circumvent these negative results, we introduce the concept of \textit{Sybil Welfare Invariant} mechanisms, where a mechanism maintains its worst-case welfare under Sybil-strategies for every set of prior beliefs even when the mechanism is not Sybil-proof. Finally, we prove that the Shapley value mechanism for public excludable goods holds this property, and so deduce that the worst-case social cost of this mechanism is the $n$th harmonic number $\mathcal H_n$ even under equilibrium of the game with Sybil strategies, matching the worst-case social cost bound for cost-sharing mechanisms. This finding carries important implications for decentralized autonomous organizations (DAOs), indicating that they are capable of funding public excludable goods efficiently, even when the total number of agents is unknown.

The paper addresses the question of whether it is appropriate to talk about `mechanical minds' at all, and whether ChatGPT models can indeed be thought of as realizations of that. Our paper adds a semantic argument to the current debate. The act of human assertion requires the formation of a veridicality judgment. Modification of assertions with modals (John must be at home) and the use of subjective elements (John is obviously at home) indicate that the speaker is manipulating her judgments and, in a cooperative context, intends her epistemic state to be transparent to the addressee. Veridicality judgments are formed on the basis of two components: (i) evidence that relates to reality (exogenous evidence) and (ii) endogenous evidence, such as preferences and private beliefs. `Mechanical minds' lack these two components: (i) they do not relate to reality and (ii) do not have endogenous evidence. Therefore they lack the ability to form a belief about the world and a veridicality judgments altogether. They can only mimic that judgment, but the output is not ground in the very foundations for it.

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimizing operating procedures and increasing productivity. The integration of IoT in this complicated setting is hindered by specific barriers that need a thorough examination. Prominent barriers to IoT implementation in the cold supply chain are identified using a two-stage model. After reviewing the available literature on the topic of IoT implementation, a total of 13 barriers were found. The survey data was cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This research applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among those barriers, "regularity compliance" and "cold chain networks" are key drivers for IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate the barrier interactions. In the second phase of this research, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system as a whole benefits. The research findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

It is well established that to ensure or certify the robustness of a neural network, its Lipschitz constant plays a prominent role. However, its calculation is NP-hard. In this note, by taking into account activation regions at each layer as new constraints, we propose new quadratically constrained MIP formulations for the neural network Lipschitz estimation problem. The solutions of these problems give lower bounds and upper bounds of the Lipschitz constant and we detail conditions when they coincide with the exact Lipschitz constant.

Most of the existing Mendelian randomization (MR) methods are limited by the assumption of linear causality between exposure and outcome, and the development of new non-linear MR methods is highly desirable. We introduce two-stage prediction estimation and control function estimation from econometrics to MR and extend them to non-linear causality. We give conditions for parameter identification and theoretically prove the consistency and asymptotic normality of the estimates. We compare the two methods theoretically under both linear and non-linear causality. We also extend the control function estimation to a more flexible semi-parametric framework without detailed parametric specifications of causality. Extensive simulations numerically corroborate our theoretical results. Application to UK Biobank data reveals non-linear causal relationships between sleep duration and systolic/diastolic blood pressure.

AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy don't necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.

This paper presents a theoretical analysis and practical approach to the moral responsibilities when developing AI systems for non-military applications that may nonetheless be used for conflict applications. We argue that AI represents a form of crossover technology that is different from previous historical examples of dual- or multi-use technology as it has a multiplicative effect across other technologies. As a result, existing analyses of ethical responsibilities around dual-use technologies do not necessarily work for AI systems. We instead argue that stakeholders involved in the AI system lifecycle are morally responsible for uses of their systems that are reasonably foreseeable. The core idea is that an agent's moral responsibility for some action is not necessarily determined by their intentions alone; we must also consider what the agent could reasonably have foreseen to be potential outcomes of their action, such as the potential use of a system in conflict even when it is not designed for that. In particular, we contend that it is reasonably foreseeable that: (1) civilian AI systems will be applied to active conflict, including conflict support activities, (2) the use of civilian AI systems in conflict will impact applications of the law of armed conflict, and (3) crossover AI technology will be applied to conflicts that fall short of armed conflict. Given these reasonably foreseeably outcomes, we present three technically feasible actions that developers of civilian AIs can take to potentially mitigate their moral responsibility: (a) establishing systematic approaches to multi-perspective capability testing, (b) integrating digital watermarking in model weight matrices, and (c) utilizing monitoring and reporting mechanisms for conflict-related AI applications.

Artificial intelligence (AI) has the potential to transform education with its power of uncovering insights from massive data about student learning patterns. However, ethical and trustworthy concerns of AI have been raised but are unsolved. Prominent ethical issues in high school AI education include data privacy, information leakage, abusive language, and fairness. This paper describes technological components that were built to address ethical and trustworthy concerns in a multi-modal collaborative platform (called ALLURE chatbot) for high school students to collaborate with AI to solve the Rubik's cube. In data privacy, we want to ensure that the informed consent of children, parents, and teachers, is at the center of any data that is managed. Since children are involved, language, whether textual, audio, or visual, is acceptable both from users and AI and the system can steer interaction away from dangerous situations. In information management, we also want to ensure that the system, while learning to improve over time, does not leak information about users from one group to another.