Search Results (518)

This study proposes methods of computer simulation to study and optimize the cybersecurity of Small Modular Nuclear Reactors (SMRs). SMRs hold the potential to help build a clean and sustainable power grid but will struggle to gain widespread adoption without public confidence in their security. SMRs are emerging technologies and potentially carry higher cyber threats due to remote operations, large numbers of cyber-physical systems, and cyber connections with other industrial concerns. A method of agent-based computer simulations to model the effects, or payoff, of collaboration between cyber defenders, power plants, and cybersecurity vendors is proposed to strengthen SMR cybersecurity as these new power generators enter into the market. The agent-based model presented in this research is intended to illustrate the potential of using simulation to model a payoff function for collaborative efforts between stakeholders. Employing simulation to heighten cybersecurity will help to safely leverage the potential of SMRs in a modern and low-emission energy grid. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

We propose a life-expectancy pay-off function (LEP) for determining optimal cancer treatment within a control theory framework. The LEP averages life expectancy over all future outcomes, outcomes that are determined by key events during therapy such as tumour elimination (cure) and patient death (including treatment related mortality). We analyse this optimisation problem for tumours treated with chemotherapy using tumour growth models based on ordinary differential equations. To incorporate tumour elimination we draw on branching processes to compute the probability distribution of tumour population extinction. To demonstrate the approach, we apply the LEP framework to simplified one-compartment models of tumour growth that include three possible outcomes: cure, relapse, or death during treatment. Using Pontryagin’s maximum principle (PMP) we show that the best treatment strategies fall into three categories: (i) continuous treatment at the maximum tolerated dose (MTD), (ii) no treatment, or (iii) treat-and-stop therapy, where the drug is given at the MTD and then halted before the treatment (time) horizon. Optimal treatment strategies are independent of the time horizon unless the time horizon is too short to accommodate the most effective (treat-and-stop) therapy. For sufficiently long horizons, the optimal solution is either no treatment (when treatment yields no benefit) or treat-and-stop. Patients, thus, split into an untreatable class and a treatable class, with patient demographics, tumour size, tumour response, and drug toxicity determining whether a patient benefits from treatment. The LEP is in principle parametrisable from data, requiring estimation of the rates of each event and the associated life expectancy under that event. This makes the approach suitable for personalising cancer therapy based on tumour characteristics and patient-specific risk profiles. Full article

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC. Full article

This study explains how integrating with foreign suppliers fortifies a buying firm’s supply-chain resilience, captured here as heightened market responsiveness. Drawing on information-processing theory, we argue that supplier integration equips buyers with richer, faster information flows that enable timely adaptation to market shocks. Extending value-congruence theory, we posit that this resilience dividend depends on simultaneous cultural alignment at two levels—national and organizational. Survey data from 174 manufacturing firms engaged in international buyer–supplier relationships across East Asia, North America, Latin America and Europe were analyzed via hierarchical regression. Results confirm that foreign supplier integration has a positive main effect on market responsiveness. Crucially, a significant three-way interaction (integration × national-culture congruence × organizational-culture congruence) reveals that the responsiveness—and thus resilience—payoff materializes only when both cultural layers are highly congruent; congruence at just one layer is insufficient. By demonstrating the contingent, multilevel nature of resilience benefits, this research advances the global supply-chain literature in three ways: (1) it unites information-processing and value-congruence perspectives to clarify when integration generates adaptive capability; (2) it positions dual-level cultural fit as a prerequisite for resilient performance; and (3) it offers region-spanning evidence that guides managers in designing culturally attuned integration strategies to withstand market turbulence. Full article

The optical flow problem and image registration problem are treated as optimal control problems associated with Fokker–Planck equations with controller u in the drift term. The payoff is of the form ${\displaystyle \frac{1}{2}}|y\left(T\right)-y_1{|}^2+\alpha {\int }_0^T{|u\left(t\right)|}_4^{4}dt$, where $y_1$ is the observed final state and $y=y^u$ is the solution to the state control system. Here, we prove the existence of a solution and obtain also the Euler–Lagrange optimality conditions which generate a gradient type algorithm for the above optimal control problem. A conceptual algorithm to compute the approximating optimal control and numerical implementation of this algorithm is discussed. Full article

This paper introduces a novel four-party evolutionary game model to analyze cooperation dynamics in High-Value Mixed Cropping (HMC) systems integrating non-pesticide cacao, cashew nut, and free-range chicken farming within circular and sharing economy frameworks. The model uniquely examines strategic interactions among local government and three farming family types (cacao, cashew, and chicken), incorporating both regulatory mechanisms and cooperative behaviors. Through rigorous stability analysis and MATLAB simulations based on empirical data from Southeast Vietnam, we identify precise conditions for Evolutionarily Stable Strategies (ESSs) that sustain long-term cooperation. Our results demonstrate that government incentives (subsidies, technical support) and reputational sanctions critically shape farmers’ and consumers’ payoffs, thereby steering the system toward collective action equilibria. In particular, increasing the strength of positive incentives or reputational benefits enlarges the basin of attraction for full-cooperation ESSs, regardless of initial strategy distributions. Conversely, overly punitive sanctions can destabilize collaborative outcomes. These findings underscore the pivotal role of well-balanced policy instruments in fostering resilience, innovation, and resource circulation within rural agroecosystems. Finally, we propose targeted policy recommendations, such as graduated subsidy schemes, participatory monitoring platforms, and cooperative branding initiatives, to reinforce circular economy practices and accelerate progress toward the United Nations Sustainable Development Goals. Full article

This paper analyzes the pricing dynamics of product warranties by developing a three-player hierarchical game model involving a manufacturer, an independent service agent, and a consumer. The model provides a scenario where the manufacturer and the agent form a coalition to coordinate pricing strategies, while interacting non-cooperatively with the consumer. In this framework, the manufacturer sets the product’s sale price, including the base warranty, while the agent determines the price of extended maintenance services. The key contribution is the application of the Shapley value to equitably distribute the coalition’s profits based on each member’s contribution—a novel approach in the warranty pricing literature. We detail the characteristic functions that define the coalition’s structure and present computer simulations to estimate the expected costs associated with maintenance services. A comprehensive sensitivity analysis is applied to report how changes in parameters influence equilibrium strategies and players’ payoffs. The results provide strategic insights into how manufacturers and agents can coordinate to optimize pricing, capture consumer surplus, and improve decision-making in warranty service markets. Full article

Non-cooperative games in normal form are specified by a player set, sets of player strategies, and payoff functions. Cooperative games, meanwhile, are specified by a player set and a worth function that maps coalitions of players to payoffs they can feasibly achieve. Although these games study distinct aspects of social behavior, this paper proposes a novel attempt at relating the two models. In particular, cooperative games may be represented by a non-cooperative game in which players can freely sign binding agreements to form coalitions. These coalitions inherit a joint strategy set and seek to maximize collective payoffs. When these coalitions play against one another, the equilibrium payoffs for each coalition coincide with what is predicted by the worth function. This paper proves sufficient conditions under which cooperative games can be represented by non-cooperative games. This paper finds that all strictly superadditive partition function form (PFF) games can be represented under Nash equilibrium (NE) and rationalizability; that all weakly superadditive characteristic function form (CFF) games can be represented under NE; and that all weakly superadditive PFF games can be represented under trembling hand perfect equilibrium (THPE). Full article

This paper reveals a simple methodology to create local-correlation models suitable for the closed-form pricing of two-asset financial derivatives. The multivariate models are built to ensure two conditions. First, marginals follow desirable processes, e.g., we choose the Geometric Brownian Motion (GBM), popular for stock prices. Second, the payoff of the derivative should follow a desired one-dimensional process. These conditions lead to a specific choice of the dependence structure in the form of a local-correlation model. Two popular multi-asset options are entertained: a spread option and a basket option. Full article

I argue that the universally accepted assumption that in realist fiction a character narrator’s narration contributes to their characterization needs to be complicated. Working with a conception of narrative as rhetoric that highlights readerly interest in the author’s handling of the mimetic, thematic, and synthetic components of narrative, I suggest that the question about narration as characterization is one about the relation between the mimetic (character as possible person) and synthetic (character as invented construct) components. In addition, understanding the mimetic-synthetic relation requires attention to issues at the macro and micro levels of such narratives. At the macro level, I note the importance of (1) the tacit knowledge, shared by both authors and audiences, of the fictionality of character narration, which means authors write and readers read with an interest in its payoffs; and of (2) the recognition that character narration functions simultaneously along two tracks of communication: that between the character narrator and their narratee, and that between the author and their audience. These macro level matters then provide a frame within which authors and readers understand what happens at the micro level. At that level, I identify seven features of a character’s telling that have the potential to be used for characterization—voice, occasion, un/reliability, authority, self-consciousness, narrative control, and aesthetics. I also note that these features have their counterparts in the author’s telling. Finally, I propose that characterization via narration results from the interaction between the salient features of the character’s telling and their counterparts in the author’s telling. I develop these points through the analysis of four diverse case studies: Mark Twain’s Huckleberry Finn, Robert Browning’s “My Last Duchess,” Nadine Gordimer’s “Homage,” and Ernest Hemingway’s A Farewell to Arms. Full article

In this work, we study n-agent Bayesian games with m-dimensional vector types and linear payoffs, also called linear multidimensional Bayesian games. This class of games is equivalent with n-agent, m-game uniform multigames. We distinguish between games that have a discrete type space and those with a continuous type space. More specifically, we are interested in the existence of pure Bayesian Nash equilibriums for such games and efficient algorithms to find them. For continuous priors, we suggest a methodology to perform Nash equilibrium searches in simple cases. For discrete priors, we present algorithms that can handle two-action and two-player games efficiently. We introduce the core concept of threshold strategy and, under some mild conditions, we show that these games have at least one pure Bayesian Nash equilibrium. We illustrate our results with several examples like the double-game prisoner’s dilemma (DGPD), the game of chicken, and the sustainable adoption decision problem (SADP). Full article

I consider an underexplored possible explainer of the “surprising” results of Ultimatum Game experiments, namely, that Proposers and Recipients consider following only some of all the logically possible strategies of their Ultimatum Game. I present an evolutionary analysis of different games having the same set of allowable Proposer offers and functions that determine Proposer and Recipient payoffs. For Unrestricted Ultimatum Games, where Recipients may choose from among any of the logically possible pure strategies, populations tend to evolve most often to Nash equilibria where Proposers make the lowest allowable offer. However, for Threshold Reduced Ultimatum Games, where Recipients must choose from among minimum acceptable offer strategies, and for Range Reduced Ultimatum Games, where Recipients must choose from among pure strategies that spurn offers that are “too high” as well as “too low”, populations tend to evolve most often to Nash equilibria where Proposers offer substantially more than the lowest possible offer, a result that is consistent with existing Ultimatum Game experimental results. Finally, I argue that, practically speaking, actual Proposers and Recipients will likely regard some reduction of the Unrestricted Ultimatum Game as their game because, for them, the strategies of this reduction are salient. Full article

Federated Learning (FL), a key paradigm in privacy-preserving and distributed machine learning (ML), enables collaborative model training across decentralized data sources without requiring raw data exchange. FL enables collaborative model training across decentralized data sources while preserving privacy. However, selecting appropriate clients remains a major challenge, especially in heterogeneous environments with diverse battery levels, privacy needs, and learning capacities. In this work, a centralized reward-based payoff strategy (RBPS) with cooperative intent is proposed for client selection. In RBPS, each client evaluates participation based on locally measured battery level, privacy requirement, and the model’s accuracy in the current round computing a payoff from these factors and electing to participate if the payoff exceeds a predefined threshold. Participating clients then receive the updated global model. By jointly optimizing model accuracy, privacy preservation, and battery-level constraints, RBPS realizes a multi-objective selection mechanism. Under realistic simulations of client heterogeneity, RBPS yields more robust and efficient training compared to existing methods, confirming its suitability for deployment in resource-constrained FL settings. Experimental analysis demonstrates that RBPS offers significant advantages over state-of-the-art (SOA) client selection methods, particularly those relying on a single selection criterion such as accuracy, battery, or privacy alone. These one-dimensional approaches often lead to trade-offs where improvements in one aspect come at the cost of another. In contrast, RBPS leverages client heterogeneity not as a limitation, but as a strategic asset to maintain and balance all critical characteristics simultaneously. Rather than optimizing performance for a single device type or constraint, RBPS benefits from the diversity of heterogeneous clients, enabling improved accuracy, energy preservation, and privacy protection all at once. This is achieved by dynamically adapting the selection strategy to the strengths of different client profiles. Unlike homogeneous environments, where only one capability tends to dominate, RBPS ensures that no key property is sacrificed. RBPS thus aligns more closely with real-world FL deployments, where mixed-device participation is common and balanced optimization is essential. Full article

COVID-19 school closures forced German high-school graduates (Abitur 2022 cohort) to prepare for their final examinations with lengthy learning times at home. Guided by transactional stress theory, we tested how personal resources—self-regulated learning (SRL) skills and academic self-efficacy—and contextual resources—perceived teacher support and teacher digital competence—jointly predicted perceived stress during exam preparation. A cross-sectional online survey (June–July 2022) yielded complete data from N = 2379 students (68% female; Mage = 18.3). Six latent constructs were measured with 23 items and showed adequate reliability (0.71 ≤ α/ω ≤ 0.89). A six-factor CFA fit the data acceptably (CFI = 0.909, RMSEA = 0.064). The structural equation model (CFI = 0.935, RMSEA = 0.064) explained 35% of the variance in stress and 23% of the variance in SRL—action. Academic self-efficacy (β = −0.31, p < 0.001), perceived support (β = −0.28, p < 0.001), teacher digital competence (β = −0.08, p < 0.001), COVID-19 learning disruptions (β = +0.13, p < 0.001), and gender (male = 0.32 SD lower stress, p < 0.001) had direct effects on stress. SRL—action’s direct path was small and non-significant (β = −0.02). Teacher digital competence also reduced stress indirectly through greater perceived support (standardized indirect β = −0.11, p < 0.001). The results highlight self-efficacy and perceived instructional support as the most potent buffers of pandemic-related stress, whereas cancelled lessons added strain. Boosting teachers’ digital pedagogical skills has a dual payoff—raising students’ sense of support and lowering their stress. Full article