Search Results (545)

The unbridled pursuit of statistical rationality has precipitated a crisis of value rationality in e-commerce ecosystems, leading to algorithmic involution—a dilemma characterized by destructive hyper-competition. To reconcile this theoretical paradox and explore effective governance pathways, this paper constructs a tripartite evolutionary game model involving e-commerce platforms, government regulators, and consumers. Simulation results indicate that high-intensity government deterrence constitutes the necessary stability foundation of hard constraints, while consumer activism acts as the decisive accelerator of the soft environment contingent on high synergistic gains and low information screening costs. Furthermore, a platform’s pivot toward “algorithm for good” is not driven by altruism, but by the rational calibration between short-term extractive gains and long-term benevolent returns. Sensitivity analysis confirms that reducing the ratio of these two factors is the effective lever to speed up system convergence. Finally, effective governance requires restructuring this payoff matrix by establishing dynamic penalty mechanisms and transparent low-cost feedback channels to render ethical algorithmic behavior a dominant strategy in terms of economic rationality. This research aims to guide the e-commerce ecosystem from a zero-sum game of involution toward a sustainable equilibrium of multi-party value co-creation. Full article

We introduce a class of parametric operators acting on the space of Bayesian games with continuous utility functions. Each operator induces a structured perturbation of agents’ utilities while preserving the underlying informational primitives, strategy spaces, and Bayesian updating. This construction generates a family of utility-perturbed Bayesian games that can be interpreted as continuous deformations of classical incomplete-information games in the space of payoff functions. The contribution of the paper is purely mathematical. First, we formally define a utility perturbation operator and characterize the associated class of perturbed Bayesian games. Second, under standard compactness and continuity assumptions, we prove the existence of Nash equilibria for all admissible perturbations. Third, we show that the equilibrium correspondence of the perturbed games converges upper hemicontinuously to the classical Bayesian Nash equilibrium correspondence as the perturbation parameter vanishes. Under additional differentiability and strict concavity assumptions, we establish a structural stability result: in a neighborhood of the unperturbed game, equilibria are locally unique and depend smoothly on the perturbation parameter. The equilibrium mapping is continuous, locally Lipschitz, and differentiable, implying that utility perturbations generate a stable deformation of the classical equilibrium structure rather than a qualitative departure from it. Taken together, the results identify a new operator-based framework for studying equilibrium stability and sensitivity in Bayesian games. The analysis shows that parametric perturbations of utility functions define a mathematically well-posed deformation of classical game-theoretic equilibria, providing a foundation for further work on equilibrium equivalence, stability, and comparative statics in non-cooperative games. Full article

The development of a robust and well-designed carbon emission governance framework is critical to achieving effective carbon reduction. To explore carbon reduction and regulation behaviour between governments and enterprises based on government rewards, penalties, regulations, and publicity and disclosure from the media, this study used evolutionary game theory to construct an evolutionary game model. In this model, strong and weak regulation are two strategies that can be selected by the government; truthful reports and underreporting of carbon emissions are two strategies that can be used by enterprises. Hence, we performed theoretical analysis and numerical simulations in this study. The results show that different strategy selections are influenced by an initial payoff matrix and initial parameter selection and construction. Under certain conditions, to impel the government to choose the strong regulation and the enterprises to choose the truthful report strategy, this study suggests decreasing the cost of government regulations and increasing the probability of publicity of the governments’ strong regulation behaviour and the enterprises’ truthful reporting of carbon emissions. Finally, increasing the penalty of the enterprises’ underreporting behaviour and the probability of disclosure on the behaviour of weak regulation and underreporting of carbon emissions. Full article

This paper studies optimal dividend and capital injection strategies with active exit options under a jump-diffusion model. We introduce a piecewise terminal payoff function to capture stop-loss exits (for deficits) and profit-taking exits (for surpluses), enabling shareholders to dynamically balance risk and return. Using the dynamic programming principle, we derive the associated quasi-variational inequalities (QVIs) and characterize the value function as the unique viscosity solution. To address analytical challenges, we employ the Markov chain approximation method, constructing a controlled Markov chain that closely approximates the jump-diffusion dynamics. Numerical solutions of the approximated problem are obtained via value iteration. The numerical results demonstrate how the value function and optimal strategies respond to different claim distributions (comparing Exponential and Pareto cases), key model parameters, and exit payoff functions. The numerical study further validates the algorithm’s convergence and examines the stability of solutions with respect to domain truncation in the QVI formulation. Full article

The information released to investors in financial markets takes various forms. We understand range information as information about the upper and lower bounds that the payoff of a risky asset may reach in the future. This study develops rational expectation models to explore the market impacts of disclosing such information. Our model shows that its disclosure can decrease market price sensitivity to private signal and increase market liquidity. Furthermore, the market impact of its disclosure depends on the position and precision of the range disclosed. When the linear combination of private signal and noise trading volume is distant from the disclosed range, the reaction of price to a variation in private signal will almost vanish, whereas movement in the disclosed range can efficiently impact price. Under certain conditions, such as a higher proportion of informed traders or a larger size of noise trading in the market, disclosing range information is more likely to reduce asset price and raise capital cost. Full article

The Nash equilibrium is a central concept in game theory, widely used across economics, social sciences, computer science, and artificial intelligence. However, computing Nash equilibria, especially in multi-player games, is a complex and computationally challenging task. Among the various types of Nash equilibria, the unique pure-strategy Nash equilibrium payoffs possess particularly desirable properties that make them suitable for deeper analysis and application. In this paper, we propose a first-order logical framework for three-player finite games, inspired by the notion of Pareto optimality, to identify a class of games with unique pure-strategy Nash equilibrium payoffs. By utilizing a SAT solver and the finite verifiability of ternary clauses, we automatically discover several families of three-player games that exhibit unique pure-strategy Nash equilibrium payoffs. This approach provides new insights into the computational aspects of game theory and offers an automated method for discovering novel game-theoretic structures. Full article

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars. Full article

This paper addresses a critical inconsistency in models of the term structure of interest rates (TSIR), where zero-coupon bonds are priced under risk-neutral measures distinct from those used in equity markets. We consider a unified TSIR framework that treats zero-coupon bonds as European options with deterministic payoffs, ensuring that they are priced under the same risk-neutral measure that governs equity derivatives. Using put–call parity, we extract zero-coupon bond implied yield curves from S&P 500 index options and compare them with the US daily treasury par yield curves. As the implied yield curves contain maturity time T and strike price K as independent variables, we investigate the $K$—dependence of the implied yield curve. Our findings, that at-the-money option-implied yield curves provide the closest match to treasury par yield curves, support the view that the equity options market contains information that is highly relevant for the TSIR. By insisting that the risk-neutral measure used for bond valuation is the same as that revealed by equity derivatives, we offer a new organizing principle for future TSIR research. Full article

Behavioral deviations from subjective expected utility theory, most famously captured by the Allais paradox and the Ellsberg paradox, have inspired extensive theoretical and experimental research into risk and ambiguity preferences. While the existing analyze these paradoxes independently, little work explores how such heterogeneously biased agents interact in networked strategic environments. Our paper fills this gap by modeling a convergent Markov–network game between Allais-type and Ellsberg-type players, each endowed with fully enriched loss matrices that reflect their distinct probabilistic and ambiguity attitudes. We define convergent priors as those inducing a spectral radius of <1 in iterated enriched matrices, ensuring iterative convergence under a matrix-based update rule. Players minimize their losses under these priors in each iteration, converging to an equilibrium where no further updates are feasible. We analyze this convergence under three learning regimes—homophily, heterophily, and type-neutral randomness—each defined via distinct neighborhood learning dynamics. To validate the equilibrium, we construct a risk-neutral measure by transforming losses into payoffs and derive a riskless rate of return representing players’ subjective indifference to risk. This applies risk-neutral pricing logic to behavioral matrices, which is novel. This framework unifies paradox-type decision makers within a networked Markovian environment (stochastic adjacency matrix), extending models of dynamic learning and providing a novel equilibrium characterization for heterogeneous, ambiguity-averse agents in structured interactions. Full article

This study empirically investigates the relationship between Environmental, Social, and Governance (ESG) scores and the financial performance of Mexican companies traded at Bolsa Mexicana de Valores (BMV), based on firm value and profitability. The study used a quantitative method of correlational research. Using data from the Refinitiv, the study analyzes 103 companies operating in 37 different industries listed on the BMV over five years (2019–2023), excluding financial institutions. Ordinary least squares (OLS) regressions revealed a statistically significant, positive correlation between ESG scores associated with higher return on assets (ROA) and market value measured by Tobin’s Q). Stakeholder theory serves as the theoretical foundation, as ESG initiatives may enhance long-term value for stakeholders. The study found that ESG efforts contribute positively to ROA and Tobin’s Q of public companies in Mexico. This study focuses exclusively on Mexican companies, expanding the existing literature. Corporate decision makers and investors can gain insights into ESG’s role in Mexican companies’ financial strategy and stakeholder value creation. Full article

With the accelerated expansion of the Internet of Things (IoT), massive distributed and heterogeneous devices are increasingly exposed to severe security threats. Traditional centralized intrusion detection systems (IDS) suffer from significant limitations in terms of privacy preservation and communication overhead. Federated Learning (FL) offers an effective paradigm for building the next generation of distributed IDS; however, it remains vulnerable to poisoning attacks in open environments, and existing client selection strategies generally lack robustness and security awareness. To address these challenges, this paper proposes an Uncertainty-Guided Evolutionary Game-Theoretic (UEGT) Client Selection mechanism. Built upon evolutionary game theory, UEGT integrates Shapley value, gradient similarity, and data quality to construct a multidimensional payoff function and employs a replicator dynamics mechanism to adaptively optimize client participation probabilities. Furthermore, uncertainty modeling is introduced to enhance strategic exploration and improve the identification accuracy of potentially high-value clients. Experimental results under adversarial scenarios demonstrate that UEGT maintains stable convergence even under a high fraction of malicious participating clients, achieving an average accuracy exceeding 89%, which outperforms several mainstream client selection and robust aggregation methods. Full article

Switched reluctance motors (SRMs) are promising for applications such as air compressors due to their robust structure and fault tolerance, but suffer from high torque ripple and radial electromagnetic forces that cause vibration and noise. This paper proposes a game-theoretic multi-objective design optimization framework to enhance electromagnetic performance by simultaneously maximizing average torque and minimizing radial force. The optimization problem is transformed into a game model where objectives are treated as players with strategy spaces derived through fuzzy clustering and correlation analysis. Particle swarm optimization (PSO) is employed to solve the payoff functions under both novel cooperative and non-cooperative game scenarios of SRMs’ structural design. Finite element analysis (FEA) validates the optimized motor topology, showing that the cooperative game model achieves a balanced performance with high torque density and reduced vibration, meeting the requirements for air compressor drives. The proposed method effectively resolves the weight selection challenge in traditional multi-objective optimization and demonstrates strong engineering feasibility. Full article

The rapid advancement of artificial intelligence has driven a surge in demand for computing power. As the core computing infrastructure, data centers have expanded in scale, escalating electricity consumption and magnifying a regional mismatch between computing capacity and energy resources: facilities are concentrated in the energy-constrained East, while the renewable-rich West possesses vast, untapped hosting capacity. Focusing on cross-regional data-center migration under the “Eastern Data, Western Computing” initiative, this study constructs a tripartite evolutionary game model comprising the Eastern Local Government, the Western Local Government, and data-center enterprises. The central government is modeled as an external regulator that indirectly shapes players’ strategies through policies such as energy-efficiency constraints and carbon-quota mechanisms. First, we introduce key parameters—including energy efficiency, carbon costs, green revenues, coordination subsidies, and migration losses—and analyze the system’s evolutionary stability using replicator-dynamics equations. Second, we conduct numerical simulations in MATLAB 2024a and perform sensitivity analyses with respect to energy and green constraints, central rewards and penalties, regional coordination incentives, and migration losses. The results show the following: (1) Multiple equilibria can arise, including coordinated optima, policy-failure states, and coordination-impeded outcomes. These coordinated optima do not emerge spontaneously but rather depend on a precise alignment of payoff structures across central government, local governments, and enterprises. (2) The eastern regulatory push—centered on energy efficiency and carbon emissions—is generally more effective than western fiscal subsidies or stand-alone energy advantages at reshaping firm payoffs and inducing relocation. Central penalties and coordination subsidies serve complementary and constraining roles. (3) Commercial risks associated with full migration, such as service interruption and customer attrition, remain among the key barriers to shifting from partial to full migration. These risks are closely linked to practical relocation and connectivity constraints—such as logistics and commissioning effort, and cross-regional network latency/bandwidth—thereby potentially trapping firms in a suboptimal partial-migration equilibrium. This study provides theoretical support for refining the “Eastern Data, Western Computing” policy mix and offers generalized insights for other economies facing similar spatial energy–demand asymmetries. Full article

This paper introduces a novel framework for designing fair and sustainable unemployment benefits, grounded in cooperative game theory and real-time fiscal policy. The labor market is modeled as a coalitional game, where a random subset of participants is employed, generating stochastic economic output. To ensure fairness, we adopt equal employment opportunity as a normative benchmark and propose a dichotomous valuation rule that assigns value to both employed and unemployed participants. Within a continuous-time, balanced budget framework, we derive a closed-form payroll tax rate that is fair, debt-free, and asymptotically risk-free. This tax rule is robust across alternative objectives and promotes employment, productivity, and equality of outcome. The framework naturally extends to other domains involving random bipartitions and shared payoffs, such as voting rights, health insurance, road tolling, and feature selection in machine learning. Our approach offers a transparent, theoretically grounded policy tool for reducing poverty and economic inequality while maintaining fiscal discipline. Full article

Data, as a key production factor in modern logistics systems, plays a crucial role in enhancing industry efficiency and promoting supply chain coordination. To address challenges in data sharing among logistics enterprises—such as conflicts of interest, unequal risk allocation, and insufficient security governance—this study develops a tripartite evolutionary game model involving logistics enterprises, data partners, and supervisory institutions. The payoff matrix incorporates prospect theory to account for risk attitudes, loss–gain perceptions, and subjective judgments. Stable equilibrium points are derived using the Jacobian matrix, and numerical simulations examine strategic evolution under varying parameters. Results indicate that increased returns for data partners reduce their motivation to provide truthful data, while higher enterprise profits suppress logistics enterprises’ willingness to share. Compensation levels have limited impact, whereas excessively high supervision subsidies weaken participation and oversight across all parties. Stronger penalties and higher-level enforcement significantly promote compliance and positive system evolution. Enterprise investment positively correlates with data-sharing behavior, and risk preferences of all parties accelerate convergence to stable equilibria. Conversely, excessively low risk preference in supervisory institutions may lead to an unstable “sharing–false data–non-regulation” pattern. These findings provide theoretical support and policy guidance for designing a dynamic governance mechanism that balances incentives, constraints, and collaboration, thereby facilitating secure and effective logistics data sharing and informing the development of the data factor market. Full article