Search Results (133)

Upstream small- and medium-sized enterprises (SMEs) in emerging economies often face severe credit constraints. These constraints hinder green transformation by limiting green R&D investment and production capacity. To address this, we develop a Stackelberg-based governance strategy selection framework. We first analyze cost-sharing (CS) and equity-sharing (ES) contracts and then extend them by incorporating external financing, where the retailer’s contractual commitment serves as an operational guarantee. This integration leads to two incentive-financing bundles, namely CS-F and ES-F. Three main findings emerge. First, capital constraints fundamentally shape the feasibility of green supply chain governance by creating a trade-off between green R&D and physical production. Specifically, the CS contract is feasible only within an intermediate capital range, whereas the ES contract is infeasible. Second, the incentive-financing bundles relax capital constraints and expand the feasible governance region. Although all feasible governance strategies promote green R&D investment, the ES-F bundle remains more sensitive to parameter variations. Third, the optimal governance strategy depends primarily on firms’ capital conditions, shifting across CS, CS-F, and ES-F. Notably, a distributive-efficiency paradox emerges: even when the ES-F bundle yields greater total surplus, a higher sharing ratio violates the retailer’s individual rationality and prevents its adoption. We introduce an asymmetric Nash bargaining mechanism to address this paradox. The mechanism determines transfer payments endogenously and restores the efficient governance outcome. Overall, our findings help supply chain managers select appropriate governance strategies based on observable firm-level capital conditions. Full article

Public opinion event response requires not only timely decisions but also transparent and trustworthy collaboration among multiple stakeholders. To address delayed responses, fragmented collaboration, and information opacity, this study first proposes a collaborative-decision model based on blockchain for public opinion event response and then develops a blockchain-driven adaptive consensus method to improve consensus efficiency and decision quality. In the proposed model, public opinion information is mined to identify the attribute categories and weights of response alternatives, while collaborative-decision quality is evaluated by integrating decision reliability, opinion convergence, and individual comprehensive weights derived from social network influence. On this basis, smart contracts are designed to support transparent, traceable, and automated consensus processes. The adaptive consensus method dynamically terminates the consensus process by considering public opinion crisis levels and individual consensus differentiation. A utility-maximizing feedback mechanism is further introduced to improve consensus quality, and smart contracts are used to detect the adjustment willingness of inconsistent individuals and implement an elastic incentive mechanism. Case analysis and simulation experiments verify the effectiveness and robustness of the proposed model and method, showing their potential to support trustworthy collaborative decision-making in public opinion event response under uncertain and time-sensitive conditions. Full article

This paper studies how trust can be supported in exchange environments where potential responders make prior declarations about future return behavior. While prior work has separately examined communication, competition, or contract enforcement, how these mechanisms interact remains underexplored. We modify the standard trust game by introducing two potential responders who declare their intended returns before one is selected, before experimentally varying responder-side competition and contract enforcement in a 2 × 2 design. The results reveal an asymmetric relationship: competition significantly increases trust and realized trustworthiness, whereas contract enforcement alone does not generate a statistically significant increase in trust. However, when competition is combined with enforcement, the effect is substantially amplified, producing the highest levels of trust and trustworthiness. Mechanism evidence shows that competition drives higher declarations and selection incentives, while enforcement eliminates the declaration-fulfillment gap, making competitive declarations credible. These findings suggest that competition and enforcement are complements rather than substitutes—a result with implications for the design of online platforms and other markets where trust depends on prior declarations. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

This paper investigates whether prediction market settlements create incentives for temporary price pressure in Bitcoin spot markets. Using high-frequency data from February 2025 to January 2026 and actual contract-level data from Polymarket and Kalshi to identify economically relevant contract strikes, we document basis divergence between settlement oracle exchanges (Coinbase) and non-constituent exchanges (Binance) during expiry windows. Employing a difference-in-differences framework with month fixed effects, we find that a one standard deviation increase in strike proximity is associated with a 6.7 basis point constituent exchange price deviation during settlement windows. The estimate is precise under the baseline minute-level HAC specification, while exact paired-month permutation inference based on 12 settlement events yields $p=0.0256$; equal-weight event aggregation produces a larger negative estimate, indicating event heterogeneity. Monthly directional patterns are suggestive, though stricter event-level and above-versus-below-strike tests provide mixed evidence on directional asymmetry. Taken together, these findings provide reduced-form evidence consistent with settlement-related incentives and may raise broader settlement-design considerations for decentralized financial systems. However, the analysis does not directly observe trader intent or the underlying mechanism. Full article

The authorization and operation of the public data supply chain is an important pathway for unlocking the value of public data and cultivating the data element market. However, in practice, it faces challenges such as insufficient data supply and insufficient stakeholder incentives. This paper focuses on the differentiated subsidy policies of the fiscal department, constructing a differential game model involving multiple participants, including data providers, data managers, and data operators. The paper systematically compares the optimal effort decisions of each stakeholder, the evolution trajectory of public data product value, and the trajectory of overall system profits under two subsidy models: cost subsidies and transaction subsidies. It further analyzes the regulatory role of revenue distribution ratios and cost-sharing contracts in shaping the effectiveness of these subsidy mechanisms. The study finds that: (1) cost subsidies provide more balanced and stable incentives for all stakeholders and contribute more to the final value trajectory of public data products; transaction subsidies are more effective in improving overall system profits but offer weaker incentives for the supply and management sides, requiring flexible use in conjunction with cost-sharing contracts; (2) cost-sharing contracts play a regulatory role under different subsidy models and effectively reduce the data provider’s dependence on fiscal subsidies under the transaction subsidy mechanism; (3) the revenue distribution ratio only positively affects the effort decisions of the supply and management sides under the transaction subsidy model, and the optimal subsidy ratio of the fiscal department is closely related to the revenue distribution ratio. Therefore, differentiated subsidy strategies should be implemented based on specific decision-making contexts and internal revenue distribution ratios. This paper reveals the synergistic incentive mechanism between differentiated subsidy models and cost-sharing contracts, providing a theoretical basis for the design of subsidy policies for public data authorization and operation. Full article

Geographical indication (GI) products are often understood through terroir, certification, and branding, yet less attention has been paid to the feedback dynamics through which their market value is amplified and destabilized. This paper applies systems thinking to the post-harvest value–trust subsystem of Xinhui Chenpi, a traditional Chinese aged citrus pericarp product with GI protection, to examine how value amplification and systemic vulnerability emerge through interacting feedback mechanisms. Drawing on 28 semi-structured interviews conducted between September 2023 and December 2024, supplemented by participant observation, policy and standard documents, field-based market observations, and contextual media materials, the study develops a preliminary causal loop diagram (CLD) with 15 endogenous feedback variables, 3 boundary value-input variables, 4 exogenous contextual inputs, and 21 causal links (18 endogenous and 3 value-input). The model identifies two reinforcing loops and two balancing loops through which price expectations, holding incentives, credible circulation supply, perceived scarcity, misrepresentation, and trust erosion interact. The trust-erosion loop shows how premium-driven misrepresentation increases claim uncertainty, weakens open-market consumer trust, reduces credible open-market liquidity, and further contracts credible circulation supply. Buyer exit and delayed supply response operate as limited balancing mechanisms because their effects are segment-dependent and constrained by aging and verification delays. The proposed CLD suggests that high-value mechanisms in aging-dependent GI products may also generate structural vulnerabilities, with implications for managing consumer trust, claim verification, and credible circulation in premium markets. Full article

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms. Full article

Water conservancy infrastructure is essential for sustainable socio-economic development, but quality failures caused by information asymmetry among stakeholders may undermine long-term project performance. Existing studies rarely integrate early completion benefits into quality control decisions or investigate governance strategies under different information conditions. This study develops a three-level principal-agent model involving the owner, supervisor, and contractor by incorporating early completion incentives into the quality supervision framework. The optimal quality monitoring level (Pa), quality guarantee deposit (S), and penalty mechanism (F) are derived under symmetric, asymmetric, and incomplete information scenarios, where probability density functions are introduced to characterize behavioral uncertainty under incomplete information. The results show that early completion incentives require a balance between schedule acceleration and quality risk control. Under sufficient contractor quality probability-based behavior, increased time effort reduces the owner’s supervision demand, whereas schedule compression combined with weak quality control increases the need for financial constraints. Under asymmetric and incomplete information, higher penalties and adaptive deposit strategies are required to mitigate moral hazard. Moreover, contractor probability-based behavior and supervisor performance exhibit substitution effects on owner supervision, indicating that strong partner probability-based behavior can reduce monitoring costs. These findings provide practical guidance for designing adaptive contracts, including differentiated supervision strategies and probability-based behavior-based deposit mechanisms, thereby enhancing the resilience and sustainability of water infrastructure supply chains. Full article

This paper investigates the dynamic coopetition and capacity-sharing strategies between an Integrated Manufacturer and a Developer under R&D uncertainty, focusing on the governance of strategically scarce capacity. By constructing a two-stage game model, we analyze how government intervention and risk-hedging mechanisms influence the allocation of idle strategically scarce capacity in innovation-driven industries. The findings reveal a two-sided paradoxical behavioral pattern: in the low-probability R&D interval, rather than relying on safe contract manufacturing, the Integrated Manufacturer counter-intuitively reduces collaborative duration to aggressively gamble on its immature product. Conversely, in the high-probability R&D interval, where conventional wisdom predicts an aggressive pivot to self-production, the manufacturer paradoxically extends or maintains the contract manufacturing duration, driven by the partner’s full cost-sharing incentive mechanism. Furthermore, To maximize the total supply output of strategically scarce resources during collaboration, we uncover a non-linear ‘counterproductive subsidy trap’ and propose a binary ‘critical mass’ policy rule: governments should either withhold subsidies entirely or commit sufficient funding to bypass the supply deficit zone. This framework provides a theoretical foundation for managing scarcity in capital-intensive sectors such as biopharmaceuticals and semiconductors. Full article

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value. Full article

The Engineering-Procurement-Construction (EPC) general contracting model has emerged as the dominant delivery method for large-scale infrastructure and industrial projects in China. However, contemporary EPC project cost control remains plagued by critical industry challenges, including fragmented cross-stage coordination, pervasive data silos, and the shallow integration of digital technologies into core management processes. This study considers three key stakeholders—government regulators, project owners, and EPC general contractors—and develops a tripartite evolutionary game model to analyze the strategic interactions underlying intelligent cost control in EPC projects. We examine the evolutionary stability of each stakeholder’s strategy selection, explore how various factors influence tripartite strategic choices, and further investigate the stability of equilibrium points in the game system. The key findings are summarized as follows: (1) Strengthening government incentives and penalties simultaneously promotes owners’ investment in intelligent cost control systems and general contractors’ active collaborative cost management. However, excessive incentive intensity undermines the government’s regulatory effectiveness. (2) Establishing a revenue-sharing mechanism for excess cost savings fully stimulates the spontaneous cooperation willingness of owners and general contractors, serving as the cornerstone for market-oriented operation of intelligent cost control. (3) Reducing owners’ intelligent construction investment costs and general contractors’ collaborative control costs effectively addresses practical implementation barriers and accelerates the digital upgrading of engineering cost management. Finally, numerical simulations are performed using MATLAB R2020b to validate theoretical findings. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

In the digital twin (DT) network, effective edge data processing is essential to meet the real-time requirements of DT models. However, edge servers (ESs) are self-interested and have limited computation resources. The virtual content operator (VCO) cannot observe their true computing capabilities, leading to participation reluctance and information asymmetry. To address these challenges, this paper proposes a contract-learning integration method for computing incentive and data offloading. A two-dimensional computation-reward contract incentive mechanism is designed to motivate ESs to provide computation resources for data pre-processing, where both continuous and discrete distributions of ES types are considered. Then, ESs upload the processed results to the VCO for DT model mapping, synchronization, and final construction. Based on the individual rationality and incentive compatibility constraints, the optimal incentive reward and computing resource allocation strategies are analytically derived to maximize the VCO’s utility. Then, based on the signed contracts, a multi-agent double deep Q-network algorithm is developed to jointly optimize the binary data offloading decision, transmission bandwidth, and transmission power for the minimal system delay. The algorithm learns adaptive strategies in the dynamic network environment and mitigates Q-value overestimation. Numerical results demonstrate that the proposed method improves system performance in terms of computing incentive and data offloading. Full article

The commissioning of private suppliers to conduct technological research and development (R&D) has become a central instrument for the public sector to promote technological innovation in public goods. However, information asymmetry and goal divergence between public principals and suppliers create moral hazard problems that can undermine innovation efficiency. Purely output-based incentive contracts are often insufficient to curb suppliers’ opportunistic behavior, especially when R&D outputs are uncertain and difficult to measure ex ante. This raises the need to complement incentive contracts with supervision mechanisms and to jointly optimize the structure of incentives and monitoring efforts. Building on principal–agent theory, this paper develops an incentive model that explicitly incorporates both supervision intensity and regulatory difficulty and analyzes how these factors shape suppliers’ R&D efforts and the public sector’s benefit levels. The results show, first, that appropriately designed supervision can increase suppliers’ willingness to invest in R&D and thereby help to strengthen the effectiveness of incentives. Second, incentive contracts need to be adjusted in line with supervision intensity: by reallocating rewards based on both observed outputs and supervision results, the public principal can induce higher effort levels. Third, as regulatory difficulty rises, the marginal effectiveness of supervision changes; under high regulatory difficulty, excessive supervision may even weaken incentive effects, implying that supervision intensity should be kept within a moderate range. Fourth, there exists an interior level of supervision intensity that balances monitoring costs against incentive benefits and thus maximizes the principal’s overall expected payoff. Viewed from a system engineering perspective, commissioned public goods R&D constitutes a complex multi-actor system subject to external disturbances in which incentive and supervision mechanisms operate as joint control structures regulating system behavior. The findings provide analytical support and policy-relevant insights for designing and calibrating supervision and incentive mechanisms in technological innovation projects for public goods. Full article