Decision making and welfare effects in manufacturer's closed-loop supply chain for recycling and remanufacturing: A risk signal game theory perspective

ABSTRACT A manufacturer with private information about product quality sells through a retailer to end consumers. By hiding wholesale pricing contracts from end consumers, the manufacturer can hide his private information, eliminate signaling distortions and earn higher (expected) profit compared to observable wholesale pricing as well as direct selling; consumers may also earn higher surplus under such contracts even though they do not learn true product quality. Policies that mandate disclosure of quality or of upstream contracts can reduce welfare relative to the equilibrium with hidden contracts. We formalize this interaction as a class of intermediated signaling games—distinct from standard signaling models because of the hidden interaction with the intermediary—and introduce a new belief refinement, IC‐I, tailored to such games and analogous to the Intuitive Criterion.

Live-streaming selling has grown rapidly worldwide, and many brands are partnering with high-popularity influencers to boost sales. However, traffic manipulation can mislead brands into overestimating popularity, thereby distorting decisions on compensation, product pricing and live-streaming rewards. In this study, we build a brand–influencer signalling game to show how influencers use observable, costly signals to gain brand recognition. Equilibria are refined using the Cho–Kreps Intuitive Criterion to rule out unreasonable pooling outcomes. We find that commission and monitoring acceptance serve as credible signals of popularity. In equilibrium, the high-popularity influencer selects a higher commission and higher monitoring acceptance while the low-popularity influencer retains symmetric-information benchmark levels. Anticipating a higher revenue share, the brand optimally lowers prices and rewards when partnering with the high-popularity type. We then examine the role of monitoring acceptance and show that suppressing this signal imposes large losses, with identification relying excessively on commission, amplifying price and reward distortions and pushing both parties’ payoffs further from the symmetric-information benchmark. Allowing monitoring acceptance to share the identification burden attenuates these distortions and brings profits closer to symmetric-information benchmark levels. We recommend explicitly embedding monitoring-acceptance clauses and third-party verification in live-streaming contracts to support sustainable industry development.

This paper introduces the Bateson Game, a signaling game in which ambiguity over the governing rules of interaction (interpretive frames), rather than asymmetry of information about player types, drives strategic outcomes. We formalize the communication paradox of the “double bind” by defining a class of games where a Receiver acts under uncertainty about the operative frame, while the Sender possesses private information about the true frame, benefits from manipulation, and penalizes attempts at meta-communication (clarification). We prove that the game’s core axioms preclude the existence of a separating Perfect Bayesian Equilibrium. More significantly, we show that under boundedly rational learning dynamics, the Receiver’s beliefs can become locked into one of two pathological states, depending on the structure of the Sender’s incentives. If the Sender’s incentives are cyclical, the system enters a persistent oscillatory state (an “ambiguity trap”). If the Sender’s incentives align with reinforcing a specific belief or if the Sender has a dominant strategy, the system settles into a stable equilibrium (a “certainty trap”), characterized by stable beliefs dictated by the Sender. We present a computational analysis contrasting these outcomes, demonstrating empirically how different parametrizations lead to either trap. The Bateson Game provides a novel game-theoretic foundation for analyzing phenomena such as deceptive AI alignment and institutional gaslighting, demonstrating how ambiguity can be weaponized to create durable, exploitative strategic environments.

Purpose This paper reviews the application of game theory in finance, focusing on its role in modeling strategic interactions among market participants. It synthesizes classical models such as Nash equilibrium and signaling games while integrating emerging themes including behavioral finance, sustainability-linked decisions, decentralized finance (DeFi) and artificial intelligence (AI)-driven agents. The study aims to highlight how game-theoretic frameworks inform financial decision-making, market design and governance and to identify conceptual gaps and future research directions. Design/methodology/approach The study employs a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol, complemented by bibliometric mapping using VOSviewer. It analyzes 78 peer-reviewed articles published between 2000 and 2025 across five finance domains: asset pricing, corporate finance, investment strategies, financial markets and behavioral finance. Conceptual frameworks and taxonomies are developed to categorize game-theoretic models by strategic orientation and information structure, offering a structured synthesis of theoretical advancements and practical applications. Findings Game theory enhances understanding of strategic behavior in finance, particularly under conditions of asymmetric information and market complexity. Key findings include the relevance of signaling games in initial public offerings pricing, repeated games in environmental, social and governance commitments and mechanism design in DeFi governance. The review identifies gaps in behavioral integration, empirical validation and modeling of decentralized ecosystems. It proposes future research directions involving multi-agent learning, adaptive mechanism design and sustainability-linked financial strategies. Research limitations/implications The review is limited by its focus on published literature and may exclude emerging models in unpublished or proprietary research. Empirical validation of proposed frameworks remains a future research priority. Practical implications The paper offers actionable insights for regulators, investors and policymakers by applying game-theoretic tools to systemic risk management, portfolio allocation and financial regulation in digitized markets. Originality/value This study provides a novel synthesis of game theory’s evolution in finance, introducing conceptual frameworks that integrate behavioral, technological and sustainability-linked dimensions.

A signaling game between retail investors and firms: ESG washing as a strategic response

Navigating channel conflicts: dynamics of sales effort in manufacturer encroachment

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions.

ABSTRACT This paper investigates the optimal cooperation model of a new energy vehicle (NEV) supply chain comprising one NEV manufacturer and one power battery supplier. Considering asymmetric quality information, this paper develops the signaling game and the Stackelberg game to compare three cooperation models: the wholesale model, the joint venture model, and the patent licensing model. The results show that (1) under the wholesale model with asymmetric quality information, the separating equilibrium is a stable dominant equilibrium. The H‐type supplier may upwardly distort the wholesale price to separate from the L‐type supplier. (2) Compared with the wholesale model, the joint venture model and the patent licensing model may not be better. The choice of the optimal cooperation model is related to the cost of the high‐quality power battery, the joint venture share, and the fixed patent licensing fee. (3) The information sharing enables Pareto improvements in the ex‐ante profits. Furthermore, government subsidies can stimulate consumption and enhance ex‐ante profits, but their effects vary substantially across different cooperation models.

Greenwashing in ESG information disclosure: An intertemporal signaling game approach

In modern labor markets, rising educational attainment among job applicants often fails to translate into corresponding productivity differences. This phenomenon, commonly referred to as credential inflation, raises concerns about the signaling power of education. This paper explores credential inflation through the lens of signaling games in microeconomic theory and investigates how changing cost structures and labor market screening contribute to inefficient signaling. Specifically, a Perfect Bayesian Equilibrium (PBE) model is used, where job applicants privately know their ability level and choose their education level to signal productivity to employers. When the cost of education becomes less sensitive to ability, pooling equilibria emerge, where both high- and low-ability workers choose the same level of education. As a result, education loses its signaling power and inflation of educational requirements occurs. This paper concludes that credential inflation is a rational outcome under certain market conditions and propose policy responses to mitigate its effects on labor market sorting.

This study examines supply chain coordination mechanisms in India's Ethanol Blended Petrol (EBP) Program, integrating petrol and ethanol producers to meet transportation fuel demand while addressing environmental, energy security, and sustainability goals. Using a Stackelberg game model with Karush–Kuhn–Tucker (KKT) conditions, we analyse the effects of centralised versus decentralised decision-making on optimising coordination under regulatory constraints. We explore two mechanisms – Revenue Sharing (RS) and Cost Allocation (CA) – to assess their impact on profitability, carbon emission reduction, and consumer demand. Findings reveal that Centralized Decision-Making (CDM) achieves lower retail prices and higher emission reductions, while Decentralized Decision-Making (DDM) results in higher prices and lower reductions due to lack of coordination and information asymmetry, further analysed using a Bayesian Stackelberg signalling game. RS and CA effectively optimise coordination by aligning incentives, balancing costs, and improving environmental outcomes. Sensitivity analysis highlights the roles of carbon reduction coefficients, consumer preferences, and government policies (taxes and subsidies) in shaping EBP's viability. We provide a strategic framework for policymakers to design regulatory interventions, refine subsidies, and implement effective mechanisms that promote sustainable fuel adoption, stabilise prices, and embed environmental sustainability into operational decisions, supporting India's transition to a cleaner and more resilient transportation sector.

This paper develops a framework to extend the strategic form analysis of cursed equilibrium (CE) developed by Eyster and Rabin (2005) to multistage games. The approach uses behavioral strategies rather than normal form mixed strategies and imposes sequential rationality. We define and characterize properties of cursed sequential equilibrium (CSE) and apply it to four canonical economic applications: signaling games, reputation building, durable goods monopoly, and the dirty faces game. These applications illustrate various implications of CSE, show how and why it differs from sequential equilibrium and CE, and provide evidence from laboratory experiments that support the empirical relevance of CSE. (JEL C72, C73, D42, D82, D83)

Abstract We compare different forms of communication in the context of cheap talk sender-receiver games. While previous experiments find evidence supporting the comparative statics prediction that more preference divergence leads to less information transmission, there is also a consistent pattern of overcommunication and exaggeration, not predicted by theory, in which subjects convey more information than predicted in equilibrium. The latter of these findings may be due to the restricted nature of the message space in most experimental cheap talk games, encouraging subjects to engage in exaggeration artificially, rather than allowing it to emerge naturally. We tested this hypothesis with an incentivized lab experiment, and found evidence both phenomena persist with natural language (text-based) communication. Moreover, we probe the consequences of this expanded message space for outcomes, showing that senders benefit more than receivers, but that the most notable effect is that text messages improve efficiency.

ABSTRACT Motivated by supply chain finance (SCF) practices, we propose a new financing mode, i.e. discount negotiation on interest rate between the capital-constrained supply chain and the bank. Specifically, with credit strength, a core supplier can help the capital-constrained retailer (a small- and medium-sized enterprise [SME]) negotiate for a possible discount on interest rate from the bank. Such a discount can ease the SME retailer’s financing burden. Correspondingly, the bank would set negotiation barriers to limit this discount for its own interests. To reveal the underlying credit supporting mechanism, we establish two Stackelberg games: benchmark model without – and negotiation model with – the core supplier’s credit support. It shows that the supply chain members may join forces to conceal this core supplier’s real credit level to earn more benefits at the bank’s expense. Further, a signalling game is extended to analyse the bank’s lending decision with possible untruthfulness of the capital-constrained supply chain on the credit level under information asymmetry. This results in a pooling equilibrium with invalid credit signal transmission. Subsequently, a revenue-sharing contract for coordinating the credit supporting scheme under information asymmetry is designed. Overall, our study carries significant practical implications for implementing the discount negotiation mode in SCF (M11).

We consider information transmission between a sender, who has finitely many types, and a receiver, who must choose a decision in a real interval. The payoffs depend on the sender’s type and the receiver’s decision. We assume that the payoff functions are well-behaved. We characterize the pure strategy perfect Bayesian equilibrium outcomes as incentive-compatible partitions of the sender’s types. We propose an algorithm, which starts from the finest partition. Then, at every step, if the current partition is not incentive compatible, a random type of the sender improves its payoff, and the receiver best responds. We show that every possible run of the algorithm converges to a unique incentive-compatible partition [Formula: see text]. This partition [Formula: see text] is such that any partition with more cells than [Formula: see text] is not incentive compatible, so the algorithm determines to which extent information transmission can be effective. The partition [Formula: see text] also satisfies some refinement criteria for perfect Bayesian equilibria in sender-receiver games. Furthermore, in a discrete version of a popular class of examples (namely, if the sender’s type is uniformly distributed and payoff functions are quadratic, with a constant upward bias for the sender), [Formula: see text] ex ante Pareto dominates every other incentive-compatible partition.

The research focuses on privacy protection in the Internet of Things environment. A model based on evolutionary theory game and signal game mechanism is proposed to analyze and optimize privacy protection strategies. The study introduces evolutionary game theory and signal game mechanism to construct a game model between users, devices, network operators, and attackers. Detailed discussions are conducted on factors such as privacy protection needs, information asymmetry, and privacy leakage risks. The proposed Multi-stage Signal Game and Deep Learning Model for IoT Privacy Protection (IoT-PSGDL) performed the best on privacy protection effectiveness, at 98.25% on the CIC IoT dataset, with a policy update speed of 7.42 updates/second and a system response time of 35.12ms. Compared with other models, the proposed model performed well in multiple metrics, such as privacy protection persistence (97.56%), communication latency (54.12ms), and data storage security (96.75%). In addition, privacy protection strategies such as data encryption performed the best in the experiment, with a privacy protection success rate of 96.72% and the lowest privacy leakage probability of only 2.14%. The significance of the research lies in providing an efficient and dynamically optimized privacy protection strategy that can effectively respond to various privacy threats in complex Internet of Things environments.

When less is more: Evolutionary dynamics of deception in a sender–receiver game

Iterated exclusion of implausible types in signaling games

Modern cybersecurity challenges require dynamic defense mechanisms able to waiting for antagonistic strategies at the same time as balancing operational constraints. This study unifies recreation theory and operations studies (OR) to create an adaptive framework for countering sophisticated cyber threats, empirically tested the use of the QRID-2025-Dataset-Small.Csv. By synthesizing Stackelberg games (modeling hierarchical attacker-defender interactions), evolutionary video games (taking pictures long-term antagonistic evolution), and signaling games (addressing deception) with OR methods—including blended-integer programming and Markov choice processes—the framework optimizes useful resource allocation and selection-making beneath uncertainty. Empirical results spotlight a 65.7% discount in superior chronic danger (APT) breaches and a three.41 go back on investment (ROI) for ransomware mitigation, surpassing rule-based totally and machine mastering benchmarks. The technique achieves linear scalability ((O(n))) and adapts to heterogeneous environments, inclusive of high-noise situations (30% of instances), overcoming barriers of static or fragmented solutions. The take a look at contributes a pioneering integration of game-theoretic equilibrium evaluation with stochastic OR optimization, proven towards real-global value metrics (CostTime, CostMoney, CostEnergy) derived from 500 assault simulations. Practical programs are demonstrated in time-touchy sectors like healthcare and commercial manage structures (SCADA), where fee-effective speedy response is critical. By merging strategic adversary modeling with operational efficiency, this work advocates for future improvements in AI-driven actual-time threat prediction and behavioral technological know-how to beautify human-centric protection strategies. The framework gives a scalable, replicable model for protecting essential infrastructure in opposition to escalating cyber risks, urging cross-disciplinary collaboration between academia and enterprise.