payoff analysis

This paper identifies how blockchain protocol malleability elevates miners’ time preference, undermines long-run cooperative equilibria, and incentivizes a strategic shift from productive investment toward political rent-seeking and influence contests. Method: Drawing on Austrian capital theory (Böhm-Bawerk, Mises, Hayek) and repeated game frameworks, the authors construct an institution–behavior interaction model to analyze how protocol immutability functions as a critical institutional anchor—reducing time preference, enhancing calculability, and reinforcing strategic consistency. Contribution/Results: The study pioneers the systematic integration of time preference theory into blockchain incentive design, demonstrating that rigid, rule-based protocols restore entrepreneurial confidence by mitigating uncertainty and intertemporal distortion, thereby enabling sustainable network equilibria grounded in credible commitment and long-horizon coordination.

This study addresses strategic competition among multiple agents for divisible, scarce resources—such as financial assets or computational capacity—under conditions of asymmetric information or the absence of a common prior. By developing a game-theoretic framework that integrates market price dynamics, the work extends the existence, uniqueness, and computationally efficient characterization of Bayesian Nash equilibria to partial-information settings with a common prior, and further establishes convergence guarantees for simultaneous learning dynamics when no common prior is assumed. Theoretical contributions include a rigorous characterization of equilibrium properties and an upper bound on the Price of Anarchy. Empirically, simulations based on real-world financial data validate both the convergence of the proposed algorithms and the efficacy of the resulting strategic behaviors.

Frequent consensus rule changes undermine long-term miner cooperation in blockchain systems by inducing short-termism and coordination failure. Method: We develop a repeated game model under stochastic rule shocks, formalizing “institutional noise” as a driver of intertemporal decision distortion, and analyze equilibrium dynamics via game-theoretic reasoning and numerical simulation. Contribution/Results: We identify critical thresholds at which rational mining behavior shifts from cooperative to arbitrage- or rent-seeking strategies. Even minor rule uncertainty significantly increases miners’ time preference and destabilizes cooperative equilibria. Empirical validation confirms that protocol rigidity is essential for computational sustainability and ecosystem stability. The study reframes blockchain protocol design through a constitutional economics lens, establishing institutional stability—not merely technical efficiency—as a foundational principle of decentralized governance.

This paper investigates realistic multi-stage resource allocation games where payoffs depend on supply-demand balance and participant engagement—increased resource investment yields higher returns, but insufficient player participation incurs profit losses. We propose a generalized multi-stage game framework that unifies canonical settings such as Colonel Blotto and receding-horizon games. Under weighted proportional fairness allocation, we derive a stage-wise payoff structure conforming to the Tullock contest success function. Theoretically, we establish sufficient conditions for the existence and uniqueness of Nash equilibria, design a semi-decentralized iterative algorithm applicable to any number of players, and provide a semi-analytical equilibrium solution for the Blotto variant. Empirical evaluation demonstrates the model’s effectiveness in intelligent mobility resource competition, significantly improving modeling fidelity and computational efficiency in complex, dynamic competitive environments.

This paper examines a dynamic dividend distribution game between two financially constrained firms competing in a shared market, where default risk generates a monopoly externality—upon one firm’s default, the surviving firm captures the entire market and enjoys higher profits. Method: We formulate a two-player singular stochastic control game with absorbing boundaries (representing default states) and derive, for the first time, an explicit feedback-type Nash equilibrium using stochastic control theory, Hamilton–Jacobi–Bellman (HJB) equations, and absorption boundary analysis. Contribution/Results: We obtain closed-form solutions for optimal dividend policies and equilibrium payoffs under two distinct equilibrium regimes: coexistence and dominance. Crucially, we identify a novel mechanism whereby the firms’ initial capital levels endogenously determine the prevailing equilibrium type. The analysis quantifies how competitive market structure simultaneously shapes micro-level financial decisions and macro-level systemic risk.

This study addresses the challenge of balancing revenue maximization and risk management in wholesale electricity markets with high renewable penetration, where bidding strategies must navigate the complexities of both day-ahead and real-time markets. To this end, the authors develop a high-fidelity two-stage bidding simulation environment grounded in empirical PJM market data and propose MARS-DA, a hierarchical multi-agent reinforcement learning framework. MARS-DA features a meta-controller that dynamically coordinates a “safe agent” and a “speculative agent” to enable risk-aware bidding decisions. The work introduces the first open-source, standardized reinforcement learning benchmark tailored to two-settlement electricity markets. Experimental results demonstrate that the proposed approach significantly improves risk-adjusted returns under extreme price volatility, outperforming existing methods while exhibiting robust adaptability to evolving market mechanisms.

This study investigates how participants’ understanding of equilibrium strategies transfers across different mechanisms. To this end, it introduces the concept of “strategic analogy,” which extends traditional notions of strategic equivalence by simultaneously remapping both actions and types. The paper develops a knowledge representation framework grounded in payoff comparisons to formally characterize strategic understanding. Integrating tools from mechanism design, equilibrium analysis, and knowledge representation, the work demonstrates that, provided a clear correspondence between actions and types is established, equilibrium reasoning can be effectively transferred across strategically analogous mechanisms. The proposed framework applies broadly to settings such as single-item auctions, scoring auctions, and nonlinear pricing with capacity constraints, offering both a theoretical foundation and practical pathways for cross-mechanism strategic transfer.

This study investigates how the design of decentralized electricity trading platforms influences prosumers’ strategic behavior and market efficiency. By formulating a finite-horizon dynamic game model integrated with a multi-agent differentiable clearing framework, perfect conditional ε-equilibrium analysis, and Cournot-style market power modeling, the paper quantifies strategic interactions among prosumers under varying pricing mechanisms, information disclosure policies, and energy storage ownership structures, and their impacts on grid settlement costs and user welfare. Results show that strategic behavior in the baseline scenario increases settlement costs by approximately 6%, whereas optimized platform design or decentralized storage ownership substantially mitigates strategic supply distortions. Despite such strategic actions, the platform still reduces electricity bills for passive users by about 40%, with only an 8% erosion of these gains, demonstrating the mechanism’s effectiveness in enhancing both efficiency and equity.

This study addresses the challenges of investment recovery and pricing in shared infrastructure systems, where uncertainty in revenue, heterogeneous risk preferences, and resource congestion complicate decision-making. To tackle these issues, the authors propose a risk-aware Stackelberg game in which the provider, as the leader, jointly optimizes capacity provisioning and access pricing, while users, as followers, commit to usage through take-or-pay contracts, accounting for operational costs, congestion effects, and their own risk aversion. The framework innovatively incorporates Conditional Value-at-Risk (CVaR) to model heterogeneous risk preferences, establishes the existence of equilibrium, and devises a polynomial-time approximation algorithm with provable performance guarantees. Numerical evaluations in a mobile edge computing setting reveal that higher user risk aversion leads to lower system capacity, prices, and provider profit, yet significantly improves the provider’s probability of achieving profitability.