We study peer-to-peer (P2P) insurance contracting between a risk-averse P2P reinsurer and multiple risk-averse peers in an asymmetric Nash-bargaining framework, where all agents seek to improve expected utility relative to their disagreement points. Consistent with the expected value premium principle, we impose a price-fairness condition requiring each peer's expected contribution to be based on a common loading applied to the peer's expected loss. To justify the bargaining formulation relative to a standard fixed-weight weighted-sum optimization problem, we provide an axiomatic characterization showing that the Nash bargaining solution satisfies properties well suited to voluntary P2P insurance contracting in small pools. We establish the existence and uniqueness of the optimal contract and derive first-order characterizations for the full-, partial-, and zero-reinsurance regimes. To address subgroup formation, we develop computationally tractable sufficient conditions that rule out viable coalitional deviations, both with and without price fairness. Our numerical study investigates the impact of price fairness and pool size on the optimal contract and agents'welfare. Price fairness reduces dispersion in risk allocations and certainty-equivalent loadings among peers. Regarding pool size, welfare need not increase monotonically, highlighting that risk-pool expansion depends not only on diversification but also on the evolution of bargaining power.
We settle the worst-case approximability of consumer-surplus maximization in general multidimensional mechanism-design environments. We do so through two black-box reductions from welfare maximization to the agents'total utility. Our first reduction turns exact welfare maximization into a prior-free, universally truthful and ex-post individually rational mechanism that preserves at least a $1/H_n$ fraction of optimal welfare as expected consumer surplus. The guarantee holds for $n$ agents with arbitrary nonnegative valuations over a finite outcome space, where $H_n$ is the $n$-th harmonic number. The factor $H_n$ is worst-case optimal, including its constant, even for a single-item auction with a known i.i.d. prior and Bayesian incentive compatibility. Our second reduction allows existing truthful welfare approximation mechanisms to be reused for surplus maximization. For valuation classes closed under scaling, it converts any ex-post individually rational, truthful $\alpha$-approximation for welfare with nonnegative payments into an $O(\alpha\log(n))$-approximation for surplus. Our sharp guarantee resolves the welfare-approximation aspect of the open question of Hartline and Roughgarden [2008] on the power of money burning beyond $k$-unit auctions, and the question of Ezra et al. [2025] concerning optimal surplus guarantees for broader valuation classes. It also replaces the outcome-dependent $O(\log|\mathcal{O}|)$ guarantee of Fotakis et al. [2015] with the tight agent-dependent factor $H_n$. These results yield polynomial-time mechanisms with the exact $H_n$ guarantee for gross-substitutes. They also give prior-free, universally truthful approximations of $O(H_n\log^2\log m)$ for XOS valuations and $O(H_n\log^3\log m)$ for subadditive valuations using demand and value queries, where $m$ is the number of items.
We introduce a repeated dynamic incentive framework for characterizing when"compliance", or full-effort honest service provision, is incentive compatible in Decentralized Physical Infrastructure Networks (DePIN). We model quality control in this setting as a repeated moral-hazard problem between the protocol and each provider, where compliance is enforced by both slashing posted collateral and the discounted threat of demotion to probation tiers on a reputation ladder. Our main contribution is the"deterrence ratio"$\Gamma$, the worst-case ratio of a deviation's private gain to its marginal probability of detection. We find that if any profitable deviation does not increase the fail probability relative to compliance, then binary public-outcome protocols cannot deter it. When all profitable deviations have positive detection gaps and no weakly costlier action is less likely to fail, compliance is sequentially incentive compatible if and only if immediate slashing plus discounted reputation loss is at least $\Gamma$ at every reputation tier. We use this condition to formulate a protocol design problem mapping service primitives to stake requirements, reward schedules, probation rules, and audit frequency.
We study optimal investment for insurers managing participating (profit-sharing) contracts under probability distortion and probability benchmark (aspiration) constraints. The problem combines three theoretical complexities: (i) nonconcave effective utilities induced by embedded guarantees and surplus-sharing rules, (ii) probability weighting capturing behavioral aspects of long-horizon decisions, and (iii) aspiration-type constraints formalizing solvency requirements. Using quantile formulations and concavification techniques, we derive explicit closed-form solutions for optimal terminal wealth and trading strategies in both complete and incomplete Black-Scholes markets. Our utility class accommodates the piecewise hyperbolic absolute risk aversion (PHARA) family and covers nonconcavities arising naturally in insurance contexts. The framework reveals how probability distortion weakens lock-in behavior and induces time inconsistency: under inverse S-shaped distortions, insurers overestimate upside probabilities and increase risky investment relative to undistorted benchmarks. Asymptotic analysis and numerical illustrations demonstrate regime switches in optimal policies driven by regulatory thresholds and capital constraints. Our results extend the hope-fear-aspirations framework of He and Zhou (2016) and provide practical insights for managing insurance balance sheets under behavioral preferences and solvency constraints.
The random-order interpretation of the Shapley value specifies both a terminal allocation and a payment path: when a player enters, she receives her marginal contribution at that moment. We ask whether players would voluntarily follow this path. A player may prefer to wait if her marginal contribution is expected to rise as the coalition grows. This creates a tension in convex games. The Shapley allocation belongs to the core, but, except in additive games, the associated marginal-contribution payment path does not support voluntary entry. We separate these two objects by introducing payment flows that divide the surplus created at each coalition transition. Every efficient allocation can be implemented by a local, budget-balanced voluntary flow when signed payments are available. We select a canonical implementation by minimizing the distance from the Shapley flow, and show that the problem simplifies sharply in symmetric cardinality games. We then consider equal residual sharing, under which any transition surplus not paid to the entrant is divided equally among incumbents. The closest voluntary flow in this class selects an allocation endogenously. With two player types, the selected allocation is a game-dependent affine combination of the Shapley and equal-division allocations. Examples show both the role of payment restrictions and a possible conflict between voluntary entry and coalitional stability.
This paper investigates the strategic and welfare properties of endogenous population partitioning (secession) within large-population anonymous games featuring strategic heterogeneity. We consider a continuum-player framework with a binary action space where players are categorized either as fol- lowers, who experience positive network externalities from conformity, or as contrarians, who seek distinctiveness via anti-conformism. We fully characterize the set of Nash equilibria and establish con- ditions under which costless secession yields structural Pareto improvements. We demonstrate that in any strategically mixed society, every mixed-strategy Nash equilibrium admits a Pareto-improving se- cession. With finitely many types, secession systematically mitigates coordination frictions, enhancing both individual payoffs and aggregate utility. Furthermore, we characterize social planner configura- tions optimizing weighted aggregate utility, establishing a formal mathematical isomorphism between optimal jurisdictional design and the theory of Bayesian persuasion solved via concavification. Finally, we derive the structural conditions governing migration stability when subgroups can unilaterally re- locate across distinct societies.
The VCG family and the AGV mechanism are two classical approaches to efficient implementation in the static social choice problem. In 2024, Cs\'oka et al. showed that AGV has critical weaknesses. In contrast, the transferable-utility Guaranteed Utility Mechanism (TU-GUM) retains all the standard desirable properties of AGV while adding further ones, including collusion-proofness, because it implements efficiency in Guaranteed Utility Equilibrium. TU-GUM also applies to a more general dynamic setting with multiple extensions. Moreover, TU-GUM is a special case of an even more general and robust mechanism that combines contingent first-price tendering with the coordinated execution of dynamic stochastic multi-agent projects through a surprisingly simple rule. This paper summarizes and connects existing results from a different perspective, with some minor new observations.
Endre Csóka· 0 citations
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