We study strategic bargaining for peaceful settlement before a conflict escalates into war (modeled as an all-pay auction), comparing two protocols: offering a take-it-or-leave-it side payment versus requesting one. Unlike in mediation models, the proposer here can signal private information and influence the opponent’s beliefs by varying the payment proposal, which renders the prospect of peace tenuous. In the bribing model, peace can be implemented through a continuum of bribes but cannot be secured. Conversely, in the requesting model, peace security is possible, yet it can only be sustained through a single, specific request.
Keywords: All-pay auctions; side payments; signaling; conflict resolution; peaceful settlement
JEL codes: D44; D74; D82 [page::1]
This paper provides empirical evidence that shared autobidding software synchronizes bids across batteries owned by different operators, especially after the ERI scarcity-disclosure reform [page::1][page::2]. Bids from the same provider co-move more strongly in the same region due to provider-driven forecasts and implementation, and co-movement intensifies with provider concentration [page::9][page::11]. A dynamic Bellman analysis estimates the marginal value of stored energy and, via counterfactual reclearing, tests whether observed bidding reflects owner-level incentives or internalisation of same-provider profits; results reject owner-level only deviations for near-margin shares roughly 30-70% and support internalisation across the path [page::19][page::22]. The conduct test finds evidence of internalisation when the focal provider's near-margin MW share is about 30-70%, with lambda around 0.75-1, while outside this range the test loses power [page::25][page::27]. The identified conduct implies a consumer-cost of about \$5.5 million per year, concentrated in the two major autobidder providers (Tesla Autobidder and Fluence Mosaic) [page::28][page::30]. FCAS deviations show no conduct signal, suggesting the observed effects are primarily in energy bidding rather than FCAS services [page::16][page::17]. Overall, the findings contribute to the policy debate on algorithmic collusion and highlight that concentration in the upstream provider layer can shape market outcomes [page::4][page::31].
This paper derives the exact chi-square distributions and benchmark values for Jensen–Shannon Divergence (JSD) and Kullback–Leibler Divergence (KLD), and benchmarks them against PSI in monitoring distribution shifts of credit default probabilities generated by Merton, Merton with Jump, and Stochastic Volatility with Jump models. Key findings: JSD shows superior Type I error control near 5% but sacrifices power at small samples; KLD offers higher sensitivity; PSI tends to have higher power in larger samples. The work provides practical guidance on binning, sample-size requirements (min(n,m) ≥ 400), and when to use permutation/bootstrap corrections for sparse data, with empirical validation showing consistent discrimination of PD distributions across models [page::1]
本文提出一个统一的信用扩张理论以解释1999-2019年美国家庭住房周期中的三大事实:在繁荣期,收入增速与按揭增速在同城 ZIP 码间呈负相关,但跨城则呈正相关;繁荣区的受冲击城在金融信用扩张中显示出更强的波动;最严重的衰退城往往也经历最强的复苏。作者构建以净出口增长暴露度为核心的本地经济驱动因素,区分政府担保按揭(GSEM)与私有标签按揭(PLM)的制度差异,利用国际贸易文献的格拉VD(gravity)模型工具建立工具变量以识别PLM的信用扩张对房价与按揭的因果影响,并通过“低-高(LMH)”双重差分和多项鲁棒性检验排除纯投机驱动的解释。研究给出若干跨城与同城内的双重差分预测,以及在繁荣期、衰退期与复苏期的三段式时间演化结论;政策含义聚焦于放贷端的监管与抵御系统性信用扩张的工具。核心证据来自跨城图景、双重差分结果及对投机渠道的对比检验,参照文中图表(如图2、图9等)支撑关键结论 [page::7][page::65][page::39]。
This paper develops an early asset-pricing framework for compute, a scarce, non-storable input central to the AI economy. It argues that storage-based no-arbitrage links cannot directly price compute futures, since a GPU-hour cannot be stored for delivery in the future; instead, futures should be priced via the expected future spot rental price at delivery, adjusted for a risk premium arising from hedging pressure. Using term rental data to construct synthetic futures, the authors build the first compute futures return panel sorted by GPU generation and maturity, finding preliminary evidence of a positive compute risk premium consistent with hedging pressure by compute providers. The analysis highlights how synthetic forward pricing, forward-looking spot expectations, and basis risk shape the evolving price discovery process for AI infrastructure. [page::4][page::5][page::28][page::30]