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在本文章中，我们对顶刊《Management Science》于2026年6月份在线发布的文章中进行了精选（共12篇），并总结其基本信息，旨在帮助读者快速洞察行业新动态。本月MS发文聚焦资产定价与行为金融、社会公平与组织机制、以及平台运营与数据驱动决策科学，研究非线特征与异质信念驱动的量化投资策略、兼顾社会公平机会的法选拔与序贯搜索机制、平台经济中信息红利与物流运力的配置权衡、以及有限注意力约束下的组织复杂扭曲等前沿问题。法涵盖度学习与因果机器学习、混整数锥规划与对偶优化法、博弈论机制设计、以及准实验微观计量与大规模调查实验等技术。
荐文章1
● 题目：The Tragedy of Complexity
复杂的悲剧
● 作者：Martin Oehmke , Adam Zawadowski
● 发布时间：2026-6-1
● 摘要：
Complexity can create value. At the same time, understanding more complex goods requires more of an agent’s attention. We show that equilibrium complexity is generally inefficient when agents face competing demands on their limited attention. Because attention allocation is hump-shaped in complexity, equilibrium complexity is distorted toward intermediate levels: well-understood goods are inefficiently complex, whereas less well-understood goods are oversimplified. We apply our model to financial institutions facing regulatory bodies and CEOs interacting with corporate divisions.
复杂能够创造价值。但与此同时，理解复杂的商品需要消耗行为主体（agent）多的注意力。我们研究表明，当行为主体有限的注意力面临多重竞争需求时，均衡状态下的复杂通常是率的。由于注意力分配与复杂之间呈现倒 U 型（驼峰状）关系，均衡复杂会向中间水平扭曲：原本容易被理解的商品表现出低的复杂化，而原本不易被理解的商品则被过度简化。我们将该模型应用于面对监管机构的金融机构，以及与企业各部门进行博弈的席执行官（CEO）
荐文章2
● 题目：Selecting and Testing Asset-Pricing Models: A Stepwise Approach
资产定价模型的选择与检验：种逐步法
● 作者：Guanhao Feng , Wei Lan , Hansheng Wang , Jun Zhang
● 发布时间：2026-6-2
● 摘要：
The asset-pricing literature emphasizes factor models that minimize pricing errors but overlooks unselected candidate factors that could enhance the performance of test assets. This paper proposes a framework for factor model selection and testing by (i) selecting the optimal model that spans the joint efficient frontier of test assets and all candidate factors and (ii) testing pricing performance on both test assets and unselected candidate factors. Our framework updates a baseline model (e.g., capital asset-pricing model) sequentially by adding or removing factors based on asset-pricing tests. Ensuring model selection consistency, our framework utilizes the asset-pricing duality; minimizing cross-sectionally unexplained pricing errors aligns with maximizing the Sharpe ratio of the selected factor model. Empirical evidence shows that workhorse factor models fail asset-pricing tests, whereas our proposed eight-factor model is not rejected and exhibits robust out-of-sample performance.
资产定价域的文献通常侧重于寻找能够使定价误差小化的因子模型，但往往忽视了那些虽未被选中、却能提升测试资产表现的候选因子。本文提出了个用于因子模型选择与检验的新型框架，其核心包括：(i) 选择能够张成测试资产与所有候选因子“联有前沿（joint efficient frontier）”的优模型；(ii) 对该模型在测试资产以及未选中候选因子上的定价表现同时进行检验。
我们的框架基于资产定价检验的结果，通过依次添加或剔除因子，对基准模型（如资本资产定价模型，CAPM）进行序贯新。为确保模型选择的致，该框架利用了资产定价的对偶，即：在横截面上小化未被解释的定价误差，等价于大化所选因子模型的夏普比率。实证证据表明，传统的主流核心因子模型普遍法通过资产定价检验，而本文提出的八因子模型则未被拒，并且展现出稳健的样本外表现。
荐文章3
● 题目：Dropping Standardized Testing for Admissions Trades Off Information and Access
在招生中取消标准化考试：信息与获取机会的权衡
● 作者：Nikhil Garg , Hannah Li, Faidra Monachou
● 发布时间：2026-6-2
● 摘要：
We investigate how an on-demand service platform’s mechanism to share demand-supply mismatch information spatially affects drivers’ relocation decisions and the platform’s matching efficiency. We consider three mechanisms motivated by practice; the platform shares demand-supply mismatch information about either zones(s) with excess demand with all drivers (surge information sharing, common practice today), all zones with all drivers (full information sharing), or zone(s) with excess demand only with drivers sufficiently close by (local information sharing). We develop a game-theoretic model with three zones wherein drivers in two non-surge zones decide whether to relocate to the surge zone with excess demand. We incorporate two spatial aspects: drivers’ relocation costs and initial supply across different non-surge zones. Theoretically, full information sharing can hurt the platform’s matching efficiency compared with surge information sharing under low relocation costs because drivers in non-surge zones facing high demand locally do not chase the surge as much. Local information sharing is strictly dominated by other mechanisms in terms of matching efficiency when the supply of drivers near the surge zone is limited and weakly dominated otherwise by surge information sharing. We test these theory predictions in the laboratory with human participants as drivers in an environment where theoretical matching efficiency is highest with surge and lowest with local information sharing. Experimentally, the platform serves fewer customers than predicted with surge information sharing because drivers relocate too often, compromising efficiency in non-surge zones. In contrast, the platform serves more customers than predicted with full and local information sharing, and these mechanisms perform at least as well in matching efficiency as surge. Therefore, sharing demand-supply mismatch information either fully or in a targeted manner (as in local) can help to alleviate coordination problems on a platform. A behavioral equilibrium incorporating loss aversion through mental accounting and decision errors describes drivers’ behavior in our experiments better than the rational equilibrium.
本文围绕存在公平考量的容量受限选拔问题，探究信息价值与准入机会在其中的作用机制。研究构建了套统计歧视分析框架：每位申请者具备多项特征，且可能采取策略行为。该模型正式刻画了单特征的双重权衡关系 —— 特征既可能发挥正向的信息甄别价值，也可能因不同社会群体对该特征的获取机会不均等，产生负向的排斥应。这分析框架可直接应用于招生域取消标准化考试的政策辩论。
研究核心结论表明，是否取消考试分数这类选拔特征，法脱离其他特征构成的整体信息环境、以及该要求对申请者池结构的影响而孤立决策。取消某项特征会减少每位申请者的可观测信息量，尤其不利于识别非传统背景申请者的能力，反而可能加剧群体差异。但当特征本身存在准入壁垒时，信息环境与准入壁垒对申请者池规模的影响会形成度复杂的交互作用。
进步，本文拓展了双校竞争、考试存在参与成本的场景，学生可策略决定是否参加考试。理论显示，学生的考试参与行为可能呈现非单调特征。文章刻画了两校的政策均衡状态，证明每所学校取消考试的优决策，关键依赖于另所学校的考试政策。后通过校准模拟验证：在符现实的参数场景中，取消标准化考试既可能让所有评价指标同步，也可能致所有指标同步恶化。
荐文章4
● 题目：Deep Parametric Portfolio Policies
度参数化投资组策略
● 作者：Frederik Simon , Sebastian Weibels , Tom Zimmermann
● 发布时间：2026-6-3
● 摘要：
We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies generate 43-102 basis points higher monthly certainty equivalent returns compared with linear policies. Looking beyond expected returns, nonlinear portfolio policies better capture the complex relationship between investor preferences and firm characteristics but the benefits of using complex models vary with investor preferences. Results hold across different utility functions and remain robust to transaction costs and short-selling restrictions. Overall, economic regularization constrains model complexity much like statistical regularization but emerges endogenously from investor preferences.
本文采用度经网络构建任意复杂度的参数化投资组策略保亭泡沫板专用胶厂，以实现投资者用大化。研究发现，风险厌恶可发挥经济正则化机制的作用：投资者的风险厌恶程度越，对模型复杂度的约束作用越强。实证结果表明，与线参数化投资组策略相比，度参数化投资组策略的月度确定等价收益出 43 至 102 个基点。
跳出期望收益的单视角，非线投资组策略能够地刻画投资者偏好与公司特征之间的复杂关联，但复杂模型的增益果会随投资者偏好的差异而变化。上述结论在不同用函数设定下均成立，且纳入交易成本、空限制等现实约束后结果依然稳健。总体而言，经济正则化与统计正则化具备相似的模型复杂度约束果，但前者是由投资者偏好内生衍生的约束机制。
荐文章5
● 题目：Dynamic Portfolio Selection and Asset Pricing Under Neo-Additive Probability Weighting
新加概率权重下的动态投资组选择与资产定价
● 作者：Xue Dong He , Yu Sun
● 发布时间：2026-6-4
● 摘要：
We study a dynamic portfolio selection problem in which an agent trades a stock and a risk-free asset with the objective of maximizing the rank-dependent utility of their wealth at the terminal time of the investment horizon. Because of time inconsistency, we consider three types of agents, namely precommitted, sophisticated, and naive agents, who differ from each other in whether they are aware of the time inconsistency and whether they have self-control. Assuming a neo-additive probability weighting function, we solve the strategies of these agents. We find that the precommitted agent takes a loss-exit strategy, leading to a positively skewed terminal wealth, and that the sophisticated agent is less willing to participate in the stock market than the precommitted and naive agents. We also study equilibrium asset pricing and find that with a precommitted representative agent, stock returns exhibit a reversal effect, and the initial stock price is lower than in the case of a naive representative agent or a sophisticated representative agent.
本研究探讨了动态投资组选择问题。在此问题中，决策者通过交易种股票和种风险资产，目标是在投资期末大化其财富的秩依用。由于概率权重通常会致行为的时间不致，我们考虑了三类经典的决策者，即预先承诺型、精明型和幼稚型决策者。它们之间的核心区别在于是否能意识到自身的时间不致，以及是否具备自我控制能力。
在假设新加概率权重函数的情形下，我们并求解了这三类决策者的优投资策略。研究发现，预先承诺型决策者会采取种止损离场策略，从而致其期末财富分布呈现正偏态；此外，与预先承诺型和幼稚型决策者相比，精明型决策者参与股票市场的意愿明显较低。我们还进步研究了市场的均衡资产定价，结果表明，当市场由预先承诺型的代表经济主体主时，股票收益率会表现出反转应，且其初始股票价格要低于由幼稚型或精明型代表经济主体主时的价格。
荐文章6
● 题目：Markovian Search with Ex Ante Constraints: Theory and Applications to Socially Aware Algorithmic Hiring
带事前约束的马尔可夫搜索：理论及其在兼顾社会意识法招聘中的应用
● 作者：Mohammad Reza Aminian , Vahideh Manshadi , Rad Niazadeh
● 发布时间：2026-6-5
● 摘要：
We study and develop an algorithmic framework for incorporating “ex ante” constraints—constraints on outcomes that hold only on average—into stateful sequential search problems with costly inspection. Our framework encompasses the classical Weitzman’s Pandora’s box and its extensions to joint Markovian scheduling, which model richer processes such as multistage search with multiple layers of inspection. Ex ante constraints are particularly motivated by social considerations in algorithmic hiring, where they can adjust outcome distributions to promote equity and access. Although most work in the algorithmic fairness literature in computer science and economics has focused on incorporating such constraints into machine learning tasks like classification and regression, far less attention has been devoted to operational problems such as sequential search, with their unique intricacies. Our work aims to bridge this gap. Building on the optimality of index-based policies in the unconstrained versions of these problems, we show that optimal policies under a single ex ante constraint (e.g., demographic parity) retain an index-based structure but require (i) dual-based adjustments of the indices and (ii) randomization between two such adjustments via a “tie-breaking rule,” both easy to compute and economically interpretable. We then extend our results to multiple affine constraints by reducing the problem to a variant of the exact Carathéodory problem and providing a polynomial-time algorithm that constructs an optimal randomized dual-adjusted index-based policy satisfying all constraints simultaneously. For general affine and convex constraints, we develop a primal-dual algorithm that randomizes over a polynomial number of dual-based adjustments, yielding a near-feasible, near-optimal policy. These results rely on the key observation that a suitable relaxation of the Lagrange dual function for these constrained problems admits index-based policies akin to those in the unconstrained setting. Finally, through a numerical study, we investigate the implications of imposing socially aware ex ante constraints and their socially desirable outcomes.
本研究探讨并开发了个法框架，旨在将事前约束（即仅在平均意义上成立的结果约束）引入到带有考察成本的有状态序贯搜索问题中。我们的框架涵盖了经典的魏茨曼潘多拉魔盒模型及其在联马尔可夫调度中的扩展，这些模型能够刻画为复杂的过程，例如带有全位考察的多阶段搜索。事前约束的提出特别受到了法招聘中社会因素的启发，在这场景下，这类约束可以通过调整结果分布来促进公平和获取机会。尽管计机科学和经济学中关于法公平的大多数文献都集中在如何将此类约束引入分类和回归等机器学习任务中，但对于序贯搜索等具有特复杂的运营管理问题，关注却少得多。本研究旨在填补这空白。
基于这些问题在约束版本中指数型策略的优，我们证明了在单事前约束（例如人口统计学平权）下，优策略仍保持指数型结构，但需要基于对偶原理对指数进行调整，并通过种平局决胜规则在两种此类调整之间进行随机化。这两者都易于计且具有经济学可解释。随后，通过将问题转化为精确卡拉西奥多里问题的个变体，我们将研究结果扩展到了多个仿射约束的情形，并提供了个多项式时间法，用以构建个能同时满足所有约束的优随机化对偶调整指数型策略。针对般的仿射约束和凸约束，万能胶生产厂家我们开发了种原始-对偶法，该法在多项式数量的对偶调整上进行随机化，从而产生个近似可行且近似优的策略。这些结果依赖于个核心发现，即对于此类受约束问题，其拉格朗日对偶函数的某种适当松弛形式，允许存在类似于约束设定下的指数型策略。后，通过数值研究，我们探讨了实施兼顾社会意识的事前约束的意义及其带来的社会期望结果。
荐文章7
● 题目：The Value of Last-Mile Delivery in Online Retail
线上售中“后公里”配送的价值
● 作者：Zhikun Lu , Ruomeng Cui , Tianshu Sun , Lixia Wu
● 发布时间：2026-6-8
● 摘要：
Last-mile delivery, the most expensive stage of the shipment process, has become increasingly important in online retail, raising the strategic question of whether firms should outsource it to customers via pickup stations or offer home delivery. In this paper, we examine the economic value of last-mile delivery to inform this high-stakes decision. Partnering with Cainiao, Alibaba’s logistics platform, we exploit a quasi-experiment where home delivery service was sequentially rolled out to pickup stations in 2021. A staggered difference-in-differences design reveals that home delivery significantly increases sales and customer spending on Alibaba’s retail platform. Because last-mile delivery is labor-intensive and capacity constrained, effectively allocating delivery resources is crucial. To address this challenge, we propose a novel operations-aware targeting framework that integrates causal machine learning with constrained optimization to identify and prioritize the most valuable customers. The framework incorporates both capacity and fairness constraints to maximize sales while maintaining equitable service access. We further extend it with routing optimization, enabling value-aware delivery planning that jointly considers sales uplift and spatial efficiency. We demonstrate that the resulting targeting and routing policies significantly improve revenue performance. Taken together, we show that last-mile delivery is not merely a cost center but also a revenue driver. By adopting tailored logistics strategies that balance customer needs with resource constraints, online retailers can unlock substantial value and win the last mile of e-commerce.
后公里配送是物流流程中成本的阶段，在线上售中的重要日益凸显，这也引发了个战略问题：企业应当通过自提点将这环节外包给客户，还是提供送货上门服务。本文研究了后公里配送的经济价值，旨在为这重大决策提供依据。
我们与阿里巴巴旗下的物流平台菜鸟网络作，利用了2021年送货上门服务逐步行至各驿站的这准实验。多时点双重差分设计的研究结果表明，送货上门服务显著提升了阿里巴巴售平台上的销量和客户支出。由于后公里配送属于劳动密集型且受到运力约束，如何有分配配送资源变得至关重要。为了应对这挑战，我们提出了个新型的运营感知型目标定位框架，该框架将因果机器学习与受约束优化相结，用以识别具价值的客户并进行优先排序。该框架同时纳入了运力和公平约束，在保持公平的服务获取机会的同时实现销量的大化。我们进步引入了路径优化对其进行扩展，从而实现了将销量提升与空间率进行协同考虑的价值感知型配送规划。
结果表明，由此产生的目标定位与路径规划策略显著提升了营收表现。综上所述，我们表明后公里配送不仅是个成本中心，是个营收增长点。通过采用在客户需求与资源约束之间取得平衡的定制化物流策略，线上售商能够释放出巨大的价值，并在电子商务的后公里竞争中赢得主动。
荐文章8
● 题目：Randomized Robust Price Optimization
随机化鲁棒价格优化
● 作者：Xinyi Guan, Velibor V. Mišić
● 发布时间：2026-6-9
● 摘要：
The robust multiproduct pricing problem is to determine the prices of a collection of products to maximize the worst-case revenue, where the worst case is taken over an uncertainty set of demand models that the firm expects could be realized in practice. A tacit assumption in this approach is that the pricing decision is a deterministic decision: the prices of the products are fixed and do not vary. In this paper, we consider a randomized approach to robust pricing, where a decision maker specifies a distribution over potential price vectors to maximize its worst-case revenue over an uncertainty set of demand models. We formally define this problem—the randomized robust price optimization problem—and analyze when a randomized price scheme performs as well as a deterministic scheme versus when it yields a benefit. We also propose solution methods for obtaining an optimal randomization scheme over a discrete set of candidate price vectors and show how these methods are applicable for common demand models, such as the linear, semi-log, and log-log demand models. We numerically compare the randomized and deterministic approaches on a variety of synthetic and real problem instances; on instances derived from a real grocery retail scanner data set, we show that the improvement in worst-case revenue can be as high as 92. Using the same grocery retail scanner data set, we also show that the randomized approach can produce price prescriptions that achieve higher out-of-sample revenue than the nominal and deterministic robust approaches.
鲁棒多产品定价问题旨在确定组产品的价格以大化坏情况下的收益，这里的坏情况是从企业预期实际可能发生的需求模型不确定集中取出的。该法的个默认假设是，定价决策是个确定决策，即产品的价格是固定且保持不变的。在本文中，我们考虑了种鲁棒定价的随机化法，决策者通过指定潜在价格向量的概率分布，来大化需求模型不确定集下坏情况的收益。我们正式定义了这问题——即随机化鲁棒价格优化问题，并分析了随机化价格案在何时与确定案表现相同，而在何时能带来额外收益。
我们还提出了在离散候选价格向量集上获取优随机化案的求解法，并展示了这些法如何适用于常见的需求模型，例如线、半对数和双对数需求模型。我们在多种成例和实际问题例上对随机化法和确定法进行了数值对比。在基于真实食品杂货售扫描数据集的例中，我们证明了坏情况收益的提升幅度可达92。同样利用该食品杂货售扫描数据集，我们还表明，与名义法和确定鲁棒法相比，随机化法所产生的价格策略能够实现的样本外收益。
荐文章9
● 题目：Exact Logit-Based Product Design
基于Logit模型的精确产品设计
● 作者：İrem Akchen, Velibor V. Mišić
● 发布时间：2026-6-9
● 摘要：
The share-of-choice product design problem is to find the product, as defined by its attributes, that maximizes market share arising from a collection of customer types or segments. When customers follow a logit model of choice, the market share is given by a weighted sum of logistic probabilities, leading to the logit-based share-of-choice product design problem. In this paper, we develop a methodology for solving this problem to provable optimality. We first analyze the complexity of this problem and show that this problem is theoretically intractable: it is NP-Hard to solve exactly, even when there are only two customer types, and it is furthermore NP-Hard to approximate to within a nontrivial factor. Motivated by the difficulty of this problem, we propose three different mixed-integer exponential cone programs of increasing strength for solving the problem exactly, which allow us to leverage modern integer conic program solvers such as Mosek. Using both synthetic problem instances and instances derived from real conjoint data sets, we show that our methodology can solve large instances to provable optimality or near optimality in operationally feasible time frames and yields solutions that generally achieve higher market share than previously proposed heuristics.
选择份额产品设计问题旨在寻找由其属定义的优产品，以大化由组客户类型或细分市场所带来的市场份额。当客户遵循Logit选择模型时，市场份额由逻辑概率的加权和给出，从而引出了基于Logit的选择份额产品设计问题。在本文中，我们开发了种将该问题求解至可证明优解的法。
我们先分析了该问题的复杂度，并表明该问题在理论上是难解的：即使只有两种客户类型，精确求解该问题也是NP-难的，而且在非平凡因子范围内进行近似求解同样是NP-难的。针对该问题的求解难度，我们提出了三种强度递增的混整数指数锥规划模型来精确求解该问题，从而使我们能够利用Mosek等现代整数锥规划求解器。利用成问题例以及源自真实联分析数据集的例，我们表明，我们的法能够在实际运营可行的时间范围内，将大规模例求解至可证明的优或接近优，并且所得到的解通常比以往提出的启发式法能获得的市场份额。
荐文章10
● 题目：Climate Transition Beliefs
气候转型信念
● 作者：Marco Ceccarelli , Stefano Ramelli
● 发布时间：2026-6-12
● 摘要：
We study an overlooked driver of heterogeneity in green investment behavior: differences in expectations about the long-term trajectory of the energy transition (climate transition beliefs). Drawing on a survey of U.S. retail investors, we document considerable heterogeneity in climate transition beliefs at different long-term horizons. Transition optimism is positively associated with expected green financial performance and preferences for green investments, particularly among investors without strong pro-environmental attitudes. Two preregistered survey experiments provide causal evidence that transition beliefs play a key role in shaping green return expectations and investment decisions. Distinguishing between heterogeneous beliefs and preferences is crucial for a better understanding of the motivations behind green investment behavior.
本研究探讨了绿投资行为异质中个被忽视的驱动因素：对能源转型长期路径预期的差异，即气候转型信念。基于对美国散户投资者的调查，我们证实了在不同的长期时限下，气候转型信念存在着巨大的异质。对转型的乐观态度与绿资产的预期财务表现以及绿投资偏好显著正相关，在那些没有强烈环保态度的投资者中尤为明显。两项预先注册的调查实验提供了因果证据，表明转型信念在塑造绿收益预期和投资决策面起着关键作用。区分异质的信念与偏好，对于入地理解绿投资行为背后的动机至关重要。
荐文章11
● 题目：Information Design of Online Platforms
在线平台的信息设计
● 作者：T. Tony Ke , Song Lin , Michelle Y. Lu
● 发布时间：2026-6-15
● 摘要：
We study how an online platform strategically uses information to both guide consumer search and influence sellers’ targeted advertising. Our model unifies personalized recommendations and targeted ads under an information design framework. We illustrate a fundamental tradeoff facing the platform between improving match efficiency and extracting seller surplus by inducing their competition for prominence. The optimal information design may be socially inefficient because it balances the tradeoff by limiting consumer search and mixing the matched product with a long tail of unmatched ones for recommendation. This implies that sponsored targeted advertising on retail platforms may introduce match inefficiency.
本研究探讨了在线平台如何策略地利用信息来引消费者搜索，并同时影响的广告投放。我们的模型在统的信息设计框架下，将个化荐与广告结在起。我们阐明了平台所面临的个根本权衡：是在提升匹配率，还是通过诱竞争曝光度来榨取剩余。优的信息设计在社会层面上可能是低的，因为平台为了平衡上述权衡，会限制消费者的搜索行为，并在荐时将度匹配的产品与长尾的非匹配产品相混杂。这意味着售平台上的赞助广告可能会致匹配率的下降。
荐文章12
● 题目：Using Neural Networks to Guide Data-Driven Operational Decisions
利用经网络指数据驱动的运营决策
● 作者：Saman Lagzi , Ningyuan Chen , Joseph Milner
● 发布时间：2026-6-15
● 摘要：
We propose deep neural networks for data-driven stochastic optimization. Using historical data (covariates, decisions, costs), we propose to train a neural network to predict the objective value as a function of both the decision and covariate. After training, for a given covariate, this predicted objective is optimized over the decision variables using gradient-based methods with analytical gradients and Hessians. Performance is characterized by neural network generalization bounds. Comprehensive experiments on newsvendor, personalized assortment pricing, and call center staffing problems demonstrate our method’s strength over existing approaches such as conditional stochastic optimization and analytical approximations, especially when (i) the objective function is unknown, (ii) moderate to large data sets are available, or (iii) the problem structure resists simple parametric approximations.
本研究提出利用度经网络来解决数据驱动的随机优化问题。借助历史数据（包括协变量、决策和成本），我们通过训练经网络，将目标函数值预测为决策变量与协变量的函数。训练完成后，对于给定的协变量，利用带有解析梯度和海森矩阵的基于梯度的法，围绕决策变量对该预测目标进行优化求解。模型的能通过经网络的泛化界来进行刻画。
在报童问题、个化品种定价以及呼叫中心人员配备问题上的实验表明，本文法相较于条件随机优化和解析近似等现有法具有显著优势，尤其适用于以下场景：(i) 目标函数形式未知；(ii) 拥有中大规模的数据集；(iii) 问题结构难以通过简单的参数化法进行近似。相关词条:不锈钢保温 塑料管材设备 预应力钢绞线 玻璃棉板厂家 pvc管道管件胶
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