越览（112）——精读期刊论文的6过去15年发生了什么（1）

摘要：This issue of the post will introduce what has happened to the journal article "Improving emergency responsiveness with management

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今天小编为大家带来“越览（112）——精读期刊论文

《Improving emergency responsiveness with

management science》的

6 过去十五年发生了什么（1）”。

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Today, the editor brings you the

＂Yue Lan (112)：Intensive reading of the journal article＇Improving emergency responsiveness with

management science:

6 What has happened in the past fifteen years (1)＇＂.

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍期刊论文《Improving emergency responsiveness with management science》的过去15年发生了什么（1）。

This issue of the post will introduce what has happened to the journal article "Improving emergency responsiveness with management science" in the past 15 years from three aspects: mind mapping, intensive reading content, and knowledge supplement (1).

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

本段介绍了过去15年《管理科学》期刊中应急响应系统出版物数量下降的原因。

This paragraph describes the reasons for the decline in the number of emergency response system publications in the journal Management Science over the past 15 years.

第一个原因是因为已有模型基本满足需求。前几十年开发的模型已解决应急系统管理的核心问题，并广为传播。部分模型被咨询机构改进并整合进专有软件，限制了公开发表。此外，尽管原模型仍有改进空间，但管理者可能抵制更复杂的方法，且公共部门技术转移存在挑战。

The first reason is that existing models are generally sufficient. Models developed in previous decades have addressed core issues in emergency system management and have been widely disseminated. Some models have been improved by consulting agencies and integrated into proprietary software, limiting their public release. In addition, although there is still room for improvement in the original models, managers may resist more complex approaches, and there are challenges in technology transfer in the public sector.

第二个原因是对决策支持模型的需求下降。20世纪60-70年代，美国犯罪率上升、社会动荡加剧，经济压力下资源紧张，应急系统需高效配置资源。而90年代经济繁荣、犯罪减少、应急部门资源充足，对最优配置模型的需求降低。

The second reason is the decline in demand for decision support models. In the 1960s and 1970s, the US crime rate rose, social unrest intensified, resources were tight under economic pressure, and the emergency system needed to allocate resources efficiently. In the 1990s, the economy prospered, crime decreased, and emergency departments had sufficient resources, so the demand for optimal allocation models decreased.

应急响应系统研究的一个独特特征是高度依赖客户组织的赞助，否则研究难以推进。不同于库存管理或排队理论，构建有意义的应急系统模型需要深入了解组织的运作和目标。当需求较低、缺乏政治推动力和资金支持时，学者难以独立开展研究。

A unique feature of emergency response system research is that it is highly dependent on sponsorship from client organizations, without which research is difficult to advance. Unlike inventory management or queuing theory, building meaningful emergency system models requires a deep understanding of the organization's operations and goals. When demand is low and there is a lack of political momentum and funding, it is difficult for scholars to conduct research independently.

纽约市兰德研究所（NYCRI）工作成功的关键因素包括规模大、持续时间长，以及咨询与研究的结合。一个稳定的核心团队在六年内解决了从概念化到实施的多个问题，并得到了计算机系统人员、数据分析师和管理团队的支持。

Key factors in the success of NYCRI’s work include its large size, long duration, and combination of consulting and research. A stable core team tackled multiple problems from conceptualization to implementation over six years, supported by computer systems personnel, data analysts, and a management team.

四、知识补充（Knowledge supplement）

决策支持模型是一种基于数据、数学方法和逻辑规则的工具，旨在帮助决策者更科学、合理地分析复杂问题，并制定最佳或最优决策。这类模型通常用于处理不确定性、资源分配、优化选择和预测分析等问题，广泛应用于企业管理、医疗健康、公共安全、供应链管理、金融分析和应急响应系统等领域。

Decision support models are tools based on data, mathematical methods and logical rules, designed to help decision makers analyze complex problems more scientifically and rationally, and make the best or optimal decisions. Such models are usually used to deal with problems such as uncertainty, resource allocation, optimization selection and predictive analysis, and are widely used in corporate management, healthcare, public safety, supply chain management, financial analysis and emergency response systems.

不同于完全依赖人类经验或直觉的传统决策方式，决策支持模型利用系统化、数据驱动的方法，使决策更加透明、可解释，并减少人为偏见和失误。尽管最终决策仍需由人来做，但这些模型提供了强有力的分析依据和参考，提高了决策的科学性和效率。

Unlike traditional decision-making methods that rely entirely on human experience or intuition, decision support models use systematic, data-driven methods to make decisions more transparent and explainable, and reduce human bias and errors. Although the final decision still needs to be made by people, these models provide a strong analytical basis and reference, improving the scientificity and efficiency of decision-making.

决策支持模型的主要特点：

The main features of decision support models are:

数据驱动：依赖历史数据、实时数据或模拟数据，利用数据分析方法辅助决策。

Data-driven: Rely on historical data, real-time data or simulated data, and use data analysis methods to assist decision-making.

优化与模拟：采用数学优化、模拟、博弈论等方法，评估不同决策方案的效果。

Optimization and simulation: Use mathematical optimization, simulation, game theory and other methods to evaluate the effectiveness of different decision-making options.

辅助决策：提供定量分析和可行性建议，但不直接替代人类决策者的判断。

Decision support: Provides quantitative analysis and feasible suggestions, but does not directly replace the judgment of human decision makers.

可调整性：能够根据环境变化、市场波动、政策调整等因素进行动态优化和改进。

Adjustability: Ability to dynamically optimize and improve based on environmental changes, market fluctuations, policy adjustments and other factors.

跨学科性：结合运筹学、统计学、人工智能、经济学和计算机科学等多学科方法，提高决策质量。

Interdisciplinarity: Combining multidisciplinary approaches such as operations research, statistics, artificial intelligence, economics, and computer science to improve decision-making quality.

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