2017美国数学建模ICM F题（政策）翻译迁移到火星（Migration to Mars: Utopian Workforce of the 2100 Urban Society）

小鱼儿 2022-07-11 07:52 171阅读 0赞

**F题中文翻译：**

问题F：迁移到火星：2100城市社会的乌托邦劳动力

国际机构，星际金融与勘探政策实验室（LIFE），最近（在今年的2095年）完成了一系列短期计划的生活实验我们的邻居星球，火星。新技术，包括个性化人工增强单元，将很快使人类能够在2100年之前在火星上居住制造的城市。第一波移民潮叫做人口零，将包括10,000人。

LIFE代理机构启动了UTOPIA：2100项目，旨在为22世纪创造最佳员工队伍，为所有人提供最高质量的生活，并在未来100年实现可持续发展愿景。在过去20年里，几个计划的社区已经在地球上设计和建造，测试了几个计划的生活条件。这些社区由经济学，政府，劳动力和司法系统中的平等主义原则驱动。

LIFE正在寻求一套数学和计算模型，通知国际火星联盟（ICM）政府如何设计一个他们可以用零人口实施的经济劳动力教育系统。为了决定采用什么程序，LIFE聘请了最合格的决策者和数据科学家，目的是制定一套政策，实现向火星的迁移。您的三人政策建模小组是顾问和决策者小组的一部分。 ICM已要求您的小组制定一个政策模式和报告，这将产生一套政策建议，将创造一个可持续的生活计划，并将使火星在2100年的生活经验甚至比地球上的一年更好的2095。

网络科学，系统科学，复杂系统，组织和工业心理学以及其他跨学科领域的新工具为理解社会和政府系统提供了新的见解，具有处理可扩展性问题的重要能力（与小群体和大群体相关），模态（多层）和动态（随时间的变化）。

人口零旨在在许多劳动力和社会生活因素中获得最佳条件（注意另一个团队正在负责健康政策，因此ICM要求您从您的分析中排除医疗保健）。人口零的使命是通过最大限度地提高经济产出（GDP）和幸福来创造一个可持续的社会在其公民的工作地点。当然，这两个目标可能是相反的，因此政策建议必须考虑平衡因素，如：

●收入：确保适当的补偿，使所有人都能负担得起基本必需品（住所，食物，衣服）。

●教育：提供高质量的教育，使公民为22世纪的需要和挑战做好准备。

●平等：改善妇女在劳动力中的保留，特别是在她们在地球上代表不足或受到歧视的领域。

您的ICM定向任务是：

1.定义与人口零三个优先因素（收入，教育和社会平等）相关的参数和具体结果。需要考虑的一些问题是：a）最低工资和工资分配（收入）; b）高效劳动力所需的技能;获得这些技能所需的治理类型和基础设施（教育）;和c）产假和陪产假，负担得起的儿童保育，以确保人们能够留在劳动力（社会平等）。

一个。确定并定义将在未来十年（2100-2110年）的三个因素中显示出积极成果的具体结果。考虑每个因素的目标是什么;例如，目标是改善所有公民的生活质量或提高系统的产出数量。

b。人口的主要特征（例如人口统计，人口规模和工作条件）将有助于这些结果？

C。创建将用于通过识别和定义三个因素中的每一个的关键参数来评估系统是否满足其目标的度量。

2.你被要求生成一个10,000人的样本人口移民到火星。从人口普查数据集提取数据（链接到一个数据集在下面提供）或综合一个。

一个。从你的数据集中，识别和分析人口零模拟的人口统计特征。分析和描述人口分布，如性别，种族，年龄和教育水平。

b。考虑公民的因素分布，这也将有助于实现UTOPIA：2100 - 建立一个和平，合作，平等社会的目标。您的数据是否足以确定这些因素？例如，是否应考虑创新者与生产者的分配？熟练劳动与非熟练劳动？家庭与单身人士？

链接到PUMS数据（如果您希望使用此人口普查数据）：

o PUMS数据可以通过以下链接找到：

[http://www.census.gov/programs-s ... mentation/pums.html][http_www.census.gov_programs-s ... mentation_pums.html]

[http://www2.census.gov/programs- ... a/pums/2015/1-Year/][http_www2.census.gov_programs- ... a_pums_2015_1-Year]

o这些链接显示如何提取R中的数据：

[https://stat.ethz.ch/R-manual/R- ... se/html/sample.html][https_stat.ethz.ch_R-manual_R- ... se_html_sample.html]

[https://cran.r-project.org/web/packages/sampling/sampling.pdf][https_cran.r-project.org_web_packages_sampling_sampling.pdf]

o此链接显示如何在MATLAB中提取数据：

[https://www.mathworks.com/help/s ... n=www.mathworks.com][https_www.mathworks.com_help_s ... n_www.mathworks.com]

3.建立一个包括三个确定因素（收入，教育和社会平等）的模型。使用您在任务1中创建的参数，定义未来10年成功社会的关键要素。当综合这三个因素时，参数之间的关键相互依存关系是什么？是否需要额外的限制来保持10年期间的结果？应该多久对模型进行评估，以确保继续满足UTOPIA 2100的目标？在这一时期可能影响模型的可行性的经济，社会，文化和其他全球性因素是什么？基于这些因素和约束，回答以下：

一个。确定最佳最低工资和工资分配，以最好地管理福利（更高的生活质量）和支持那些较不能提供劳动服务的人之间的紧张。

b。识别您的模型中可以通过新想法的贡献得到最大改进的术语。描述激励这些新想法的贡献的激励。

C。什么是最好的育儿和陪产假/产假策略？

4.现在您已经为三个因素创建了模型，继续将这些模型合并到全局模型中。在任务3中，您设计了一个模型，为整个社会提供最佳结果。现在，考虑该模型将如何为不同的组运行？

一个。确定你的员工的主要分组，并确定他们的主要优先事项。例如，非技术劳动力可能涉及工作时间，残疾照顾，儿童保育和最低工资，而专业人员的优先考虑可能是休假，培训和育儿假。您的模型将决定您考虑哪些子组。您可能需要开发新参数以充分评估每个组的优先级。

b。理解每个小组将有一套不同的需求，观点和成功标准，分析在收入，教育和平等方面满足他们的需求的程度。例如，您的模型在不同教育水平上的功能是否不同？不同年龄段？不同的文化价值观？你的模型对女性或男性是否更好？家庭如何受到影响？

C。考虑到您已确定的小组，您以前的模型可能不再产生最佳结果。通过添加新的约束或参数来调整模型，以优化不同子组的需求。目标是使子群体的优先成果最大化，而不会显着减少全球结果。

5. LIFE计划在未来100年内分阶段实施额外的迁移。

一个。您的模型对于各种迁移阶段的群体选择有多敏感？这个人口的人口分布是否会显着改变结果？你的抽样程序如何影响你的模型？如果未来几年的移民和增长将类似于人口零（一次在一个新的制造城市10,000人），你将如何改变您的模型为下几次迁移？你的招聘和选拔过程是否可持续？

b。这个长远计划与十年计划有很大不同吗？你的10年愿景和建议中有哪些元素对于100年愿景是不可持续的？识别任何新的参数或约束，以确保您的模型在整个22世纪继续有效。

6.在令人震惊的新闻中，科学家发现地球与行星尺寸彗星碰撞的威胁。我们需要疏散行星地球，并尽可能多的人移动到火星住在扩大的制造城市。

一个。您的模型是否仍然有效？如果迁移发生在阶段，它会有所不同吗？

b。研究您的模型的鲁棒性，并评论其对更大规模迁移的一般敏感性。

C。说明您的模型相对于主要迁移的优势和弱点。

7.写一份针对LIFE主任的政策建议，其中包括基于您的模型的收入，教育，平等政策等因素，并根据ICM的指示。你的建议会根据人口零的组成和规模而改变吗？解释推理，使您得到您的建议，并分析您期望实现的结果。

您的ICM提交应包括1页的摘要表，1-2页的政策建议和您的解决方案（不超过20页），最多23页。 注意：附录和参考文献不计入23页的限制。  
  
英文原文：

PROBLEM F: Migration to Mars: Utopian Workforce of the 2100 Urban Society

The international agency, Laboratory of Interstellar Financial & Exploration Policy (LIFE), has recently (in this year of 2095) completed a series of short-term planned living experiments on our neighbor planet, Mars. New technologies, including personalized artificial augmentations units, will soon enable humans to inhabit manufactured cities on Mars by 2100. The first wave of migration, called Population Zero, will include 10,000 people.

The LIFE agency launched project UTOPIA: 2100, with the goal of creating an optimal workforce for the 22nd century to give all people the greatest quality of life with a vision of sustainability for the next 100 years. Over the last 20 years, several planned communities have been designed and built across Earth that tested several planned living conditions. These communities are driven by egalitarian principles in economics, government, workforce, and justice systems.

LIFE is seeking a set of mathematical and computational models that will inform the International Coalition on Mars (ICM) government on how to design an economic-workforce-education system that they can implement with Population Zero. In order to decide what procedure to follow, LIFE has hired the most qualified policy makers and data scientists with the goal to develop a set of policies to realize the migration to Mars. Your three-person policy modeling team is part of the group of advisors and policy makers. ICM has asked your group for a policy model and report that will result in a set of policy recommendations that will create a sustainable life-plan and will make the living experience on Mars in the year 2100 even better than the Earthly one in the current year of 2095.

New tools in network science, systems science, complex systems, organizational & industrial psychology, and other interdisciplinary fields provide new insights for understanding social and governmental systems, with important capabilities to deal with issues of scalability (relevant for both small and large populations and effects), modality (multiple layers), and dynamics (changes over time).

Population Zero aims to have optimal conditions in many workforce and social living factors (note that another team is being tasked with health policy, so ICM has asked that you exclude health care from your analysis). The mission of Population Zero is to create a sustainable society by maximizing both economic output (GDP) and happinessin the work place for its citizens. Of course, these two goals can be in opposition, so the policy recommendation has to consider balancing factors, such as:

● Income: Ensure adequate compensation so that all people can afford fundamental necessities (shelter, food, clothes).

● Education: Provide high quality education that prepares citizens for the needs and challenges of the 22nd Century.

● Equality: Improve the retention of women in the workforce, particularly in fields where they have been underrepresented or discriminated against on Earth.

Your ICM-directed tasks are:

1. Define parameters and specific outcomes related to the three priority factors (income, education, and social equality) in Population Zero. Some issues to consider are: a) minimum wage and salary distribution (income); b) skills required for an efficient workforce; types of governance and infrastructure needed to obtain these skills (education); and c) maternity and paternity leave, affordable childcare to ensure people can remain in the workforce (social equality).

a. Identify and define the specific outcomes that would indicate positive results across the three factors for the next decade (years 2100-2110). Consider what the goal is for each of these factors; for example, is the objective to improve the quality of living for all citizens or improve quantity of output of the system.

b. What are the major features of the population (eg. demographics, population size, and working conditions) that would contribute to these outcomes?

c. Create metrics that you will use to evaluate whether the system is meeting its objective by identifying and defining the critical parameters for each of the three factors.

2. You have been asked to generate a sample population of 10,000 people to emigrate to Mars. Extract data from a census dataset (link to one is provided below) or synthesize one.

a. From your data set, identify and analyze the demographic characteristics of this simulation of Population Zero. Analyze and describe demographic distributions, such as gender, ethnicity, age, and education levels.

b. Consider the distribution of citizens in terms of factors that will also help to meet goals of UTOPIA: 2100 – to build a peaceful, cooperative, egalitarian society. Are your data sufficient to determine these factors? For example, should the distribution of innovators versus producers be considered? Of skilled versus unskilled labor? Of families versus single people?

Link to PUMS data (if you desire to use this census data):

o PUMS data can be found via following links:

 http://www.census.gov/programs-surveys/acs/technical-documentation/pums.html

 http://www2.census.gov/programs-surveys/acs/data/pums/2015/1-Year/

o These links show how to extract the data in R:

 https://stat.ethz.ch/R-manual/R-devel/library/base/html/sample.html

 https://cran.r-project.org/web/packages/sampling/sampling.pdf

o This link show how to extract the data in MATLAB:

 https://www.mathworks.com/help/stats/datasample.html?requestedDomain=www.mathworks.com

3. Build a model that includes the three identified factors (income, education, & social equality). Using the parameters that you created in task 1, define the key elements of a successful society for the next 10 years. When integrating these three factors, what are the critical interdependencies among the parameters? Are there additional constraints required to preserve the outcomes over the 10 year period? How often should the model be evaluated to ensure the goals of UTOPIA 2100 continue to be met? What might be economic, social, cultural, and other global factors that might affect the viability of the model over that period? Based on these factors and constraints, answer the following:

a. Determine the optimal minimum wage and salary distribution to best manage the tension between wellbeing (higher quality of life) and support for those less equipped to provide labor services.

b. Identify terms in your model that can be most improved through contribution of new ideas. Describe the incentives to motivate contribution of those new ideas.

c. What is the best childcare and paternity/maternity leave strategies?

4. Now that you have created models for the three factors, proceed to merge these models into a global model. In task 3, you designed a model to provide optimal outcomes for society, at large. Now, consider how the model will function for different groups?

a. Identify the major subgroups of your workforce, and identify their main priorities. For example, unskilled labor force might be concerned with work hours, disability care, child care, and minimum wage, while the priorities of the professional workforce may be time off, training, and parental leave. Your model will dictate which subgroups you consider. You might have to develop new parameters to adequately evaluate each groups’ priorities.

b. With the understanding that each group will have a different set of needs, perspectives, and criteria for success, analyze how closely their needs are met in terms of income, education, and equality. For example, does your model function differently across educational levels? Different ages? Different cultural values? Does your model function better for women or men? How are families affected?

c. With the consideration of the subgroups that you have identified, your previous model may no longer produce optimal outcomes. Adjust the model by adding new constraints or parameters to optimize the needs of the different subgroups. The goal is to maximize the priority outcomes of the subgroups without significantly reducing the global outcomes.

5. LIFE has planned additional migration phased over the next 100-years.

a. How sensitive is your model to the population selection for various migration phases? Does the demographic distribution of this population significantly change the outcomes? How does your sampling procedure affect your model? If migration and growth in future years will be similar to Population Zero (10,000 people in a new manufactured city at a time), how would you change your model for the next few migrations? How sustainable are your recruitment and selection processes?

b. Is this long-term plan substantially different than the 10-year plan? Are there elements in your 10-year vision and recommendations that are not sustainable for the 100 year vision? Identify any new parameters or constraints that will ensure your model continues to be effective for the entire 22nd century.

6. In shocking news, scientists discover a threat of a collision of Earth with a planet sized comet. We need to evacuate planet Earth and move as many people as possible to Mars to live in enlarged manufactured cities.

a. Is your model still functional? Would it make a difference if migrations occurred in phases?

b. Study the robustness of your model and comment on its general sensitivity to a much larger scale migration.

c. State the strengths and weaknesses of your model relative to a major migration.

7. Write a policy recommendation addressed to the director of LIFE that includes the factors of income, education, equality policies based on your model and according to the directions of ICM. Will your recommendations change depending on the composition and size of the Population Zero? Explain the reasoning that led you to your recommendations and analyze the results you are expecting to achieve.

Your ICM submission should consist of a 1 page Summary Sheet, a 1-2 page policy recommendation, and your solution (not to exceed 20 pages) for a maximum of 23 pages. Note: The appendix and references do not count toward the 23 page limit.

References:

https://www.kansascityfed.org/publications/community/transformworkforce

https://www.kansascityfed.org/~/media/files/publicat/community/workforce/transformingworkforcedevelopment/book/transformingworkforcedevelopmentpolicies.pdf

http://www.economist.com/blogs/freeexchange/2012/01/chinas-labour-force

[http_www.census.gov_programs-s ... mentation_pums.html]: http://www.census.gov/programs-surveys/acs/technical-documentation/pums.html
[http_www2.census.gov_programs- ... a_pums_2015_1-Year]: http://www2.census.gov/programs-surveys/acs/data/pums/2015/1-Year/
[https_stat.ethz.ch_R-manual_R- ... se_html_sample.html]: https://stat.ethz.ch/R-manual/R-devel/library/base/html/sample.html
[https_cran.r-project.org_web_packages_sampling_sampling.pdf]: https://cran.r-project.org/web/packages/sampling/sampling.pdf
[https_www.mathworks.com_help_s ... n_www.mathworks.com]: https://www.mathworks.com/help/stats/datasample.html?requestedDomain=www.mathworks.com