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CAREER: An accessible, dynamic model of pricing applied to wage and housing purchases

职业：适用于工资和住房购买的易于理解的动态定价模型

基本信息

批准号：
2237119
负责人：
Peter Kvam
金额：
$ 69.68万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2028-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237119&HistoricalAwards=false
关键词：
CAREER accessible dynamic model pricing

项目摘要

Employers across the United States are facing a crisis of labor supply, driven in part by changes in the working conditions employees desire and the wages they perceive as fair. Simultaneously, prospective homebuyers are faced with rising costs and fierce competition for housing – with aging adults, veterans, minorities, and low-income families hit hardest by rising home prices. This project seeks to understand how people assign value in these types of transactions (wages, homebuying), focusing on how individuals respond to competition and market changes. The project team is partnering with financial education programs on money management and first-time homebuyers to understand how differences between people – such as their tolerance for risks or willingness to delay purchases until a future date – predict their success on job and housing markets. The findings will allow us to better understand the psychology of value, create educational resources to help people adopt better strategies for finding jobs or housing, and identify public policy interventions that can alleviate housing and labor shortages.Across a series of experiments, this project tests computational models of pricing in multi-alternative, multi-attribute settings. These experiments manipulate simulated market competitiveness, delays, risks, and attributes of the choice alternatives in order to examine the dynamics of price-setting among individuals and test the cognitive mechanisms of pricing models. To improve the accessibility and efficiency of pricing model fitting and comparison, the project embeds them in neural networks trained to map observed pricing data onto generative model parameters. These will be added to online tools, teaching materials, and workshops to make them widely available. The pricing models will be used to estimate risk and delay aversion, bias, and pricing dynamics in multi-alternative choice experiments. The model parameters will also be used to predict the outcomes of job and home searches among participants in financial education programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国各地的雇主正面临劳动力供应危机，部分原因是员工期望的工作条件和他们认为公平的工资发生了变化。与此同时，潜在的购房者面临着不断上涨的成本和激烈的住房竞争-老年人，退伍军人，少数民族和低收入家庭受到房价上涨的打击最大。该项目旨在了解人们如何在这些类型的交易（工资，购房）中分配价值，重点关注个人如何应对竞争和市场变化。该项目团队正在与有关资金管理和首次购房者的金融教育项目合作，以了解人们之间的差异-例如他们对风险的容忍度或推迟购买的意愿-如何预测他们在就业和住房市场上的成功。这些发现将使我们更好地理解价值心理学，创造教育资源，帮助人们采取更好的策略来寻找工作或住房，并确定公共政策干预，可以缓解住房和劳动力短缺。通过一系列实验，该项目测试了多选择，多属性设置的定价计算模型。这些实验操纵模拟的市场竞争力，延迟，风险和属性的选择方案，以检查个人之间的价格制定的动态和测试的认知机制的定价模型。为了提高定价模型拟合和比较的可访问性和效率，该项目将它们嵌入到经过训练的神经网络中，以将观察到的定价数据映射到生成模型参数。这些将被添加到在线工具，教学材料和研讨会中，以使其广泛使用。定价模型将被用来估计风险和延迟厌恶，偏见，和定价动态多方案选择实验。该模型参数还将用于预测金融教育项目参与者的求职和家庭搜索结果。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。