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The Economic Foundation for Neural Computing Systems

神经计算系统的经济基础

基本信息

批准号：
9005969
负责人：
Andrew Whinston
金额：
$ 1.86万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1990
资助国家：
美国
起止时间：
1990-03-01 至 1991-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9005969&HistoricalAwards=false
关键词：
Economic Foundation Neural Computing Systems

项目摘要

This is a Small Grant for Exploratory Research which uses the framework of mathematical economics to model the coordination of amultitude of computational units engaged in large-scale problem solving. The authors are working on developing a detailed formalism of a massively parallel computing system that lends itself to a representation as an economic resource allocation problem. A computation will be regarded as a production process that yields a specific output and requires certain inputs. Just as production of commodities are organized into parallel and serial stages with intermediate goods, a computational problem will be organized into parallel and serial steps with intermediate outputs. Inputs and outputs will be priced, and profits will be defined for each computational unit. Entry and exit of individual units based on rofitability can be interpreted as a form of adaptive learning. Mathematical economic will allow the characterization of optimal prices that will achieve the system objective efficiently. The authors will study the existence, optimality, and stability of equilibrium prices and allocations, the practical implementation of this economic solution, and the potential learning behavior driven by profitability. The research is significant in that it explores the way in which knowledge from economic science and knowledge fromcomputer science can be integrated to produce new knowledge.

这是一个探索性研究的小资助金，它使用数理经济学的框架来模拟从事大规模问题解决的计算单位的能力协调。作者正在致力于开发一种大规模并行计算系统的详细形式主义，该系统适合表示为一个经济资源分配问题。计算将被视为产生特定输出并需要某些输入的生产过程。正如商品的生产被组织成具有中间产品的并行和串联阶段一样，计算问题也将被组织成具有中间产品的并行和串联步骤。将对投入和产出进行定价，并为每个计算单位定义利润。基于适应性的个别单元的进入和退出可被解释为适应性学习的一种形式。数学经济学将允许描述最优价格，从而有效地实现系统目标。作者将研究均衡价格和配置的存在、最优性和稳定性，这一经济解决方案的实际实施，以及由盈利驱动的潜在学习行为。这项研究的意义在于，它探索了如何将经济科学的知识和计算机科学的知识整合起来，以产生新的知识。