The Economic Foundation for Neural Computing Systems

这是一项探索性研究的小额资助，它使用数学经济学的框架来模拟大规模问题解决中大量计算单元的协调。作者正致力于开发一种大规模并行计算系统的详细形式化方法，该系统可以将其表现为经济资源分配问题。计算将被视为产生特定输出并需要特定输入的生产过程。就像商品生产被组织成带有中间产品的平行和串行阶段一样，计算问题将被组织成带有中间输出的平行和串行步骤。投入和产出将被定价，利润将被定义为每个计算单位。个体单位基于盈利能力的进入和退出可以解释为一种适应性学习的形式。数学经济学将允许描述将有效实现系统目标的最优价格。作者将研究均衡价格和分配的存在性、最优性和稳定性，这种经济解决方案的实际实施，以及由盈利能力驱动的潜在学习行为。该研究的意义在于，它探索了经济科学知识和计算机科学知识如何整合以产生新知识的途径。

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.