MOD: Modeling the Dynamics of Technological Evolution

MOD：技术进化动态建模

• 批准号: 0738187
• 负责人: James Farmer
• 金额: $ 38.07万
• 依托单位: Santa Fe Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0738187&HistoricalAwards=false
• 关键词: MOD; Modeling; Dynamics; Technological; Evolution

The aim of this study is to develop an empirically based, quantitative model of the dynamics of technological evolution. The goal of this model is to explain how technologies are related in a dynamical network and why certain technologies improve faster than others. The project focuses on examining both incremental improvements and radical new discoveries that are based on fundamental scientific advances. This problem is approached from diverse points of view, with an interdisciplinary team including economists, engineers, physicists, biologists, and a sociologist.While model construction is the main focus of this project, several tools are developed in the process, taking advantage of the researchers' experience in complex systems analysis. These tools include developing best-practice guidelines for making technological performance forecasts. Work on portfolio theory, and specifically in designing portfolios under increasing returns in the energy sector, should also provide a useful decision-making tool for public and private actors investing in low carbon energy technologies. This study consists of an empirical component, theory and model construction, and a simulation component. In the empirical component of this project several large data sets are constructed and analyzed. The project begins with testing of Wright's law, which states that the cost of manufacturing a given technology decreases as a power law when plotted against the cumulative number of units produced. Alternative performance curves are explored, including replacing cost with other measures of performance, and replacing cumulative number of units with other measures of prevalence. The study focuses on both radical new discoveries and incremental improvements, by relating patent and scientific literature data to performance curves. The empirical work includes broad studies of as many technologies as possible, as well as a few in-depth studies of technologies where there is a more detailed view of all the factors that influence technological improvement. These detailed case studies largely deal with energy technologies, an area with societal relevance. The theoretical component improves on existing models of technological evolution. The operating assumption is that technologies must be studied as part of an ecology of related entities, including all internal component technologies and all external technologies that influence these components. The focus here is on making more realistic models of these network effects. Connections to innovation in biology and the role of selection are explored.The simulation component consists of the construction of toy models of technological innovation and a study related open problems in portfolio theory. Wright's Law implies increasing returns, so consequently technologies that are developed early accumulate an advantage over technologies that are developed later. What is the optimal approach to investing when technologies follow Wright's Law? In this context, the portfolio problem defines a highly nonlinear stochastic dynamical system whose properties are far from obvious. The case studies focus on the low-carbon energy sector.Understanding the likely outcomes of public investments in research and development and of the influence of market transformation programs on technological innovation is critical for effective public policy formulation. The focus here is on energy related policy, and in particular on the problem of public investments to create low-carbon energy technologies. The results of this work will be shared with the international private investment and public policy communities through the London Accord project. The results will also be shared with students from a variety of institutions.

本研究的目的是建立一个基于经验的、定量的技术演进动态模型。这个模型的目的是解释技术是如何在动态网络中相互关联的，以及为什么某些技术比其他技术进步得更快。该项目侧重于研究基于基础科学进步的渐进式改进和根本性的新发现。这个问题是由一个包括经济学家、工程师、物理学家、生物学家和社会学家在内的跨学科团队从不同的角度来解决的。虽然模型构建是这个项目的主要焦点，但在这个过程中，利用研究人员在复杂系统分析方面的经验，开发了几个工具。这些工具包括制定技术性能预测的最佳实践指南。投资组合理论方面的工作，特别是在能源部门回报不断增加的情况下设计投资组合方面的工作，也应该为投资低碳能源技术的公共和私人行为体提供有用的决策工具。本研究由实证部分、理论与模型构建部分和仿真部分组成。在本项目的实证部分，构建和分析了几个大型数据集。该项目首先测试了赖特定律（Wright’s law），该定律指出，制造一种特定技术的成本随着生产单位的累积数量呈幂次定律下降。探讨了其他性能曲线，包括用其他性能度量代替成本，用其他流行度量代替累计单位数。该研究通过将专利和科学文献数据与性能曲线联系起来，重点关注激进的新发现和渐进式改进。实证工作包括对尽可能多的技术的广泛研究，以及对技术的一些深入研究，其中对影响技术改进的所有因素有更详细的看法。这些详细的案例研究主要涉及能源技术，这是一个与社会相关的领域。理论部分改进了现有的技术演化模型。操作假设是，必须将技术作为相关实体生态的一部分来研究，包括所有内部组成部分技术和影响这些组成部分的所有外部技术。这里的重点是为这些网络效应建立更现实的模型。探讨了生物创新与选择的作用之间的联系。仿真部分包括技术创新玩具模型的构建和投资组合理论中相关开放问题的研究。莱特定律意味着收益递增，因此早期开发的技术相对于后期开发的技术积累了优势。当技术遵循莱特定律时，最佳的投资方式是什么？在这种情况下，投资组合问题定义了一个性质远不明显的高度非线性随机动力系统。案例研究集中在低碳能源领域。了解公共研发投资的可能结果，以及市场转型项目对技术创新的影响，对于有效制定公共政策至关重要。这里的重点是与能源有关的政策，特别是创造低碳能源技术的公共投资问题。这项工作的成果将通过《伦敦协议》项目与国际私人投资界和公共政策界分享。调查结果也将与来自不同院校的学生分享。