The Economics of Experimentation in the Design of New Products and Processes

建议#9416177在许多领域，研究人员传统上认为，为了获得高成功率，通过创建和测试几个完好的实验来进行研究是经济的。大自然采取了一种不同的方法--创建许多实验，然后筛选出少数几个成功的实验(随机突变和适者生存)。每种实验策略的成本效益取决于设计实验与“运行”实验并分析结果的相对成本。今天，由于使用了新的或大大改进的实验方法，如模拟、大规模筛选和遗传算法技术，与新产品和新工艺设计相关的实验步骤的相对成本在许多领域都受到了根本性的影响。通常，这些技术的效果是使“自然”的实验方法变得越来越划算。这反过来又改变了许多领域正在(或应该)进行实验的方式。这项研究考察了新形式的(通常是计算机辅助的)实验在许多领域的经济性，并将它们与更传统的实验方法的经济性进行比较。这项研究的结果将为学者们提供新的见解，让他们了解新产品和新工艺设计中的实验经济学。这些发现还将使新产品和工艺的开发商和政府决策者能够就他们在开发新产品和工艺期间可能使用的试验策略的成本效益做出更好的判断。为了便于分析，实验的实施包括四个步骤：(1)构思或设计实验；(2)建造进行该实验所需的(物理或虚拟)仪器；(3)运行实验；(4)分析结果。这四个步骤中的每一个都有成本。这些成本的绝对和相对大小会影响实验策略。因此，从逻辑上讲，实验者可能会努力用更便宜的实验方法取代昂贵的实验方法。而且，如果步骤(1)--设计一项实验--比步骤(3)--运行该实验--便宜得多，那么在逻辑上，人们可能会期望实验者在步骤4--设计上投入更多资金--如果他或她这样做可以减少所需的实验运行的成本或频率的话。最近发展起来的实验技术，如模拟、大规模筛选和遗传算法技术，可以从根本上影响实验所涉及的四个步骤的绝对和相对成本。共15对实验，共30例。每一对都将包含一个以研究领域的传统方式进行的实验，以及一个非常类似类型的实验，使用如上文所述的新实验技术进行。在分析这些实验对的成本和收益之后，建立和测试假设，即哪些技术在实验条件下将最有效，其特征是关键变量的已知水平，如用作实验投入的材料或设计的成本。

Proposal #9416177 In many fields, researchers have traditionally found it economical to do research by creating and testing a few, well though-out experiments in the expectation of a high successful rate. Nature takes a different approach -creating many experiments and then screening for the few successful ones (random mutation and survival of the fittest). The cost-effectiveness of each experimental strategy depends on the relative cost of designing experiments versus "running" them and analyzing the results. Today, the relative costs of experimental steps associated with the design of new products and processes is being radically affected in many fields due to the use of new or greatly improved experimental methods such as simulation, mass screening and genetic algorithm techniques. Often, the effect of these techniques is to make "nature's" experimental approach increasingly cost-effective. This, in turn, is changing the way experimentation is being (or should be) done in many fields. This research examines the economics of the new forms of (often, computer assisted) experimentation in a number of fields, and to compare them with the economics of more traditional experimental methods. The findings of this research will provide academics with new insight into the economics of experimentation in the design of new products and processes. The findings will also enable developers of new products and processes and governmental decision makers to make better judgments with respect to the cost-effectiveness of experimentation strategies they may use during the development of new products and processes. For purposes of analysis, the execution of an experiment as involving a four-step cycle: (1) one conceives of or designs an experiment; (2) one builds the (physical or virtual) apparatus needed to conduct that experiment; (3) one runs the experiment; (4) one analyzes the result. Each of these four steps has a cost. The absolute and relative magnitude of these costs can affect exp erimental strategies. Thus, experimenters might logically strive to replace expensive experimental methods with cheaper ones. And, if step (1) - design of an experiment- is much cheaper than step (3) - running that experiment- one might logically expect an experimenter to invest more in step 4 -design- if by doing so he or she can reduce the cost or frequency of required experimental runs. Recently-developed experimental techniques such as simulation, mass screening and genetic algorithm techniques can radically affect the absolute and relative cost of the four steps involved in experimentation. 15 pairs of experiments (30 cases total) are studied. Each pair will contain one experiment performed in a manner that is traditional for the filed studied, and one experiment of a very similar type performed using a novel experimental technique such as those mentioned just above. Analyses of cost and benefits of these experimental pairs are followed by the creation and testing of hypotheses as to which techniques will be most effective in experimental conditions characterized by known levels of key variables such as the cost of generating materials or designs to be used as experimental inputs.