A functional approach for studying technological progress: Application to information technology

Koh, Hyun Hee; Magee, Christopher L.

doi:10.1016/j.techfore.2006.06.001

Cited by 121 publications

(88 citation statements)

References 24 publications

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“…1 Koh and Magee [15,16] claim an exponential function of time (Moore's law) predicts the performance of several different technologies. Goddard [11] claims costs follow a power law in production rate rather than cumulative production.…”

Section: Reserved For Publication Footnotesmentioning

confidence: 99%

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The Role of Design Complexity in Technology Improvement

et al. 2009

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We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d − 1 other, dependent components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the entire cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: The more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity is more complicated, depending on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.experience curve | learning curve | progress function | performance curve | design structure matrix | evolution of technology T he relation between a technology's cost c and the cumulative amount produced y is often empirically observed to be a power law of the formwhere the exponent α characterizes the rate of improvement. This rate is commonly termed the progress ratio 2 −α , which is the factor by which costs decrease with each doubling of cumulative production. A typical reported value [9] is 0.8 (corresponding to α ≈ .32), which implies that the cost of the 200th item is 80% that of the 100th item. Power laws have been observed, or at least assumed to hold, for a wide variety of technologies [2,18,9], although other functional forms have also been suggested and in some cases provide plausible fits to the data 1 . We give examples of historical performance curves for several different technologies in Figure 1. The relationship between cost and cumulative production goes under several different names, including the "experience curve", the "learning curve" or the "progress function". The terms are used interchangeably by some, while others assign distinct meanings [9,29]. We use the general term performance curve to denote a plot of any performance measure (such as cost) against any experience measure (such as cumulative production), regardless of the context. Performance curve studies first appeared in the 19th century [10,6], but their application to manufacturing and technology originates from the 1936 study by Wright on aircraft produc...

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Section: Reserved For Publication Footnotesmentioning

confidence: 99%

“…The most commonly used proxy is cumulative production, but other possibilities include cumulative investment, installed capacity, R&D expenditure, or even time. The best proxy for innovation effort is a subject of serious debate in the literature [15,16,11,26,22].…”

Section: Testing the Model Predictionsmentioning

confidence: 99%

The Role of Design Complexity in Technology Improvement

et al. 2009

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“…Our analysis uses functional performance metrics originally proposed by Koh and Magee (2006) 1 . These include information storage (per unit volume), bandwidth, and calculations per second, as well as their costs, making a total of six different performance metrics, as summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…These include information storage (per unit volume), bandwidth, and calculations per second, as well as their costs, making a total of six different performance metrics, as summarized in Table 1. Koh and Magee (2006) contains an extensive historical database, in many cases going back in time for more than a hundred years. Although there are many missing values (times for which no observations are available), their data are unrivaled in terms of scope and reliability, covering long time periods with high quality 2 .…”

Section: Introductionmentioning

confidence: 99%

“…But is the progress of IT really exponential? In this paper we examine long time series of data documenting progress in information technology gathered by Koh and Magee (2006). We analyze six different historical trends of progress for several technologies grouped into the following three functional tasks: information storage, information transportation (bandwidth), and information transformation (speed of computation).…”

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Superexponential long-term trends in information technology

Nagy¹,

Farmer²,

Trancik³

et al. 2011

Technological Forecasting and Social Change

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Moore's Law has created a popular perception of exponential progress in information technology. But is the progress of IT really exponential? In this paper we examine long time series of data documenting progress in information technology gathered by Koh and Magee (2006). We analyze six different historical trends of progress for several technologies grouped into the following three functional tasks: information storage, information transportation (bandwidth), and information transformation (speed of computation). Five of the six datasets extend back to the nineteenth century. We perform statistical analyses and show that in all six cases one can reject the exponential hypothesis at statistically significant levels. In contrast, one cannot reject the hypothesis of superexponential growth with decreasing doubling times. This raises questions about whether past trends in the improvement of information technology are sustainable.

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Towards quantification of the role of materials innovation in overall technological development

Magee

2012

Complexity

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Executive SummaryThis report develops a method for quantitatively assessing the role of materials innovation in overall technological development. The report demonstrates the method for one specific case and defines the key requirements to use it in a number of other cases. The new method involves the comparative examination of overall technical capability metrics with performance metrics at more detailed levels of progress where materials and process innovation dominates the progress. This analysis is supplemented by exploration of the specific technical capabilities utilized in technological development areas of interest.It is specifically found that about 2/3 of the total progress in computation over the past 40 years has been due to materials/process innovations. It is also found that making reasonably reliable estimates in other functional areas such as energy storage, information transmission, etc. could be possible if more attention were paid to the development and collection of technical progress metrics at the level of materials and processes (such as Moore's Law has done for information transformation). Examination of what is known leads to three other key (but more speculative) findings: 1) Materials/process innovation contributes at least 20% of the progress in all areas examined; 2) The contribution of materials/process innovations in energy storage are possibly 80% or higher; 3) The relative contribution of materials/process innovation to overall technological progress has grown in the past few decades.

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A functional approach for studying technological progress: Application to information technology

Cited by 121 publications

References 24 publications

The Role of Design Complexity in Technology Improvement

The Role of Design Complexity in Technology Improvement

Superexponential long-term trends in information technology

Towards quantification of the role of materials innovation in overall technological development

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