Debunking the Learning Curve

Goddard, Chris I.

doi:10.1109/tchmt.1982.1136009

“…The most commonly used proxy is cumulative production, but other possibilities include cumulative investment, installed capacity, research and development expenditure, or even time. The best proxy for innovation effort is a subject of serious debate in the literature (34)(35)(36)(37)(38).…”

Section: Testing the Model Predictionsmentioning

confidence: 99%

Role of design complexity in technology improvement

McNerney

¹

,

Farmer

²

,

Redner

³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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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 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 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 depends 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.design structure matrix | experience curve | learning curve | performance curve

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“…Goddard [11] claims costs follow a power law in production rate rather than cumulative production. Multivariate forms involving combinations of production rate, cumulative production, or time have been examined by Sinclair et al [26] and Nordhaus [22].…”

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

McNerney

¹

,

Farmer

²

,

Redner

³

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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“…Advanced technology levelized costs evolve as a function of changing generation share according to the Goddard model [44]. The model includes 10 electricity generation technologies, and the generation mix evolves deterministically toward a target.…”

Section: Discussionmentioning

confidence: 99%

“…The strategies correspond to the alternative future generation mixes outlined in Table II. Advanced technology levelized costs evolve according to the Goddard model [44], which attributes changes in unit cost to economies of scale that arise due to changes in annual production. The deterministic evolution of the generation mix toward the target prescribed by each strategy is illustrated in Figure 1.…”

Section: Stochastic Simulation Modelmentioning

confidence: 99%

Evaluation of post-Fukushima Japanese electricity strategies: A stochastic simulation model

Leibowicz

¹

2014

Int. J. Energy Res.

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SUMMARY Japan has not settled on a coherent long‐term electricity strategy for its post‐Fukushima reality. In this analysis, a stochastic electricity sector simulation model is formulated and used to evaluate alternative strategies prescribing different generation mixes for 2050. The generation mix evolves deterministically, demand is endogenous, and social cost of carbon is randomly sampled. The model treats fossil fuel prices and advanced technology cost dynamics stochastically. Monte Carlo simulation produces a distribution of net present social cost for each strategy, and the alternatives are assessed based on the median (expected cost) and standard deviation (risk). Aggressively expanding renewables is a promising strategy; it has the lowest expected cost and moderate risk. Whether this is preferred to substantial nuclear generation is sensitive to parameter assumptions, but a general finding is robust: severely limiting nuclear generation and substituting liquefied natural gas (LNG) and other fossil fuels is costly and risky. Principal component analysis confirms the vulnerability of such strategies to LNG price and social cost of carbon uncertainty. Japan's dominant short‐term response to Fukushima has been a dramatic decrease in nuclear generation coupled with increased use of fossil fuels, particularly LNG. This study suggests that continuing this approach would constitute a poor long‐term Japanese electricity strategy. Copyright © 2014 John Wiley & Sons, Ltd.

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Debunking the Learning Curve

Cited by 23 publications

References 5 publications

Role of design complexity in technology improvement

Role of design complexity in technology improvement

The Role of Design Complexity in Technology Improvement

Evaluation of post-Fukushima Japanese electricity strategies: A stochastic simulation model

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