Statistical Basis for Predicting Technological Progress
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{"title"=>"Statistical Basis for Predicting Technological Progress", "type"=>"journal", "authors"=>[{"first_name"=>"Béla", "last_name"=>"Nagy", "scopus_author_id"=>"48861738500"}, {"first_name"=>"J. Doyne", "last_name"=>"Farmer", "scopus_author_id"=>"16143273100"}, {"first_name"=>"Quan M.", "last_name"=>"Bui", "scopus_author_id"=>"55320225900"}, {"first_name"=>"Jessika E.", "last_name"=>"Trancik", "scopus_author_id"=>"9634402000"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368446829", "sgr"=>"84874525419", "issn"=>"19326203", "arxiv"=>"1207.1463", "pmid"=>"23468837", "scopus"=>"2-s2.0-84874525419", "doi"=>"10.1371/journal.pone.0052669", "isbn"=>"1932-6203"}, "id"=>"981da419-0027-32e5-8302-8cb6c98b6192", "abstract"=>"Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly tied. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation.", "link"=>"http://www.mendeley.com/research/statistical-basis-predicting-technological-progress", "reader_count"=>182, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>2, "Student > Doctoral Student"=>6, "Researcher"=>37, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/975575"], "description"=>"<p>The solid line is the expected forecast and the dashed line is the expected error.</p>", "links"=>[], "tags"=>["pv", "costs", "photovoltaics2", "exponential"], "article_id"=>643273, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669.g006", "stats"=>{"downloads"=>0, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_projection_of_future_PV_electricity_costs_from_the_Photovoltaics2_historical_data_set_1977_8211_2009_using_Moore_s_exponential_functional_form_/643273", "title"=>"A projection of future PV electricity costs from the Photovoltaics2 historical data set (1977–2009) using Moore's exponential functional form.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 17:22:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/975573"], "description"=>"<p>The data-specific contribution to the slope, , is plotted against the data specific contribution to the intercept, , and compared to the ellipse of two standard deviation errors. The best forecasts are obtained for those found in the lower left quadrant, such as Beer, Sodium, RefinedCaneSugar, and Aluminum.</p>", "links"=>[], "tags"=>["datasets", "deviate", "pooled"], "article_id"=>643271, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669.g005", "stats"=>{"downloads"=>1, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_illustration_of_how_individual_datasets_deviate_from_the_pooled_data_/643271", "title"=>"An illustration of how individual datasets deviate from the pooled data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 17:22:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/975572"], "description"=>"<p>The value of the Wright parameter is plotted against the prediction based on the Sahal formula, where is the exponent of cost reduction and the exponent of the increase in cumulative production.</p>", "links"=>[], "tags"=>["exponentially", "decreasing"], "article_id"=>643270, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669.g004", "stats"=>{"downloads"=>0, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_illustration_that_the_combination_of_exponentially_increasing_production_and_exponentially_decreasing_cost_are_equivalent_to_Wright_s_law_/643270", "title"=>"An illustration that the combination of exponentially increasing production and exponentially decreasing cost are equivalent to Wright's law.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 17:22:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/975569"], "description"=>"<p>The plot shows the predicted root absolute log error vs. forecasting horizon using each of the functional forms (see Eq. (6)). The performance of the five hypotheses shown is fairly similar, though Goddard is worse at short horizons and SKC and Moore are worse at long horizons.</p>", "links"=>[], "tags"=>["errors", "hypothesized"], "article_id"=>643267, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669.g002", "stats"=>{"downloads"=>1, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_illustration_of_the_growth_of_errors_of_each_hypothesized_law_vs_time_/643267", "title"=>"An illustration of the growth of errors of each hypothesized law vs. time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 17:22:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/975583"], "description"=>"<div><p>Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly the same. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation.</p> </div>", "links"=>[], "tags"=>["predicting", "progress"], "article_id"=>643281, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669", "stats"=>{"downloads"=>22, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Statistical_Basis_for_Predicting_Technological_Progress__/643281", "title"=>"Statistical Basis for Predicting Technological Progress", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-01 17:24:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/975570"], "description"=>"<p>We have chosen these examples to be representative: The top row contains an example with one of the worst fits, the second row an example with an intermediate goodness of fit, and the third row one of the best examples. The fourth row of the figure shows histograms of values for fitting and for the 62 datasets.</p>", "links"=>[], "tags"=>["examples", "logarithm", "industry-wide"], "article_id"=>643268, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669.g003", "stats"=>{"downloads"=>1, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Three_examples_showing_the_logarithm_of_price_as_a_function_of_time_in_the_left_column_and_the_logarithm_of_production_as_a_function_of_time_in_the_right_column_based_on_industry_wide_data_/643268", "title"=>"Three examples showing the logarithm of price as a function of time in the left column and the logarithm of production as a function of time in the right column, based on industry-wide data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 17:22:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/975567"], "description"=>"<p>The mean value of the logarithmic hindcasting error for each dataset is plotted against the hindcasting horizon , in years. An error of , for example, indicates that the predicted value is three times as big as the actual value. The longest data-sets are: PrimaryAluminum (green), PrimaryMagnesium (dark blue), DRAM (grey), and Transistor (red).</p>", "links"=>[], "tags"=>["errors", "wright"], "article_id"=>643265, "categories"=>["Information And Computing Sciences", "Mathematics", "Science Policy", "Ecology"], "users"=>["Béla Nagy", "J. Doyne Farmer", "Quan M. Bui", "Jessika E. Trancik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052669.g001", "stats"=>{"downloads"=>0, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_illustration_of_the_growth_of_errors_with_time_using_the_Wright_model_/643265", "title"=>"An illustration of the growth of errors with time using the Wright model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-01 17:22:08"}

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Relative Metric

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