We do so by, first, arguing that critics operate with a narrow and incorrect notion of how engineering actually works, and of what the reliance on ideas from engineering entails. Second, we diagnose and diffuse one significant source of concern about appeals to engineering, namely that they are inherently and problematically metaphorical.
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We suggest that there is plenty of fertile ground left for a continued, healthy relationship between engineering and biology. We constantly adopt new technologies, conceive new ideas, meet new people, and experiment with new situations. Occasionally, we as individual, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level.
Innovations occur throughout social, biological, and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. What seems to be key in the successful modeling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, and technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new.
We employ a two step estimation approach, and match two firm-level data sets for OECD countries, which allows us to relax the linearity assumption of the canonical Griliches knowledge capital model. Previous empirical studies have only focused on the organization-level conditions of exaptation. To test our hypotheses, we analyse a large sample of U. Based on our findings, we discuss a number of implications of exaptation for the management of innovation as well as for policy makers.
We then propose new variants in which the probability of the arrival of new colors is itself subject to adaptive change depending on the success of past innovations and discuss applications to evolutionary models of technologies and industries. We numerically simulate different specifications of these urns with adaptively changing mutation rate and show that they can account for complex patterns of evolution in which periods of exploration and innovation are followed by periods in which the dynamics of the system is driven by selection among a stable set of alternatives.
When scientists decide whether or not to work on a big new problem, they weigh the potential rewards of a major discovery against the costs of setting aside other projects. These self-interested choices can potentially spread researchers across problems in an efficient manner, but efficiency is not guaranteed.
We use simple economic models to understand such decisions and their collective consequences. This convention of Open Science is thought to accelerate collective discovery, but we find that it need not do so. The ability to share partial results influences which scientists work on a particular problem; consequently, Open Science can slow down the solution of a problem if it deters entry by important actors.
Technologies must provide some level of performance and price for specific applications before they will begin to diffuse and technologies that experience rapid rates of improvement are more likely to become economically feasible for a growing number of applications than are other technologies. Drawing from a large data base on rates of improvement, this paper describes a set of plausible futures that are very different from ones that are presented in public forums.
Since the literature emphasizes cost reductions through increases in cumulative production, this paper explores cost and performance improvements from a new perspective. We identity three mechanisms — materials creation, process changes, and reductions in feature scale — that enable these improvements to occur and use them to extend models of learning and invention. These mechanisms can also apply during post-commercial time periods and further research is needed to quantify the relative contributions of these three mechanisms and those of production-based learning in a variety of technologies.
We correspondingly demonstrate how the pursuit of technological trajectories based on systematic increases in the size and power capacities of units pushed a new class of professionals, skills and procedures to the forefront of business decision-making.
From onwards, forecasting framed and sharpened organisational insight into problems. Drawing on archival data on coal-fired, oil-fired and nuclear powered stations in England and Wales, the final section proceeds to measure the gap between reality and forecasts and singles out three major hypotheses to explain forecasting errors: inability to predict rapid changes outside the model inter-fuel substitution ; disregard of technical shortcomings in replication and standardisation, and overconfidence in extrapolating cost reductions at higher capacity levels.
Lessons from the upscali. The paper addresses this issue by analyzing performance trends and patent output over time for 28 technological domains. In addition to patent output, production and revenue data are analyzed for the integrated circuits domain.
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The key findings are Sahal's equation is verified for additional effort variables for patents and revenue in addition to cumulative production where it was first developed. The power law and effort exponents determined are dependent upon the choice of effort variable but the time dependent exponent is not. Overall, the results are interpreted as indicating that Moore's law is a better description of longer-term technological change when the performance data come from various designs whereas experience curves may be more relevant when a singular design in a given factory is considered.
In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal.
The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process. In The Age of Intelligent Machines, inventor and visionary computer scientist Raymond Kurzweil probes the past, present, and future of artificial intelligence, from its earliest philosophical and mathematical roots to tantalizing glimpses of 21st-century machines with superior intelligence and truly prodigious speed and memory.
Generously illustrated and easily accessible to the nonspecialist, this book provides the background needed for a full understanding of the enormous scientific potential represented by intelligent machines as well as their equally profound philosophic, economic, and social implications. Running alongside Kurzweil's historical and scientific narrative are 23 articles examining contemporary issues in artificial intelligence. He was the principal developer of the first print-to-speech reading machine for the blind and other significant advances in artificial intelligence technology.
Articles by: Charles Ames. Margaret A. Harold Cohen. Daniel C. Edward A. George Gilder. Douglas R. Michael Lebowitz. Margaret Litven. Blaine Mathieu. Marvin Minsky. Allen Newell.
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Brian W. Seymour Papert. Jeff Pepper. Roger Schank and Christopher Owens. Sherry Turkle. Mitchell Waldrop. Introduces the general philosophy of response surface methodology, and details least squares for response surface work, factorial designs at two levels, fitting second-order models, adequacy of estimation and the use of transformation, occurrence and elucidation of ridge systems, and more.
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Some results are presented for the first time. Includes real-life exercises, nearly all with solutions. This paper studies these using a nonequilibrium price formation rule, developed in the context of trading with market orders. Value investing does not necessarily cause prices to track values.
When value investing and trend following are combined, even though there is little linear structure, there can be boom—bust cycles, excess and temporally correlated volatility, and fat tails in price fluctuations. Profits can be decomposed in terms of aggregate pairwise correlations.
Under reinvestment of profits this leads to a capital allocation model that is equivalent to a standard model in population biology. An investigation of market efficiency shows that patterns created by trend followers are more resistant to efficiency than those created by value investors, and that profit maximizing behavior slows the progression to efficiency.
Order of magnitude estimates suggest that the timescale for efficiency is years to decades. We evaluated technological forecasts to determine how forecast methodology and eight other attributes influence accuracy. We also evaluated the degree of interpretation required to extract measurable data from forecasts. We found that, of the nine attributes, only methodology and time horizon had a statistically significant influence on accuracy. Forecasts using quantitative methods were more accurate than forecasts using qualitative methods, and forecasts predicting shorter time horizons were more accurate that those predicting longer time horizons.
While quantitative methods produced the most accurate forecasts, expert sourcing methods produced the highest number of forecasts whose events had been realized, indicating that experts are best at predicting if an event will occur, while quantitative methods are best at predicting when. We also observed that forecasts are as likely to overestimate how long it will take for a predicted event to occur as they are to underestimate the time required for a prediction to come to pass. Additionally, forecasts about computers and autonomous or robotic technologies were more accurate than those about other technologies, an observation not explained by the data set.
Finally, forecasts obtained from government documents required more interpretation than those derived from other sources, though they had similar success rates. Forecasting approaches vary, including quantitative such as patent analysis and qualitative methods such as expert elicitation using the Delphi method. We discuss a new method and system for predicting technology futures by harnessing the predictive information made available by society in open sources.
Here, we evaluate the phrase discovery component using a dataset of technology forecast statements. We describe a novel approach for harnessing a collective crowdsourced predictive ability available through publicly made technology-related statements by automatically determining significant convergences on technology forecasts. We evaluate our approach using a corpus of science-related articles and demonstrate that passive crowdsourcing may be a powerful source of technology-related predictive intelligence. Prediction markets exploit the information transmission property of markets to improve forecasts of future events.
Participants in a prediction market buy and sell assets that pay off if the underlying event occurs. Prices in a prediction market can be interpreted as consensus probabilities for the underlying events. Here we formulate Moore's law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution.
This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology at a given point in the future.
Despitethe star-studded array of academic lords and knights who were willingto testify on his behalf, neither MI5 nor the Special Branch seem tohave trusted him, and no less a person than Roy Jenkins, the then HomeSecretary, signed off on the refusal to naturalize him.
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According to Google Scholar, by the25th of January , that is, just twenty-five days intothe new year, thirty-three papers had been published citingLakatos in that year alone, a citation rate of over one paperper day. Introductory texts on the Philosophy of Science typicallyinclude substantial sections on Lakatos, some admiring, some critical,and many an admixture of the two see for example Chalmers andGodfrey-Smith The premier prize for the best book in thePhilosophy of Science funded by the foundation of a wealthy andacademically distinguished disciple, Spiro Latsis is named in hishonour.
Moreover, Lakatos is one of those philosophers whose influenceextends well beyond the confines of academic philosophy. Of thethirty-three papers citing Lakatos published in the first twenty-fivedays of , at most ten qualify as straight philosophy.
Therest are devoted to such topics as educational theory, internationalrelations, public policy research with special reference to thedevelopment of technology , informatics, design science, religiousstudies, clinical psychology, social economics, political economy,mathematics, the history of physics and the sociology of the family. Obviously, we cannot settle the matterin an Encyclopedia entry but we hope to say enough to illuminate theissue. Spoiler alert: so far as the Philosophy of Science isconcerned, we tend to favor the English interpretation.