The Learning Curve and Durable-Goods Production

We investigate the effect of a learning curve on the production of durable goods by examining a durable-goods monopolist in a two-period model. If the monopolist faces a learning curve, the model shows that the equilibrium quantity of the first- (second-) period products will be smaller (larger) than if there were no learning curve. Consequently, in cases where the original production cost is sufficiently large, the presence of a learning curve drives down total profits.

A Cognitive Model of the Learning Curve

This article provides a cognitive foundation of the parameters that regulate a model of the learning curve. Organizational learning and its actual occurrence are linked to the number of available categories and to the amount of information to be processed.Learning Curve, Organization of Production, Team Work

A New-Keynesian model of the yield curve with learning dynamics: A Bayesian evaluation

We estimate a New-Keynesian macro-finance model of the yield curve incorporating learning by private agents with respect to the long-run expectation of inflation and the equilibrium real interest rate. A preliminary analysis shows that some liquidity premia, expressed as some degree of mispricing relative to no-arbitrage restrictions, and time variation in the prices of risk are important features of the data. These features are, therefore, included in our learning model. The model is estimated on U.S. data using Bayesian techniques. The learning model succeeds in explaining the yield curve movements in terms of macroeconomic shocks. The results also show that the introduction of a learning dynamics is not sufficient to explain the rejection of the extended expectations hypothesis. The learning mechanism, however, reveals some interesting points. We observe an important difference between the estimated inflation target of the central bank and the perceived long-run inflation expectation of private agents, implying the latter were weakly anchored. This is especially the case for the period from mid-1970s to mid-1990s. The learning model also allows a new interpretation of the standard level, slope, and curvature factors based on macroeconomic variables. In line with standard macro-finance models, the slope and curvature factors are mainly driven by exogenous monetary policy shocks. Most of the variation in the level factor, however, is due to shocks to the output-neutral real rate, in contrast to the mentioned literature which attributes most of its variation to long-run inflation expectations

Learning curves and changing product attributes: the case of wind turbines

The heuristic concept of learning curves describes cost reductions as a function of cumulative production. A study of the Liberty shipbuilders suggested that product quality and production scale are other relevant factors that affect costs. Significant changes of attributes of a technology must be corrected when assessing the impact of learning-by-doing. We use an engineering-based model to capture the cost changes of wind turbines that can be attributed to changes in turbine size. We estimate the learning curve and turbine size parameters using more than 1500 price points from 1991 to 2003. The fit between model and empirical data confirms the concept

Reconciling modern machine learning practice and the bias-variance trade-off

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine learning practice. The bias-variance trade-off implies that a model should balance under-fitting and over-fitting: rich enough to express underlying structure in data, simple enough to avoid fitting spurious patterns. However, in the modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered over-fit, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double descent" curve subsumes the textbook U-shaped bias-variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine learning models delineates the limits of classical analyses, and has implications for both the theory and practice of machine learning

Financial model calibration using consistency hints

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market