Search results

In this chapter, we present fragments of previously unpublished correspondence between Ludwig Lachmann and G. L. S. Shackle on the nature of institutions. This correspondence allows us to rationally reconstruct a theory of institutions, which extends Lachmann’s theoretical work. Shackle pointed out to Lachmann that institutions might be inputs into economic activities and that they themselves may be reproduced and transformed by these activities. Lachmann in turn contended that institutions consist of “instruments of interpretation.” We develop the concept of “instruments of interpretation” as a subset of institutions. These instruments are mental models and cognitive tools which are (1) inputs complementary to capital goods (2) jointly produced, reproduced, and transformed through economic activity. We suggest that in contrast to privately produced capital goods, parts of the institutional infrastructure are produced jointly as shared goods because the use of certain institutional elements is non-exclusive and non-subtractable; these elements – instruments of interpretation – are produced and reproduced by sharing and contributions through a process of joint production. This chapter explicitly connects two different but essential themes in Lachmann’s work: capital, and institutions. By combining these two strands of Lachmann’s work, we are able to demonstrate that there is a cross-complementarity between institutional orders and capital structures. This connection in turn provides a thicker understanding of the workings of markets.

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

In recent years, the problem of classification with imbalanced data has been growing in popularity in the data-mining and machine-learning communities due to the emergence of an abundance of imbalanced data in many fields. In this chapter, we compare the performance of six classification methods on an imbalanced dataset under the influence of four resampling techniques. These classification methods are the random forest, the support vector machine, logistic regression, k-nearest neighbor (KNN), the decision tree, and AdaBoost. Our study has shown that all of the classification methods have difficulty when working with the imbalanced data, with the KNN performing the worst, detecting only 27.4% of the minority class. However, with the help of resampling techniques, all of the classification methods experience improvement on overall performances. In particular, the Random Forest, in combination with the random over-sampling technique, performs the best, achieving 82.8% balanced accuracy (the average of the true-positive rate and true-negative rate).

We then propose a new procedure to resample the data. Our method is based on the idea of eliminating “easy” majority observations before under-sampling them. It has further improved the balanced accuracy of the Random Forest to 83.7%, making it the best approach for the imbalanced data.

Several prominent data-mining studies have evaluated the performance of neural networks (NNs) against traditional statistical methods on the two-group classification problem in discriminant analysis. Although NNs often outperform traditional statistical methods, their performance can be hindered because of failings in the use of training data. This problem is particularly acute when using NNs on smaller data sets. A heuristic is presented that utilizes Mahalanobis distance measures (MDM) to deterministically partition training data so that the resulting NN models are less prone to overfitting. We show this heuristic produces classification results that are more accurate, on average, than traditional NNs and MDM.

One fundamental question in normative ethics concerns how norms influence human behavior and discussions within normative ethics would be facilitated by a classification that treats human actions/behavior and moral norms within the same functional framework. Based on evolutionary analysis of benefits and costs, we distinguish five categories of human action. Four of these – self-interest, kin selection, group egoism, and cooperation – are basically results of gene selection, benefit the individual's genetic interest and may be described as “broad self-interest.” In contrast, the fifth category, unselfishness, is more likely a result of cultural influences. All the five categories of action are influenced by three broad moral spheres, each of which represents many norms that have a common denominator. Thus, a sphere of integrity concerns the individual's right to act in his/her interest and against those of other individuals. A sphere of reciprocal morality deals with rules for various forms of cooperation. An altruistic sphere has to do with the obligations to generate advantages for others. Ethics can be viewed as a dynamic conflict among various norms within and between these spheres. The classical conflict is that between the integrity and altruistic spheres. However, we argue that the prime antagonism may be that between the altruistic and reciprocal spheres; the main impact of altruistic ideals may not be the reputed one of counteracting egoism, but subversively thwarting reciprocal morality.

Risk preferences play a critical role in almost every facet of economic activity. Experimental economists have sought to infer the risk preferences of subjects from choice behavior over lotteries. To help mitigate the influence of observable, and unobservable, heterogeneity in their samples, risk preferences have been estimated at the level of the individual subject. Recent work has detailed the lack of statistical power in descriptively classifying individual subjects as conforming to Expected Utility Theory (EUT) or Rank Dependent Utility (RDU). I discuss the normative consequences of this lack of power and provide some suggestions to improve the accuracy of normative inferences about individual-level choice behavior.