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As described in the z-Tree (Zurich Toolbox for Readymade Economic Experiments) tutorial (Fischbacher, 2002), use of the z-Tree software for designing and administering research experiments is quite straightforward. z-Tree is likely to be a viable alternative for accounting researchers interested in administering a computerized experimental game, and it could offer some advantages over web-based administration or use of complicated spreadsheet tools. With little or no programming experience and no familiarity with z-Tree, a researcher can likely program the example experiment described herein to be functional within a day and ready to administer within a week. Thus, one appeal of z-Tree is the ease of programming and execution and possibly reduced investment of time by the researcher. This tutorial describes use of the z-Tree software to program a noninteractive accounting research experiment; as such, it should serve as a supplemental and more specific tutorial, with many illustrations, for accounting researchers considering using the z-Tree program.

We address the problem of selecting a topological design for a network having a single traffic source and uncertain demand at the remaining nodes. Solving the associated fixed charge network flow (FCF) problem requires finding a network design that limits both the fixed costs of establishing links and the variable costs of sending flow to the destinations. In this paper, we discuss how to obtain a sequence of optimal solutions that arise as the demand intensity varies from low levels to high. One of the network design alternatives associated with these solutions will be chosen based upon the dominant selection criteria of the decision maker. We consider both probabilistic and non-probabilistic criteria and compare the network designs associated with each. We show that the entire sequence of optimal solutions can be identified with little more effort than solving a single FCF problem instance. We also provide solution approaches that are relatively efficient and suggest good design alternatives based upon approximations to the optimal sequence.

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.

Throughout the world, at one period or another in its history, it has been the practice to cultivate tree species and agricultural crops in intimate combination in most of the countries. The history of cultivating trees and crops in home gardens, social tree planting, protecting and managing forests, appreciating wildlife, and sustaining the beauties of nature in Sri Lanka go back to more than about 25 centuries. In chronicles, there are some references on social tree planting practices, and home gardens planted with flowering and fruit-bearing trees in Sri Lanka. Because of the traditions, influencing factors of the existing environment, and nature of agroforestry, the numerous examples of agroforestry practices are found in all agro climatic and ecological zones of Sri Lanka. Today, the traditional knowledge of agroforestry is being developed and expanded with the objective of improving living standards, especially the rural communities in Sri Lanka.

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.

Auditing textbooks include summary level coverage of the American Institute of Certified Public Accountants (AICPA) Code of Professional Conduct, but textbook coverage is too brief to support a strong understanding of auditor independence. Independence rules have the force of professional law for the independent auditor (PCAOB, 2015). Threats to firm independence can arise from events and circumstances such as investments in the client, loans from the client, past-due fees, contingent fees, deposits in the client, gifts and job offers. Student test results from a five-year rotation of alternative auditor independence lecture support materials demonstrate that using the actual AICPA Code of Professional Conduct reduces student performance. However, this drag on student performance was mostly offset by the positive impacts of simultaneous use of an independence decision tree developed for this chapter and tested as a teaching material for classrooms use.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.