Journal articles on the topic 'Tree of decisions'
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Scott, Jessie, and David Betters. "Economic Analysis of Urban Tree Replacement Decisions." Arboriculture & Urban Forestry 26, no. 2 (March 1, 2000): 69–77. http://dx.doi.org/10.48044/jauf.2000.008.
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Urban forest managers often are required to make decisions about whether to retain or replace an existing tree. In part, this decision relies on an economic analysis of the benefits and costs of the alternatives. This paper presents an economic methodology that helps address the tree replacement problem. The procedures apply to analyzing the benefits and costs of existing trees as well as future replacement trees. A case study, involving a diseased American elm (Uimus americana) is used to illustrate an application of the methodology. The procedures should prove useful in developing economic guides for tree replacement/retention decisions.
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TOFAN, Cezarina Adina. "Method of decision tree applied in adopting the decision for promoting a company." Annals of "Spiru Haret". Economic Series 15, no. 3 (September 30, 2015): 47. http://dx.doi.org/10.26458/1535.
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The decision can be defined as the way chosen from several possible to achieve an objective. An important role in the functioning of the decisional-informational system is held by the decision-making methods. Decision trees are proving to be very useful tools for taking financial decisions or regarding the numbers, where a large amount of complex information must be considered. They provide an effective structure in which alternative decisions and the implications of their choice can be assessed, and help to form a correct and balanced vision of the risks and rewards that may result from a certain choice. For these reasons, the content of this communication will review a series of decision-making criteria. Also, it will analyse the benefits of using the decision tree method in the decision-making process by providing a numerical example. On this basis, it can be concluded that the procedure may prove useful in making decisions for companies operating on markets where competition intensity is differentiated.
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AMPUŁA, Dariusz. "Prediction of Post-Diagnostic Decisions for Tested Hand Grenades’ Fuzes Using Decision Trees." Problems of Mechatronics Armament Aviation Safety Engineering 12, no. 2 (June 30, 2021): 39–54. http://dx.doi.org/10.5604/01.3001.0014.9332.
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The article presents a brief history of creation of decision trees and defines the purpose of the undertaken works. The process of building a classification tree, according to the CHAID method, is shown paying particular attention to the disadvantages, advantages, and characteristics features of this method, as well as to the formal requirements that are necessary to build this model. The tree’s building method for UZRGM (Universal Modernised Fuze of Hand Grenades) fuzes was characterized, specifying the features of the tested hand grenade fuzes and the predictors used that are necessary to create the correct tree model. A classification tree was built basing on the test results, assuming the accepted post-diagnostic decision as a qualitative dependent variable. A schema of the designed tree for the first diagnostic tests, its full structure and the size of individual classes of the node are shown. The matrix of incorrect classifications was determined, which determines the accuracy of incorrect predictions, i.e., correctness of the performed classification. A sheet with risk assessment and standard error for the learning sample and the v-fold cross-check were presented. On the selected examples, the quality of the resulting predictive model was assessed by means of a graph of the cumulative value of the lift coefficient and the "ROC" curve
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Jiang, Daniel R., Lina Al-Kanj, and Warren B. Powell. "Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds." Operations Research 68, no. 6 (November 2020): 1678–97. http://dx.doi.org/10.1287/opre.2019.1939.
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In the paper, “Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds,” the authors propose an extension to Monte Carlo tree search that uses the idea of “sampling the future” to produce noisy upper bounds on nodes in the decision tree. These upper bounds can help guide the tree expansion process and produce decision trees that are deeper rather than wider, in effect concentrating computation toward more useful parts of the state space. The algorithm’s effectiveness is illustrated in a ride-sharing setting, where a driver/vehicle needs to make dynamic decisions regarding trip acceptance and relocations.
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Li, Jiawei, Yiming Li, Xingchun Xiang, Shu-Tao Xia, Siyi Dong, and Yun Cai. "TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation." Entropy 22, no. 11 (October 24, 2020): 1203. http://dx.doi.org/10.3390/e22111203.
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Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at different stages: (1) a novel James–Stein Decision Tree (JSDT) is proposed to generate better knowledge representations for DNNs, especially when the input data are in low-frequency or low-quality; (2) the DNNs output high-performing prediction result from the knowledge embedding inputs and behave as a teacher model for the following tree model; and (3) a novel distillable Gradient Boosted Decision Tree (dGBDT) is proposed to learn interpretable trees from the soft labels and make a comparable prediction as DNNs do. Extensive experiments on various machine learning tasks demonstrated the effectiveness of the proposed method.
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Heshmatol Vaezin, S. M., J. L. Peyron, and F. Lecocq. "A simple generalization of the Faustmann formula to tree level." Canadian Journal of Forest Research 39, no. 4 (April 2009): 699–711. http://dx.doi.org/10.1139/x08-202.
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The economic decision model serving as an objective function in forest economics was conceived originally by Faustmann at the stand level. Nevertheless, the tree level seems to be an appropriate scale for analysis, especially for harvesting decisions and certain estimations both at tree and stand levels. However, the Faustmann formula cannot be directly applied to the tree level. The present research has provided certain tree-level formulations of the Faustmann formula, including, in particular, tree expectation value (TEV) and land expectation value (LEV). TEV and tree-level LEV formulas were developed by analyzing the Faustmann formula under deterministic conditions. Unlike previous tree-level decision models presented in the forest economics literature, TEV and tree-level LEV formulas incorporate the expectation value of the land occupied by trees and its variability over time as well as the interaction between trees and their trajectories (cutting age). The proposed formulas were then compared with the Faustmann formula using the first-order condition of optimal harvest age. The TEV and tree-level LEV formulas appeared to be absolutely compatible with the Faustmann formula. The utility of the proposed formulas was then illustrated with application examples, including target diameter, stand expectation value, TEV, LEV, and the value of damage to beech trees or stands in northeastern France.
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Rautenberg, Tamlyn, Annette Gerritsen, and Martin Downes. "Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer." Diagnostics 10, no. 3 (March 14, 2020): 158. http://dx.doi.org/10.3390/diagnostics10030158.
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Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the structure of decision trees and treating sequential diagnostics as independent. These errors have consequences for the accuracy of model results, and thereby impact on decision making. This paper sets out to overcome these errors using color to link fundamental epidemiological calculations to decision tree models in a visually and intuitively appealing pictorial format. The paper is a must-read for modelers developing decision trees in the area of diagnostics for the first time and decision makers reviewing diagnostic reimbursement models.
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Marzouk, Mohamed, and Emad Mohamed. "Modeling bid/no bid decisions using fuzzy fault tree." Construction Innovation 18, no. 1 (January 2, 2018): 90–108. http://dx.doi.org/10.1108/ci-11-2016-0060.
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Purpose Decisions by construction contractors to bid (or not to bid) require the thorough assessment and evaluation of factors relevant to the decision, as well as the quantification of their combined impact, to produce successful bid/no-bid decisions. The purpose of this study is to present a fuzzy fault tree model to assist construction contractors to more efficiently bid for future projects. Design/methodology/Approach The proposed model consist of two stages: first, identification of the factors that affect bidding decision using a questionnaire survey after an extensive literature review, and second, usage of the identified factors to build a fuzzy fault tree model to simulate the bidding decision. Findings A list of 15 factors that affect bid/no-bid decisions was identified. Analysis of factors revealed that the highest-ranking factors were related to financial aspects of the project. A case study is presented to demonstrate the capabilities of the model, and a fuzzy important analysis is performed on the basic events to demonstrate the differences between three contractors’ bid/no-bid decisions. The results reveal that there is variation between the decisions of each contractor based on their willingness to participate. Besides, the influence of evaluation factors on the final decision for each contractor is different. Originality/value The study contributes to the body of knowledge on tendering and bidding practices. The proposed model incorporated the fuzzy set theory, which suits human subjectivity. The proposed methodology overcomes the limitations of previous models as it can, using the linear pool opinion principle, combine and weigh the evaluations of multiple experts. In addition, the model is convenient for situations where historical data are not available.
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Abate, Tensaye, and Temesgen Yohannes. "Socio-Economic Determinants of Smallholder Tree Plantation in Basona-Werana Woreda in the North Shoa of Amhara Regional State, Ethiopia." Caraka Tani: Journal of Sustainable Agriculture 37, no. 1 (November 22, 2021): 15. http://dx.doi.org/10.20961/carakatani.v37i1.54247.
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Tree growing by smallholders is an emerging livelihood strategy in Basona-Werana <em>Woreda</em> of the North Shoa Zone of Amhara Regional State. The objective of this study was to identify socio-economic determinants of the smallholder tree growing in the study area. Data were collected from the household survey, key informants and focus group discussions. The binary logistic regression model was employed to identify the socio-economic determinants of smallholder tree growing behavior. According to the study, about 55% of tree growers generated their livelihood income from tree planting whereas 72% of non-growers generated income from livestock. Family size of the household and age positively and significantly affected tree planting decisions at P < 0.10 and P < 0.01, respectively. Meanwhile, livestock ownership and distance to the market were negatively and significantly influenced the decision to tree planting at P < 0.01 and P < 0.05, correspondingly. Similarly, total household income positively and significantly (P < 0.01) affected tree planting decisions. This study concluded that the socio-economic circumstances of smallholder farmers must be taken into account in the formulation of initiatives and policies aimed at encouraging smallholders to grow trees in their farming systems to improve livelihood and sustainable agricultural production.
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Luna, José Marcio, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone, Jerome H. Friedman, Timothy D. Solberg, and Gilmer Valdes. "Building more accurate decision trees with the additive tree." Proceedings of the National Academy of Sciences 116, no. 40 (September 16, 2019): 19887–93. http://dx.doi.org/10.1073/pnas.1816748116.
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The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.
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Hernández, Víctor Adrián Sosa, Raúl Monroy, Miguel Angel Medina-Pérez, Octavio Loyola-González, and Francisco Herrera. "A Practical Tutorial for Decision Tree Induction." ACM Computing Surveys 54, no. 1 (April 2021): 1–38. http://dx.doi.org/10.1145/3429739.
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Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.
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Tofan, Cezarina Adina. "Decision Tree Method Applied in Cost-based Decisions in an Enterprise." Procedia Economics and Finance 32 (2015): 1088–92. http://dx.doi.org/10.1016/s2212-5671(15)01572-5.
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Wu, Mike, Sonali Parbhoo, Michael Hughes, Ryan Kindle, Leo Celi, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez. "Regional Tree Regularization for Interpretability in Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6413–21. http://dx.doi.org/10.1609/aaai.v34i04.6112.
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The lack of interpretability remains a barrier to adopting deep neural networks across many safety-critical domains. Tree regularization was recently proposed to encourage a deep neural network's decisions to resemble those of a globally compact, axis-aligned decision tree. However, it is often unreasonable to expect a single tree to predict well across all possible inputs. In practice, doing so could lead to neither interpretable nor performant optima. To address this issue, we propose regional tree regularization – a method that encourages a deep model to be well-approximated by several separate decision trees specific to predefined regions of the input space. Across many datasets, including two healthcare applications, we show our approach delivers simpler explanations than other regularization schemes without compromising accuracy. Specifically, our regional regularizer finds many more “desirable” optima compared to global analogues.
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Azad, Mohammad, and Mikhail Moshkov. "Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions." Procedia Computer Science 35 (2014): 368–77. http://dx.doi.org/10.1016/j.procs.2014.08.117.
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Tan, Xiaoyang, Satoshi Hirabayashi, and Shozo Shibata. "Estimation of Ecosystem Services Provided by Street Trees in Kyoto, Japan." Forests 12, no. 3 (March 7, 2021): 311. http://dx.doi.org/10.3390/f12030311.
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Street trees are integral components of urban green infrastructure. The importance of benefits provided by street trees has motivated the development of various tools to quantify the value of ecosystem services. The i-Tree Eco is a widely applied method for quantifying urban forest structure, ecosystem services, and values. Since its first release in 2006, i-Tree Eco has been successfully utilized in over 100 countries around the world. This study described one of the first applications of the i-Tree Eco international project in Kyoto, Japan, by customizing the models and parameters to enhance the accuracy of analysis results. Kyoto’s street trees are prominently dominated by Ginkgo (Ginkgo biloba L.), Trident Maple (Acer buergerianum Miq.), Japanese Zelkova (Zelkova serrata (Thunb.) Makino.), Tuliptree (Liriodendron tulipifera L.), Flowering dogwood (Cornus florida L.), London Planetree (Platanus × acerifolia), Plum/cherry (Prunus spp.), and Weeping willow (Salix babylonica), which account for 92% of the 1230 sample trees and deliver ecosystem service benefits at US$71,434.21 annually or US$58.07/tree/year. The annual value of each function was estimated at US$41.34/tree for carbon storage and sequestration, US$3.26/tree for stormwater runoff reduction, US$11.80/tree for adverse health mitigation effects, and US$1.67/tree for energy savings. The street tree species of Kyoto city that produce the highest average annual benefits are among the largest trees currently in the population, including P. × yedoensis (US$225.32/tree), Z. serrata (US$123.21/tree), S. babylonica (US$80.10/tree), and P. × acerifolia (US$65.88/tree). Our results demonstrated a comprehensive understanding of street trees benefits for Kyoto city, providing baseline information for decision-makers and managers to make effective urban trees management decisions, developing policy, and setting priorities.
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Erokhina, O. V. "Implementation of Machine Learning Methods to solve Political Problems." Humanities and Social Sciences. Bulletin of the Financial University 10, no. 3 (November 2, 2020): 67–73. http://dx.doi.org/10.26794/2226-7867-2020-10-3-67-73.
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The political reality described by non-linearity of processes and significant influence of informal mechanisms of interaction of decision-making subjects-political elites and leaders. One of the most promising areas that open up new opportunities for studying political problems is the synthesis of “traditional” methodology of political analysis and methods of artificial intelligence. The purpose of this work is to build a methodological model in the form of a “decision tree” and use it to analyze the process of making personnel decisions concerning the heads of the subjects of the Russian Federation. Chronological limits: 2000–2020 years. It is necessary to solve the following tasks to achieve such a goal. 1. Study the main trends and priorities of the Federal centre’s personnel policy in relation to regional heads in the period under review. 2. Identification of key factors affecting the political positions of the leaders of regions of the Russian Federation based on the analysis of data on the performance parameters of the regional executive power. 3. Generalization of data on personnel decisions made in 2000–2018 and the construction of a “decision tree” that provides grounds for predicting future personnel decisions following the selected algorithm. The article substantiates the adequacy of using the decision tree for analyzing and predicting political decisions on the example of the personnel policy of federal government structures in relation to the regions of the Russian Federation. Based on the results of the work, 13 formal and informal criteria for assessing the stability of political positions of governors with varying degrees of probability of their use in the process of making personnel decisions were formulated. The author proposes to use these criteria as attributes for training the decision tree. Due to the significant amount of data, the paper presents a fragment of the decision tree that clearly illustrates the possibility of using the C 4.5 algorithm to solve political problems.
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Petter, Joshua, Paul Ries, Ashley D’Antonio, and Ryan Contreras. "How Are Managers Making Tree Species Selection Decisions in the Pacific Northwest of the United States?" Arboriculture & Urban Forestry 46, no. 2 (March 1, 2020): 148–61. http://dx.doi.org/10.48044/jauf.2020.011.
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Trees provide an array of social, economic, and ecological benefits; furthermore, trees on public land are critical for providing those benefits to people who cannot afford their own trees. It is important to know how managers make trade-offs and prioritize different tree selection criteria in order to target educational campaigns at the state or regional level. Primary contacts for Tree City USA designated cities were surveyed across the Pacific Northwest. Of these municipalities, 79 out of 151 responded (52.3% response rate), with 6 municipalities providing responses from different departments for a total of 85 responses. Currently, there are primarily descriptive statistics in relation to tree species selection. This study provides a framework for future statistical analysis and greater exploration of how municipalities and managers are selecting tree species. Results were analyzed with a Mann-Whitney U test to compare International Society of Arboriculture (ISA) Certified Arborists® to those who are not certified across various tree species selection criteria. Another Mann-Whitney U test was used to compare small (≤ 50,000) and large (> 50,000) municipalities across the same criteria. ISA Certified Arborists® showed statistically significant differences from those who are not certified in a number of tree species selection criteria. ISA Certified Arborists® also differed in urban forest management on a city-wide scale, particularly in favoring greater tree species diversity. The differences in urban forest management between ISA Certified Arborists® and noncertified—and between municipality sizes—can help to influence future educational campaigns targeted toward increasing urban forest health and resiliency.
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Pokorná, Pavla, and Jarmila Šebestová. "Profit Reinvestment: Main Motives Supporting Financial Decisions." e-Finanse 15, no. 4 (December 1, 2019): 34–43. http://dx.doi.org/10.2478/fiqf-2019-0026.
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AbstractReinvestment decisions are based on basic the economic literacy of entrepreneurs because they do not want to affect future liquidity or development activities. The main goal of the article is to suggest a simple decision tree model to describe profit reinvestments in a general way based on results of a primary pilot study (128 interviews), where reinvestment behaviour is affected by specific factors like risk taking, competitive advantage or business experience. After that a decision-making tree is suggested to explain the process of reinvestment as determined by the manager.
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Mišić, Velibor V. "Optimization of Tree Ensembles." Operations Research 68, no. 5 (September 2020): 1605–24. http://dx.doi.org/10.1287/opre.2019.1928.
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Florkowski, Wojciech J., and Orville M. Lindstrom. "Opinions and Perceptions of Having a Live-cut Leyland Cypress as a Christmas Tree." HortTechnology 5, no. 2 (April 1995): 161–64. http://dx.doi.org/10.21273/horttech.5.2.161.
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Leyland cypress (×Cupressocyparis Leylandii) is becoming increasingly important as a live-cut Christmas tree yet it differs from trees currently familiar to most customers. Results of a consumer survey provide an opportunity for growers to adjust planting and marketing decisions. Questionnaires were completed while respondents displayed the tree at their residences. Opinions about the tree referred to tree features and compared them with features of other types of Christmas trees and inquired about the care given to the tree and its disposal. In general, respondents were consistent in their favorable assessment of Leyland cypress as a live Christmas tree with respect to several characteristics including tree shape twig density, and maintenance of fresh appearance over time. Recycling was the primary form of tree disposal.
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Maceika, Augustinas, Andrej Bugajev, Olga Regina Šostak, and Tatjana Vilutienė. "Decision Tree and AHP Methods Application for Projects Assessment: A Case Study." Sustainability 13, no. 10 (May 14, 2021): 5502. http://dx.doi.org/10.3390/su13105502.
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This research is dedicated to the modelling of decision process occurring during the implementation of construction projects. Recent studies generally do not assess the robustness of the decisions regarding the possible changes during the construction project implementation. However, such an assessment might increase the reliability of the decision-making process. We addressed this gap through a new model that combines the decision-making process modelling with the AHP method and includes the analysis of model stability concerning stakeholders’ behaviour. We used the Analytic Hierarchy Process (AHP) and Decision tree methods to model the decision-making process. The proposed model was validated on a case study of multiple construction projects. The assessment was performed from individual investor’s and independent expert’s perspectives. The criteria for the assessment were selected according to the principles of sustainability. We performed the sensitivity analysis, making it possible to assess the possible changes of the decisions depending on the potential patterns of the decision-makers’ behaviour. The results of the study show that, sometimes, small fluctuations in the project factors affect the project selection indicating the possible lack of the robustness of the project decisions.
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Ilina, E. P. "Methods and Models for Employment of the Expert Analytical Knowledge in Organization Decision Making. Part I. Decisions Knowledge Models." PROBLEMS IN PROGRAMMING, no. 1 (January 2016): 089–101. http://dx.doi.org/10.15407/pp2016.01.089.
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The paper is devoted to the formal representation of the organization decision by providing of the special kind of the organization knowledge models. Being employed in the organization information system these models give an ability for reusing of organization experience. Moreover they help to support analytical operations for quality man-agement of organization decisions and decision making processes. The main components of the proposed knowledge models system are the Ontological Model for Decisions Support and the Decision Field of Organization. First of them includes such the categories of concepts as Gall, Gall Tree, Operation, Problem Situation, Side Influence, Decision, Package of Decisions, Value, Value tree. Their models are represented as the systems of the partial definitions for the concept that belong to the certain category and the set of rules for interpretation of each role position in such partial definition. Incomplete conceptual knowledge may be also represented in these models. Models proposed may be used for representation of the Organization Decisions Field and for constructing the mechanism of its analysis that is a subject of the next part of this article.
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Gunduz, Murat, and Hamza M. A. Lutfi. "Go/No-Go Decision Model for Owners Using Exhaustive CHAID and QUEST Decision Tree Algorithms." Sustainability 13, no. 2 (January 15, 2021): 815. http://dx.doi.org/10.3390/su13020815.
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Go/no-go execution decisions are one of the most important strategic decisions for owners during the early stages of construction projects. Restructuring the process of decision-making during these early stages may have sustainable results in the long run. The purpose of this paper is to establish proper go/no-go decision-tree models for owners. The decision-tree models were developed using Exhaustive Chi-square Automatic Interaction Detector (Exhaustive CHAID) and Quick, Unbiased, Efficient Statistical Tree (QUEST) algorithms. Twenty-three go/no-go key factors were collected through an extensive literature review. These factors were divided into four main risk categories: organizational, project/technical, legal, and financial/economic. In a questionnaire distributed among the construction professionals, the go/no-go variables were asked to be ranked according to their perceived significance. Split-sample validation was applied for testing and measuring the accuracy of the Exhaustive CHAID and QUEST models. Moreover, Spearman’s rank correlation and analysis of variance (ANOVA) tests were employed to identify the statistical features of the 100 responses received. The result of this study benchmarks the current assessment models and develops a simple and user-friendly decision model for owners. The model is expected to evaluate anticipated risk factors in the project and reduce the level of uncertainty. The Exhaustive CHAID and QUEST models are validated by a case study. This paper contributes to the current body of knowledge by identifying the factors that have the biggest effect on an owner’s decision and introducing Exhaustive CHAID and QUEST decision-tree models for go/no-go decisions for the first time, to the best of the authors’ knowledge. From the “sustainability” viewpoint, this study is significant since the decisions of the owner, based on a rigorous model, will yield sustainable and efficient projects.
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Deptuła, A., and R. Kh Kurmaev. "Application of decision logic trees and game-tree structures in analysis of automatic transmission gearboxes." Trudy NAMI, no. 3 (October 6, 2021): 6–21. http://dx.doi.org/10.51187/0135-3152-2021-3-6-21.
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Introduction (problem statement and relevance). The graphs, logic and game-tree structures methods have been used in mechanics. The purpose of modeling an automatic gearbox with graphs can be versatile, namely: determining the transmission ratio of individual gears, analyzing the speed and acceleration of individual rotating elements.The purpose of the study. The article presents the application of decision trees in the analysis of automatic gearboxes modeled with the Hsu graph.Methodology and research methods. The paper presents a method of generating game tree structures that allow to change the values of decision parameters in the issues of decision making and knowledge generation. Specifying the rank of importance, in which order you should change individual items to active, allows you to detect the so-called redundant or temporarily redundant components for a given gear currently under consideration.Scientific novelty and results. At each stage of optimization, a tree is generated, selecting the optimal decisions. Then, vertices can be added to the tree that represent the optimal responses of the system to changes in arithmetic construction parameters.Practical significance. The most important in this regard will be the selection of the optimal programming environment with the possibility of installing the program in laboratory
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Oakes, Lindsey R., D. Rose Ewald, Muhsin Michael Orsini, and Robert W. Strack. "The Photovoice Decision Tree: Legal Considerations and Ethical Implications for Photographs and Captions." Health Promotion Practice 23, no. 2 (March 2022): 250–61. http://dx.doi.org/10.1177/15248399211053892.
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This article presents a photovoice decision tree that serves as a guide for making lawful and ethical decisions during the portions of the photovoice process that involve photograph selection, caption development, and public display of photographs and captions. Lawful and ethical considerations encompass privacy of person, privacy of place, illegal acts and obscenity, defamation, representation of truth versus actual malice, and opinion versus assertion of fact, but do not address pursuing and obtaining institutional review board approval for photovoice projects and/or other important steps of photovoice projects that are beyond the scope of this article. The decision tree presumes that a comprehensive photo release process was completed with all photovoice participants and collected from any individual captured within a photograph. The decision tree has important implications for research and practice, including movement of photovoice practitioners beyond the required institutional review board approval for research projects to consider lawful and ethical issues associated with photograph selection, caption development, and public display of photographs and captions. This decision tree can serve as a meaningful tool for all photovoice practitioners and participants to guide their lawful and ethical decisions.
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Church, Bryan K., Wei Jiang, Xi (Jason) Kuang, and Adam Vitalis. "A Dollar for a Tree or a Tree for a Dollar? The Behavioral Effects of Measurement Basis on Managers' CSR Investment Decision." Accounting Review 94, no. 5 (November 1, 2018): 117–37. http://dx.doi.org/10.2308/accr-52332.
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ABSTRACT We experimentally investigate how managers' decisions to invest discretionary resources in the company's corporate social responsibility (CSR) initiatives are affected by whether the investment decision is denominated in financial or nonfinancial measures (i.e., the measurement basis used for decision making). We posit that nonfinancial measures bring attention to the society-serving nature of CSR investments, thus activating the pro-CSR social norms of the company and managers' personal CSR norms. Norm activation, in turn, influences managers' investment decisions to the extent that social norms are congruent with personal norms. As predicted, we find that the level of CSR investment is higher under a nonfinancial measurement basis than under a financial measurement basis, but only when the manager is personally supportive of CSR. Supplemental analysis indicates that CSR-supportive managers continue to invest more under a combined financial/nonfinancial measurement basis than under a financial measurement basis only. Theoretical and practical implications are discussed. JEL Classifications: C91; M41.
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Bacchieri, Antonella, and Ermanno Attanasio. "L’analisi delle decisioni negli studi di farmacoeconomia." Farmeconomia. Health economics and therapeutic pathways 6, no. 2 (June 15, 2005): 141–52. http://dx.doi.org/10.7175/fe.v6i2.831.
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This paper is a review of the decision tree methodology. This is a very useful technique in complex decision making, when the consequences of the decisions are distant in time and the information upon which we can rely is uncertain. Decision trees are the basic structure underlying most applications of decision analysis in medicine. However, in this review we only cover their application to the pharmaco-economic field. The main steps of this decision analysis are explained. Thereafter, a case study from the literature is used as an example, i.e. an application of the decision tree analysis to a study aimed at comparing two different drugs in the treatment of gastro-esophageal reflux. The main focus of our paper is on the statistical aspects, which include the definition and quantification of the outcome variables, the definition and quantification of the probabilities of occurrence of the uncertain events considered in the decision tree, and the sensitivity analysis. The knowledge of the basic laws of the probability theory is mandatory for assigning correct values to the parameters of the decision tree (outcomes and probabilities). Finally, the sensitivity analysis is an important part of the work to be performed in the last stage of the decision analysis in order to measure the degree of robustness of the results when varying the assumptions.
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Harris, J. Roger, and Nina Bassuk. "Tree Planting Fundamentals." Arboriculture & Urban Forestry 19, no. 2 (March 1, 1993): 64–70. http://dx.doi.org/10.48044/jauf.1993.012.
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Transplanting imposes severe physiological stress because over 95% of the root system is removed when the tree is dug. The landscape designer and contractor can make decisions that will improve the chances of success. Advantages and disadvantages of purchasing trees produced by different production methods are discussed from the landscape contractor's point of view. Current research concerning key steps in the planting process is reviewed, and recommendations are made.
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Cheng, Kuo-Chih, Mu-Jung Huang, Cheng-Kai Fu, Kuo-Hua Wang, Huo-Ming Wang, and Lan-Hui Lin. "Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques." Sustainability 13, no. 6 (March 11, 2021): 3100. http://dx.doi.org/10.3390/su13063100.
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This study attempts to integrate the decision tree algorithm with the Apriori algorithm to explore the relationship among financial ratio, corporate governance, and stock returns to establish a stock investment decision model. The sports and leisure related industries are employed as the research target. The data are collected and processed for generating decision tree and association rules. Based on the analysis outcome, an investment decision model is constructed for investors expecting to decrease their investment risks and further increase their profits. This stock investment decision model is one type of multiple-criteria decision-making model. This study makes three critical contributions to investors. (1) It proposes a systematical model of exploring related data through the decision tree algorithm and the Apriori algorithm to reveal the implicit investment knowledge. (2) An effective investment decision model is established and expected to provide a reference basis during stock-picking decisions. (3) The investment decision model is enhanced with implicit rules found among variables using association rules.
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Christias, Panagiotis, Ioannis N. Daliakopoulos, Thrassyvoulos Manios, and Mariana Mocanu. "Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support." Mathematics 8, no. 5 (May 3, 2020): 717. http://dx.doi.org/10.3390/math8050717.
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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.
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Gessesse, Berhan, Woldeamlak Bewket, and Achim Bräuning. "Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia." Solid Earth 7, no. 2 (April 20, 2016): 639–50. http://dx.doi.org/10.5194/se-7-639-2016.
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Abstract. Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 = 37.29, df = 15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed.
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Samsami, Mahsa, and Ralf Wagner. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis." Journal of Risk and Financial Management 14, no. 7 (July 1, 2021): 299. http://dx.doi.org/10.3390/jrfm14070299.
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Ignoring endogeneity when assessing investors’ decisions carries the risk of biased estimates for the influence of exogeneous marketing variables. This study shows how to overcome this challenge by using Pólya trees in the quantification of impacts on investors’ decisions. A total of 2255 investors recruited for this study received and opened a digital marketing newsletter about investing daily. Given the nature of investors’ decisions characterized by heterogeneity and endogeneity, the response model is assessed with the Dirichlet process mixture and estimated with the Markov chain Monte Carlo method. Digital marketing substantially exceeds the impact of investor experience, but both have a significant positive impact on investors’ trading volume. Findings obtained with the Dirichlet process mixture as a flexible model indicate that digital marketing even with latent endogenous factors makes an underlying contribution to the investors’ actions in the stock market.
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Evison, David C., and Luis A. Apiolaza. "Incorporating economic weights into radiata pine breeding selection decisions." Canadian Journal of Forest Research 45, no. 1 (January 2015): 135–40. http://dx.doi.org/10.1139/cjfr-2014-0363.
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This article introduces the concept of “robust selection”, which proposes tree selection based on the stochastic simulation of economic values to account for the inherent uncertainty of economic weights used in tree selection for breeding programs. The proposed method uses both median ranking and ranking variability as criteria for breeding selection. Using consensus genetic and economic parameters from the New Zealand Radiata Pine Breeding Company program, we compare three selection strategies: deterministic application of economic weights from a vertically integrated bioeconomic model, an equal-weight index often used in operations, and robust selection. All strategies aim to increase value for a breeding objective that includes four traits, i.e., volume, stem sweep, branch size, and wood stiffness (measured as modulus of elasticity), based on a selection index that considers five criteria, i.e., stem diameter at breast height (1.3 m), straightness, branching score, wood density, and modulus of elasticity. Two-thirds of the selected trees were unique for each of the selection strategies. Robust selection achieved the best realised gain for three of the four selection criteria and was the middle performer in the last selection criteria. Considering the high intrinsic uncertainty of economic weights, we suggest that the relevant criterion for the selection of individuals is the maximum median ranking, subject to an acceptable level of variation in that ranking, rather than their narrow performance under a single economic scenario. This will lead to tree selections that perform well under a wide range of economic circumstances.
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Benali, Khaled, and Sidi Ahmed Rahal. "OntoDTA: Ontology-Guided Decision Tree Assistance." Journal of Information & Knowledge Management 16, no. 03 (July 31, 2017): 1750031. http://dx.doi.org/10.1142/s0219649217500319.
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The effective application of a Decision Tree (DT) process is beset with many difficult and technical decisions about the choice of algorithms, parameters, evaluation, etc. Therefore, we propose assistance by using ontologies for addressing the above-mentioned challenges that face the non-specialist DT miner (person). Ontologies have been used in various research areas such as computer science, including data mining tools. In this paper, we propose the realisation of a domain ontology for DT OntoDTA to empower the non-specialist DT miner throughout the key phases of the DT process. OntoDTA ontology contains the knowledge of DT and provides a common terminology that can be shared and processed by DT miners.
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Knoke, Thomas, and Andreas Hahn. "Baumartenvielfalt und Produktionsrisiken: Ein Forschungseinblick und -ausblick | Diversity of tree species and risk: A research insight and outlook." Schweizerische Zeitschrift fur Forstwesen 158, no. 10 (October 1, 2007): 312–22. http://dx.doi.org/10.3188/szf.2007.0312.
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Scientific approaches to analyze silvicultural decisions and their consequences for financial risk are scarce, and yet, considering financial risk and risk correlation offers the opportunity to use adequate approaches from financial theory for the valuation of silvicultural decisions. Based on a classical financial approach, this paper analyzes the silvicultural problem of tree species choice and develops a perspective for silvicultural and forest economic oriented research. It provides the conventional «Faustmann-approach» with financial risk and risk correlation generated by Monte-Carlo simulation. This enables both, the application of the theory of portfolio selection according to Markowitz and the theorem of capital separation according to Tobin to derive the optimal tree species composition, which is principally independent from the degree of risk-aversion if a risk-free investment alternative exists. While supporting the ecological idea of tree species diversity, this simple financial approach provides an interesting research perspective. In future, the two species example has to be extended to more species, different sites and the integration of ecological effects between tree species when mixed in small units such as groups. Furthermore, possible changes in the risk profile of different tree species should be considered; for example, by means of the «information-gap» decision theory. In conclusion, one can see great potential in combining ecological and financial research to support silvicultural decisions for the Central European forest science.
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Rossi, Luca, Maria Elena Menconi, David Grohmann, Antonio Brunori, and David J. Nowak. "Urban Planning Insights from Tree Inventories and Their Regulating Ecosystem Services Assessment." Sustainability 14, no. 3 (February 1, 2022): 1684. http://dx.doi.org/10.3390/su14031684.
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With the uncertainties that our societies are living with (the COVID-19 pandemic and climate change), it becomes essential to provide urban planners and decision-makers with state-of-the-art and user-friendly methodologies to incorporate ecosystem service considerations into their designs for resilient cities. In this regard, urban forests play a crucial role. The quantification of the ecosystem services is geo-specific and needs studies in different urban contexts. At this scope, we evaluated the urban forest of a neighborhood of a densely built-up Italian city (Perugia) with a low level of urban greenery management and with a tree inventory still in progress. Furthermore, we defined a tool helpful in tree-planting decisions and management. This paper involves citizens in field research for trees inventory. Then, it uses i-Tree Eco to evaluate four ecosystem services (carbon storage and sequestration, pollution removal, and runoff avoided) provided by 373 inventoried urban trees belonging to 57 species. Our results show that Italian Municipal tree inventories do not adequately represent their urban forest and that citizens’ participation provides a cost-effective method for integrating field data. Finally, the paper develops an easy tool helping local administrations enhance the ecosystem services provisions in urban green design.
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van Haaster, F., and T. E. Kleynhans. "DECISION TREE MODELLING TO SUPPORT INVESTMENT DECISIONS ON FLOWER PRODUCTION FOR THE NORTHERN HEMISPHERE." Agrekon 41, no. 1 (March 2002): 75–96. http://dx.doi.org/10.1080/03031853.2002.9523587.
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Solway, Alec, and Matthew M. Botvinick. "Evidence integration in model-based tree search." Proceedings of the National Academy of Sciences 112, no. 37 (August 31, 2015): 11708–13. http://dx.doi.org/10.1073/pnas.1505483112.
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Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.
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Park, Eun Young, Myungsun Yi, Hye Sook Kim, and Haejin Kim. "A Decision Tree Model for Breast Reconstruction of Women with Breast Cancer: A Mixed Method Approach." International Journal of Environmental Research and Public Health 18, no. 7 (March 30, 2021): 3579. http://dx.doi.org/10.3390/ijerph18073579.
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The number of breast reconstructions following mastectomy has increased significantly during the last decades, but women are experiencing a number of conflicts with breast reconstruction decisions. The aim of this study was to develop a decision tree model of breast reconstruction and to examine its predictability. Mixed method design using ethnographic decision tree modeling was used. In the qualitative stage, data were collected using individual and focus group interviews and analyzed to construct a decision tree model. In the quantitative stage, the questionnaire was developed questions based on the criteria identified in the qualitative stage. A total of 61 women with breast cancer participated in 2017. Five major criteria: recovery of body image; impact on recurrence; recommendations from others; financial resources; and confirmation by physicians. The model also included nine predictive pathways. It turns out that the model predicted 90% of decisions concerning whether or not to have breast reconstruction. The findings indicate that the five criteria play a key role in decision-making about whether or not to have breast reconstruction. Thus, more comprehensive issues, including these five criteria, need to be integrated into an intervention for women with breast cancer to make their best decision on breast reconstruction.
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Zylberberg, Ariel. "Decision prioritization and causal reasoning in decision hierarchies." PLOS Computational Biology 17, no. 12 (December 31, 2021): e1009688. http://dx.doi.org/10.1371/journal.pcbi.1009688.
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From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.
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Gessesse, B., W. Bewket, and A. Bräuning. "Determinants of farmers' tree planting investment decision as a degraded landscape management strategy in the central highlands of Ethiopia." Solid Earth Discussions 7, no. 4 (November 13, 2015): 3245–70. http://dx.doi.org/10.5194/sed-7-3245-2015.
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Abstract. Land degradation due to lack of sustainable land management practices are one of the critical challenges in many developing countries including Ethiopia. This study explores the major determinants of farm level tree planting decision as a land management strategy in a typical framing and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and binary logistic regression model. The model significantly predicted farmers' tree planting decision (Chi-square = 37.29, df = 15, P<0.001). Besides, the computed significant value of the model suggests that all the considered predictor variables jointly influenced the farmers' decision to plant trees as a land management strategy. In this regard, the finding of the study show that local land-users' willingness to adopt tree growing decision is a function of a wide range of biophysical, institutional, socioeconomic and household level factors, however, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system have positively and significantly influence on tree growing investment decisions in the study watershed. Eventually, the processes of land use conversion and land degradation are serious which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, devising sustainable and integrated land management policy options and implementing them would enhance ecological restoration and livelihood sustainability in the study watershed.
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Tsai, Wan-Min, Heping Zhang, Eugenia Buta, Stephanie O’Malley, and Ralitza Gueorguieva. "A modified classification tree method for personalized medicine decisions." Statistics and Its Interface 9, no. 2 (2016): 239–53. http://dx.doi.org/10.4310/sii.2016.v9.n2.a11.
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Rada, Roy, and Hayden Wimmer. "Decision Trees and Financial Variables." International Journal of Decision Support System Technology 9, no. 1 (January 2017): 1–15. http://dx.doi.org/10.4018/ijdsst.2017010101.
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A decision tree program for forecasting stock performance is applied to Compustat's Global financial statement data augmented with International Monetary Fund data. The hypothesis is that certain Compustat variables will be most used by the decision tree program and will provide insight as to how to make investing decisions. Surprisingly, the authors' experiments show that the most frequently used variables come from the International Monetary Fund and that variables provided exclusively for Financial Industry stocks were not useful for forecasting financial stock performance. These experiments might be part of a constellation of such experiments that help people map financial forecasting problems to the variables most useful for solving those problems. The research shows the value of using decision tree methodologies as applied to finance.
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Zaky, Muhammad Husnan, and Ni Made Purnami. "GREEN MARKETING MIX BERPENGARUH TERHADAP KEPUTUSAN PEMBELIAN PRODUK BIG TREE FARMS DI LOTUS FOOD SERVICES." E-Jurnal Manajemen Universitas Udayana 9, no. 2 (February 3, 2020): 678. http://dx.doi.org/10.24843/ejmunud.2020.v09.i02.p14.
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The green marketing mix approach is believed to be able to increase the integration of environmental issues in all aspects of company activities. This study aims to determine the effect of green marketing mix variables (green product, green price, green place and green promotion) on purchasing decisions for Big Tree Frams products. The research sample was determined by a purposive sampling method of 125 consumers. Data collection was carried out by distributing questionnaires to consumers of Big Tree Farms products at Lotus Food Services Jimbaran, Badung. The data analysis technique in this study used multiple linear regression analysis. The conclusions from the results of the study are, Green marketing mix (green product, green price, green place and green promotion) has a positive and partially significant effect on the purchasing decisions of Big Tree Frams products at Lotus Food Services Jimbaran, Badung.
Keyword : Green Marketing Mix,Buying Decision
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Maceika, Augustinas, Andrej Bugajev, and Olga R. Šostak. "The Modelling of Roof Installation Projects Using Decision Trees and the AHP Method." Sustainability 12, no. 1 (December 19, 2019): 59. http://dx.doi.org/10.3390/su12010059.
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In this work, the process of roofing projects’ execution is considered. The proper analysis of this process is important to optimise the behaviour of a project’s participants and to perform risk evaluation. The main result of this work is methodology, which can be used to optimise a project owner’s decisions and potentially can be applied for risk control or integrated into expert systems. This methodology includes the application of a decision tree and AHP (analytic hierarchy process) method to perform the modelling for roof installation project selection. In the proposed approach, a decision tree describes the process with nodes representing the states of a project. The tree includes the decision on whether to sell the project results or not, which requires the estimation of the subjective opinion of the project owner. These subjective values are used in the decision tree leaves. We propose to perform this estimation with the AHP method and describe how to do it in this paper. A particular example was considered. The proposed methodology was applied to that case, and all details of the process and results are provided. Using the proposed methodology, the adapted version of a specific, current situation model of project participants’ behaviours can be formed, allowing one to make the most efficient decisions in the light of the existing constraints. The application of results can increase the investor protection and contribute to the general sustainability of investments.
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SHIVELY, GERALD E. "Economic policies and the environment: the case of tree planting on low-income farms in the Philippines." Environment and Development Economics 3, no. 1 (February 1998): 83–104. http://dx.doi.org/10.1017/s1355770x98000059.
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This paper investigates the potential for economic policies to influence environmental outcomes in low-income agriculture. The analysis focuses on how changes in agricultural prices influence land-use decisions and environmental indicators. Tree-planting decisions of low-income farmers in the Philippines are examined using a stochastic dynamic household model. Empirical analysis and dynamic simulations demonstrate that changes in relative prices and relative price risks are important determinants of tree-planting decisions. Results show that small holders are sensitive to risks associated with tree crops, but that price risk is not an insurmountable barrier to tree crop adoption. The relationship between cropping patterns and two environmental indicators - erosion and species diversity - are discussed.
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Alsaleem, Maan Y. Anad, and Safwan O. Hasoon. "COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES." EUREKA: Physics and Engineering 1 (January 31, 2020): 56–61. http://dx.doi.org/10.21303/2461-4262.2020.001132.
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Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.
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Lin, Lin, Hao Guo, Yancheng Lv, Jie Liu, Changsheng Tong, and Shuqin Yang. "A machine learning method for soil conditioning automated decision-making of EPBM: hybrid GBDT and Random Forest Algorithm." Eksploatacja i Niezawodnosc - Maintenance and Reliability 24, no. 2 (March 7, 2022): 237–47. http://dx.doi.org/10.17531/ein.2022.2.5.
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There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.
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O’Herrin, Keith, Richard Hauer, Kaitlyn Pike, and Jess Vogt. "Homebuilder Activities and Knowledge of Tree Preservation during Construction: Comparison of Practitioners in Rural and Urban Locations." Sustainability 14, no. 5 (February 26, 2022): 2753. http://dx.doi.org/10.3390/su14052753.
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Preservation of existing trees is one of the few tools available to communities seeking to maintain or increase tree canopy coverage. This study compared the knowledge and activities of builders in an urban locale with a strict tree preservation ordinance and rigorous enforcement against a rural locale with no tree preservation ordinance. Overall, there were more similarities than differences between the two groups though some of those differences are very important. Urban builders and rural builders scored a very similar average of correct responses on questions testing their knowledge: 63% and 65%, respectively. The major difference between urban and rural appears to be in activities as dictated by ordinance. Urban builders were more likely to consult tree preservation experts and use tree fence to create tree protection zones. The successful tree preservation outcomes in the urban community are likely a direct result of ordinance requirements and enforcement by the City Forester, not builders’ knowledge or their conscious decisions.
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Olivero-Lora, Sofia, Elvia Meléndez-Ackerman, Luis Santiago, Raúl Santiago-Bartolomei, and Diana García-Montiel. "Attitudes toward Residential Trees and Awareness of Tree Services and Disservices in a Tropical City." Sustainability 12, no. 1 (December 22, 2019): 117. http://dx.doi.org/10.3390/su12010117.
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Attitudes toward urban residential trees and awareness of their ecosystem services and disservices may play an important role in management decisions of private residential green spaces with important consequences to urban sustainability. In 2011, 397 household surveys were conducted in six locations of the Río Piedras Watershed (San Juan, Puerto Rico) to evaluate residents’ attitudes toward residential and neighborhood trees and their association with household socio-demographic factors, how awareness of services and disservices relate to the spatial proximity of trees (home versus neighborhood), and whether attitudes are associated with yard management (tree abundance). Most residents self-reported positive attitudes toward trees in general and these appeared to be more frequent than self-reported negative attitudes. Respondents recognized more tree services (emphasizing shade, lower temperature, food, and ornamental/aesthetics) and fewer disservices (emphasizing maintenance hardship, property damage, and power line obstruction). Not all tree services and disservices were equally recognized, and differences in the spatial context of trees and residents may contribute to the variation in residents’ awareness of tree ecosystem services or disservices. Variation in positive attitudes partially explained the current variation in yard tree abundance, along with residents’ age, housing tenure, yard size, and watershed location. Results have direct implications for urban forest planning and management in residential contexts.