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Journal articles on the topic 'Decision'

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Published: 4 June 2021
Last updated: 7 July 2024

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1

Owens, T. J. "Decision, Decisions, Decisions." Seismological Research Letters 76, no. 2 (March 1, 2005): 177. http://dx.doi.org/10.1785/gssrl.76.2.177-a.

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2

Rigopoulos, Evangelos. "DECODING EDUCATIONAL DECISIONS: TRACING THE EVOLUTION OF DECISION-MAKING THEORIES." Global Multidisciplinary Journal 03, no. 03 (March 1, 2024): 01–06. http://dx.doi.org/10.55640/gmj-abc219.

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Decoding Educational Decisions: Tracing the Evolution of Decision-Making Theories explores the historical trajectory and development of decision-making theories within educational contexts. This paper provides a comprehensive examination of the key theories that have shaped decision-making processes in education over time, shedding light on their evolution, implications, and practical applications. Drawing upon a wide range of scholarly literature and historical perspectives, this study offers valuable insights into how decision-making frameworks have influenced educational practices and policies. By tracing the evolution of these theories, educators and policymakers can gain a deeper understanding of the factors influencing decision-making processes in education and make informed choices to enhance teaching, learning, and organizational effectiveness.
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3

Natal, Gerald, and Barbara Saltzman. "Decisions, decisions, decisions: decision fatigue in academic librarianship." Journal of Academic Librarianship 48, no. 1 (January 2022): 102476. http://dx.doi.org/10.1016/j.acalib.2021.102476.

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4

Hossfeld, Steffen. "The Advantage of Digital Decision Making for Strategic Decisions – Proofed by a Supply Chain Case." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 3, no. 5 (2017): 7–20. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.35.1001.

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This paper will discuss the advantage of decision making supported by a digital system and will provide an overview of an empiric analysis researched on this topic. Decision making in organizations is a significant system implied task of managers and therefore a broad area in scientific research, not only in the disciplines management or business studies – even from technical to humanistic disciplines. Nowadays the trend of digitalization captures all areas of life especially in business, as well as the typical management task of decision making. Triggered by the digitalization trend business will move toward an autonomous decision making of machines or cyber systems. The important step toward autonomous decisions or decision support (cyber systems will prepare a decision, but finally executed by a human) will be the next development step for decision making. Designed algorithmic models for these decisions will use the content of classical decision models to reach maximum utility. Hence, a view on classical decision making will illuminate the basis for these models, from researchers like von Neumann/Morgenstern or Bernoulli. Furthermore, digitalization changes the process of decision making, especially focussing on the behavioral part of decisions, discussed from Simon, Selten or Tversky/Kahneman. Due to reason that today the human aspect of decision making is sustainable in organizations. The hypothesis that the digital set up for decision making in organizations will increase the efficiency of strategic decisions will be proofed by an empiric study. The research method for the empirical part of this paper is a questionnaire. It is online questionnaire which will be answered by professionals and scholars. As a typical example of a strategic decision, the author explains a business case in the supply chain function of organizations. A section of the well-known SCOR model will be tested on digitalization characteristics. The research findings of the questionnaire will illustrate that the digitalization of supply chain processes is in the scope of managers and consultants, to reach higher efficiency by increased turnover or by reducing costs. The result of this paper verifies an improved decision process by usage of digital features, but the capability of the entire digital possibilities is not fully achieved yet
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5

TOFAN, Cezarina Adina. "Optimization Techniques of Decision Making - Decision Tree." Advances in Social Sciences Research Journal 1, no. 5 (September 30, 2014): 142–48. http://dx.doi.org/10.14738/assrj.15.437.

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6

McCall, Storrs. "Decision." Canadian Journal of Philosophy 17, no. 2 (June 1987): 261–87. http://dx.doi.org/10.1080/00455091.1987.10716436.

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We all make decisions, sometimes dozens in the course of a day. This paper is about what is involved in this activity. It's my contention that the ability to deliberate, to weigh different courses of action, and then to decide on one of them, is a distinctively human activity, or at least an activity which sets man and the higher animals apart from other creatures. It is as much decisio as ratio that constitutes the distinguishing mark of human beings. Homo may not always be rationalis, but he is always decidens.The paper is about practical decision, although there is also another important kind of decision that I won't discuss, namely cognitive decision. The difference is roughly that which distinguishes the function of a jury from that of a judge, at least in jury trials. The jury's job is to arrive at an answer to the question: guilty or not guilty? This is a cognitive decision, a decision that, on the basis of the evidence, such-and-such is the case. The judge, apprized of the jury's cognitive decision, makes the practical decision to sentence the guilty party to 20 years. On some other occasion, I hope to be able to say something about cognitive decision, as well as about a third kind of decision named value decision.
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7

Mechitov, Alexander, Helen Moshkovich, and Ronald Klimberg. "VERBAL DECISION ANALYSIS FOR STRATEGIC DECISIONS." International Journal of Business Research 22, no. 1 (March 1, 2022): 26–37. http://dx.doi.org/10.18374/ijbr-22-1.4.

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8

Howard, Ronald A. "Speaking of Decisions: Precise Decision Language." Decision Analysis 1, no. 2 (June 2004): 71–78. http://dx.doi.org/10.1287/deca.1030.0005.

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9

Steele, Katie, Helen M. Regan, Mark Colyvan, and Mark A. Burgman. "Right Decisions or Happy Decision‐makers?" Social Epistemology 21, no. 4 (October 2007): 349–68. http://dx.doi.org/10.1080/02691720601159711.

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10

Horvath, Stephan, and Yung‐Shain Wu. "Voiced/unvoiced decision using sequential decisions." Journal of the Acoustical Society of America 86, no. 6 (December 1989): 2477. http://dx.doi.org/10.1121/1.398400.

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11

Kostbade Hughes, Katherine, and Wendy B. Young. "Decision Making Stability of Clinical Decisions." Nurse Educator 17, no. 3 (May 1992): 12–16. http://dx.doi.org/10.1097/00006223-199205000-00010.

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12

Linkov, Igor, Matthew E. Bates, Benjamin D. Trump, Thomas P. Seager, Mark A. Chappell, and Jeffrey M. Keisler. "For nanotechnology decisions, use decision analysis." Nano Today 8, no. 1 (February 2013): 5–10. http://dx.doi.org/10.1016/j.nantod.2012.10.002.

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13

Youngner, Stuart J., Donald J. Murphy, and Joanne Lynn. "Decision making in support: Sentinel decisions." Journal of Clinical Epidemiology 43 (January 1990): S67—S71. http://dx.doi.org/10.1016/0895-4356(90)90223-c.

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14

Tavakoli, Manouche, Huw TO Davies, and Richard Thomson. "Aiding clinical decisions with decision analysis." Hospital Medicine 60, no. 6 (June 1999): 444–47. http://dx.doi.org/10.12968/hosp.1999.60.6.1139.

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15

Azad, Mohammad, Igor Chikalov, and Mikhail Moshkov. "Representation of Knowledge by Decision Trees for Decision Tables with Multiple Decisions." Procedia Computer Science 176 (2020): 653–59. http://dx.doi.org/10.1016/j.procs.2020.09.037.

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16

Barksdale, Cheryl, and Anne D. Smith. "Decisions, Decisions . . . Resources and Tools for Complex Decision Making." Journal of Management Education 38, no. 6 (August 6, 2014): 894–98. http://dx.doi.org/10.1177/1052562914545226.

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17

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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18

Lipstein, Ellen A., Daniel J. Lovell, Lee A. Denson, Sandra C. Kim, Charles Spencer, Richard F. Ittenbach, and Maria T. Britto. "High Levels of Decisional Conflict and Decision Regret When Making Decisions About Biologics." Journal of Pediatric Gastroenterology and Nutrition 63, no. 6 (December 2016): e176-e181. http://dx.doi.org/10.1097/mpg.0000000000001425.

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19

Geurtzen, R., J. F. M. van den Heuvel, J. J. Huisman, E. M. Lutke Holzik, M. N. Bekker, and M. Hogeveen. "Decision-making in imminent extreme premature births: perceived shared decision-making, parental decisional conflict and decision regret." Journal of Perinatology 41, no. 9 (July 20, 2021): 2201–7. http://dx.doi.org/10.1038/s41372-021-01159-7.

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20

Smith, Claire, and Soomi Lee. "DECISIONS, DECISIONS: CHARACTERIZING WORKERS' DAILY DECISION PROCESSES DURING LEISURE TIME." Innovation in Aging 6, Supplement_1 (November 1, 2022): 14–15. http://dx.doi.org/10.1093/geroni/igac059.051.

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Abstract Decisions during adulthood set the foundation for healthy aging, but descriptions of healthy and unhealthy decision processes are missing. We extracted latent profiles of daily decision resources (energy and affect) and linked them to daily leisure activity. Diary data was collected from working adults (N=83; Mage=37 years) over the ten workdays (N=693). We identified three daily decision profiles consistent with the Decision Triangle – (1) logical (energetic, unemotional), (2) automatic (less energetic, unemotional), and (3) visceral (unenergetic, highly emotional) – and one additional profile, (4) mild visceral (moderately unenergetic, moderately emotional). Daily logical decision-making related to more “want” leisure activities (i.e., aligned with desires/interests) and the greatest variety in leisure activities. Automatic engaged in the most chores. Visceral engaged in the fewest social activities and least variety in leisure activities. Our findings advance understanding of specific decision processes during leisure, which may have consequences for health and well-being as a person ages.
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21

Keltch, Brain, Yuan Lin, and Coskun Bayrak. "Advanced decision support for complex clinical decisions." Journal of Biomedical Science and Engineering 03, no. 05 (2010): 509–16. http://dx.doi.org/10.4236/jbise.2010.35071.

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22

ROTHERT, MARILYN L., and ANNETTE M. O’CONNOR. "Health Decisions and Decision Support for Women." Annual Review of Nursing Research 19, no. 1 (January 2001): 307–24. http://dx.doi.org/10.1891/0739-6686.19.1.307.

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Women are more likely to live longer with chronic illness and have a longterm relationship with their health care provider; this requires a situation in which patients and providers have a role in managing illness. In this chapter, the authors provide a conceptual overview of decision making along with key issues: historical concepts related to patients and providers, consumerism, informed choice/consent, patient rights, shared decision making, patient involvement, as well as an overview of models of patient/provider partnerships. This review builds on the work of O’Connor et al. (1999), which resulted in a Cochrane review of decision aids and focuses the examination of patient decision aids that support women’ decisions regarding health treatment or screening. The authors conclude with a look to the future and recommendations for research in the area of shared decision making and health care decision aids.
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23

van Rest, David, David J. Hickson, Richard J. Butler, David Cray, Geoffrey R. Mallory, and David C. Wilson. "Top Decisions: Strategic Decision-Making in Organizations." Journal of the Operational Research Society 39, no. 7 (July 1988): 697. http://dx.doi.org/10.2307/2582197.

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24

Miller, Gale, David J. Hickson, Richard J. Butler, David Cray, Geoffrey R. Mallory, and David C. Wilson. "Top Decisions: Strategic Decision-Making in Organizations." Contemporary Sociology 16, no. 5 (September 1987): 649. http://dx.doi.org/10.2307/2069756.

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25

Xue, Yexiang, Arthur Choi, and Adnan Darwiche. "Basing Decisions on Sentences in Decision Diagrams." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 842–49. http://dx.doi.org/10.1609/aaai.v26i1.8221.

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The Sentential Decision Diagram (SDD) is a recently proposed representation of Boolean functions, containing Ordered Binary Decision Diagrams (OBDDs) as a distinguished subclass. While OBDDs are characterized by total variable orders, SDDs are characterized by dissections of variable orders, known as vtrees. Despite this generality, SDDs retain a number of properties, such as canonicity and a polytime apply operator, that have been critical to the practical success of OBDDs. Moreover, upper bounds on the size of SDDs were also given, which are tighter than comparable upper bounds on the size of OBDDs. In this paper, we analyze more closely some of the theoretical properties of SDDs and their size. In particular, we consider the impact of basing decisions on sentences (using dissections as in SDDs), in comparison to basing decisions on variables (using total variable orders as in OBDDs). Here, we identify a class of Boolean functions where basing decisions on sentences using dissections of a variable order can lead to exponentially more compact SDDs, compared to OBDDs based on the same variable order. Moreover, we identify a fundamental property of the decompositions that underlie SDDs and use it to show how certain changes to a vtree can also lead to exponential differences in the size of an SDD.
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26

Corner, James L., and Patricia D. Corner. "Characteristics of Decisions in Decision Analysis Practice." Journal of the Operational Research Society 46, no. 3 (March 1995): 304. http://dx.doi.org/10.2307/2584324.

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27

Louis, Meryl Reis, David J. Hickson, Richard J. Butler, David Cray, Geoffrey R. Mallory, and David C. Wilson. "Top Decisions: Strategic Decision-Making in Organizations." Administrative Science Quarterly 32, no. 4 (December 1987): 627. http://dx.doi.org/10.2307/2392899.

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28

Piercey, C. Darren. "Emphasizing Nonword Decisions in Word-Decision Performance." Psychological Reports 103, no. 1 (August 2008): 97–101. http://dx.doi.org/10.2466/pr0.103.1.97-101.

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A robust finding in the lexical decision literature is that decisions to words are made more quickly and accurately than decisions to nonwords. When instructions are presented to participants prior to an experiment, an emphasis is usually placed on identifying words. This study assessed whether instructing participants to emphasize nonword decisions would affect the performance of the speed and accuracy of identification. A total of 98 individuals took part, 49 in a Word Instruction condition and 49 in a Nonword Instruction condition. Analysis indicated changes in emphasis on words versus nonwords decreased the difference in mean reaction time between word and nonword decisions. An interesting finding is that the manipulation of instructions affected reaction times to words but not to nonwords. The analysis of accuracy yielded no significant comparisons. Further research is required to assess the importance of the finding that the manipulation of instructions affects only word decisions.
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29

PIERCEY, C. DARREN. "EMPHASIZING NONWORD DECISIONS IN WORD-DECISION PERFORMANCE." Psychological Reports 103, no. 5 (2008): 97. http://dx.doi.org/10.2466/pr0.103.5.97-101.

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30

Dubois, Frank L. "Reducing Post-Decision Dissonance in International Decisions." Journal of Teaching in International Business 11, no. 2 (March 2001): 55–69. http://dx.doi.org/10.1300/j066v11n02_04.

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31

Rest, David van. "Top Decisions: Strategic Decision-Making in Organizations." Journal of the Operational Research Society 39, no. 7 (July 1988): 697–98. http://dx.doi.org/10.1057/jors.1988.121.

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32

Corner, James L., and Patricia D. Corner. "Characteristics of Decisions in Decision Analysis Practice." Journal of the Operational Research Society 46, no. 3 (March 1995): 304–14. http://dx.doi.org/10.1057/jors.1995.45.

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33

Liu, Dun, and Decui Liang. "Three-way decisions in ordered decision system." Knowledge-Based Systems 137 (December 2017): 182–95. http://dx.doi.org/10.1016/j.knosys.2017.09.025.

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34

Rosman, Andrew J., and Jean C. Bedard. "Lenders' Decision Strategies and Loan Structure Decisions." Journal of Business Research 46, no. 1 (September 1999): 83–94. http://dx.doi.org/10.1016/s0148-2963(98)00117-9.

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35

Na, WoonBong, Roger Marshall, and Arch G. Woodside. "Decision system analysis of advertising agency decisions." Qualitative Market Research: An International Journal 12, no. 2 (April 3, 2009): 153–70. http://dx.doi.org/10.1108/13522750910948761.

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36

Krupa, Tadeusz, and Teresa Ostrowska. "Decision-Making in Flat and Hierarchical Decision Problems." Foundations of Management 4, no. 2 (December 1, 2012): 23–36. http://dx.doi.org/10.2478/fman-2013-0008.

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Abstract The article is dedicated to the modelling of the essence of decision-taking processes in flat and hierarchical decision problems. In flat decision problems particular attention is drawn to the effectiveness of strategies in seeking decision variants on solution decomposition trees, taking into account the strength of their predefined contradictions. For hierarchical decision processes, the issue of iterative balancing of global (hierarchical) decisions is expressed, based on the valuation of the significance of flat decisions.
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37

Lawson, Stephanie J., Mark R. Gleim, and Michael D. Hartline. "Decisions, decisions: variations in decision-making for access-based consumption." Journal of Marketing Theory and Practice 29, no. 3 (January 10, 2021): 358–74. http://dx.doi.org/10.1080/10696679.2020.1855990.

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38

Causer, Joe, and Paul R. Ford. "“Decisions, decisions, decisions”: transfer and specificity of decision-making skill between sports." Cognitive Processing 15, no. 3 (January 11, 2014): 385–89. http://dx.doi.org/10.1007/s10339-014-0598-0.

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39

Reddy, Harinath. "Decision management." Journal of Management and Science 1, no. 1 (March 31, 2020): 123–26. http://dx.doi.org/10.26524/jms.2020.13.

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The phrase decision management can also be expressed as being an enterprise decision management or EDM or that of BDM or business decision management.This will be encompassing any aspects in relation to that of management, building and designing of systems which take decisions in an automated way. These are often used by organisations to be managing the kind of interactions they have with their suppliers, employees and customers. Computing or computerisation has been changing the ways in which organisations are bound to be making decisions. More and more decisions are being made in an automated way.
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40

Reddy, Harinath. "Decision management." Journal of Management and Science 10, no. 2 (June 30, 2020): 24–27. http://dx.doi.org/10.26524/jms.2020.2.6.

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The phrase decision management can also be expressed as being an enterprise decision management or EDM or that of BDM or business decision management.This will be encompassing any aspects in relation to that of management, building and designing of systems which take decisions in an automated way. These are often used by organisations to be managing the kind of interactions they have with their suppliers, employees and customers. Computing or computerisation has been changing the ways in which organisations are bound to be making decisions. More and more decisions are being made in an automated way.
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41

Joo, Sung Jun, Leor N. Katz, and Alexander C. Huk. "Decision-related perturbations of decision-irrelevant eye movements." Proceedings of the National Academy of Sciences 113, no. 7 (February 1, 2016): 1925–30. http://dx.doi.org/10.1073/pnas.1520309113.

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It is well established that ongoing cognitive functions affect the trajectories of limb movements mediated by corticospinal circuits, suggesting an interaction between cognition and motor action. Although there are also many demonstrations that decision formation is reflected in the ongoing neural activity in oculomotor brain circuits, it is not known whether the decision-related activity in those oculomotor structures interacts with eye movements that are decision irrelevant. Here we tested for an interaction between decisions and instructed saccades unrelated to the perceptual decision. Observers performed a direction-discrimination decision-making task, but made decision-irrelevant saccades before registering their motion decision with a button press. Probing the oculomotor circuits with these decision-irrelevant saccades during decision making revealed that saccade reaction times and peak velocities were influenced in proportion to motion strength, and depended on the directional congruence between decisions about visual motion and decision-irrelevant saccades. These interactions disappeared when observers passively viewed the motion stimulus but still made the same instructed saccades, and when manual reaction times were measured instead of saccade reaction times, confirming that these interactions result from decision formation as opposed to visual stimulation, and are specific to the oculomotor system. Our results demonstrate that oculomotor function can be affected by decision formation, even when decisions are communicated without eye movements, and that this interaction has a directionally specific component. These results not only imply a continuous and interactive mixture of motor and decision signals in oculomotor structures, but also suggest nonmotor recruitment of oculomotor machinery in decision making.
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42

Lye, Ashley, Wei Shao, Sharyn Rundle‐Thiele, and Carolyn Fausnaugh. "Decision waves: consumer decisions in today's complex world." European Journal of Marketing 39, no. 1/2 (January 1, 2005): 216–30. http://dx.doi.org/10.1108/03090560510572098.

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PurposeThe purpose of this paper is to examine the dominant consumer decision theory models and understand why that theory has received little empirical validation. A “decision waves” theory is proposed – an alternative, multi‐phase approach to decision making using image theory. An approach to validating empirically the multi‐phase theory is outlined.Design/methodology/approachThis conceptual paper examines the foundations of modern consumer decision theory and argues for a more representative model of actual consumer decisions.FindingsDecision waves provide a theoretical approach to represent more accurately consumer decision making and improve understanding in this foundational component of marketing. Decision waves do not change detailed empirical findings: however, they do change the macro perspective of how those findings are assembled for marketing.Research limitations/implicationsAn empirical test of decision waves theory is ongoing.Practical implicationsThe concepts outlined in this paper will change segmentation, positioning and how tactical plans are developed within the marketing mix, particularly for promotional strategies.Originality/valueA theoretical approach that represents decision making more accurately will bring us closer to understanding this foundational component of marketing. It provides a basis for differentiation in congested markets.
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43

Stacey, D., J. Kryworuchko, C. Bennett, M. Murray, S. Mullan, and F. Légaré. "Decision coaching to prepare patients for making health decisions - a systematic review of decision coaching in trials of patient decision aids." International Journal of Evidence-Based Healthcare 9, no. 3 (September 2011): 327. http://dx.doi.org/10.1097/01258363-201109000-00089.

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44

Azad, Mohammad, and Mikhail Moshkov. "Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions." European Journal of Operational Research 263, no. 3 (December 2017): 910–21. http://dx.doi.org/10.1016/j.ejor.2017.06.026.

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45

Javalgi, Rajshekhar G., and Hemant K. Jain. "Integrating multiple criteria decision making models into the decision support system framework for marketing decisions." Naval Research Logistics 35, no. 6 (December 1988): 575–96. http://dx.doi.org/10.1002/1520-6750(198812)35:6<575::aid-nav3220350606>3.0.co;2-9.

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46

Quilodrán, Carlos, Matías Kirmayr, Bárbara Valente, Javier Pérez-Bracchiglione, Luis Garegnani, and Juan Víctor Ariel Franco. "The GRADE approach, Part 2: Evidence to decision frameworks—outlining decision-making in health." Medwave 21, no. 04 (May 27, 2021): e8182-e8182. http://dx.doi.org/10.5867/medwave.2021.04.8182.

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The GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology provides a framework for assessing the certainty of the evidence and making recommendations. The Evidence to Decision Framework (EtD) is a transparent and structured system for formulating health recommendations. Once the problem is identified and the certainty of the evidence is assessed, EtD provides several criteria for formulating a recommendation. These criteria include the trade-off between benefits and harms, patients’ values and preferences, acceptability, feasibility, resource use, and impact on equity. The resulting recommendations may differ in strength (strong or weak) and direction (for or against). The process is transparent, allowing other users to adjust the framework of recommendations by modifying the criteria to fit the desired context through an adaptation-adoption process. Given the extensive information available on EtD and the GRADE methodology in general, this narrative review seeks to explain the main concepts involved in decision-making in health by using simplified and friendly descriptions, accompanied by practical examples, thus facilitating its understanding by inexperienced readers.
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47

Zhang, Jia, Xiang Wang, Fang Deng, Bin Xin, and Wenjie Chen. "Battlefield Agent Decision-Making Based on Markov Decision Process." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (March 15, 2017): 221–27. http://dx.doi.org/10.20965/jaciii.2017.p0221.

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Battlefield decision-making is an important part of modern information warfare. It can analyze and integrate battlefield information, reduce operators’ work and assist them to make decisions quickly in complex battlefield environment. The paper presents a dynamic battlefield decision-making method based on Markov Decision Processes (MDP). By this method, operators can get decision support quickly in the case of incomplete information. In order to improve the credibility of decisions, dynamic adaptability and intelligence, softmax regression and random forest are introduced to improve the MDP model. Simulations show that the method is intuitive and practical, and has remarkable advantages in solving the dynamic decision problems under incomplete information.
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48

Marshall, Peter. "Using decision analysis for stand-level silvicultural decisions." Forestry Chronicle 67, no. 4 (April 1, 1991): 384–88. http://dx.doi.org/10.5558/tfc67384-4.

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A decision analytic approach to assessing stand-level silvicultural decisions is illustrated using planting density and spacing decisions for coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Information from several stand projection procedures was combined into a single analysis. The value of "perfect information" was calculated as an indicator of the value of additional response information. The value of perfect information should not be used as the sole justification for gathering treatment response information because it fluctuated with the economic assumptions made and the alternatives evaluated. However, it provides a relative ranking of treatment response requirements and a means for quantifying the risk associated with silvicultural decisions. The decision analytic approach proved flexible as a means for combining growth response information from several sources into a compromise decision.
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49

Liu, Shin Ta, and Robert T. Clemen. "Making Hard Decisions, an Introduction to Decision Analysis." Technometrics 34, no. 3 (August 1992): 365. http://dx.doi.org/10.2307/1270059.

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50

Tang, Antony, Rick Kazman, and George Fairbanks. "Decision-Making Principles for Better Software Design Decisions." IEEE Software 38, no. 6 (November 2021): 98–102. http://dx.doi.org/10.1109/ms.2021.3102358.

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