Decision Rule for Investment in Reusable Code

2010 ◽  
pp. 154-160
Author(s):  
Roy Gelbard
Keyword(s):  
Decision Making ◽  
Software Development ◽  
Decision Rule ◽  
Development Time ◽  
Real Life ◽  
Improve Quality ◽  
Restricted Model ◽  
Code Unit ◽  
New Models ◽  
Unlimited Time

Reusable code helps to decrease code errors, code units and therefore development time. It serves to improve quality and productivity frameworks in software development. The question is not HOW to make the code reusable, but WHICH amount of software components would be most beneficial (i.e. costeffective in terms of reuse), and WHAT method should be used to decide whether to make a component reusable or not. If we had unlimited time and resources, we could write any code unit in a reusable way. In other words, its reusability would be 100%. However, in real life, resources and time are limited. Given these constraints, decisions regarding reusability are not always straightforward. The current chapter focuses on decision-making rules for investing in reusable code. It attempts to determine the parameters, which should be taken into account in decisions relating to degrees of reusability. Two new models are presented for decisions-making relating to reusability: (i) a restricted model, and (ii) a non-restricted model. Decisions made by using these models are then analyzed and discussed.

Decision Rule for Investment in Frameworks of Reuse

2009 ◽  
pp. 1013-1021
Author(s):  
Roy Gelbard
Keyword(s):  
Decision Making ◽  
Software Development ◽  
Development Time ◽  
Real Life ◽  
Cost Effective ◽  
Improve Quality ◽  
Restricted Model ◽  
Code Unit ◽  
New Models ◽  
Unlimited Time

Reuse helps to decrease development time, code errors, and code units. Therefore, it serves to improve quality and productivity frameworks in software development. The question is not HOW to make the code reusable, but WHICH amount of software components would be most beneficial, that is, cost-effective in terms of reuse, and WHAT method should be used to decide whether to make a component reusable or not. If we had unlimited time and resources, we could write any code unit in a reusable way. In other words, its reusability would be 100%. However, in real life, resources are limited and there are clear deadlines to be met. Given these constraints, decisions regarding reusability are not always straightforward. The current research focuses on decision-making rules for investing in reuse frameworks. It attempts to determine the parameters, which should be taken into account in decisions relating to degrees of reusability. Two new models are presented for decision-making relating to reusability: (i) a restricted model and (ii) a non-restricted model. Decisions made by using these models are then analyzed and discussed.

Decision Rule for Investment in Frameworks of Reuse

2009 ◽  
pp. 140-147
Author(s):  
Roy Gelbard
Keyword(s):  
Decision Making ◽  
Software Development ◽  
Development Time ◽  
Real Life ◽  
Cost Effective ◽  
Improve Quality ◽  
Restricted Model ◽  
Code Unit ◽  
New Models ◽  
Unlimited Time

Reuse helps to decrease development time, code errors, and code units. Therefore, it serves to improve quality and productivity frameworks in software development. The question is not HOW to make the code reusable, but WHICH amount of software components would be most beneficial, that is, cost-effective in terms of reuse, and WHAT method should be used to decide whether to make a component reusable or not. If we had unlimited time and resources, we could write any code unit in a reusable way. In other words, its reusability would be 100%. However, in real life, resources are limited and there are clear deadlines to be met. Given these constraints, decisions regarding reusability are not always straightforward. The current research focuses on decision-making rules for investing in reuse frameworks. It attempts to determine the parameters, which should be taken into account in decisions relating to degrees of reusability. Two new models are presented for decision-making relating to reusability: (i) a restricted model and (ii) a non-restricted model. Decisions made by using these models are then analyzed and discussed.

scholarly journals ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1456
Author(s):  
Stefka Fidanova ◽  
Krassimir Todorov Atanassov
Keyword(s):  
Decision Making ◽  
Knapsack Problem ◽  
Propositional Logic ◽  
Optimization Problems ◽  
Real Life ◽  
Combinatorial Optimization Problems ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Multiple Constraint

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

Decision-making styles in a real-life decision: Choosing a college major

2006 ◽  
Vol 41 (4) ◽  
pp. 629-639 ◽  
Author(s):  
Kathleen M. Galotti ◽  
Elizabeth Ciner ◽  
Hope E. Altenbaumer ◽  
Heather J. Geerts ◽  
Allison Rupp ◽  
...  
Keyword(s):  
Decision Making ◽  
College Major ◽  
Real Life ◽  
Decision Making Styles ◽  
Life Decision

Decision rules, escalation of commitment and sensitivity to framing in group decision-making

2016 ◽  
Vol 54 (7) ◽  
pp. 1649-1668 ◽  
Author(s):  
Petru Lucian Curseu ◽  
Sandra G. L. Schruijer ◽  
Oana Catalina Fodor
Keyword(s):  
Decision Making ◽  
Decision Rule ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Rules ◽  
Escalation Of Commitment ◽  
Group Level ◽  
Content Type ◽  
The Impact ◽  
Collaborative Decision

Purpose – The purpose of this paper is to test the influence of collaborative and consultative decision rules on groups’ sensitivity to framing effect (FE) and escalation of commitment (EOC). Design/methodology/approach – In an experimental study (using a sample of 233 professionals with project management experience), the authors test the effects of collaborative and consultative decision rules on groups’ sensitivity to EOC and FE. The authors use four group decision-making tasks to evaluate decision consistency across gain/loss framed decision situations and six decision tasks to evaluate EOC for money as well as time as resources previously invested in the initial decisions. Findings – The results show that the collaborative decision rule increases sensitivity to EOC when financial resources are involved and decreases sensitivity to EOC when time is of essence. Moreover, the authors show that the collaborative decision rule decreases sensitivity to FE in group decision making. Research limitations/implications – The results have important implications for group rationality as an emergent group level competence by extending the insights concerning the impact of decision rules on emergent group level cognitive competencies. Due to the experimental nature of the design, the authors can probe the causal relations between the investigated variables, yet the authors cannot generalize the results to other settings. Practical implications – Managers can use the insights of this study in order to optimize the functioning of decision-making groups and to reduce their sensitivity to FEs and EOC. Originality/value – The study extends the research on group rationality and it is one of the few experimental attempts used to understand the role of decision rules on emergent group level rationality.

Moral Decision Making in Business: A Phase-Model

2000 ◽  
Vol 10 (4) ◽  
pp. 773-803 ◽  
Author(s):  
Aviva Geva
Keyword(s):  
Decision Making ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Ethical Problem ◽  
Real Life ◽  
Phase Model ◽  
Second Phase ◽  
Illustrative Case ◽  
Moral Decision ◽  
Ethical Problems

Abstract:The traditional model of ethical decision making in business suggests applying an initial set of principles to a concrete problem and if they conflict the decision maker may attempt to balance them intuitively. The centrality of the ethical conflict in the accepted notion of “ethical problem” has diverted the attention of moral decision modelers from other ethical problems that real-world managers must face—e.g., compliance problems, moral laxity, and systemic problems resulting from the structures and practices of the business organization. The present article proposes a new model for ethical decision making in business—the Phase-model—designed to meet the full spectrum of business-related ethical problems. Drawing on the dominant moral theories in business literature, the model offers additional strategies for tackling ethical issues beyond the traditional cognitive operations of deductive application of principles to specific cases and the balancing of ethical considerations. Its response to the problems of moral pluralism in the context of decision making lies in its structural features. The model distinguishes between three phases of the decision-making process, each having a different task and a different theoretical basis. After an introductory stage in which the ethical problem is defined, the first phase focuses on a principle-based evaluation of a course of action; the second phase provides a virtue-based perspective of the situation and strategies for handling unsettled conflicts and compliance problems; and the third phase adapts the decision to empirical accepted norms. An illustrative case demonstrates the applicability of the model to business real life.

Kenneth Fisher’s heuristics

2016 ◽  
Vol 8 (2) ◽  
pp. 130-148
Author(s):  
Carlo Massironi ◽  
Giusy Chesini
Keyword(s):  
Decision Making ◽  
Conceptual Analysis ◽  
Investment Decision ◽  
Real Life ◽  
Human Action ◽  
Theory Of Knowledge ◽  
Constructivist Theory ◽  
Individual Investors ◽  
Content Type ◽  
Radical Constructivist

Purpose The authors are interested in building descriptive – real life – models of successful investors’ investment reasoning and decision-making. Models designed to be useful for trying to replicate and evolve their reasoning and decision-making. The purpose of this paper, a case study, is to take the substantial material – on innovating the investing tools – published in four books (2006/2012, 2010, 2011, 2015) by a US stock investor named Kenneth Fisher (CEO of Fisher Investments, Woodside, California) and sketch Fisher’s investment innovating reasoning model. Design/methodology/approach To sketch Fisher’s investment innovating reasoning model, the authors used the Radical constructivist theory of knowledge, a framework for analyzing human action and reasoning called Symbolic interactionism and a qualitative analytic technique called Conceptual analysis. The authors have done qualitative research applied to the study of investment decision-making of a single professional investor. Findings In the paper, the authors analyzed and described the heuristics used by Fisher to build subsequent generations of investing tools (called by Fisher “Capital Markets Technology”) to try to make better forecasts to beat the stock market. The authors were interested in studying the evolutive dimensions of the tools to make forecasts of a successful investor: the “how to build it” and “how to evolve it” dimension. Originality/value The paper offers an account of Kenneth Fisher’s framework to reason the innovation of investing tools. The authors believe that this paper could be of interest to professional money managers and to all those who are involved in the study and development of the tools of investing. This work is also an example of the use of the Radical constructivist theory of knowledge, the Symbolic interactionist framework and the Conceptual analysis to build descriptive models of investment reasoning of individual investors, models designed to enable the reproduction/approximation of the conceptual operations of the investor.

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