Estimating the probability of forecasted events

Madera, Alexander G.

doi:10.14419/ijaes.v4i1.6146

Cited by 5 publications

(14 citation statements)

References 13 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The topic of BM co-creation is most frequently discussed among available literature on OBMI [23][24][25][26]. Ebel et al define BM cocreation as the activity of "developing busi-ness models together with customers in a collaborative manner" [24].…”

Section: Bm Co-creationmentioning

confidence: 99%

“…Particularly, the role of customers and suppliers as valuable participants in the BMI process has been highlighted in the literature. Ogilvie [25] argues that engaging directly with customers and prospective partners helps to co-create solutions that deliver value to all stakeholders involved. Buur and Gudiksen [21] arrive at the conclusion that the "discussion of business models within the company, and with suppliers and customers is necessary to ensure competitive edge".…”

Section: Bm Co-creationmentioning

confidence: 99%

“…Similarly, Ebel et al [24] denote target group involvement as crucial for successful BMI. Looking at co-creation from a customer-perspective, Ogilvie claims that customers embraced the invitation to participate in BM co-creation sessions and were enthusiastic about the idea to co-create a new solution [25].…”

Section: Bm Co-creationmentioning

confidence: 99%

“…seeking external feedback and target group involvement in early stages of BM development, is vital to the success of BMI [24,26]. Thus, through using an early BM validation approach, firms can test the com-mercial viability of a new BM concept before making investments beyond planning [23,25]. Early BM validation helps firms to reduce the high degrees of uncertainty and risk entailed in BMI.…”

Section: Early Bm Validationmentioning

confidence: 99%

See 3 more Smart Citations

Untitled

2017

Bus. Inform.

View full text Add to dashboard Cite

Section: Bm Co-creationmentioning

confidence: 99%

Section: Bm Co-creationmentioning

confidence: 99%

Section: Bm Co-creationmentioning

confidence: 99%

Section: Early Bm Validationmentioning

confidence: 99%

See 2 more Smart Citations

Untitled

2017

Bus. Inform.

View full text Add to dashboard Cite

“…At that, even if the estimated quantitative factors are accessible to the subject's sensory perception, they remain uncertain due to the impossibility of measuring them directly (the height, depth, width, etc.). As it was shown in numerous researches on decision-making psychology, prediction and assessment (please see below in more detail), as a rule, people erroneously estimate the uncertain factors, which leads to incorrect, often inadequate assessments of the situation and, consequently, the decisions taken (Thurstone, 1954;Kozielecki, 1979;Kahneman et al, 2005;Werth, 2004;Larichev, 2006;Madera a, b, 2014;Madera, 2016;Mirkin, 2014;Plous, 1993;Kahneman, 2014;Hammond et al, 2017;Borcherding, et al, 1995, World Energy Outlook. IEA, 1994-2017World Oil Outlook.…”

Section: Introductionmentioning

confidence: 99%

Ways of People’s Estimating Uncertain Quantitative Factors for Decision-Making in Economy and Business

Madera¹

2019

JBEP

View full text Add to dashboard Cite

Decisions taken by a subject in economy and business are based on preliminary estimation of the uncertain factors. Along with this, there is still no unambiguous answer to the question about what methods subjects resort to when estimating the uncertain quantitative factors. This question is extremely topical for mathematical modeling of making the best decisions in economy, finance, business processes, and social structures under the conditions of uncertainty. This work is dedicated to research into the methods using which a subject estimates the uncertain quantitative factors. The researches have shown that the subjective estimation of the quantitative uncertain factors is an interval one, and the actual values of an estimated factor are equally distributed within the estimate interval. At the same time, the point estimations are used rather rarely and are not adequate to the subject's psychology of estimation.

show abstract

Modeling and optimization of business processes and process systems under conditions of uncertainty

Madera¹

2017

Bus. Inform.

Self Cite

View full text Add to dashboard Cite

This paper is devoted to mathematical modeling and optimization of business processes and process systems under conditions of uncertainty. At present, modeling of business processes is mainly descriptive, which does not allow quantitative modeling and optimization in the design of processes and process systems. In addition, the existing methods of decision-making in business processes are based on the assumption that the decisive factors are deterministic. Despite uncertainty of the real processes caused by the uncertainty of future costs of resources, the market environment, economy, fi nances, etc., the factors of an uncertain future are either not taken into account, or are believed to be the same as those observed currently. In this paper, a stochastic interval mathematical optimization model is developed. This model allows us to simulate in a quantitative way the business processes and process systems in which they take place, taking into account the uncertainties of the future state of the economy, fi nances, market environment, costs of resources, as well as future realization of chances and risks related to the productive, supporting, and service processes. The criterion for optimality of the model is the maximization of the smallest deviation of the projected chances and risks, which makes it possible to make the best decision in the case that the most unfavorable conditions for the business process occur in the future. The criterion of optimality adopted in the mathematical model takes into account not only the uncertainty of the future state of the economy, fi nance, and market environment, but also the psychology of decision-making and the subjective nature of judgments and estimates. We present a concept and method for estimating the inductive (logical, subjective) probabilities of the occurrence of uncertain projected business process factors. The models and methods developed in the paper make it possible to carry out mathematical modeling and optimization of business processes in a variety of activities without restrictions on the complexity of the structural model of the business process, the qualitative and quantitative composition of the connections in the process systems. On their basis, a software package for the quantitative design of business processes and process systems under conditions of uncertainty can be developed.

show abstract

Estimating the probability of forecasted events

Cited by 5 publications

References 13 publications

Untitled

Untitled

Ways of People’s Estimating Uncertain Quantitative Factors for Decision-Making in Economy and Business

Modeling and optimization of business processes and process systems under conditions of uncertainty

Contact Info

Product

Resources

About