Methods of Decision Making

2016

In decision under uncertainty individual decision makers (farmers) have to choose one of a set number of alternatives with complete information about their outcomes but in the absence of any information or data about the probabilities of the various state of nature. This paper examines a decision making under uncertainty in agriculture. The classical approaches of Wald’s, Hurwicz’s, Maximax, Savage’s and Laplace’s are discussed and compared in case study of oil pumpkin production and selling of pumpkin oil. The computational complexity and usefulness of the criterion are further presented. The article is concluded with aggregate the results of all observed criteria and business alternatives in the conditions of uncertainty, where the business alternative 1 is suggested.

Expert Systems with Applications

Managerial decision-making often involves the consideration of multiple criteria with high levels of uncertainty. Multi-attribute utility theory, a primary method proposed for decision-making under uncertainty, has been repeatedly shown to be difficult to use in practice. This paper presents a novel approach termed Simulated Uncertainty Range Evaluations (SURE) to aid decision makers in the presence of high levels of uncertainty. SURE has evolved from an existing method that has been applied extensively in the pharmaceutical and speciality chemical sectors involving uncertain decisions in whole process design. The new method utilises simulations based upon triangular distributions to create a plot which visualises the preferences and overlapping uncertainties of decision alternatives. It facilitates decisionmakers to visualise the not-so-obvious uncertainties of decision alternatives. In a real-world case study for a large pharmaceutical company, SURE was compared to other widely-used methods for decision-making and was the only method that correctly identified the alternative eventually chosen by the company. The case study demonstrates that SURE can perform better than other existing methods for decision-making involving multiple criteria and uncertainty.

2017

One of the main goals of precision agriculture researches is to define a suitable decision support system (DSS) for farm management with the goal of optimizing returns on inputs while preserving resources. Therefore, our long-term objective is the development of an adequate DEM model which can be adapted in the actual decision support system (DSS) to create a new model driven decision support system (MDDS) to refine operational parameters. In this study a preliminary step of our study is discussed: the predicted role of DEM modelling in decision making and a possible numerical model for corn stalk. The consequences of the study clearly demonstrate that the discrete element method is capable to reinforce agricultural decision support systems in the future and it is worth further analysing it.

2011

There are ambiguities and vagueness in solar radiation records during a day. As a consequence, the development and adaptation of automatic knowledge acquisition techniques under uncertainty is entirely advisable. Among them fuzzy sets theory formulizes and analyses the situations in which the uncertainty is due to non-precise environment. Any good decision is based on information. Decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. From our findings, we conclude that data warehousing and data mining are essentials for an effective decision support system.

1991

A variation on the conventional Delphi was used to assemble an informational summary of expert opinion regarding the risks involved with the application of sewage sludge to farmland. The aim was to reduce uncertainties surrounding the associated health and environmental risks so agreement among citizens, farmers, and regulators could be reached. An expert panel was assembled for one day to take part in a structured communication process modeled after the Delphi. A two-part questionnaire using Likert scaling and open questions was iterated among rotating subgroups to build consensus and define disagreement. Plenary discussions were held between iterations to foster peer review. There was consensus about the risks of heavy metals, pathogens, and nutrients; but clear disagreement about the risks of organic toxins. Existing state regulations were deemed inadequate only for lead and some organic toxins. Expert quantitative ratings were found to differ radically for two hypothetical contexts: academic and public. Statement of the Problem: Decision Under Uncertainty Rarely do decision makers feel they have enough information available to make a decision about which they can be confident. Among regulators, politicians, managers, and public officials, considerable effort is expended in reducing uncertainty surrounding decisions. Uncertainty reflects incomplete knowledge. Depending on the type of knowledge, different techniques have proved effective at resolving uncertainties. Uncertain knowledge about factual evidence (random error, statistical error) can be resolved by scientific methodology and the peer review process, whereas uncertain knowledge about values (social priorities or preferences) can be reduced by political discourse or democratic voting procedures. But the domains of these two procedures to reduce uncertainty are not inclusive to all kinds of knowledge. Uncertainties about predicting future events and uncertainties

India is an agricultural Country and 56.6% of its population depends on agriculture. Designing a Decision Support System (DSS) for crop cultivation enables the farmers to make effective decision for higher yield. The parameters that are considered for the enhancement of a seasonal crop growth are type of soil and season, Insects-Pests management, irrigation methodologies. The main aim of the work is to develop a system that can provide information about the expected yield in each season with better accuracy. The decisions available to them currently are only a shallow guide for farmers due to them being unaware of various methodologies. Inefficiency in a farmers" decision results in the low productive rate of paddy than the expected rate. The growth of seasonal crops is decided basically by two factors, namely the soil type and the season. Also based on the crop type the farmer must adoptively use the insecticides and fertilizers. Thus the parameters to be considered are identified and the simulation is tested using a tool called "Netica". Based on the performance the DSS guides the farmers to improve the crop growth. The simulated results can be extended for real time usage in mobile application.

2011

This contribution discusses the concepts of validity and uncertainty in Decision Support System (DSS). In the development of any DSS, validation and uncertainty analysis are critical steps. Environmental problems are not only complex and multi-factorial, but also carrying, in most cases, a large amount of uncertain information. Uncertainty management must therefore be incorporated in environmental DSS and the evaluation process is not complete without uncertainty analysis. Validation is also part of the support system's evaluation and is designating the process of establishing the usefulness and relevance for the decision-making model for the intended application. Different approaches for assessing model validity, validation techniques and the relation between validation, verification and evaluation, together with different types and sources of uncertainty in environmental DSS and methods of representing and managing uncertainty are reviewed. Bibliography Beven K. (1993).Prophecy, reality and uncertainty in distributed hydrological modelling. Advances in Water Research 16, 41-51.[This paper presents a critical insight of the problems raised by the modeling of distributed hydrological systems] Kaufmann A.,Gupta M. M.(1985). Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold Company, New York.[This is a classical.]

Integrated Assessment, 2003

The aim of this paper is to provide a conceptual basis for the systematic treatment of uncertainty in model-based decision support activities such as policy analysis, integrated assessment and risk assessment. It focuses on the uncertainty perceived from the point of view of those providing information to support policy decisions (i.e., the modellers' view on uncertainty) -uncertainty regarding the analytical outcomes and conclusions of the decision support exercise. Within the regulatory and management sciences, there is neither commonly shared terminology nor full agreement on a typology of uncertainties. Our aim is to synthesise a wide variety of contributions on uncertainty in model-based decision support in order to provide an interdisciplinary theoretical framework for systematic uncertainty analysis. To that end we adopt a general definition of uncertainty as being any deviation from the unachievable ideal of completely deterministic knowledge of the relevant system. We further propose to discriminate among three dimensions of uncertainty: location, level and nature of uncertainty, and we harmonise existing typologies to further detail the concepts behind these three dimensions of uncertainty. We propose an uncertainty matrix as a heuristic tool to classify and report the various dimensions of uncertainty, thereby providing a conceptual framework for better communication among analysts as well as between them and policymakers and stakeholders. Understanding the various dimensions of uncertainty helps in identifying, articulating, and prioritising critical uncertainties, which is a crucial step to more adequate acknowledgement and treatment of uncertainty in decision support endeavours and more focused research on complex, inherently uncertain, policy issues.

In global agricultural, decision making at all levels are increasing rapidly due to unreliable and uneven distribution of combined experi-ence of complex processes associated with the food production. Besides flaws in human initiative judgement have far-reaching consequences for the sustainable agriculture development. Therefore, aiding the reliability and deficiencies of human judgement and quality decision-making has been a major focus of research throughout the history particularly with the development of Decision Support Systems (DSSs). DSS is interactive computer-based system that help decision makers solve unstructured problems under complex and uncertain conditions by providing access to data through procedures and analytical reasoning. These systems have been designed to address complex tasks involving multiple disciplines of agriculture, enabling us to mimic the necessary requirements of crop with respect to the land characteristics so that the optimum objectives speci-fied by the user is obtained. Experimental use of these systems has resulted in improved integrated crop production and management with restrict-ed use of the natural resources together with improved farm income and reduced negative environmental impact. With the advent of powerful computation, efficient database management system, on-line analytical processing (OLAP), improved knowledge representation, efficient searching techniques, data warehousing and mining, Geographical Information System (GIS) and Remote Sensing (RS); DSS are being extensively used world-wide using state of art technologies, integrating multiple models for sustainable agriculture development from farm to fork, despite “Problem of Implementation”. Once the barrier is crossed, it will give a leading role to DSS in global sustainable agriculture.

Future Internet

Several studies address the topic of Information and Communication Technologies (ICT) adoption in irrigated agriculture. Many of these studies testify on the growing importance of ICT in influencing the evolution of the sector, especially by bringing down information barriers. While the potentialities of such technologies are widely investigated and confirmed, there is still a gap in understanding and modeling decisions on ICT information implementation. This gap concerns, in particular, accounting for all the aspects of uncertainty which are mainly due to a lack of knowledge on the reliability of ICT and on the errors of ICT-information. Overall, such uncertainties might affect Decision Makers’ (DM’s) behavior and hamper ICT uptake. To support policy makers in the designing of uncertainty-management policies for the achievement of the benefits of a digital irrigated agriculture, we further investigated the topic of uncertainty modelling in ICT uptake decisions. To do so, we reviewe...