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Contents

• Introduction
• The main purpose and aims of the graduation paper
• Actuality of the graduation paper
• The research methods used in the graduation paper
• The structure of the graduation paper
• Key definitions and concepts in the graduation paper
• Simulation modeling
• Expert forecasting
• Cognitive modeling
• Decision tables
• Conclusion
• References

Introduction

As Thomas L. Saaty once said, all people are fundamentally decision makers and everything we do consciously or unconsciously is the result of a decision. (T.L. Saaty, 2008) It is very important to note that not all the gathered information is useful for improving our understanding and judgments. If decisions are only made intuitively, one is inclined to believe that all kinds of information are helpful and the larger the quantity, the better. However, that is not true. It is believed that to make a decision one needs to know the problem, the need and purpose of the decision, stakeholders and groups affected, and the alternative actions.

Today, decision-making theory has become a science. It is helpful as a means of making the process of decision-making more transparent in all its aspects. Naturally, numerous approaches have been proposed in order to achieve this goal. The subject of the present paper, particularly, is a number of approaches to modeling the problem situations that occur in the process of decision-making.

It is widely recognized that the modeling of the alternative problem situations significantly increases the effectiveness of the decisions made, which makes the subject of the present study an interesting and topical issue.

The main purpose and aims of the graduation paper

The purpose of the graduation paper is the review and the analysis of the various approaches to modeling the problem situations of decision-making, as well as the key concepts and definitions related to these approaches. Particularly, the subject of the study includes several approaches:

· Simulation modeling

· Expert forecasting

· Cognitive modeling

· Decision tables

The aims of the graduation paper are as following:

· The definition of the characteristics and features of the approaches;

· The analysis of the necessity of the approaches;

· The comparative characteristics of the approaches;

· Advantages and disadvantages of the approaches;

· The examples of the implementation of the approaches.

The result of the study is expected to be a thorough and complete analysis of the chosen approaches.

Actuality of the graduation paper

The subject of the paper is the modeling of the problem situations of decision-making. When a decision is made, the person making it should take into consideration the connection of the problem on which he focuses with other problematic areas. Only modeling the alternative problem situations one can increase the effectiveness of the decisions. The problem should be identified, as well as the necessity and content of its solvation, the criteria of the decision-making, the interested party, and the alternative actions. It is of a strong necessity to be aware of the various approaches to modeling the problem situations of decision-making, in order to make the decision that is the most effective.

The research methods used in the graduation paper

In order to conduct the thorough analysis of the approaches to modeling the problem situations of decision-making, it is necessary to study a variety of publications devoted to this subject. The topic is acute and popular; therefore, there are many articles and research publications.

Furthermore, the study of the specific literature allows conducting an explicit analysis of the chosen approaches to modeling the problem situations, which is the main purpose of the graduation paper.

The structure of the graduation paper

The preliminary structure of the graduation paper is as following:

1. Introduction

1.1. Actuality

1.2. Main purpose and aims

1.3. Key definitions

1.4. Choice of approaches to modeling

1.5. Presentation of the structure

2. Simulation modeling

2.1. General information

2.2. Origin

2.3. Characteristics and features

2.4. Conditions of use

2.5. Examples

3. Expert forecast

3.1. General information

3.2. Origin

3.3. Characteristics and features

3.4. Conditions of use

3.5. Examples

4. Cognitive modeling

4.1. General information

4.2. Origin

4.3. Characteristics and features

4.4. Conditions of use

4.5. Examples

5. Function model based on decision tables

5.1. General information

5.2. Origin

5.3. Characteristics and features

5.4. Conditions of use

5.5. Examples

6. Conclusion

6.1. Generalization of the conducted research

6.2. The results of the research

6.3. Final results and conclusions

7. References

Key definitions and concepts in the graduation paper

A decision is a choice or judgment that you make after a period of discussion or thought. (Longman, 1995) graduation paper method structure

Decision-making is the process of thinking about a problem, idea, etc., and then making a choice or judgment. (Longman, 1995)

Then, it is necessary to examine key concepts, directly connected with the chosen approaches to modeling the problem situations of decision-making.

Simulation modeling

Simulation is “an activity or situation that produces conditions which are not real, but have the appearance of being real, used especially for testing something”. (Longman, 1995)

According to R.E. Shannon, “simulation modeling is one of the most powerful analytical tools available to the people responsible for the development and operation of complex processes and systems”. (R.E. Shannon, 1975) The idea of simulation is both simple and intuitively appealing. R.E. Shannon continues with a statement that “simulation allows its user to experiment with an actual or proposed system when it is impossible or inefficient to deal with a real object”. (R.E. Shannon, 1975) He defines simulation as “the process of designing a model of a real system and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies (within the limits imposed by a criterion or a set of criteria) for the operation of the system”. (R.E. Shannon, 1975)

Ricki G. Ingalls in his publication “Introduction to Simulation” (R.G. Ingalls, 2008) notices that simulation can mimic the dynamic behavior of a system. Regardless of how complex a system may be, it is likely that a simulation expert will be able to create a model that will evaluate it. However, the more complex a system is, the longer it takes to model, run, and evaluate.

It is necessary to specify a set of conditions that cause the emergence of problem situations, including random conditions. Then, it is possible to examine various problem situations that might occur under different circumstances.

Expert forecasting

Expert forecast is another approach to modeling the problem situations of decision-making. T. Kravchenko identifies the following main stages of expert forecast. (T. Kravchenko, 2010)

Figure 1. Stages of forecasting

In order to conduct a successful expert forecast, the following issues should be solved:

· The organizational support of forecast development;

· Identifying aims of forecasting;

· Organization of an analytical group;

· Setting up an expert committee;

· Preparing all the needed background material, suitable software, and necessary data.

Precise expert forecast can be developed only if well prepared and if competent experts are engaged. It is also important that only experienced specialists should be invited to join the expert committee. All the information sources must be authentic and reliable. Evaluations must be correctly collected and processed. (Kravchenko, Seredenko, 2011)

For proper implementation of the forecast the main task is the identification of important conditions that influence the emergence of problem situations.

Cognitive modeling

Cognitive modeling of problem situations is based on the concept of a cognitive map of a directed graph. Vertices of the cognitive map correspond to the conditions that define the situation, while directed edges illustrate the cause-and-effect relations of the conditions.

According to V. Maksimov, E. Kornoushenko, and S. Kachaev (1998), the following stages are included in any situation for which cognitive analysis is used:

· Stating the goals and objectives of the study;

· The study of the complex situation from the standpoint of the stated goals: collection and systematization and analysis of the existing statistical and qualitative data regarding the object of study and its ambient, definition of the requirements for the study, as well as conditions and restrictions;

· Identifying the main factors that influence the development of the situation;

· Defining connections between the factors by means of the cause-and-effect chain analysis (using a cognitive map in the form of a directed graph);

· The study of the interaction of the factors (using both mathematical models for the quantitative connections between the factors and subjective points of view of the experts for the qualitative interrelations);

· Verification of the cognitive model;

· Defining possible ways of development of the situation with the use of the cognitive model, discovering means of influence on the situation; thus, developing a management strategy.

Decision tables

A decision table is one of the functional models of decision making. (C.W. Ries, 1968) According to T. Kravchenko (2012), given all the differences in the models, a decision table is filled in the following way:

Figure 2. The initial form of a decision table

· Conditions considered in the process of decision-making (quadrant II)

· Actions taken as a result of the examination of the conditions (quadrant III)

· Decision rules (quadrants I and IV) that illustrate what actions from quadrant III are taken in the real situation defined by a concrete combination of the results on the examination of the conditions.

In the most primitive situation conditions in the decision table are formulated in such a way so that the answers could be stated as “yes” or “no”. Elements “yes” and “no” are the inputs of the conditions and are stated in the first quadrant. (Kravchenko, 2012)

An elementary example of a decision table for the situation “The lights went out” is illustrated below.

Table 1. An example of a primitive decision table

Conclusion

Only modeling the alternative problem situations it is possible to ensure the highest efficiency of the decisions that are made. For this purpose the following approaches are mainly used: simulation modeling, expert forecast, cognitive modeling, and the function model based on decision tables.

In this project a review of the graduation paper regarding the approaches to modeling the problem situations of decision-making was conducted. It included such features of the project as: purpose and aims, actuality of the topic, chosen approaches, and a detailed structure. The key concepts and definitions used in the graduation paper were introduced and analyzed in this project as well.

References

1. Ingalls, R.G. (2008). Introduction to Simulation. Oklahoma State University

2. Кравченко Т.К. (2012) Математический анализ экономических моделей. Метод аналитических сетей при принятии решений в условиях неопределенности. 99-101

3. Longman Group Limited, Harlow, Essex (1995). Longman Dictionary of Contemporary English. 3^rd edition. 355

4. Longman Group Limited, Harlow, Essex (1995). Longman Dictionary of Contemporary English. 3^rd edition. 356

5. Ries C.W. (1968). Decision tables. S. Afric. Comput. Bull.Vol.9. № 8.

6. Kravchenko T., Seredenko N. (2011). International Journal of the Analytical Hierarchy Process. Decision-making with the modeling of problem situations using the analytic network hierarchy process. 3 (1): 28-30

7. В.И. Максимов, Е.К. Корноушенко, С.В. Качаев. (1998) Распределенная конференция «Технологии информационного общества 98 - Россия». Когнитивные технологии для поддержки принятия управленческих решений.

8. Saaty T.L. (2008). Int. J. Services Sciences. Decision making with the analytic hierarchy process. Vol. 1, № 1. 83-84

9. Shannon R.E. (1975). Systems simulation: the art and science. University of Alabama in Huntsville.

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