Study Modul Decision Making Technology in Practice for Social Policy Analysis _ENGLISH

STUDY MODULE:

DECISION MAKING TECHNOLOGY IN PRACTICE FOR SOCIAL POLICY ANALYSIS

2012

1

Project “Study module: Decision making technology in practice for social policy analysis “ is implemented through the Policy, Advocacy, and Civil Society Development in Georgia (G-PAC)

program of East-West Management Institute (EWMI).

This program was made possible by the generous support of the American people through the United States Agency for International Development (USAID).

The contents of this publication do not necessarily reflect the views of USAID or the United States Government.

Grant #677-11-211-3016-20

Gramtee: Gori Teaching University

Project completed by professors of the Gori Teaching Univesity (Ruizan Mekvabidze, Nana Akhalaia), Ivane Javakhishvili Tbilisi State University (Malkhaz Matsaberidze, Nino Javakhishvili) and University of Gdansk (Jacob Potulski, Arkadiusz Modrzejewski)

Coordinator: Ruizan Mekvabidze

Experts: Jacob Potulski, Arkadiusz Modrzejewski,

2

STUDY MODULE: DECISION MAKING TECHNOLOGY IN PRACTICE FOR SOCIAL POLICY ANALYSIS

(for the first course MA students)

Social science research should lead to a better understanding of current societal developments and enable policy makers to propose solutions to problems and design policies that can serve the public more effectively. Governments are increasingly aware of the need and opportunities to improve the contribution of social science knowledge to policy making and are keen to realize this potential.

Can the social sciences act as an agent of societal change? How can they contribute to social practice? Can best practice in other research fields and economic sectors be a source of inspiration on new

approaches to sharing knowledge? How can the divide between the two communities - social scientists and decision makers - be narrowed?

Decision Making as a Basis of Interdisciplinary Exchange

Each individual is more likely than not to make the correct decision on her own, people are being more closed in their beliefs because there is just too much information and especially in that cases when decision making is connected with the views of public. In some countries people are spending many years to learn how to properly relate material to students, not simply make them memorize everything. We must derive a method that will keep catalytic reactions between ideologies under control. This method, should include political, policy-based, and technological instruments. Then, we should align them in the right sequence so that we can apply our reasoning, not only memory.

When asking the crowd what their opinion is regarding a particular issue the answers will undoubtedly depend on the individual's personal beliefs . If the question is framed and there is a limited selection of answers, such as in polling, the individual’s answer will be fitted their personal belief. While this will allow for an analysis of what the "majority" of the crowd prefers it does not necessarily mean that majority is correct."If each of us have less than a 50% chance of being right about a decision, a group of us will be worse at making a correct decision, with our probability of accuracy increasing towards zero as the size of the group increases.

What is Decision-making technology in practice?

The context of decision-making is the dynamics of the Knowledge Economy and the Network Society. The subject mediates analytical and conceptual competencies to interpret the dynamics and to perform plausible decision-making management. Typical topics are:

Study and values studies; Decision theory; Organizational theory; Research design; Risk and risk management; Strategic management; Modeling; Decision analysis;

The subject is premised on the assumption that organizations, universities, educational institutions are the engines of society and challenging part of managing organizations is the combination of decision-making, using of knowledge technologies and the social and cultural dynamics of individual and group processes.

A Focus on Decision-making

3

One of the peculiar characteristics of the knowledge economy is that the more knowledge there is, the less there is clarity as to what general course of action is to be followed. Knowledge is the cure to all problems. This is a result of the essential characteristic of knowledge itself. Knowledge, if it is truly knowledge, is an inherently creative dynamic. As such knowledge always opens up options and creates new capacities for action. As a society becomes more knowledge intensive, and as our economy becomes ever more knowledge dependant, the inevitable outcome is an increase in options. Options mean choices and that means decisions have to be made.

Nowadays, the focus is on very complex and integrated decision-making systems. It is not a matter of individual, but rather of the organic functioning of the organization or society as a whole. The organic understanding of decision-making emerged in the 1980's and was reinforced in the 1990's in conjunction with two significant developments, as are:

Knowledge Management ICT development

Information systems have become ubiquitous. On the one hand they create conditions to knowledge creation, but on the other hand they are the primary sources of enormous ambiguity and confusion. All of this has a massive bearing on the way organizations and societies work, how they are managed, how decisions are made.

The Problem

Basic social policy problems such as trend of social policy practice and research has not adequately been addressed. The study's objectives were to identify, describe and analyze what social policy research and what it does and the extent to which they address social problems and identify future research priority and direction. The understanding of social policy is perceived synonymously with social welfare policy. There is a certain consensus concerning various barriers in the decision making process. These include:

a/ Ideological problems that constrain the formulation of reform agendas;b/ Historical separation between researchers, policymakers, service providers, administrators, managers, etc.c/ Different conceptions of risk at the individual or public level;d/ Media interference, which can both confuse the issue by publicizing results inappropriately e ) Circulation of researchf/ The lack of Research process from the decision-making process.

For many social policy makers and practitioners, the conception of social policy excludes economic development and economics. Unforeseen problems emerged. There was also the emergence problem of financing of education and teaching of decision making theory at the university level.

For many social policy makers and practitioners, the conception of social policy excludes economic development and economics. Unforeseen problems emerged. There was also the emergence problem of financing of education and teaching of decision making theory at the university level. Under the project (grant #677-11-211-3016-20) is prepeared the study module for the first course MA students “Decision making technologies in practice for social policy analysis” with four syllabi:

1. Introduction in Decision Making;2. Statistics for Social science;3. Decision Making Tools and Techniques (programme software STATA);4. Decision Making Methods.

The module will be introduced at Gori Teaching University as an obligatory course. SYLLABUS # 1. INTRODUCTION TO DECISION MAKING

4

Focus: Decision is universal phenomenon that accomplishes life of individuals, groups as well as institutions and organizations. Each of us have to make decisions during whole period of our life. We make ordinary, prosaic decisions like what to eat for dinner or very important or strategic decisions about choice of our professional career or decisions which have global implications. Decision making (DM) is a sense of personal, family, economic, political and social life. Maybe that is why DM is a subject complicated and hard for analysis. It is problem which concerns represents of numerous and different branch of science and humanity, i.e. economists, political scientists, sociologists etc. Module 1. introduces students to basic issues according to DM. It provides elementary knowledge that is helpful and useful for certain estimation of decision situation and making a propter decision. In module we point on three levels of situations implicating DM process: micro, mezzo and macro. We include in these levels essential problems of contemporary civilization which are found by individuals, business, TNCs, NGOs as well as public institutions. These problems create basic conditions which force us to make concrete decisions. In Module we concern experience of different social sciences: economy, political science, communication science, sociology, management science and psychology. Thanks that we can provide for our students complementary knowledge about DM phenomenon, what implicates more effective preparation to application this knowledge in practical life.

Aim:basic theoretical and practical knowledge about essential of problems solving in decision making

Skills: diagnosis of problems and threatens in contemporary world, DM’s ability in different level of social, economic and political life,

Teaching methods: lectures, class discussion, essays, work papers

Grading:

Components Max. ballintermediate testing 1 20intermediate testing 2 20

Individual presentation 10

Group presentation 10

essay + final exam 10 + 30=40Total 100Total possible grade points: Percentage of Total

Possible Grade PointsA 91-100%B 81-90%C 71-80%D 61-70%Fx 51-60Failing <51%

Theme 1: Decision Science (DS) and Decision Making (DM) process – general introduction

Subject: DS, Decision-maker, decisional situation, decisional problem, elements of DM process, condition of DM, structuring of decisional problem, techniques and tools of DM, problems and “decision trees”.

Abstract: There are three forms of applied thinking that we all need: decision making, problem solving and creative thinking. Decision making is about deciding what action to take; it usually involves choice between options. The objects of problem solving are usually a solution, answer or conclusion. The outcome of creative thinking, by contrast, is new idea. Decision sciences refer to broad

5

interdisciplinary field interested in all aspects of human decision making. The emerging field of decision sciences is concerned with understanding and improving decision making of individuals, groups and organizations. In general decision making sciences examines a process of decision making at multiple levels, starting with individual choice, then moving to the group, organization and societal levels. At issue is not only how decision makers „solve problems”, but also how they come to identify and accept such problems and learn from the results of their actions.

Decisions indicate our behaviors. Their quality influences on our daily life. That’s why DM absorbs much times. Nobody is free from DM. Everyone makes decision almost permanently. We can define decision as a choice of any activity from the collection of different alternatives of other activities which are possible to do in determined time. Decision can also means a conscious refraining from any action. Each decision indicates any choice and choice needs at least two alternative ways of resolving problems which could be chosen. Each possibility of choice is correlated with decision-maker’s collection of opinions and convictions according to results of decision.

DM analyze concentrates especially on DM process. We define this process as a series of acts which lead us to make a decision. The DM process is divided into stages. We can point following of them: (1) definition of decisional problem (diagnosis of situation); (2) collecting information; (3) analysis of possible variants of decision; (4) evaluation and comparison of the variants; (5) making a decision.

Problems to recognize: What’s decisional situation? What’s a decisional problem? What’re basic elements of DM process?

Practical examples: In specialist literature we can find many examples of DM in different situations. These decisions are made by various subjects, e.g. individuals, public institutions, NGOs and Church hierarchy.

The example of individual DM: woman who has to decide about have a child during her professional career and social activity. She has a choice: be pregnant and to have a child or to continue her career and present social life. The first variant interrupts in professional and social life. But when woman delay to the decision about the child, it can indicate problems of getting pregnant in older age. That’s why she has to decide when she has the best opportunities to be pregnant and when the costs of that are the lowest but chances for pregnant are still high.

The example of DM in public policy: the public decision maker has to choose from the different variants of political or economic strategy. Each variant has own advantages and disadvantages. Public decision maker has to choose only one variant and to calculate its costs. That’s why reliable information is so important for DM process. Decision maker has to analyze different aspects and to predict causes of his/her decisions. Let’s look at Georgian exemplification. Georgian government tries to define the priorities of foreign policy. It can choose for example (1) integration with euro-atlantic structures; (2) neutrality; (3) cooperation with Near East countries. Every option means any consequences but also losses. The 1st option indicates long way to integration which requires structural reforms in economy, democratization and liberalization of social and political sphere, stabilization in internal and external relations, it may also lead to a deterioration in relations with neighbors (Russia). The 2nd option means balancing between external powers but doesn’t guarantee safety in international relations. The 3rd option gives especially economic benefits but can indicate reduction of democratic standards. Detailed analysis of advantages and disadvantages can make simpler and more rational decision.

Discussions (conversational problems): How can DS change our life?

6

Theme 2: Decision making theory and analysis

Subject: decision theory, normative and descriptive decision theory, decision matrix, process of DM

Abstract: Decision theory (DT) is an interdisciplinary project to which philosophers, economist, psychologists, computer scientists and statisticians contribute their experience. DT is the product of the join effort of economists, mathematicians, philosophers, social scientist and statisticians toward making sense of how individuals and groups make or should make decision. The applications of DT range form very abstract speculations by philosophers about ideally rational agents to practical advice from decision analysts trained in business school. There is a distinguish between descriptive and normative decision theory. Descriptive DT seeks to explain and predict how people actually make decision. Normative DT seeks to yield prescriptions about what decision makers are rationally required or ought to do. Another important division within decision theory is that between decision made by individuals and those made by groups.

Contemporary decision analysis (DA) that is basic method used in DT tries to elaborate interdisciplinary approach to the DM problematic and to combine behavioral and normative aspects of the theory. The aim is cognitive as well as practical.

Problems to recognize:What is different between normative and descriptive DT? What is helpful in decision matrix? Why does DT have interdisciplinary character?

Practical examples: DA is an useful and valuable research tool for Political Science. It let us understand politics as a decisional process. Decision is explanandum – what we shell explain – in DT. Independent variables create explanans – this what explains a decision being explanandum. Thanks to use DA Political Science fulfills its essential function, i.e. explanation as well as forecasting.

Discussions (conversational problems): What is helpful and useful in DT?

Theme 3: Information in DM process

Subject: the importance of information in our life, information technology, information chaos and decision making, problem of selection of information, the role of information in decision making process, decision under ignorance and decision under information chaos, paradox of choice

Abstract: We live in the world of information. Information is a value that leads us to effective activity and makes easier DM process. Thanks information we can choose the most optimal decision. That’s why information is a useable value. Every man searches, obtains, transforms and uses information. We need information before we make decision. It should concern two aspects: (1) alternatives of choice; (2) features of these alternatives. In the previous decade, we have experienced one of the greatest transformations in history in the way societies create, store, distribute and use information. In the late twentieth century, many social scientists and other social commentators came to characterize the world as evolving into an “information society”. Central to this claim was the notion that new social uses of information, and particularly application of scientific knowledge, are transformed social life in fundamentals ways. In the reality of information society the base research question is how is social culture and decision-making interrelated in the information society and with respect to phenomena such digital divides. The basic question from viewpoint of information technology is: what can the computer offer to decision makers and how it can support their work? Another question is what are threatens of growing information chaos for the process of decision making. Information is a critical aspect of decision making. Data processing, selecting information has a big influence on our decision.

7

In the contemporary world we have problem with numerous and often opposite information. Man has limited “information capacity”. Too much information is rather a problem. Computers can be very useful for us in DM process. They help us to select information.

Problems to recognize: What is role of information in the decision making process? What is uncertainty? Why and how uncertainty is one of the factors which create decision making difficult? How to avoid a problem of uncertainty? What is a data processing? What is the information chaos? How to make good decision in a situation of information chaos? How to select information? Who is a “gamekeepers”?

Practical example: We can point to the consumer and political decisions as an exemplification of using information in DM process. In the first case a consumer tries to gain following data: (a) product brands available in the market; (b) features of these products (quality, price, durability etc.). Advertising is a main way of popularize knowledge on products. But is not a reliable source. That’s why consumer has to gain additional details on products. We can ask sellers, friends, other users, experts etc. But we need information to make our decision more optional at least in our opinion. In the second case we can emphasis on new information technologies (Internet) and mass media (especially TV) as a basic source of information on public life. People more often absolutely believe and trust these source of information. That’s why mass media can create public opinion. But also they select information, deciding which ones are valuable and worthy of dissemination (gatekeeping). In this assessment they are guided by advertisers, interest groups, own point of view, political factors, political correctness etc. In this way we gain selected information very often accompanied by commentary. That’s why we can get relative opinions which pretend to be absolute truth. But diversity of source of information facilitates to obtain reliable information.

Discussion (conversational problems): How can electronic tools help us in propter data processing?

Theme 4: DM in the conditions of uncertainty and risk

Subject: definitions of risk, nature of contemporary risk, positive aspects of risk, source of risk, risk management, institutional rules, identification of risk and avoid of risk, decision under risk (probability and utility), measure of risk.

Abstract: Perceptions of risk are socially constructed and created through the framing efforts of various institutional actors. The resulting struggle over meaning is particularly acute when the issues contain many unknown elements—as is the case with emerging technologies. Risk is a function, that is, probability times the magnitude of loss. If our present knowledge and the method we follow do not allow for said values to be attached to the magnitude and probability of the expected harm, we should rather speak of a threat or hazard facing us in a condition of uncertainty, and reserve the concept of risk for those situations that can be properly defined as such. The risk management and the risk manager are mandatory elements for a success activity in business as well as in politics. Avoiding the risks is a sure way to failure. A higher risk can lead to a higher benefit. The higher risk is the absence of the risk. If the risk exists, through risk management it can be controlled, but if the risk is not identified, certainly there are hidden risks which difficult to control. An improper risk management can generate important financial, political and even human losses. It is necessary to implement a risk management system which becomes an important objective both for the project itself and for those involved in realizing it.

8

Decisions in the condition of risk (probabilistic decisions) are determined by uncertain results of considered activities, e.g. success in a brand new business depends on the general economic situation. But our uncertainty is not absolute one. We can estimate probability of results (risk of losses and chance of profits). Generally, making decisions in the condition of risk our choice will lead to the one of the defined probabilities.

Many of our decisions we make in the conditions of risk. Decisional analysis leads us to the different theories which tries to determine the way of DM in conditions of risk and when risk may give us a chance for success. Risk management as well as DM in the conditions of risk are the most important and the most expanded section of decisional analysis. In the contemporary society very often defined as ‘risk society’ we can notice that number of situations of risk still increases that’s why we need still to make decisions in the conditions of risk. We have to habituate to this situation and try to minimize appearance of the negative results accompanying our decisions.

Problem to recognize: How do we define a ‘risk’? When do we take a risk? What are probabilities? How can we use methods to measure probabilities? How do we response to a risk? How can we estimate a risk?

Practical example: In the end of the 20th century the majority of climatologists agrees that climate in the Earth is permanently warming. They convince that glaciers will disappear from Earth and level of ocean will be higher which will lead to flooding many lands. But 30 years earlier climatologists declared risk of opposite situation – return to the Ice Age. And also nowadays we can meet this same opinions. Decision makers should decide which forecasts are closer to the truth, i.e. which one will fulfill. They need valuable and reliable data. Thanks that they could predict the most real scenario of climate changes. But risk is not eliminated. Because forecasting gives us only probabilistic knowledge. Reduction or increase of emission of CO2 leads us to opposite results. World can be effected by global warming or new glaciations.

Discussion (conversational problems): Is it possible to avoid risk or is risk natural part of our life and we can only minimize risk using different decision making methods and tools?

Theme 5: Individual in DM process

Subject: rationality, rational choice theory, the role of emotions in DM, sociology of emotions, ethical rules in DM

Abstract: For strategic investigations the understanding of the nature of human decision is a central problem. Individual decision theory has concentrated on the problem of how individuals may best further their personal interest. Whatever these interest may be. Decision making is usually defined as a mental process, which involves judging multiple options or alternatives, in order to select one, so as to best fulfill the aims or goals of the decision maker. Decision making is traditionally viewed as rational process where reason calculates the best way to achieve the goal. Investigations from different areas of cognitive science have shown that human decision and actions are much more influenced by intuition and emotional responses then it was previously thought. Rational choice theory and game theory are normative theories. They thus presuppose an idealized rational individual who informed, with ordered preferences and with complete inside computer. In oppose to rational and game theories many philosophers, neuroscientist and psychologist have pointed out that emotions play an extremely

9

important role not only in goal setting but also in decision making. They consider that the emotional responses play an important role in decision making process.

For our consideration behavioral theories are especially useful. Former economic ones pointed only on the rational nature of human beings and created “economic man”. Behavioral approaches characteristic for social psychology and sociology focus on social and individual factors of DM. they want to answer the question on psychological – emotional and rational determinants of DM process. That’s why we can find in these theories such categories as personality, emotions, frustration, fear, stress etc.

Problems to recognize: Can human decision be rational? What is the homo economicus? What is role of interests in our decision? What is the framing? What is role of emotion in our decision? What is role of the stress in our decision? What is an intuition?

Practical example: The most important problem in political analysis is rationality of citizens during elections. Economic theory created by Anthony Downs (An ecnomic theory. New York, 1957) tries to convince us that citizens during elections behavior like consumers in the market. Political parties create election programs which are similar to the products. Citizens can choose one of them like consumer choose a product in the shop. It is normative theory that speaks rather how we should choose than how we choose in real life. Behavioral theories based on empirical researches are opposite to the normative one. They try convince that despite rationality people are guided by irrational and subjective factors (emotions, stress, prejudices and stereotypes.

Discussion (conversation problems): What is better way of DM: intuition or cold calculation of profits and losses?

Theme 6: Dynamic of small social groups and DM

Subject: group decision theory, group decision making approaches, groupthink (thinking or conforming), nature and dynamics of small groups, logic of collective behavior

Abstract: Human life is also social life because people live in different groups. Family is the most elementary one. Groups create complex organization called society. That’s why we live in social groups, decisions made inside these groups are very important problem of DS. Groups make different decisions which can be prosaic – for example meeting time or so serious as a jury verdict.

When can we talk about group DM? When at least two persons make a decision. Persons have to be interrelated or interdependent. DM in groups is defected by different difficulties, e.g. extension of time of DM, groupthink, group polarization, competition within the group etc. But DM in groups has advantages also. It makes easier to get information. Decision could be more acceptable that decision made and imposed by individual.

Effective decision made in group depends on propter management of group DM. That’s why we need learn psychological issues concern DM as well as methods of group choice.

Problems to recognize: Haw we make decision in small groups? What is the group thinking? What are dangers in groupthink? What is the role of leader in group decision making? What is a dynamics of small group? Can teams make good decision? Is bringing together diverse people can lead us to more innovative and creative solutions to complex problem? What is the “brainstorm”?

10

Practical example: Jury in Anglo-Saxon system of law is the best example of group DM. The people who are jurymen have to commonly decide on guilt or innocence of the accused. The empirical researches show some regularities in DM of jury. It is very seldom situation (1-2%) when one juryman being opposed to the rest of jury convinces others to her/his point of view. Individual rather agrees with the majority then blocks the adoption of the verdict. We are conformists. That’s why final verdict is usually the same what firstly opinion of majority. The level of acceptation of decision depends on numbers of dominating fraction. Minority in another way presents own point of view. It rather suggests and indicates on doubt. Hardly ever minority formulates statements and opinions.

Discussion: What is influence of group’s pressure on our personal decision?

Theme 7: Organizational and institutional conditions of DM

Subject: organizational effectives, institutional frames, logic of institutional activity, institutional theory, ISO.

Abstract: Formal groups become organizations or institutions and have official structures which are responsible for DM process. They endeavor to gain statutory targets. Organization means an organizing of activities and human behavior and assumes relatively stable frames and principles of proceeding and behavior in concrete situations. We are noticing nowadays plenty different and specialized organizations and institutions responsible for specific areas of life. We meet these organizations and institutions in different social contexts: economy (banks, corporations, chambers of Commerce, trade unions etc.), education system (universities, schools), health care (hospitals, clinics), political and social life (NGOs, political parties, Parliament, executive, self-government). Institutions and organizations meet our needs. Their activity is determined by formal procedures which limit manifestations of spontaneity and formalize our behavior. In many case individual has to be subordinated to the procedures and internal norms.

Problem solving and DM is important part of every management. People in different organizations and institutions permanently solve any problems. Each corporation, NGO, political party or institution etc. has many problems in its functioning. Especially managers very often stay in the face of various problematic situations. In every firm as well as other form of organization the problem is determined as a each met condition that seriously restricting effectiveness of organizational activity. The problem solving is a main task of managers and leaders. The very important thing is recognition of problems. Managers and leaders should be able to propter definition of problematic situations and to find way of its solution. Every organizations have they own rules of taking activity which makes frames to our decision. I our social activity there is not only a “mind frame” but also organizational and institutional frames. Study of organizational behaviors is essential part of knowledge of decision making process. Formally defined organizational behavior – OB for short – is the study of individuals and groups in organizations.

Problem to recognize: How institution works? How institutional and organizational frames shape the process of decision making? How organizational resources help people to make good decision? How organizational frames disturb people to make good decision? What is the “organizational behavior” and why is it so important? How and why are we regarded behavior in organizational setting? Does it reflect the pursuit of rational interest and the exercise of conscious choice, or is behavior primarily shaped by conventions, routines, and habits?

11

Practical example:International Organization for Standardization (ISO) is a peculiar example of institutionalization and standardization of activities. ISO’s norms determine rules of DM process. All decisions have to be based on system of management of quality and be indicated by former researches and analysis. That’s why organization or institution should define which output data are necessary in repetitive DM process. In the next step it should determine identify place of measure and processing of information as well as point on methods of data collection and methods of analysis concerning computer methods. DM is based on facts, i.e. based on analysis of data.

Other example is democratic institutions whose procedures limit spontaneity and authoritarianism in DM process. Parliament is a main democratic institution in most democracies. Establishment of law is its elementary function. High degree of formalization, multistage and complexity are features of Parliament DM process. In the Polish Parliament we meet a few stages of DM process : (1) legislative initiative; (2) three readings in the lower chamber of parliament (Sejm); (3) voting; (4) two reading in the higher chamber of parliament (Senat); (5) voting: a) acceptance, b) rejection or c) amendment; (6) if b) or c) project of law is again considered by lower chamber of parliament; (7) a) signature of President or b) his veto either c) referral to the Constitutional Court; (8) if b) lower chamber of parliament can reject president veto. Each stage is strictly regulated by Constitution. Thanks that parliament decisions seem considerable and constitutional.

Discussion (conversational problem): Is it possible to make institutional and organizational frames more flexibility? How organizational standards can help us to avoid wrong decision?

Theme 8: Public choice – political decisions

Subject: political DM, theory of public choice, democratic DM process, electoral decisions, electoral systems, game theory, index of power, public sphere

Abstract: Political as a part of social choices are the specific type of decisions. Common parliament or presidential elections are the example of group choice in aspect of theory of DM but they are not in sense of social psychology. What is a social choice? Generally, it is an act of selection of available alternatives. Choice is made by individuals but it can have private or public character. Private choice concerns our ordinary life and implicates to meeting of the needs. But human being also makes decision in public sphere when his/her selection influence on him/herself and others. The theory of social choice concerns especially problem of social decision in democratic social relations. Democracy guarantees collective DM. Even if individual makes decision he/she has to have democratic legitimization.

But distance between election (will of voters) and political decisions made by decision makers having democratic legitimization is very far. Theory of public choice tries to explain this problem, using economic methods in analysis of noncommercial or quasi-commercial phenomena. That’s why it focuses also on political decisions, framing of public order as well as mechanism of providing public supplies. It is occupied in behavior of political parties, governments, parliaments and bureaucracy.

Problem to recognize: What is the way of transformation from individual preferences to collective (social) choices? What is the main paradox of democracy? How methods of voting do you know? What is economic motivation in collective activity?

12

Practical example: We can describe one paradox of democracy. 12 students should decide on method of examination. They can choose 3 options: essay, test and oral examination. All of them have to pass this same form of examination. Examiner would like students to choose the last one. But he will respect of students’ choice. Does he have possibility of influence on their choice? One of way is method called plurality. Relative majority decide on examination. Most students chose the other option that examiner would like them to choose. But he knows that other method of choice existence: sequential majority voting. In this method each student points on the most preferable alternative, less one as well as the worst one. In our hypothetic situation students chose:

The best option

Midle option

The worst option

Group A (5 students) Essay Test Oral

Group B (3 students) Test Oral Essay

Group C (4 students) Oral Essay Test

If examiner is smart he could direct the counting of votes in such a way that his option will win. He should eliminate essay in confrontation with oral examination and then he can eliminate this one in final confrontation with test.

Discussion (conversational problem): Do we better understand social choice using economic methods of explanation?

Theme 9: Complexity of contemporary social world and DM

Subject: complexity, globalization, chaos, postindustrial reality, multicriterial decisional analysis

Abstract: World in the 21th century is based on uncertainty, inconsistency, instability, risk, flexibility and fluidity of relations, identities and social structures. Contemporary civilization is determined ‘postmodern’ or ‘postindustrial’. Modern world and its order and stability is running out off. We have to habit to live in the new reality characterized by fluid borders and identities. That’s why social sciences have problem with a new paradigm of researches. Their predictable competitions are now limited. Science is not now convincing source of knowledge and information. Our knowledge is temporary as our reality. Tomorrow we can wake up in the new social, political and cultural order. Despite it social sciences try to describe and explain fluid reality.

Processes like climate change, technological innovation, rapid fluctuations in world markets change a linear, scale-free, and static worldview that has guided large parts of the scientific study of society, economics and politics. Its argued that understanding nature of contemporary world we need a new paradigm. Complex system theory is not just another theory; rather it is more general perspective of analysis. We use term complex system to denote a set of interconnected and interdependent parts. The most important features of the complex system are interconnectedness and the emergence, i.e. the fact that the whole cannot be reduced to the sum of components. It will be argued that a new science of complex system is needed and that this science will embrace the humanities and social science alongside the traditional natural and physical and engineering science within the framework of policy and applications of policy through design, implementation and management. Complex system science

13

is highly interdisciplinary and multicriterial. Generally it is necessary to have knowledge in many disciplines, including mathematics, sociology, psychology, economics, and geography and so on.

New computer technologies seem to be useful and helpful in analysis of data. Thanks them we can make many calculations. Finally we use them to DM process. Multicriterial decisional analysis is very popular in so different sciences as medicine, financial economy, management, production engineering, environmental sciences etc. We can use it also in social sciences and humanities.

Problem to recognize: What is the consequence of rising complexity of our social life? What kind of methods we can use in situations of many criteria? Does is possible to make rational decision in situations of many elements and possibilities? What is the role of multiple criteria decision methods? Which is a role of multicriterial decisional analysis? Practical example: Multicirterial decisional analysis is often used by local authorities during DM about road investments. City authority of Warsaw requested an expertise concerning building the express road to the Polish seaports located in the Northern Poland. They got to evaluate ten variants of the road. The person responsible for expertise established seven criterions being a basis of analysis of planned route: (1) functionality; (2) technical; (3) traffic; (4) safety; (5) economical; (6) geographical and (7) environmental. Each of these criterions got propter value expressed as a percentage. Thanks this multicriterial analysis experts gained the most valuable variant of road that became basis for further work and social consultation.

Discussion (conversational problem): Is it possible in our contemporary complex and liquid social life to make a prognosis for a future?

Theme 10: DM and conflict situations in the contemporary World

Subject: nature of contemporary conflicts, sources of conflict, conflict theory, keeping conflict constructive, positive aspects of conflict,

Abstract: In contemporary World conflicts are differently interrelated. Postmodern axis of conflicts runs on the lines: (1) North vs. South; (2) cultural circles (clash of civilizations); (3) universalism and globalism vs. localism; (4) industrial World vs. postindustrial World; (5) ethnic groups. We can find also scenarios which assumes ‘the end of history’ (the end of conflicts).

Conflict may be defined as disagreement between individuals and groups. Conflict is a universal phenomenon. It appears in interpersonal relations, between different social groups as well as inside business organizations and public institutions. Conflict is thus a reality for decision makers. It is not all bad. It is only when it reaches a high level and then it can be a major disruptive force that reduces organizational effectiveness. Further, it is worth when so-call absence of conflict is the other name for suppressed conflict. Hence, a conflict disguised or ‘cold war’ is more dangerous than open one or even ‘hot war’. Decision makers should accept conflict in positive sense as a way of development. When conflict is handled with great care as the fact of social life and to manage it as to maximize its beneficial impact and minimize its bad effects. Only then conflicts have a strong influence on development.

Practical example: In hospital we can find different category of employees. Directors, managers and doctors belong to the highest category. They have the highest level of responsibility as well as education and professional qualifications. They are the top earners. Nurses, paramedics, technicians, therapists, and employees of laboratory are the ‘middle category’ in hospital structure. Their salaries is

14

much less then the first category. But physical workload is much higher. Cleaners, porters, cookers are the lowest category of hospital employees. They have the lowest qualifications and salary. But they have to be still amenable. This inequalities create social tensions. Different groups compete for limited resources. Decision maker has to decide which category of employees should be awarded by bonus salary or appreciation of salary. His possibilities are limited. Consequently, employees won’t be satisfied. His main task is discharge of social tension and ensure the proper functioning of the hospital. He has to search solutions which will be acceptable by different groups. This conflict in fact is an example of postindustrial conflict. We have different categories of employees who belong to the different social groups. In the new type of conflict the people with the highest qualifications and professional position rival with people having less qualification and professional position even both categories belong to the same traditional social class.

Discussion (conversational problem): How can we avoid conflict situation?

Literature:

ენდრიუ ჰეივუდი. პოლიტიკა, თბ., 2008, გვ. 517-523

Nutt, P.A., The economics of public choice, Edward Elgar, 2007.

Mueller, D., Public choice III, Cambridge University Press, 2003.

Coleman, J.S., Foundations of social theory, Harvard University Press, 1990.

Oyster, C.K., Groups. A users Guide, McGraw-Hill, 2000

Buchanan, J.M., Tullock, G., The calculus of consent: logical foundations of constitutional democracy, University of Michigan Press, 1982.

Downs, A., An economic theory of democracy, 1967.

Ellis, D.G., Fisher, A., Small group decision making: communication and the group process, McGraw-Hill, 1993.Groups in context. A new perspective on group dynamics, ed. J. Gillette, M. McCollom, University Press of America, 1995.Janis, I.L., Groupthink. Psychological studies of policy decision and fiascoes, Cengage Learning, 1982.Olson, M., The logic of collective action: public goods and the theory of groups, Harvard University Press, 1971.

Hart, P., Groupthink in government: a study of small groups and policy failure, The John Hopkins University Press, 1994.

SYLABUS 2. STATISTICS FOR DECISION MAKING

Aim: This course concerns statistics for social sciences, which is different from “pure” statistics. Social scientists apply statistics to social problems as a tool to process and analyze quantitative and

15

categorical social data, which, quite often, requires different approach to the issues. Therefore, many assumptions and requirements are “violated”. The research design and results interpretation are of utmost importance in data processing via statistics for social sciences. The students should acquire certain knowledge and skills during the course. The knowledge of main statistical concepts of data description and testing relations among variables will be gained by the students, as well as the principles of main research designs. The skills acquired during the course are: finding of data sources, data processing and interpretation, using statistical software, making presentations.

Teaching Methods: There is minimum of lecturing during the class. The students are expected to have prepared the readings before the class; this enables dialogue and discussion of the issues. Question-answer format is used. Also, at some classes I will divide students into groups to discuss some problems. Each student will make two presentations, an individual and a group one. The main textbook is: Harold Kiess: Statistics for Behavioral Sciences, the third edition. The textbook is translated into Georgian and published by TSU Publishing.

Student will learn:

Apply and interpret data to support effective decision making Present results graphically and numerically to support negotiations Validate data quality using software applications Minimize risk while assessing uncertainty and probability Estimate outcomes and draw conclusions based on tested hypotheses Forecast results based on trends, patterns and sequences

This introductory course provides you with the techniques to support your decisions using well-established quantitative and qualitative methods. You learn to gather data, identify trends and analyze patterns to solve solving problems. By employing statistical tools, you learn to format and present information with clarity and precision

Grading:

Coponents Max. ballMidterm testing 1 20Midterm testing 2 20



Participation in the problem solving exercisases 10

essay + final exam 10 + 20=30Total 100Total possible grade points: Percentage of Total Possible Grade

PointsA 91-100%B 81-90%C 71-80%D 61-70%Fx 51-60Failing <51%

Theme 1: Statistics as a science. Statistics for Social Sciences.

Abstract:A very important issue is to understand that in social sciences statistics is used as a tool together with other major issues, such as: methodology, methods, research design. Knowledge of these

16

concepts and understanding how they work as a group is a foundation for further understanding of statistical concepts. I shall introduce main concepts of social sciences in this respect: data collection methods, sampling, experimental design, data, qualitative and quantitative data, discrete and continuous data, and scales: nominal, ordinal, interval and ratio. The issues like: what is science? What makes science different from non-science? What is statistics? will be discussed. The concept “statistics” is also used to denote measures for samples, while the same measures for populations are called parameters. Most of the students are familiar with the tools from descriptive statistics, but have not encountered concepts of inferential statistics.

Home task:Students have to look for the samples of usage of statistics in everyday life and bring the examples to class.

Problems to recognize:Students shall have to understand that statistic is used in social sciences in combination with research principles, such as data collection methods and research design.

Practical examples: Examples from social sciences will be discussed (they are provided in the textbook).

Readings: Kiess, H. (2008), Statistics for Behavioral Sciences. Pp. 1-29. Exercises, pp. 12-13; 27-29

Theme 2. Descriptive Statistics. Frequency. Measures of Central Tendency.

Abstract: The topic of the class is how to describe the data, which, quite often, come in large numbers, as in surveys. The survey data, especially categorical data, are mainly processed through frequencies. We will discuss frequencies, frequency distributions, graphs, percentiles, quartiles, mode, median and mean. We will consider qualities - the strong and weak sides of the various statistics introduced.

Home task: Students are to find a research article from social sciences where any of the statistics and parameters from the material to be covered is used. The schedule of individual presentations will be prepared.

Problems to recognize: Descriptive statistics are tools to reduce large N data to just a couple of numbers. Graphical representations of data are used to make the data easier to understand, therefore, knowledge of some of the fundamental principles, how to present data in the right way, is needed.


Readings: Kiess, H. (2008), Statistics for Behavioral Sciences. Pp. 30-72. Exercises, pp. 55-57, 69-72

Theme 3: Descriptive statistics. Measures of Variability.

Abstract: The large numbers of data are mainly introduced by 2 statistics: measures of central tendency and measures of variability. Measures of variability for various scale data will be introduced and discussed: range, inter-quartile range, semi-inter-quartile range, variance and standard deviation.

Introduction to SPSS software. I shall show the data view and the variable view pages, explain how they are developed. We will discuss the principles of developing codebooks. The spread sheet and

17

output concepts will be introduced. The students will learn how to describe the data through frequencies, graphs, measures of central tendency, measures of variability via STATA.

Home task: To practice using STATA.

Problems to recognize: Students need to understand that two statistics are needed to describe the data, either one of them is not enough to provide the full and representative picture of the data essence.

Practical examples: Examples from social sciences will be discussed (they are provided in the textbook). Also, students shall process the data files (provided by me) in SPSS with the statistics they have learned up to date.

Readings: Kiess, H. (2008), Statistics for Behavioral Sciences. Pp. 73-89. Exercises, pp. 86-89

Theme 4: Principles of Inferential statistics. Normal distribution. Probability. Estimations.

Abstract:Inferential statistics use principles of probability and distribution. Therefore, elementary knowledge of probability concept and its rules will be discussed. The qualities of normal distribution and the area under its curve will be introduced. One of the main concepts for inferential statistics is population-sample difference, how to make samples to become representative of populations, what are the ways to reach representation. Two ways of estimating the population parameters based on the sample statistics will be introduced and discussed. Point estimation and interval estimation concepts shall be presented.

Home task: Exercises, Kiess, H. (2008), Statistics for Behavioral Sciences

Problems to recognize:Students should be able to see the difference between descriptive and inferential statistics. Students need to understand that in most of the cases in social sciences the qualities of population are unknown. We can only judge about the qualities based on the samples. Therefore, we estimate the qualities of populations.


Readings: Kiess, H. (2008), Statistics for Behavioral Sciences. Pp. 90-140. Exercises, pp. 115-118; 137-140

Theme 5: Principles of Inferential statistics. Experimental design Testing statistical hypothesis; z test and one sample t-test.

Abstract:Number of concepts discussed at this class are used through the rest of the course, as they represent principles of inferential statistics. Formulating and testing statistical hypothesis, null hypothesis and an alternative hypothesis. Directions of hypothesis: one tailed and two tailed will be presented on the normal distribution curve graph. Significance levels: statistical rareness. The population and sample will be compared to each other via descriptive statistics, like mean and standard deviation and via inferential statistics, like z-test and one sample t-test.

18

Problems to recognize:Students need to understand that they can compare population and sample means, which is descriptive statistics, but they also need to use inferential statics, in this cases z and t tests, to make the comparisons legitimate.


Readings: Kiess, H. (2008), Statistics for Behavioral Sciences. Pp. 141-172. Exercises, pp. 169-172 ; Statistics for Behavioral Sciences. Pp. 173-214. Exercises, pp. 211-214

Practicing: STATA/SPSS.

Theme 6: Inferential statistics. One Way Analysis of Variance (ANOVA). Between groups design.

Abstract: Analysis of variance essentially fulfils the same task as the t-test, with the difference in research design. If there are more than 2 levels of the independent variable, or if there is more than one independent variable in a research design, t-test should be replaced with an F (Fisher) test. The F test works the same way the t-test does. During the course three types of ANOVAs will be studied. This class will concentrate on one way between subjects ANOVA.

Home task: Group project task is introduced: students should work in groups. The task will be to analyze some data files (the students should independently find these data files) using some of the techniques learned via STATA and present their results.

Problems to recognize:Students need to understand the relations between F and T distributions, that the F test is basically the same as the t-test: F-test value is the squared t-test value.




Theme 7: Inferential statistics. Two Way Analysis of Variance (ANOVA).

The content: It is again very important to understand the experimental research design used. In case of the factorial design (when two or more independent variables are used), various combinations of factor influence on the dependent variable take place: either of the factors can have influence on a dependent variable, and/or their interaction can have an influence. If interaction is statistically significant, then the main effects of factors become artifactual. If there is no interaction effect, then the main effects of factors become important (if they are statistically significant).

Problems to recognize:Students need to realize that it is very important to understand the research design and the combinations of variables. This understanding and knowledge needs to be used to interpret the data processing results (output in SPSS) in the right way.



19

Theme 8: Inferential statistics. One Way Analysis of Variance (ANOVA). Within groups design.

Abstract: At this class we come back to the design with one independent variable with more than two levels, but designed for only one group of subjects. In such cases, equivalency of groups in the experiment does not exist as a problem, as there is only one group of subjects. However, one and the same group of subjects undergoes all levels of an independent variable, or, in the other words, all conditions. This way researchers compare different means received from one and the same subjects at different times. The F test equation for this design is different from that of the between groups design, but the logic is very similar. We will process SPSS data file using three Fs.

Problems to recognize: Students need to make difference between the two designs of the experiment and understand the differences in corresponding equations of ANOVAs.



Theme 9: Inferential statistics. Non-parametric tests: Mann-Whitney and Wilcoxon signed-ranks tests.

Abstract: Z, t and F tests are parametric tests, as they require certain conditions to be met in order to use them. In social sciences, there are many cases, when these requirements are not met. In such cases social scientists use non-parametric tests. During the course we will study three such tests. This class concerns two of them. Mann-Whitney U test is used instead of t-independent and Wilcoxon T is used instead of t-dependent on the ordinal scale data.

Problems to recognize: Students need to realize when, in which cases we should consider that data distributions do not meet the criteria required for parametric tests and use their non-parametric analogs instead.




Theme 10: Inferential statistics. Non-parametric tests: Chi square tests.

Abstract:Chi square tests are used to compare data distributions on nominal scales. There are two types of the test: goodness of fit Chi square and the Chi square test of independence according to the research design. We will process the SPSS data file using Mann-Whitney U test, Wilcoxon T test and Chi square tests.

Problems to recognize: Students need to understand for which design it is appropriate to use the chi square tests.


20



Theme 11: Descriptive and Inferential Statistics: correlation

Abstract: Non-experimental data collection methods, such as survey, archival data can not provide evidence for causal relations, because one does not have sufficient grounds to claim cause and effect relation. Therefore, another most frequently used research design is a correlation one, where covariance of two “equal” variables is studied. We will discuss difference between correlation and causality. I will introduce concept of covariance and two correlation coefficients: Person and Spearman according to the scales.

Problems to recognize:It is important that students do not mix up causal and correlation designs and apply the correlation statistics appropriately.

Practical examples:Examples from social sciences will be discussed (they are provided in the textbook).



Theme 12: Descriptive and Inferential Statistics: regression and prediction. Summing up.

Abstract: Regression is based on correlation technique. If two variables are correlated and we know one of them, we can predict the other. We will discuss concepts of linear relation, slope of a line, the intercept of a line, prediction. We will process the SPSS data file. This is the final class, so we will sum up the whole knowledge and skills learned during the course. The final written test will be conducted after some time to check the knowledge and skills of the students. The test will require problem solutions via STATA.

Problems to recognize:Students should learn how to look at a task from the different perspective.



Practicing :STATA/SPSS.

SYLLABUS #3:Programme software STATA

Aim: This course concerns statistics software for the study module: Decision making technology in practice, as a tool to process and analyze quantitative and categorical social data, which, quite often, requires different approach to the issues. The research design and results interpretation are importance

21

in data processing via statistics software. The students should acquire certain knowledge and skills using STATA software for research. By the students will be gained a practical skills and knowledge of main statistical concepts of data description and testing relations among variables, as well as the principles of main research designs using statistical software STATA.

Teaching Methods: The course is a practical course and it will be integrated with the syllabi: statistics for decision making and Decision Making Methods. The classes will be provided in the computer lab with programme software STATA. The students are expected to have prepared the readings before the class; this enables discussion and question-answer format of the research issues. Each student will make presentations of tasks and solutions using spreadsheet and programme software STATA , an individual and a group one.

Teaching Technique and tools: computer class, spreadsheet and programme software STATA

Reading: handouts, STATA statistical software : Statistics, Data Management, Graphics, User’s guide, 2010

Grading: The students get grades with integration to the syllabi “statistics for decision making”

Coponents Max. ballMidterm testing 1 20Midterm testing 2 20



Participation in the problem solving exercisases 10



Introduction

Theme 1: Getting started with STATA for Windows

Abstract: To introduce students Getting started with STATA for Windows as the primary tools for learning about STATA and the other sources of information for them as are: The STATA web site: http://www.stat.com; the STATA technical bulletin ( The journal contains articles as well as updated commands and additions to STATA); Net courses: trainings via the internet; books and support materials ; technical support by telephone, fax, e-mail; on-line tutorials and sample datasets for instructions on how to run them; on-line help and lookup facilities for details. Start with STATA; STATA as a statistical package for managing analyzing and graphing data. Students must consider Data as a rectangular table of numeric and string values in which each row is an observation on all the variables and each column contains the observations on a single variables. Variables are designated by variable names. Observations are numbered sequentially from 1 to N . Dataset is data plus labelings, formats, notes, and characteristics.

22

http://www.stat.com/

a/ Numbers: A number mast contain a sign, an integer part, a decimal point, a fraction part , an “e” or “E” and a signed integer exponent. Numbers may not contains commas. For example, the number 1,2024 must be typed as 1024 or 1024. or 1024.0. The following are examples of valid numbers.

5; -5; 5.2; .5; 5.2e+2; 5.2e-2; We may add a missing value to the dateset if this number is presented according to rule above. As a convenience for FORTRAIN users you may use d or D as well as “e” to indicate exponential notation. Thus, the number 520 may be written 5.2d+2, 5.2D+2, 5, 5.2d+02, 5.2D+02.STATA ignores a missing numerical date and such date is realized as “0”- zero. Date must be described using the STATA command “describe”

b/ Data formats

A fourth “numeric” format type %d, is for dispaing elapsed dates. The %d format may be specified as simply %d or it may be followed by up to 11 charactrs that specify how the date is to be presented. Allowable characters are:

n – numeric month without a leading 0

N - numeric month with a leading 0

m – 3-letter month with the first letter capitalized

M – name of the month spelled out with the first letter capitalized

l- 3-letter month , all in lowercase

L - name of the month spelled out , all in lowercase

d – day of the month without a leading 0

D - day of the month with a leading 0

c – century without a leading 0

C - century with a leading 0

y – year within century with a leading 0

c/ Strings

A string is a sequence of printable characters typically enclosed in double quotes: “String”, “hello, World”, “x/y+3”, “1.2”

Testing the installation : start, exit, STATA editor and window (file, edit, prefs, window, help) with review and variables.

Problems to recognize: Students shall have to understand that the modern educational area does not considered without programme software without research design for analysis of issues. Students shall have to understand that for using STATA is demanded the correct expressions of data and datasets (numbers, dates, string, formats, datasets, variables) and students must control how data is displayed.

Home task: Students have to look for the samples of usage of statistics software in practice and bring the examples to class.

Readings: handouts, examples by STATA statistical software : Statistics, Data Management, User’s guide; pp. 27-31; 35-39; pp. 109 – 127

23

Practical examples: Examples from the STATA dataset will be discussed

Theme 2: Operators

Abstract: STATA has four different classes of operators: arithmetic (addition, subtraction, multiplication, division), string (the “+” sign also used to mean the string operator for the joining of two strings), relational (greator than, less than, greaterthan or equal, less than or equal, ),equal, not equal), and logical (&(and); |(or);

(not))

Problems to recognize:Students shall have to understand that like relational operators, logical operators return the value “1” for true and “0” for false. Logical operations , exept for ~ , are performed after all arithmetic and relational operations

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 127-131

Home task:Students have to look for the samples of usage of statistics software in practice and bring the examples to class.

Theme 3: Mathematical Functions

Abstract: Functions may be appear in expressions. Functions are indicated by Function name, an open parenthesis, an expression or expressions separated by commas and a close parenthesis. All numeric functions return missing when given missing values as arguments or when the result is undefined. The types of functions are:

abs(x) – absolute value

acos(x) – arc-cosine returning radians

asin(x) - arc-sine returning radians

atan(x) - arc-tangent returning radians

comb(n,k) – combinatorial functions

cos(x) – cosine of radian

exp(x) - exponential

Ln(x) – natural logarithm

lnfact(x) - natural log factorial

log10(x) – logarithm base 10

sin(x) - sine of radians

mod(x,y) – modules x with respect to y

sqrt(x) – square root

tan(x) tangent of radians

24

Problems to recognize: Student must understand logical essential using of functions for datasets and must be analysis that: a/ trigonometric functions are defined in terms of radians; b/ in case to calculate x! use round ( exp(lnfact(x),1) to ensure it is an integer.

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 131 -134

Home task:

Students have to look for the samples of usage of statistics software in practice and bring the examples to class.

Theme 4: Statistical Functions

Abstract: Statistical Functions:

Binomial(n,k, π )- probability of observing k or more successes in n trials when the probability of a success on a single trial is π

Binorm(h,k, ρ) - joint cumulative distribution ¢(h,k, ρ ) of bivariate normal with correlation ρ; cumulative over ( -∞,h] x(-∞,k ]

chiprob(df,x) – upper-tail cumulative χ quadrat with df degrees of freedom

fprob( , , F) - upper-tail cumulative F distribution with df1 numerator and df2 denominator degrees of freedon

gammap(a,x) - incomplete gamma function P(a,x)

ibeta(a,b,x) - incomplete beta function I(a,b)

invbinomial(n,k,p) – inverse binomial; for p ≤ 0.5, returns (π = probability of success on a single trial) such that the probability of observing k or more success in n trials is p1. For p>0.5 , returns π such that the probability of observing k or fewer success in n trial is 1-p.

Problems to recognize: Student must understand that Binorm(h,k) gives the cumulative bivariate normal distribution

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 133 -137

Home task: Students have to look for the samples of usage of statistics software in practice and bring the examples to class.

Theme 5: Data functions, string functions, special functions

Abstract: There will be considered :

1. Data functions : these functions will be described with examples . s is used to indicate a string literal, a string variable or another string expression; e, m,d and y are used to indicate numeric subexpression. There wil be considered: date (s1,s2); day(e); dow(e); mdy (m,d,y); month(e); year(e);

2. String functions :index(s1,s2); length (s); lower(s); ltrim(s); real(s); rtrim(s); string(n); substr(s,n1,n2); trim(s)

3. Special functions: 34 Commands for dealing with categorical variables for example of use.

25

4. Problems to recognize: Student must understand a categorical variables that divides the data into x as nearly equal-sized subsamples as possible, numbering the first group 1 , the second group 2 and so on., commands for dealing with dates, commands for dealing with string

5. Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 133 -139, STATA Press, College station, Texas; STATA Tutorial.

6. Home task: review of examples by Statistics, Data Management

Theme 6: System variables, Accessing coefficients and standard errors

Abstract: There will be considered :

1. To built in system variables that are created and updated by STATA. They are called variables because their names all begin with the underscore “---“ character

2. Accessing coefficients and standard errors: after estimating a model there must be access the coefficients and standard errors and use them in subsequent expressions,

3. Problems to recognize: Student should see predict estimation and post-estimation commands to obtain predictions and find correspondence between coefficients and variables such as : cnerg, logit, probit, regress and tobit

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 133 -142, STATA Press, College station, Texas; STATA Tutorial.

Home task: review of examples by Statistics, Data Management User’s guide; pp., STATA Press, College station, Texas

Theme 7: ANOVA models, multiple-equation models,

Abstract: There is no simple relationship between the coefficients and the variables. For continuous variables in the model _coef [varname] refers to the coefficient. For categorical variables must specify the level as well as the variable. Referring to coefficients and standard errors in multiple-equation models.

Problems to recognize: Student should type requests and specify the equation number “eqno” either as an absolute equation number or as an “indirect” equation number.

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 133 -142, STATA Press, College station, Texas

Home task: Reviewing examples by Statistics, Data Management User’s guide; pp., STATA Press, College station, Texas

Theme 8: Assessing results from STATA commands, generating lags and leads, subscripting within groups

Abstract: There are considered the STATA commands, that save important results in _result(). Individual observations on variables can be referenced by subscripting the variables. Explicit subscripting combined with with the _variable_n can be used to create lagged values on a variable. Since _n contains the observation number of the current observation, _n-1 evaluates to the observation number of the previous observation, which will be the observation from the previos time period if the data are sorted chronologically.

26

Problems to recognize: Student should observe that STATA just generated 1000 lines output and have typed quietly by patient : generate orig=vital(1) and putting quietly in front of ant STATA command suppresses all the output that command would otherwise have generates



Theme 9: Label values, using of the Labels in an expression in place of the numeric values with which they are associated

Abstract: To use a label, type the label in double quotes followed by a colon and the name of the value label. If the double-quoted label is not defined in the indicated value label, or the value label itself is not found, a missing value is returned

Problems to recognize: Student should know, since “yesno”associates “yes” with 1 and “no” with 0, we could have typed “list if answer==1” instead of what we did type. We could not have typed “list if answer==”yes” because answer is not string variable.

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 133 -148, STATA Press, College station, Texas; STATA Tutorial.STATA Technical Bulletin, 25-28,


Theme 9: Printing and preserving output

Abstract: a/ Overview: Starting and closing logs; Appending to an exiting log; temporary suspending and resuming logging; b/ Placing comments in logs; c/ Printing logs with STATA

Problems to recognize: Student should know, that the file can be printed or better, incorporated into documents student create with word processor


Home task: Review of examples by Statistics, Data Management User’s guide; pp., STATA Press, College station, Texas

Theme 10: Do-files

Abstract: a/ Description: version; Comments and blank lines in do-files; c/ Long lines and do-files; logging the output of do-files; preventing . b/ Ways to run a do-files; c/ programming with do-files: argument passing; suppressing output

Problems to recognize: Student should know, that he/she can create a disk file containing commands and instruct STATA to execute the commands stored in that file. Such files are called do-files since the command

27

that causes them to be executed it do. A do-file is a standard ASCII text file. A do-file is executed by STATA when student type do filename.

Reading: handouts; STATA statistical software, Statistics, Data Management, User’s guide; pp. 155 -165, STATA Press, College station, Texas


Ttheme 11: Ado-files

Abstract: a/ Description; b/ When does STATA look for ado-files? c/ How do we install an addition; c/ How do we add our own ado-files?

Problems to recognize: Student should know, that he/she can obtain from the STATA technical bulletin. web site: http:// www.stata.com This topic is for that students who want to consume ado-files.



SYLABUS 4. DECISION MAKING METHODS (DMM)

COURSE DESCRIPTION

DMM methods are especially helpful with large complex problems. If the manager have a little experience with similar problems, or the problem is sufficiently complex, then a quantitative or qualitative analysis of the problem can be an especially important consideration in the manager’s final decision. An analyst will concentrate on the quantitative or qualitative facts or data associated with the problem and develop mathematical expressions that describe the objectives, and other relationships that exit in the problem. By using one or more quantitave or qualitative methods the analyst will make a recommendation based on the quantitative or qualitative aspects of the problem. A manager can increase decision making effectiveness by learning more about quantitative or qualitative methodology and by better understanding its contribution to the decision making process, to compare and evaluate qualitative and quantitave or qualitative sources and ultimately to combine the two sources in order to make the best possible decision.

To successfully apply quantitative or qualitative analysis to decision making the management scientist must work closely with the manager and they can begin on developing a model to represent the problem mathematically. The best solution for the model then becomes a recommendation to the decision maker . The process of developing and solving models is the essence of the quantitative or qualitative analysis process.

28

http://www.stata.com/

The value of model-based conclusions and decisions is dependent on how well the model represents the real situation. The success of the model and approaches will depend heavily on how accurately the objective and constraints can be expressed in terms of mathematical relationships.

The analyst will attempt to identify the values of the decision variables that provide the best output or optimal solution of the model. In this case the analyst can successfully implement the model and develop system for asset allocation, financial planning, social policy development, marketing information technology, database marketing, portfolio performance measurement and so on.

An important part of the decision making analysis process is the preparation of managerial reports based on the model’s solution that can be easily understood by the decision maker. If the results of the decision making process are not correctly implemented , the entire effort may be of no value. Because implementation often requires people to do things differently, it often meets with resistance. People want to know “What’s wrong with the way we have been doing it?” and so on… At this stage, analyst focus on developing solutions that provide significant value and are easily implemented. Because people with different skills, perspectives and motivations must work together for a common goal, teamwork is essential. The group’s members take classes in team approaches, facilitation, and conflict resolution. They possess a broad range a multifunctional and multidisciplinary capabilities and are motivated to provide solutions that focus on the goals of the firm or organization.

DMM coverage a decision making analysis including a problem formulation and structuring the decision analysis problem and model building involving technical details such as computing and provides MA students with a sound conceptual understanding of the role of decision making process for management and administration.

Focus: The focus of the discipline is on the decision making process and on the role of the management in that process. There will be discussed the problem orientation of the process and show models can be used in this type of analysis. The difference between the model and the situation or managerial problem it represents is an important point. One of the important characteristics of management is to develop procedures for finding the best or optimal solution of the problem by using of DMM. This course is for MA students wanting to improve their decision-making skills through the use of modern computer tools and techniques. They will learn how to make effective decisions relating to project schedules, product design tradeoffs, project cost estimating, problem solving, and risk analyses.DMM coverage a decision making analysis including a problem formulation and structuring the decision analysis problem and model building involving technical details such as computing with spreadsheet and programme software STATA/SPSS and provides MA students with a sound conceptual understanding of the role of decision making process for management and administrationThe course coverage: Problem solving and Decision analysis; DMM: Single and Multi-attribute decision making methods; Elementary methods; Cost-benefit analysis; Pros and cons analysis; Maximin and maximax methods; MAUT methods (the Multi-attribute Utility Theory (MAUT)); Group decision making; decision Making without probabilities and with probabilities, Scoring Models, graphical solution, goal programming model, Functions, optimization problem, Optimizing using the Lagrange method, Decision and games, Game Theory and Decision making; A Major Issue with Game Theory; Decision making under uncertainty, Strategic Decision making , Managerial Decision Making Under Risk and Uncertainty, Strategic Decision-Making in the Face of

29

Uncertainty and Interaction between researchers and decision-makers as an explanatory dimension conditioning the use of research results.

STUDENT WILL LEARN:

• How to make effective individual and group decisions; • How to get down to the true cause of typical project problems using root-cause analysis; • How to apply spreadsheet techniques and programme software STATA to project management; • Why the critical path may not always be the longest path; • How to respond to imposed project completion dates which may be unrealistic; • How to make critical project decisions under uncertainty conditions; • How to make multi-stage decisions using decision trees; • How to use AHP and KTA models to make multicriteria decisions; How to use. Game Theory for Decision Making; How to consider DM under Risk and uncertainty; How to consider DM with probability and without probability; How to assess emerging risks; What is an Interaction between researchers and decision-makers as an explanatory dimension conditioning the use of research results

SKILLS AND ABILITY TO BE DEVELOPED

Skills:Being CreativeManaging InformationThinking, Decision-MakingThinking, Problem-Solving The ability to want understanding an essence of single and multi-criteria decisions;• The ability to make effective multi-criteria decisions using conventional spreadsheets and software STATA ;• The ability to make multistage decisions using decision trees; • The ability to conduct a root-cause analysis using Decision Making Tools and Techniques;The ability to state research problem, to find the need DM tools and to find the need decision. TEACHING-LEARNING METHODS

Active Teaching- Learning Methods aims are the Development students potential as individuals and to make informed and responsible decisions for living and working in the 21st century as flexible, creative, and proactive – young people who can solve problems, make decisions, think critically, communicate ideas effectively and work efficiently within teams and groups. Teacher-centred classroom Learner-centred classroom

Product-centred learning Process-centred learning

Teacher as a ‘transmitter of knowledge’ Teacher as an organiser of knowledge

Subject-specific focus Holistic learning focus

30

Focus on answering questions Asking questionsWanting to have their own say Actively listening to opinions of others

Teaching Methods

Teaching Method Effect of Teaching Method in skills developmentTraditional lecture –face –to-face Knowledge acquisition integration with studentsTeamwork extra class Cooperation, Responsibility, independence, communication

skillsTeamwork during class Cooperation, Responsibility, independence, communication

skillsClass solving Confidence, oral communication skills, written skills,

interaction skills, cognitive benefits( problem solving, judgment, understanding)

Individual homework assignment Independence, written skills, logical thoughtLibrary research Independence ( able to recognize when information is

needed and have to ability to locate and use effectively the needed information)

Individual assignment during the class

Independence, written skills, logical thought

Student seminar Self-confidence, communication(speaking ability) skills, critical thinking, interpersonal skills

Computer based activities Independence, using of software skills

PREREQUISITES :Syllabi: : #1,2,3Grading:

Coponents Max. ballMidterm testing 30Individual presentation 15


Participation in the problem solving an d decision making exercisases

15



Theme 1: Introduction to Decision Making Methods

Scope: Decision making is the study of identifying and choosing alternatives based on the values and preferences of the decision maker. Making a decision implies that there are alternative choices to be considered, and in such a case we want not only to identify as many of these alternatives as possible but to choose the one that best fits with our goals, objectives, desires, values, and so on…

31

Decision making should start with the identification of the decision maker(s) and stakeholder(s) in the decision, reducing the possible disagreement about problem definition, requirements, goals and criteria. Then, a general decision making process can be divided into the following steps: Define the problem; Determine requirements; Establish goals; Identify alternatives; Define criteria; Select a decision making tool; Evaluate alternatives against criteria; Validate solutions against problem statement.

Decision making is a key in every part of a person’s life, but it becomes especially important as an individual moves into progressively greater leadership roles. As a leader, person have to use every resource to make the best decisions.

Abstract:There will be considered the Seven-Step Formula for Decision Making and discussed a seven-step way for making better decisions:

1. Stop and Think ; 2. Clarify Goals; 3. Determine Facts; 4. Develop Some Options; 5. Consider the Consequences; 6. Choose ; 7. Monitor and Modify and Other Techniques for Decision Making as are: Prioritizing your decisions, Choosing between options by thinking about possible outcomes, Looking at the pros and cons for each possible decision , Analyzing the influences for and against making a decision, Assessing the decision from all points of view, deciding if a decision is worth making

Problem to recognize: At the end of the lesson student should be able to: 1. Discuss ways to make decisions 2. Discuss how do decide which methods would be best depending on the decision to be made The Seven-Step Ethical Formula for Decision Making .

Class discussion: Example on situation for the development of creativity

Home task: Essay writing: The problems for my decisions and the steps for solving them (500-1000 words)

Reading: Handouts , Josephson Institute of Ethics. (2005). Retrieved from http://www.josephsoninstitute.org/ on January 27, 2005. Mind Tools. (1995-2005). Essential skills for an excellent career. Retrieved from http://www.mindtools.com on October 5, 2003

Theme 2: Problem-solving and Decision-making

Scope:Education is the main priority and precondition of economical, political, cultiral development and of forming of civil society of the country. The most important factor of economic development of the country is the appropriate using of the intelect resourses for the management and decision making in the political, social and economical proceses and public organization sphere, also. First of all, it is in touch with the social areas, as: political science, public administration. In fact, the improving of the capacity in these areas is the basis of normal regulation of the country development. In this case, the educational process effective management, the devolopment that courses and curricula which have more importance for the research, analysis and estimation of current stuation in the region are the basis of practical results can be made by decision and re-evaluated the situation that were have happened. On the other side, the analysis of the curricula of the universities in Georgia have shown that in their study plans is absent the module or the discipline on decision making which increases student's skills of thinking and logical decision-making. The student have to provide an analysis all activities that are directed to Problem-solving and Decision-making. He/she have to analyze examples from our life, social sciences (politics, economics, …) will try to consider the steps of the Problem-solving and Decision-making and to estimate decisions for the concrete situations with using of Decision Making methods.

32

Abstract:There will be considered Decision analysis processes for problem-solving and decision-making as the important skills for social policy, business and life. Problem-solving often involves decision-making, and decision-making is especially important for management and leadership. There are processes and techniques to improve decision-making and the quality of decisions. Decision-making is more natural to certain personalities, so these people should focus more on improving the quality of their decisions. Problem-solving and decision-making are closely linked, and each requires creativity in identifying and developing options, for which the brainstorming technique, SWOT analysis and PEST analysis help decision-making and problem-solving. Good decision-making requires a mixture of skills: creative development and identification of options, clarity of judgment, firmness of decision, and effective implementation. For group problem-solving and decision-making, or when a consensus is required.

Problem to recognize: Students need to provide requirements of Decision analysis and decision Making Steps and, creative development and identification of options, clarity of judgment, firmness of decision, and effective implementation

Class Discussion: Example on conflict situation for the development of creativity

Home tasks:SWOT and PEST analysis: examples and templates; Decision Making Steps : examples

Reading: Handouts , http://www.businessballs.com/swotanalysisfreetemplate.htm PEST analysis method and examples, with free PEST template , SWOT analysis method and examples, with free SWOT template

Theme 3: A review of the General Decision Making Methods Using in Practice

Scope: Understanding Decision Making Methods for the various situations as are: Single criterion vs. multiple criteria, finite number of alternatives vs. infinite number of alternatives, A short analysis of the method; Multi-attribute decision making methods; Elementary methods; Cost-benefit analysis; Pros and cons analysis; Maximin and maximax methods; MAUT methods (the Multi-attribute Utility Theory (MAUT)); Group decision making;

Abstract : It is very important to make distinction between the cases whether we have a single or multiplecriteria. A decision problem may have a single criterion or a single aggregate measure like cost. Then the decision can be made implicitly by determining the alternative with the best value of the single criterion or aggregate measure. We have then the classic form of an optimization problem: the objective function is the single criterion; the constraints are the requirements on the alternatives. Depending on the form and functional description of the optimization problem, different optimization techniques can be used for the solution, linear programming, nonlinear programming, discrete optimization, etc. Consider a multi-attribute decision making problem with m criteria and n alternatives and Cost-benefit analysis (CBA) as a worldwide used technique in decision making, Elementary approaches as a simple and no computational support, the maximin method based upon a strategy that tries to avoid the worst possible performance, the approaches based on the Multi-attribute Utility Theory (MAUT), group thinking methods.

Problem to recognize: Student must Understand and analyze situations for using DM methods

Home tasks: Essay on using of SWOT and PEST analysis in practice , 500-1000 words.

33

http://www.businessballs.com/swotanalysisfreetemplate.htm

http://www.businessballs.com/brainstorming.htm

Reading: Handouts ,

Introduction to Decision Making Methods. Janos Fulop. Laboratory of Operations Research and Decision Systems, Computer and Automation Institute, Hungarian Academy of Sciences. (http://www.google.ge/#hl=ka&source=hp&q=Janos+Fulop.+Laboratory+of+Operations+Research+and+Decision+Systems%2C+Computer+and+Automation+Institute%2C+Hungarian+Academy+of+Sciences&btnG=Google+%E1%83%AB%E1%83%94%E1%83%91%E1%83%9C%E1%83%90&gbv=2&bav=on.2,or.r_gc.r_pw.,cf.osb&fp=40136ac97cab0ea3&biw=800&bih=400 )

Theme 4: Decision analysis (DA) : Problem Formulation, decision Making without probabilities and with probabilities

Scope:There is considered Decision analysis as a logical and systematic way to address a wide variety of problems involving decision-making in an uncertain environment. We introduce the method of decision analysis and the analytical model of constructing and solving a decision tree.

Abstract: DA can be used for determining an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain of risk. When Decision analysis have been conducted the uncertain future events make the final consequence uncertain. . The selected decision may provide good or excellent results or relatively unlikely future event may occur causing the selected decision alternative to provide fair or poor results. The risk associated with any decision alternative is a direct result of the uncertainty associated with the final consequence. A good decision analysis includes. Through risk analysis the decision maker is provided with probability information about the favorable as well as unfavorable consequences that may occur. There are provided the statement of the problem, will be given an influence diagrams, decision trees and will be considered approaches to decision making that do not require knowledge of the probabilities of the states of nature. These approaches is situations in which the decision maker has a little confidence to access the probabilities. There will be considered situations when we can obtain probability assessment for the states of nature. In these cases will be analyzed a risk profile for a decision alternative and provided sensitive analysis too. using various examples: a manufacturing manager must decide how much capital to invest in new plant capacity, when future demand for products is uncertain. A marketing manager must decide among a variety of different marketing strategies for a new product, when consumer response to these different marketing strategies is uncertain. An investment manager must decide whether or not to invest in a new venture, or whether or not to merge with another firm in another country, in the face of an uncertain economic and political environment. we introduce a very important method for structuring and analyzing managerial decision problems in the face of uncertainty, in a systematic and rational manner. The method goes by the name decision analysis. The analytical model that is used in decision analysis is called a decision tree.

Problem to recognize: Student must analyses the optimal decision alternative and provide the risk profile and to select a decision alternative with a good risk profile. Based on the sensitive analysis student concludes that the optimal solution for the decision problem is not particularly sensitive to the payoffs for the large complex decision alternative.

34

http://www.google.ge/#hl=ka&source=hp&q=Janos+Fulop.+Laboratory+of+Operations+Research+and+Decision+Systems%2C+Computer+and+Automation+Institute%2C+Hungarian+Academy+of+Sciences&btnG=Google+%E1%83%AB%E1%83%94%E1%83%91%E1%83%9C%E1%83%90&gbv=2&bav=on.2,or.r_gc.r_pw.,cf.osb&fp=40



Home task: To make glossary on the Decision analysis (DA), Formulate complex problems according to lecture material

Reading: handouts, Decision analysis. The article in A Journal of the Institute of Operations Research and the management Sciences. Online ISSN: 1545-8504. http://www.dynamic-ideas.com/Books/097591460/097591460-ch01-ex.pdf

David R. Andersen, Denis J. Sweeney, Tomas A. Williams. An Introduction to management science. Quantitative approaches to decision making, 10th edition, 2003. pp.624-783

Theme 5: Decision Analysis with sample information

Scope: The lecture is addressed to Efficiency of Sample Information: If the efficiency is high there is not much to gain. But if the efficiency is low you may not be able to do more because you have spent all you can hope to gain. Should we stop looking for more information about the states of nature?

Abstract: There will be shown how probability information about the states of nature affects the expected value calculations and thus the decision recommendation. How to make the best possible decision and how the decision maker may wont to seek additional information about the states of nature This new information can be used to revise or update the prior probabilities so that the final decision is based on more accurate probabilities for the states of nature. Most often , additional information is obtained through through experiments designed to provide sample information. There is provided sampling, product testing, market research studies with using of examples. By introducing the possibility a market research study problem becomes more complex. There will be using to the research study complex-size decisions and the approach used to determine the optimal decision strategy based on a backward pass through the decision tree using the following steps: a/ at chance nodes, Compute the expected value by multiplying the payoff at the end of each branch by the corresponding branch probabilities; b/ At decision nodes, select the decision branch that leads to the best expected value. There will be provided constructing a risk profile for the optimal decision associated probabilities.

Then will be introduced a method known as Analytical Hierarchy Process which allows the user to make pair wise comparison among the criteria and a series a pair wise comparisons among the decision alternatives in order to arrive at a prioritized ranking of decision alternatives.

Problem recognize: Student must analysis the decision tree and the choice of an optimal strategy requires that student knows the branch probabilities corresponding to all chance nodes and logical sequence for the decisions and the chance events.

Home task: Utility and decision making to identify the most desirable decision in practice. Preparing glossary for Decision Analysis with sample information.

Reading: handouts handouts, David R. Andersen, Denis J. Sweeney, Tomas A. Williams. An Introduction to management science. Quantitative approaches to decision making, 10th edition, 2003. pp.639-783

Theme 6: Multicriteria decision problems:Programming Formulation and graphical solution, goal programming model

Scope: The lecture is addressed to Goal programming that may be used to solve linear programs with multiple objectives with is objective view as a ”goal”. One approach to goal programming is to satisfy

35

goals in a priority sequence. Second priority goals are pursued without reducing the first priority goals, etc. for each priority level the objective function is to minimize the weighted sum of the goal deviation.

Abstract:To introduce the topic of multicriteria decision making , we consider a technique referred to goal programming and will be developed a multiple criteria solutions within the general framework of linear programming. There will be consider a scoring model as a relatively easy way to identify the best decision alternative for a multicriteria problem. We identify the goals and any constraints that reflect recourse capacities or other restrictions that may prevent achievements of the goals, determine the priority levels, define the decision variables, formulate the constraints in the usual linear programming function, for each goal develop a goal equation and write the objective function in terms of minimizing a prioritized function of the deviation variables.

Problem to recognize: to assume that for problems with one priority level only on a linear programming needs to be solved to obtain the goal programming solution and need to minimize the weighted deviations from the goals. Trade-offs are permitted among the goals because they are all at the same priority level. The goal programming approach can be used when the analyst is confronted with the infeasible solutions to an ordinary linear programming.

Home task: Formulate more complex problems, goal equations, objective function for examples

Reading: handouts, David R. Andersen, Denis J. Sweeney, Tomas A. Williams. An Introduction to management science. Quantitative approaches to decision making, 10th edition, 2003. pp.701-710

Theme 7: Multicriteria decision problems: Scoring Models

Scope:There is an information on the development of scoring models: how to build a scoring model, scorecard validation, and the monitoring of scorecard performance and predictiveness after implementation. There is considered a scoring model as a formula that assigns points based on known information to predict an unknown future outcome.

Abstract:There will be considered a scoring model as a relatively easy way to identify the best decision alternative for a multicriteria problem and demonstrate the use of a scoring model for a job selection application. A score for each decision alternative would have to be computed for each subcriterion. And will be used a percentage weights for the criteria and the wide applicability of scoring models in more complex problem situation. There are many types of scoring models commonly used in the financial services industry. Some examples include:

Credit scoring models. There are different classes of credit scoring models: Generic models. (such as the “FICO score” and other scores provided by the credit bureaus; Custom models typically make use of both credit bureau data and other application;

Behavior scoring models use credit and account performance data to determine whether to increase credit lines, re-price accounts, etc.

Collections scoring models utilize credit and account performance data to determine collections strategies. For example, how often to call a delinquent account and whether to sell an account or outsource to a collection agency.

36

Revenue scoring models are used to predict how long a customer will stay on the books, the amount of fee revenue an account will generate, etc.

Scoring models are also used in other industries and for other functions, such as insurance underwriting and marketing.

Problem to recognize: Selecting of the lowest cost alternative might be appropriate because the task involves many parameters and it is needed to formulate criterias as: skill level, cost containment, timing cost containment, hardware display and so on

Home task: Formulate more complex problems, revising examples according the book and write an essay on concrete solution (about 1000 words)

Reading: handouts, David R. Andersen, Denis J. Sweeney, Tomas A. Williams. An Introduction to management science. Quantitative approaches to decision making, 10th edition, 2003. pp.701-710; http://www.scoringmodels.com/

Theme 8: Multicriteria decision problems:The Analytic Hierarchy Process (AHP) : Establishing priorities using AHP.Multi-attribute decision making methods

Scope:The Lecture material covers the basic principals of AHP. The mathematics of the AHP and the calculation techniques are briefly explained but its essence is to construct a matrix expressing the relative values of a set of attributes. For example, what is the relative importance to the management

Abstract:The basic idea of the approach of the Analytic Hierarchy Process (AHP) is to convert subjective assessments of relative importance to a set of overall scores or weights. AHP is one of the more widely applied multiattribute decision making methods. Consider a multi-attribute decision making problem with m criteria and n alternatives. Let C1,.,Cm and A1,..,An denote the criteria and alternatives, respectively. A standard feature of multi-attribute decision making methodology is the decision table as shown below. In the table each row belongs to a criterion and each column describes the performance of an alternative. The score aij describes the performance of alternative Aj against criterion Ci. For the sake of simplicity we assume that a higher score value means a better performance since any goal of minimization can be easily transformed into a goal of maximization

Problem to recognize: to assume that a higher score value means a better performance since any goal of minimization can be easily transformed into a goal of maximization and understanding the criteria and alternatives, respectively

Class Discussion : Multicriteria decision problems: Formulation and graphical solutions

Home task: Example where have to use the Analytic Hierarchy Process (AHP), to make glossary on the lecture topic

Reading: handouts, David R. Andersen, Denis J. Sweeney, Tomas A. Williams. An Introduction to management science. Quantitative approaches to decision making, 10th edition, 2003. pp.707-730

Geoff Coyle: Practical Strategy. Open Access Material. AHP. The Analitic Hierarchy process.© Pearson Education Limited 2004

37

Theme 9: Decision making methods: Elementary methods: Pros and cons analysis, , Group Decision making

Scope:A goal of this lecture is to provide comparison of using of two various methods and then to to use then for the class discussion for the development of students creativity on applying these methods for students tasks and problem solving and then for decision making.

The lecture material is focused on the elementary method Pros and cons analysis with qualitative comparison method in which good things (pros) and badthings (cons) are identified about each alternative. Lists of the pros and cons are compared one to another for each alternative for decision making and group decision making with multiple actors (decision makers), each with different skills, experience and knowledge relating to different aspects (criteria) of the problem. each actor considers the same sets of alternatives and criteria. It is also assumed that there is a special actor with authority for establishing consensus rules and determining voting powers to the group members on the different criteria. Abstract : Elementary approaches are simple and no computational support is needed to perform the analysis. These methods are best suited for problems with a single decision maker, few alternatives and criteria that is rarely characteristic in environmental decision making.

Pros and cons analysis is a qualitative comparison method in which good things (pros) and bad things (cons) are identified about each alternative. Lists of the pros and cons are compared one to another for each alternative. The alternative with the strongest pros and weakest cons is preferred. It requires no mathematical skill and is easy to implement.

Group Decision making is a powerful but not infallible force. Collective decision making works only when each individual is more likely than not to make the correct decision on her own. , people are being more closed in their beliefs because there is just too much information. , what about the decision making especially when the views are public? I know the old saying, "There is safety in numbers". I know that in some countries people are spending many years to learn how to properly relate material to students, not simply make them memorize everything. We must derive a method that will keep catalytic reactions between those most extreme ideologies under control. This method, I assume, should include political, policy-based, and technological instruments. Then, we should align them in the right sequence so that we can apply our reasoning, not only memory.

Problem to recognize: Students will keep catalytic reactions between most extreme ideologies under control including political, policy-based, and technological instruments and should formulate the problems, use the need methods for them in the right sequence using knowledge from the lecture 1

Home task: Essay (minimum 500 words)

Reading: handouts,

http://www.decisionsciences.org/publications/relatedpublications.asp

Decision-making and Value Studies - the subject.pdf

» Value and Policy Studies - the programme.pdf

» Value and Policy Studies - the structure.pdf

38

http://www.decisionsciences.org/publications/relatedpublications.asp

Class discussion:1.There will be considered students need, tasks, problems using these methods for their problems. Each student has formulated the problem for consideration and decision making.

2. Decision-making and Value Studies - the subject.pdf

Theme 10: Game Theory and decision making : Functions, optimization problem, Optimizing using the Lagrange method, Decision and games, Game Theory and Decision making; A Major Issue with Game Theory; Decision making under uncertainty, Strategic Decision making

Scope:The lecture is concerned to decision making in organizations where the outcome depends on the decision of two or more autonomous players. A game theory model is constructed around strategic choices available to players where the preferred outcomes are clearly defined and known. Game theory aims to find optimal solutions to situations of conflict as Game theory allow us to understand the rational approach to decisions that have discrete choices and clear paths. Strategy and cooperation are some of the ways of describing the human elements of decision making. Combining these human elements with traditional game theory gives up a set of tools, approaches, and perspectives on decision making.

Abstract:There is considered the problem of the allocation of resources across time and different modes of production in order to maximize firm output — a classic line of inquiry, which is taken to a new testing ground. Game theory is the science of strategic decision-making. It is a powerful tool in understanding the relationships that are made and broken in the course of competition and cooperation . Game theory has been used to great effect in sciences To find new solutions to familiar problems that have not been satisfactorily resolved, by giving practitioners a deeper understanding of the nature of incentives, conflict, bargaining, decision-making and cooperation. Research suggests that good managers are well-informed, multi-skilled and flexible in their approach to problem-solving

Problem to recognize: To find certain central principles that would allow to analyze the solution to games, in the same way that we were able to find general principles for solving optimization problems; to ask what is most likely to happen in a game once the players are completely informed about the game they are playing. In other words, to give a situation that can be modeled as a game, what guiding principles should use in deciding the most plausible outcome of the game.

Home task: Review of the article: Matt Goldman1 Justin M. Rao. “He Got Game” Theory? Students must provide own conclusion about this article and prepare presentation for class discussion.

Reading : Charlambos D. Aliprantis (Department of Economics&mMathematics. Purdue University);, Subir K.Chakrabarti (Department of Economics, Indiana University) 2008 ;

Anthony Kelly. Decision Making using GameTheory. http:// www.cambridge.org ©CambridgeUniversityPress2003

Matt Goldman1 Justin M. Rao. “He Got Game” Theory? Optimal Decision Making and the NBA_ Department of Economics, University of California, San Diego, 2Yahoo! Research Labs, Santa Clara, CA, Feb. 7, 2010 (http://www.justinmrao.com/goldman_rao.pdf)

Class discussion: Presentation students essays http://www.iaeng.org/IJCS/issues_v32/issue_4/IJCS_32_4_12.pd

39

http://www.iaeng.org/IJCS/issues_v32/issue_4/IJCS_32_4_12.pd

Theme 11: Managerial Decision Making Under Risk and Uncertainty

Scope:The risk means different things to different people, and that they perceive risk in different ways depending on what area they are working within. Many studies have attempted to deal with this problem and studied the role of risk in their respective fields; a useful definition of risk in the field of decision-making. Their definition distinguishes three types of decision-making situations. We can say that most decision-makers are in the realms of decision-making under either: (a) Certainty, where each action is known to lead invariably to a specific outcome. (b) Risk, where each action leads to one of a set of possible specific outcomes, each outcome occurring with a known probability. (c) Uncertainty, where actions may lead to a set of consequences, but where the probabilities of these outcomes are completely unknown. A risky situation is thus a situation where the outcome is unknown to the decision-maker, i.e. he/she is not sure which outcome will occur and the uncertainty may lead to erroneous choices.

Abstract:People make decisions by following well-known paths and by following well established and built in norms and the discussion concerning Basic Underlying Assumptions. Problems that have been identified in this study are the lack of information and precise objective data, that risk and probability estimations made by the managers are often based on inadequate information and intuition, that no formal analysis is carried out, that no computer based decision tools are used in the decision making processes, and therefore most decisions are based on intuition and gut feeling. Rather than accepting risk, managers avoid it and in the classical literature it is widely accepted that most people are risk-averse, and that risk and return are positively related. Some studies, however, point out that managers may not necessarily believe that risk and return are positively related and in a study, made by 73% of the managers believed that risk was manageable.

According to one of the major tenets of portfolio analysis is that risk and return are positively correlated, i.e. if a person wants a higher return, he should, on average, also take a higher risk. a useful definition of risk in the field of decision-making. Their definition distinguishes three types of decision-making situations. We can say that most decision-makers are in the realms of decision-making under either: (a) Certainty, where each action is known to lead invariably to a specific outcome. (b) Risk, where each action leads to one of a set of possible specific outcomes, each outcome occurring with a known probability. (c) Uncertainty, where actions may lead to a set of consequences, but where the probabilities of these outcomes are completely unknown. A risky situation is thus a situation where the outcome is unknown to the decision-maker, i.e. he/she is not sure which outcome will occur and the uncertainty may lead to erroneous choices.

Rather than accepting risk, managers avoid it. Some studies, however, point out that managers may not necessarily believe that risk and return are positively related and in a study, made by 73% of the managers believed that risk was manageable.

Problem to recognize: Students have to know that problem that a manager did bring up is related to the acquisition of other companies. “I do not think that we really are aware of how to estimate different types of risk that we need to deal with” or He also can say that even though the “mathematical part” of many problems was easily solved since they have figures concerning the cash flow, the potential development and so on, they are still greatly governed by the “soft aspects” of the decision-making process or also he can say said that they often invest in projects that they believe will be good investments, and that they do not only focus on figures or the investment index

40

Home task: Revising the article http://www.palisade.com/downloads/pdf/FT_Oliver_Wyman_whitepaper.pdf

Reading: handouts,

http://www.iaeng.org/IJCS/issues_v32/issue_4/IJCS_32_4_12.pdf (Ari Riabacke, Managerial Decision Making Under Risk and Uncertainty)

David R. Andersen, Denis J. Sweeney, Tomas A. Williams. An Introduction to management science. Quantitative approaches to decision makin. 10th edition, 2003. pp.567-579; 635-639

Class discussion

What about the managers in this study – are they risk-prone or risk-averse? How can identify whether a person is risk-prone or risk-averse.

http://www.palisade.com/downloads/pdf/FT_Oliver_Wyman_whitepaper.pdf

Theme 12: Emerging Risks: Strategic Decision-Making in the Face of Uncertainty

Scope:There is undertook a study aimed at learning how today’s global organisations utilize risk management to manage their businesses better. The research took a look not just at the tools these organisations use, but at how risk information is communicated within the organisation and how that information is utilised to mitigate exposures. The study also examined executives’ opinions about the overall effectiveness of their organisations’ risk management skills and capabilities, including feedback addressing the tools and methodologies that would be of most value.

Abstract:Organisations are grappling with the conflicting challenges of near-term survival and long-term viability. High profile risks of only a few months ago, such as rising commodity and energy prices and terrorism have been overtaken by concerns about the global recession, liquidity/credit availability and regulatory intervention. The research also highlights that many organizations do not have the necessary tools and capabilities to deal with emerging risks, essentially struggling with the next key issues:

• Aligning effective risk management processes within the organizations

• Selecting the appropriate tools or methods for measuring risk

• Applying sufficient resources to interpreting and utilizing available risk information

• Communicating risk information to those who need it

The result: risk management is disconnected from financial and strategic decision making processes and provides limited value.

Problem to recognition: Student have to assess the fact that many organisations continue to use a limited range of risk identification and assessment techniques, and do not effectively communicate and apply the risk information that the organisation does collect. With increasing stakeholder focus on corporate risk management, organisations must review their efforts to ensure risk information is effectively integrated into operational and strategic decisions.

41


http://www.iaeng.org/IJCS/issues_v32/issue_4/IJCS_32_4_12.pdf


Home Task : To prepare presentation according to the Article

http://www.ftconferences.com/userfiles/file/FT_Oliver_Wyman_whitepaper.pdf

Reading: handouts, http://www.ftconferences.com/userfiles/file/FT_Oliver_Wyman_whitepaper.pdf

Stanford Strategic Decision and Risk Management:

http://strategicdecisions.stanford.edu/onDemandWebinars.htm

Class discussion: What are the best ways to approach decisions when there are multiple decision makers, each with different information, motives, and goals?

Theme 13: Interaction between researchers and decision-makers as an explanatory dimension conditioning the use of research results

Scope:The focus is directed on the relationship between the production of scientific knowledge and its use in policy formulation and implementation and on the debate on the use of research results for policy decision-making and implementation processes. New approaches place greater importance on the complexity of policymaking and the knowledge production process, which are integrated into and explained in particular political and institutional settings.

Abstract:Interrelations between researchers and decision makers have been considered a prime factor in analyzing knowledge transfer processes. Analysis of the weaves (or models) of interrelations between researchers and decision-makers is relevant when one realizes that the use of scientific knowledge depends largely on certain characteristics of the actors (researchers’ behavior and decision-makers’ receptiveness). Various authors have examined and promoted different ways of improving interrelations between researchers and decision-makers. This approach fosters a political and institutional analysis of relations between actors and organizations in the interconnections between research and policy formulation and implementation processes as a conditioning (or independent) variable in the use of knowledge or its impact on decision-making processes (dependent variable).The conceptual framework for such interactions is based on at the following research stages: defining the research questions; conducting the research, and the research findings; circulating results; and research utilization, defined as the ways by which research findings influence decision-makers.

Problem to recognize: Student must analyze that public policy analysis emerged as a science of action, a contribution by experts (analysts) to government decision-making processes. The central concern is to direct research in such a way as to be relevant, useful for action. the policy process is inherently rational, with research results being used when they exist and decision-makers calling for research when it is needed; Three remaining models – "interactive", enlightenment", and "intellectual enterprise" – and stressing that both the research and decision-making processes take place in parallel with a number of other social processes and thus play several different roles.

Home Task : Use of research results in policy decision-making, formulation, and implementation:

http://www.scielosp.org/scielo.php?pid=S0102-311X2006001300002&script=sci_arttext

42

http://www.scielosp.org/scielo.php?pid=S0102-311X2006001300002&script=sci_arttext

http://strategicdecisions.stanford.edu/onDemandWebinars.htm



Class discussion : Pittman P. Allied research: experimenting with structures and processes to increase the use of research in health policy. In: Global Forum for Health Research – final documents [CD-ROM]. Mexico DF: Global Forum for Health Research; 2004.

Project Implementation at Gori Teaching University

2012-2013

The Study module “ Decision Making Technology in Practice” will be implemented in the MA level programme of the faculty of Social Sciences, Business and Law for two (I and II) semesters, as an obligatory course in 2012-2013. Syllabus for the I semester includes a decision making -introduction course and technical tools (statistics for decision making with using STATA software) and it is completd by three syllabi (syllabi #1, #2, #3). Here is presented the syllabus “ Decision Making Technology in Practice”-1 for the I semester. Syllabus for II semester Decision Making Technology in Practice”-2 (syllabus #4 ) is presented on the pp.89-102.

GORI TEACHING UNIVERSITY

Syllabus

Discipline Decision Making Technology in Practice - 1

Faculty Social Science, Business and Law

characteristics

Level Study year Studying year Semester

ECTS hours Status (obligatory, elective)

MA 2012-13 2012-13 I 10 125 obligatory

Distribution of the lectures

Contact hours Independent work hours

Lecture Practice Group work

Seminar Lab Exam62

20 12 4 4 20 3

Responsible lecturers

Name Position Contact information consultationMalkhaz

MatsaberidzeNino Javakhishvili,

Ruizan Mekvabidze

Nana Akhalaia

Prof.Dr.

Prof.

Tel. e-mail day

Hour

[email protected]

Md

16:00-18:00

Sut

14:00:16:00

Name Position Contact information consultation

Prerequists: n/a

Course description:

Decision is universal phenomenon that accomplishes life of individuals, groups as well as institutions and organizations. Each of us have to make decisions during whole period of our life. We make ordinary, prosaic decisions like what to eat for dinner or very important or strategic decisions about choice of our professional

43

https://mail.google.com/mail/?ui=1&view=att&th=12af0289d2ed60b3&attid=0.1&disp=inline&realattid=f_gdtufsm50&zw

career or decisions which have global implications. Decision making (DM) is a sense of personal, family, economic, political and social life. Maybe that is why DM is a subject complicated and hard for analysis. It is problem which concerns represents of numerous and different branch of science and humanity, i.e. economists, political scientists, sociologists etc. The course introduces students to basic issues according to DM. It provides elementary knowledge that is helpful and useful for certain estimation of decision situation and making a propter decision. In module we point on three levels of situations implicating DM process: micro, mezzo and macro. We include in these levels essential problems of contemporary civilization which are found by individuals, public institutions, business and economics. These problems create basic conditions which force us to make concrete decisions with using statistics and program software STATA.

Aims of study course:

a/ Basic theoretical and practical knowledge about essential of problems solving in decision making;

b/ The students should acquire certain knowledge and skills during the course. The knowledge of main statistical concepts of data description and testing relations among variables will be gained by the students, as well as the principles of main research designs. The skills acquired during the course are: finding of data sources, data processing and interpretation, using statistical software, making presentations;

c/ The students should acquire certain knowledge and skills using STATA software for research.

Summary: To open the Basic theoretical and practical knowledge about essential of problems solving in decision making with finding of data sources, data processing and interpretation, making presentations and using statistical software for research designs.

Results of the studying:

Rcognizing and knowledge: Finding problems and create basic conditions which force students to make concrete decisions. In Module concern experience of different social sciences: economy, political science, communication science, sociology, management science and will be provided for our students complementary knowledge about DM phenomenon, what implicates more effective preparation to application this knowledge in practical life.

Using knowledge in research: Research, research design and results interpretation are of utmost importance in data processing via statistics for social sciences.

Practical skill for using program software STATA: By the students will be gained a practical skills and knowledge of main statistical concepts of data description and testing relations among variables, as well as the principles of main research designs using statistical software STATA

Summary: DMTP-1 introduces students to basic issues according to DM. It provides elementary knowledge that is helpful and useful for certain estimation of decision situation and making a propter decision. The knowledge of main statistical concepts of data description and testing relations among variables will be gained by the students, as well as the principles of main research designs. The students should acquire certain knowledge and skills using STATA software for research.

Teaching and Learning methods:

44

Lectures, class discussion, essays, work papers; Each student will make three presentations: two individuals and a group one. The classes will be provided in the computer lab with program software STATA. The students are expected to have prepared the readings before the class, this enables discussion and question-answer format of the research issues. Each student will make presentations of tasks and solutions using spreadsheet and programme software STATA , an individual and a group one.

Content of study course:

Date Theme1 Decision Science (DS) and DM process – general introduction: DS, Decision-maker, decisional

situation, decisional problem, elements of DM process, condition of DM, structuring of decisional problem, techniques and tools of DM

2 Decision making theory and analysis: decision theory, normative and descriptive decision theory, decision matrix, process of DM; Information in DM process: the importance of information in our life, information technology, information chaos and decision making, problem of selection of information, the role of information in decision making process, decision under ignorance and decision under information chaos, paradox of choice

3 DM in the conditions of uncertainty and risk: definitions of risk, nature of contemporary risk, positive aspects of risk, source of risk, risk management, institutional rules, identification of risk and avoid of risk, decision under risk (probability and utility), measure of risk; Individual in DM process: rationality, rational choice theory, the role of emotions in DM, sociology of emotions, ethical rules in DM

4 Dynamic of small social groups and DM: group decision theory, group decision making approaches, groupthink (thinking or conforming), nature and dynamics of small groups, logic of collective behavior; Organizational and institutional conditions of DM: organizational effectives, institutional frames, logic of institutional activity, institutional theory, ISO

5 Public choice – political decisions : political DM, theory of public choice, democratic DM process, electoral decisions, electoral systems, game theory, index of power, public sphere; Complexity of contemporary social world and DM: complexity, globalization, chaos, postindustrial reality, multi-criteria decisional analysis; DM and conflict situations in the contemporary World: nature of contemporary conflicts, sources of conflict, conflict theory, keeping conflict constructive, positive aspects of conflict

6 Statistics as a science. Statistics for Social Sciences. The role of Statistics for Decision Making. Descriptive statistics. Distribution of a frequency. Descriptive Statistics. Frequency. Measures of Central Tendency. Measures of Variability. STATA: Getting started with STATA for Windows; Date and Database

7 Descriptive Statistics. Normal distribution. Probability. Estimations. STATA: Operators

8 Principles of Inferential statistics. Normal distribution. Probability. Estimations. STATA: Mathematical Functions

9 Principles of Inferential statistics. Testing statistical hypothesis; z test and one sample t-test. Experimental design. T-tests STATA: Statistical Functions

10 Inferential statistics. One Way Analysis of Variance (ANOVA). Between groups design. Two Way Analysis of Variance (ANOVA).STATA: Data functions, string functions,

45

special functions

11 Inferential statistics. One Way Analysis of Variance (ANOVA). Within groups design. STATA: System variables, Accessing coefficients and standard errors

12 Inferential statistics. Interpretation of Analysis of Variance (ANOVA). Assessing results from STATA commands, generating lags and leads, subscripting within groups

13 Inferential statistics. Correlation. STATA: Label values, using of the Labels in an expression in place of the numeric values with which they are associated

14 Inferential statistics. Regression . STATA: Printing and preserving output

15 Non-parametric tests: Chi square tests. Mann-Whitney and Wilcoxon signed-ranks tests. STATA: Do-files; Ado-files

Criteria of the Assessment:

Components of the assessment Description Max grade2 essays in the introduction of DM (Student can choose any two topics), 300-500 words.

Topics: a/ Information in DM process ; b/ / Public choice – political decisions; c/ DM and conflict situations in the contemporary World; d/ Organizational and institutional conditions of DM; e/ Complexity of contemporary social world and DM

20

Midterm Testing Topics: statistics and STATA 20Individual Presentation Topics: Social Problems statement and tools for

problem solving ; Business Problems statement and tools for problem solving; Economic Problems statement and tools for problem solving

10

Group presentation Topic: Problems statement and problem solving of Social policy for example

10

Final Exam Testing + oral exam 10+30=40Total 100

GRADING

Total possible grade points:

Percentage of Total Possible Grade Points

A 91-100%

B 81-90%C 71-80%D 61-70%Fx 51-60F (Failing ) <51%

LITERATURE

a/ Obligatory

Nutt, P.A., The economics of public choice, Edward Elgar, 2007.

46

Mueller, D., Public choice III, Cambridge University Press, 2003.

Coleman, J.S., Foundations of social theory, Harvard University Press, 1990.

Oyster, C.K., Groups. A users Guide, McGraw-Hill, 2000

Kiess, H. (2002), Statistics for Behavioral Sciences. A Pearson Education Company, Boston, MA02116, www.ablongman.com

STATA statistical software, Statistics, Data Management, User’s guide; STATA Press, College station, Texas, 2010

b/ Adittional

Buchanan, J.M., Tullock, G., The calculus of consent: logical foundations of constitutional democracy, University of Michigan Press, 1982.

Downs, A., An economic theory of democracy, 1967.

Ellis, D.G., Fisher, A., Small group decision making: communication and the group process, McGraw-Hill, 1993.Groups in context. A new perspective on group dynamics, ed. J. Gillette, M. McCollom, University Press of America, 1995.Janis, I.L., Groupthink. Psychological studies of policy decision and fiascoes, Cengage Learning, 1982.Olson, M., The logic of collective action: public goods and the theory of groups, Harvard University Press, 1971.

Hart, P., Groupthink in government: a study of small groups and policy failure, The John Hopkins University Press, 1994.

c/ Technical tools: computer lab; Program software- STATA

LIBRARY

47

#1 - For the syllabus #1სილაბუსი თვის

Michael Resnik. Choices: An Introduction to Decision Theory , 1997 Ken Binmore. Rational Decisions (The Gorman Lectures in Economics) , 2011 Maurice J. Elias . Social Decision Making/Social Problem Solving: A Curriculum For Academic, Social And Emotional Learning: Grades 4-5 (Book and CD) , 2005 Harvey Kaye . Decision Power: How to Make Successful Decisions with Confidence, 1992 Theodore Kowalski . Handbook of Data-Based Decision Making in Education, 2008 Dennis V. Lindley. Making Decisions, 2nd Edition, 2001 Thomas Sowell . Knowledge And Decisions , 1996)Joel G. Siegel . Schaum's Quick Guide to Business Formulas: 201 Decision-Making Tools for Business, Finance, and Accounting Students , 1997Deborah Stone . Policy Paradox: The Art of Political Decision Making (Third Edition) , 2011Steve W. Williams . Making Better Business Decisions , 2001James Levin . Principles of Classroom Management: A Professional Decision-Making Model (6th Edition), 2009 David R. Henderson and Charles L. Hooper. Making Great Decisions in Business and Life , 2007 John Adair . Decision Making and Problem Solving Strategies: Learn Key Problem Solving Strategies; Sharpen Your Creative Thinking Skills; Make Effective Decisions (Sunday Times Creating Success) , 2010 David A. Welch . Decisions, Decisions: The Art of Effective Decision Making , 2001

#სილაბუსი 2 თვის - For the syllabus #2

James R. Evans . Statistics, Data Analysis, and Decision Modeling and Student CD (3rd Edition) , 2006 Maureen Berner . Statistics for Public Administration: Practical Uses for Better Decision Making, 2010

#სილაბუსი 4 თვის - For the syllabus #4

Charles Yoe . Principles of Risk Analysis: Decision Making Under Uncertainty , 2011 Joseph Harrington . Games, Strategies and Decision Making , 2008 Thomas L. Saaty Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and ... in Operations Research & , Management Science) , 2011 Itzhak Gilboa . Theory of Decision under Uncertainty (Econometric Society Monographs) , 2009 William G. Tierney . The Impact of Culture on Organizational Decision-Making: Theory and Practice in Higher Education , 2008Itzhak Gilboa . Making Better Decisions: Decision Theory in Practice . 2010Harvard Business School Press . Harvard Business Review on Decision Making , 2001

48

http://www.amazon.com/Harvard-Business-Review-Decision-Making/dp/1578515572/ref=pd_ybh_11?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Making-Better-Decisions-Decision-Practice/dp/1444336525/ref=pd_ybh_25?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Impact-Culture-Organizational-Decision-Making-Education/dp/1579222870/ref=pd_ybh_18?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Impact-Culture-Organizational-Decision-Making-Education/dp/1579222870/ref=pd_ybh_18?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Decision-Uncertainty-Econometric-Society-Monographs/dp/0521741238/ref=pd_ybh_17?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Decision-Making-Analytic-Network-Process/dp/1441941541/ref=pd_ybh_7?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE



http://www.amazon.com/Strategies-Decision-Making-Joseph-Harrington/dp/0716766302/ref=pd_ybh_2?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Principles-Risk-Analysis-Decision-Uncertainty/dp/1439857490/ref=pd_ybh_3?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Statistics-Public-Administration-Practical-Decision/dp/0873261925/ref=pd_ybh_19?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Statistics-Analysis-Decision-Modeling-Student/dp/0131888102/ref=pd_ybh_6?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Decisions-Art-Effective-Decision-Making/dp/1573929344/ref=pd_ybh_8?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Decision-Making-Problem-Solving-Strategies/dp/0749455519/ref=pd_ybh_7?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Decision-Making-Problem-Solving-Strategies/dp/0749455519/ref=pd_ybh_7?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Making-Great-Decisions-Business-Life/dp/0976854112/ref=pd_ybh_6?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Principles-Classroom-Management-Professional-Decision-Making/dp/0205625029/ref=pd_ybh_4?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Making-Better-Business-Decisions-Williams/dp/0761924221/ref=pd_ybh_1?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1ZJRY3CCXWM6M20EFHE6

http://www.amazon.com/Policy-Paradox-Political-Decision-Making/dp/0393912728/ref=pd_ybh_21?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Schaums-Quick-Guide-Business-Formulas/dp/0070580316/ref=pd_ybh_24?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Schaums-Quick-Guide-Business-Formulas/dp/0070580316/ref=pd_ybh_24?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Knowledge-Decisions-Thomas-Sowell/dp/0465037380/ref=pd_ybh_15?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Making-Decisions-2nd-Dennis-Lindley/dp/0471908088/ref=pd_ybh_14?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Handbook-Data-Based-Decision-Making-Education/dp/0415965047/ref=pd_ybh_12?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Decision-Power-Successful-Decisions-Confidence/dp/0132035480/ref=pd_ybh_11?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Social-Decision-Making-Problem-Solving/dp/0878225137/ref=pd_ybh_9?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Social-Decision-Making-Problem-Solving/dp/0878225137/ref=pd_ybh_9?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Rational-Decisions-Gorman-Lectures-Economics/dp/0691149895/ref=pd_ybh_8?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

http://www.amazon.com/Choices-Introduction-Decision-Michael-Resnik/dp/0816614407/ref=pd_ybh_5?pf_rd_p=280800601&pf_rd_s=center-2&pf_rd_t=1501&pf_rd_i=ybh&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=0WKD0EQ8044M0R377AFE

Study Modul Decision Making Technology in Practice for Social Policy Analysis _ENGLISH

Documents

Transcript of Study Modul Decision Making Technology in Practice for Social Policy Analysis _ENGLISH