What Does It Mean for a Decision Maker to Maximize Value?

Open up Journal of Applied Sciences
Vol.05 No.07(2015), Article ID:57976,12 pages
10.4236/ojapps.2015.57036

Determination Theory and Analysis: An Optima Value Creation Precursor for Organizations

Cephas A. Gbande¹, Paul T. Akuhwaⁱⁱ

¹Faculty of Administration, Department of Business Administration, Nassarawa Country Academy, Keffi, Nigeria

²OTIJOSH Resource Limited, Abuja, Nigeria

E-mail: Cgbande3@gmail.com, akuhwa.paul@gmail.com

Copyright © 2015 by authors and Scientific Research Publishing Inc.

This work is licensed nether the Creative Commons Attribution International License (CC By).

http://creativecommons.org/licenses/by/4.0/

Received fourteen June 2015; accepted 12 July 2015; published xv July 2015

ABSTRACT

Organizations brand many informed decisions such as increasing product capacity, improving human majuscule, entering a new market etc. This newspaper shows that executives have either of the two major types of decisions: programmed (structured) and nonprogrammed (unstructured) decisions. While the programmed decisions are for perfectly stable situations, the nonprogrammed decisions are for the existent earth situation surrounded past uncertainties, risks and ambiguities. For an optima value cosmos, this paper is succinct that a robust decision theory and analysis serve as a precursor. The environment of controlling keeps changing and it takes determination-making for organizations to alter proportionately to these environmental changes if they must survive. The conclusion-maker uses probability values to convert uncertainties and risks into perfect noesis poles then as to make informed decisions. Models are veritable conclusion making tools and are deterministic and probabilistic (or stochastic) for programmed and nonprogrammed decisions respectively. Real-world value optimization in this newspaper centres on decisions under pure doubtfulness and risky situations generating model fits for an optima value creation. Finally, the optima value creation models under the doubt and risk are suggested and organizations advised to use professional determination theorists and analysts as the need arise.

Keywords:

Decision-Making, Determination Environment, Programmed and Nonprogrammed Decisions, Optimization

i. Introduction

Nigh all human endeavours involve decisions, and consequently, success in business and industry is the sole preserve of the quality of controlling. This quality of decision-making and the technical progress amongst societies are inextricable and diametrically symbiotic. Theorizing nearly controlling is well-nigh the same as theorizing virtually human endeavours. Concomitantly, a Hegelian arroyo nearly controlling discourse is aforementioned equally Hegelian approach with human being endeavour discourses. However, decision theory and analysis are not quite as across-the-board as information technology focuses only on some aspects of man endeavours. In particular, it focuses on how the decision-maker (or executive) uses his/her freedom among the myriad of decision choices [1] . In situations encountered by decision theorists and analysts, there are options to choose, and the decision-maker chooses in a non-random way. The choices of the decision-maker, in these situations, are goal-directed activities. Hence, conclusion theory and analysis are concerned with the goal-directed behaviour in the presence of options [ane] . Determination making is primal to the corporate existence, operational excellence, effective operation and productivity of businesses and organizations. To the intent that organizations today are a part of decisions and afterward, organizations tomorrow are subjects of decisions to make today.

Decision-making is not an endless process but a discontinuous one. In the history of almost whatever endeavours, there are periods of controlling being crafted and other periods in which most of the decision-making implementation takes place. Conclusion theory and analysis try to throw some light, in various means, on the former type of menses (i.east. the flow of crafting decision-making) [1] . Organization every bit a system emphasizes the demand for good data and channels of communication in order to assist the effective conclusion making. The systems approach to organization, involves the isolation of these functions most directly concerned with the accomplishment of objectives and identification of chief determination areas. Recognition of the need for controlling and the attainment of goals depict attending to a sub-sectionalisation of the systems approach and the decision theory. The determination theory focuses on managerial controlling and how organizations process and use information in making decisions [two] [3] . Mod decision theory and analysis have developed since the centre of the 20th century through contributions from several academic disciplines. Although, being conspicuously an bookish subject field of its ain right now, decision theory is typically pursued past scholar-practitioners and seminal researchers who identify themselves as economists, statisticians, engineers, psychologists, operations enquiry scientists, political and social scientists, management scientists and other philosophers.

As organizations responds to diverse environmental changes, the decision-making process involves the description of objectives, the specification of issues and the search for implementation of solutions. Thus, the organization is seen every bit an information-processing network with numerous determination points. An agreement of how decisions are made helps in understanding behaviour in organizations with mechanisms by which conflict is resolved and choices are made [2] . The decision making in each functional areas of management under probabilistic situation is a complex process. For such decisions, the written report of the experiences of the executives using experience survey inquiry is required. Consequently, conclusive decision researches tests hypotheses to draw definite conclusions for implementation and after the validation of the hypotheses, a decision-making framework can be formulated [4] .

The understanding of the functioning of decision theory and analysis in shaping behavior in organizations and society's decision procedure is very crucial. Also, the critical synthesis of theoretical and conceptual foundations of decision theory and analysis considering their differences and common grounds for decision approaches in a broad range of corporate and public spheres is invaluable. The act of conclusion making, whether under certainty, doubt, risk and ambiguity is founded on both subjective and objective realms of human beliefs. Thus, the key theoretical issues of decisions under certainty, dubiety and risky conditions dealing with deterministic and probabilistic (stochastic) outcomes together are very necessary a precursor for optimal value cosmos in organizations and business. The theoretical views from the foundational theories as they relate to the factors that influence organizational abilities for decision-making based on modeling, culling choices, and past occurrences in the decision environment are brought to bear in this study. The in-depth synthesis of these theoretical problems directed to management theory and organizations with respect to why, what, how and when problems of planned (structured) and unplanned (unstructured) decisions for optimal value cosmos are likewise explained in this study.

Finally, decision-making for optimal value creation in organizations requires answers to questions, such as; what are the key problems and decision variables interacting that should be considered? What are the situational and ecology variables that form the domain of decision-making for optimal value creation in organizations? How can organizations formulate direct functions (or models) for optimal value creation of their resource? These higher up posted questions represent the cognition gaps calling for this research study. Since the real world issues are masterminds of uncertainty and risk challenging executives and managers controlling, decision analysis for optima value creation are detailed in this written report for probable conclusion solutions. Lastly, the report is arranged into five sections with the beginning foregoing section highlighting the study's overview. The 2nd section discusses the bug of the study while the 3rd section deals with theoretical framework and reviews. The quaternary section discusses methodology and analysis, with the 5th department closing the study with discussions, conclusion, and recommendations.

2. Trouble of the Study

Organizations are living organisms existing as bio-corporate ecological units in the global concern ecosystem. The business ecosystems economic system is controlled by many factors such every bit population size, industrial activities, agriculture, government policies, culture, educational organization, infrastructure and etc. The procedure of demand satisfaction in the business ecosystem constrains people and government to appoint in various socioeconomic activities for production of goods and services. Policies and guidelines of the cardinal and land governments facilitate the integration, coordination and control of all activities with the main objective of optimizing (maximizing or minimizing) socioeconomic growth and evolution. Suffice information technology to say that, the entity of the economy, at every point in time, is influenced by certain forces to accept competitive part in order to maximize its productivity for its sustenance, continuity, growth and survival [4] . Thus, the productivity of unlike organizations in the business ecosystems economy tin be optimized (i.e. improved and enhanced) through several research studies deploying decision theory and assay every bit a precursor (or design).The productivity of whatever business system being a conclusion-objective function, is the ratio between its output and input. Where the output corresponds to annual full income of the business organization organisation and the input corresponds to the annual total toll of the different resources consumed and services utilized past the business system to realize the annual product [4] . Optimization is a state of affair that maximizes or minimizes the decision-objective function to enhance effectiveness or make use of its all-time. In other words,

where; n = total menstruation in consideration, in this instance, n = ane year (12 months); t = (0, one, ii, ××× due north), = increase or difference, and Ʃ = summation.

This is a general form of decision-objective function for optimizing productivity. Decision analysis and theory is the heart of information technology as it assesses all combinations of variables and criteria for optimization. In any business organization ecosystem, this productivity is afflicted by many ecological and environmental factors that are of quantitative and qualitative characteristics. Thus, the development of models using the data of these factors augmented with a detailed survey analysis is the conclusion theory and analysis which is largely informed by the logic of rationality and the mathematical/statistical theory of probability.

Decision-making in business and organization is not an easy task and hardly follow any consequent procedure. where decisions are to be made abound. Irresolute times and environment evolve situations which business organisation and industrial problems are becoming more than and more complex [v] . The varying degrees of certainty and doubtfulness define the type of decision managers are to make and differentiate between programmed and nonprogrammed decisions. In a perfect world, organizations would take all the data necessary for making decisions. In reality, yet, some things are unknowable, thus; some decisions volition fail to solve the trouble or accomplish the desired outcome [3] [6] .

Organizations today face major business crises and human-accelerated environmental changes worldwide. Arguably, in that location is a critical demand for evidence based information to guide business concern policy. Nearly times, business decisions are taken with certainty and/or uncertainty and are pivotal to the future survival of the organization. In most cases, the managers who take these decisions volition not know whether they accept the right choices. They have to have these decisions against the backdrop of doubt or risk [7] . Too, information technology is the background of afore mentioned reasons that this report tries to answer the post-obit questions representing the problem of the written report:

1) How practice people make decisions under certainty and uncertainty and what do they consider desirable or the subjective element in their decision process?

2) What objective and subjective probabilities do decision making assign to the occurrences of different desirable outcomes in business organization and organizations?

iii) What are the all-time methods of rationalization and designing decision models to inform constructive determination process?

3. Objectives of the Report

This study's intention is to provide answers to the fashion, mode and what situational variables influences in conclusion-making. Many a managers would wish organizations to operate a perfectly stable environment which they tin easily make informed decisions. This is a perfect and utopian business ecosystem with a fixed organizational environment. The real world all the same presents a different business ecosystem which change is continuous and the only abiding with challenges that dreads the corporate existence of business and organizations requiring managers and executives to make difficult and right-fast decisions. This study explored decision-making situations and the corresponding strategies to clone a fit and congruence in conclusion-making and decision-objective function for organizational superior performance (optima value creation).

4. Theoretical Framework and Reviews

Seminal research pointed that, "a theory is an organized trunk of concepts and principles intended to explain a item miracle" [viii] . Also, it was stressed that theorizing is the process of systematically formulating and organizing ideas to understand a detail phenomenon, and concludes that, a theory is the prepare of interconnected ideas that emerges from this process [9] . Thus,the focus of this paper makes literature inevitable to address the problem, questions, and objectives [10] [eleven] . More than so, a growing and increasingly important trend in the social and behavioral sciences is to think near and attempt to understand specific enquiry problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories in a item subject, but to recollect nigh how an issue might be informed by theories developed in other disciplines. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts tin can be both invaluably enlightening and an effective style to be fully engaged in this particular written report [12] . A theoretical framework of this nature consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for a particular written report. This theoretical framework demonstrates an agreement of theories and concepts that are relevant to the topic of this written report and that relate to the broader areas of noesis beingness considered. These theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing noesis inside the limits of critical bounding assumptions. Therefore, this theoretical framework is the construction supporting theory of this research study, introduces and describes the theory that explains why the enquiry problem under study exists [xiii] .

The theoretical framework for this written report is shown below in Figure one with all major related variables. The main objective is to optimize value creation in organizations as shown in the framework, which requires inputs from decision theory and analysis as a precursor. The decision theory and analysis takes identify in a determination surroundings to clone a determination-making approach for the executives aiding a model building for determination model analytic. Alternatives are developed for selection of one all-time fit to maximize value as the optima value creation criterion in the arrangement with feedback on model fit and objective realization. Thus, literature review for this study is beneath and only for concepts of variables shown in the theoretical framework in Figure one.

4.1. Conclusion Theory and Analysis

Decision theory and analysis is the combination of descriptive and prescriptive business modelling approach to classify the degree of knowledge. The degree of knowledge is ordinarily classified in order from ignorance-uncer- tainty-run a risk-certainty. The complete knowledge (or certainty) is on the far correct and complete ignorance is on the far left. Betwixt the ii are take a chance and doubtfulness. Decision theory and analysis provides an analytical and systematic approach to depict the expected outcome of a situation, when alternative managerial actions and outcomes are compared [14] . The study of the best possible outcomes for decisions fabricated under varying atmospheric condition is decision theory or decision analysis. It involves procedures for choosing optimal decisions in the face of uncertainty,

Figure one. Theoretical framework for organizations value creation optimization.

risk and ambivalence. These are situations in which a decision is made, an event occurs, some other decision is made, some other event occurs, and so on.

In a perfect world, organizations would have all the information necessary for making decisions. In reality, notwithstanding, some things are unknowable, thus; some decisions will fail to solve the trouble or attain the desired outcome [3] [6] . Determination theory and analysis provides the conclusion maker a meaningful conceptual framework for improved determination making. It is a body of methods employed by the decision maker to select one course of activity amongst the alternative plans of activeness available [5] .

Every decision situation tin can exist organized on a scale co-ordinate the availability of information and possibility of failure. These four positions on the scale are certainty, risk, uncertainty, and ambivalence. Whereas programmed or structured decisions can be made in situations involving certainty, many decisions that managers' deal with everyday involve at least some degree of dubiousness and crave nonprogrammed or unstructured decisions making. From the foregoing, it tin can be generalized that; conclusion theory is a continuum and the central function of decision science with programmed decisions and nonprogrammed decisions at the continuum extreme ends for constructive determination problem solutions.

iv.2. Decision Environment

Information technology is desirable to know the details about resources (such as managers, employees, equipment, finance and etc.) that are required to carry out policies of the organization and at the same time keeping in mind the social and ecological environments in which the organisation functions. Cognition of such factors will help in modifying the initial gear up of decision-makers' objectives. The decision environment is the situational framework within which the decision is taken [3] [6] [fourteen] . These situations tin be certain, uncertain, risking and ambiguous.

Certainty―This is the situation where the outcome of a specified organization of decision can be predetermined with exactness or certainty. It is a determinate situation which each action volition atomic number 82 to only one or aforementioned issue. In this type of decision situation, the determination maker knows without doubt the outcome of every alternative courses of activeness because all the information the determination maker needs is fully available [5] -[7] [xiv] -[sixteen] .

Risk―This ways that a decision has clear-cut goals and that adept information is available, but the future outcomes associated with each alternative are subject field to risk. However, enough information is available to permit the probability of a successful outcome for each alternative to be estimated [five] -[7] [15] -[17] . Statistical analysis may exist used to calculate the probabilities of success or failure. Thus, the conclusion maker knows the likelihood that each various states of nature will occur―each action will lead to one issue, each with a known probability. In other words, it is a stochastic situation where there are many land of affairs and the decision maker knows the probability of occurrence of each state of matter.

Uncertainty―Managers know which goals they wish to achieve, just the information virtually alternatives and hereafter events in incomplete. Managers may take to make assumptions from which to forge the decision even though information technology volition be wrong if the assumptions are incorrect. Some of the questions concerning decision maker under dubiety include: Will the production catch up with the market place? Will the new co-operative of business be successful? [5] This perspective made Robert Robin, former US Treasury Secretary to ascertain dubiousness as a situation that even a skilful decision might produce a bad result [6] .

Ambiguity and Conflict―Ambiguity is past far the about difficult conclusion situation. Ambiguity ways that the goals to be achieved or the problem to exist solved is unclear, alternatives are hard to define, and information near outcomes is unavailable [vi] . Information technology is a situation in which something (determination alternative class) tin can exist understood in more than i way and information technology is not clear which pregnant is intended. In some situations presented by business organisation and industry, managers involved in a decision create ambiguity because they come across things differently and disagree about what they want. A highly cryptic situation can create what is sometimes called a wicked decision problem. Sometimes managers will come with a decision "solution" simply to realize that they hadn't clearly defined the existent problem to begin with [three] [6] .

4.iii. The Decision-making Arroyo

The approach managers utilize to make decisions usually falls into one of the three types: the classical model, the administrative model or the political model. These models and their diverse characteristics descriptions and differences are as shown in Table ane below. The option of model depends on manager's preference, whether the decision is programmed or nonprogrammed, and the degree of uncertainty associated with the determination [2] [six] .

four.four. Decision Modeling

The terminate-point of decision theory and analysis is model-building every bit information technology is both quantitative, quantitative and mixed-method (i.eastward. quantitative-qualitative). With respect to organizations objectives, alternative strategies and determination-making environment, there are bones theoretical constructs that are premises in decision theory for determination model-edifice to aid constructive direction decision-making. Through modeling, the assumed real globe is bathetic from the real state of affairs by concentrating on the ascendant variables that control the beliefs of the existent system. The model expresses in an amenable manner the mathematical functions that correspond the behavior of the assumed existent world. In other words, effective model must be representative of reality that is existence investigated and accept major impact on the determination [4] [fourteen] -[17] . The central to model-edifice lies in abstracting just the relevant variables that bear on the criteria of the measures-of-performance of the given system and in expressing the relationship in a suitable form. Model enrichment is accomplished through the process of changing constants into variables, adding variables, relaxing linear and other assumptions, and including randomness [14] . The top iii qualities of any model are:

1) Validity―How the model will correspond the critical aspects of the system or problem under study;

2) Usability―Whether the model can be used for the specific purposes;

iii) Value―Achieve value expectation of the user.

The conditional characteristics and dimensions of conclusion theory models are classified into eight different but interacting variables. These are: part, structure, dimensionality, and degree of certainty, time reference, and caste of generality, caste of closure, and degree of quantification. Withal, this study discusses models

Table ane. Characteristics of Decision-making Models (Daft, 2010; p. 221).

based on degree of certainty and are basically two types: the deterministic models and probabilistic or stochastic models.

Deterministic Models―If all the parameters, constants and functional relationship are assumed to be known with certainty when the decision is made, the model is said to be deterministic. Thus, in such a instance where the upshot associated with a particular grade of action is known, i.e. for a specific fix of input values, at that place is a uniquely determined output which represents the solution of the model under conditions of certainty. The results of this model assume a single value. Examples of this model include linear programming models, input-output models, and activity models, Transportation and assignment models, etc. [5] [14] .

Probabilistic (Stochastic) Models―These are models in which at least one parameter or decision variable is a random variable. Since at least ane decision variable is random, the independent variable(s), volition besides exist random. This means consequences or payoff due to certain changes in the independent variable cannot be predicted with certainty. However, it is possible to predict a pattern of values of both the variables by their probability distribution. Stochastic programming models and Bayesian models are examples of probabilistic models [5] .

4.5. The Determination Objective.

The objective of decision making takes priority in the decision making process (or decision theory). Thus, it is very important to ascertain clearly and explicitly the objectives involved in the decision process. Managers are expected to be rational (i.e. able to choose worthwhile in the calorie-free of cost-do good analysis), and are required to possess adequate value systems necessary for effective decision making [v] . That is, either the conclusion-maker has already obtained some solution of the problem and wants to retain it, or he/she wants to improve information technology to a higher degree. If in that location are alien objectives, he/she may be advised to rank the objectives in order of preference; overlapping among several objectives may be eliminated [14] .

4.6. Model Analysis

Most decision bug in real life are probabilistic (or stochastic) in nature due to changing environment that makes information incomplete, scarce and uncontrollable to the decision-maker. Thus, their models contains at to the lowest degree one parameter or determination variable that is a random variable. Since at to the lowest degree one decision variable is random, the independent variable(s), will also be random. That means consequences or payoff due to certain changes in the independent variable cannot be predicted with certainty. This requires a critical search for best-fit decisions centers on optima plan of action.

Alternative Plans of Actions (Strategies)―The problem arises simply when in that location are several courses of activeness bachelor for a solution. An exhaustive list of courses of action can be prepared in the procedure of going through trouble conception. Courses of activeness that are not feasible with respect to the objectives and resources may exist ruled out [fourteen] . The alternative plans of activeness are strategy options open up to the decision maker's choice if there is but 1 grade of action. There are express and unlimited strategies for decision making, so an explanation list of all feasible alternative plans of action or strategies should be prepared in accelerate, since the object of determination making is to pick up the best out of these limited or unlimited strategies [5] . The starting point is in formulation of decision pay-off in which the results or pay-offs of all the different possibilities or strategies that could be chosen are arranged according to the atmospheric condition or states of nature affecting the pay-off that might prevail [7] .

Decision Payoff―A numerical value (result) resulting from each possible combination of alternatives and states of nature is called payoff. The payoff values are always conditional because of unknown states of nature. Payoff is measured inside a specified period (e.thousand. after ane year). This flow is sometimes chosen the decision horizon and the tabular organization of these conditional consequence (payoff) values is known as payoff matrix [xiv] . Decision payoff is an indication of the effectiveness of the strategies. It is measured in terms of coin but in many situations, information technology is not possible to give a realistic value of money. In such a example, the executive decides in accordance with the skills and experience he/she has as to what the issue of a decision worth to the arrangement. Payoffs may be fixed (i.e. determinate in nature) or can be random (i.e. probabilistic in nature). A probabilistic payoff is determined by chance the strategy is chosen [5] [7] .

Selection of Desired Alternative―Once feasible alternatives are developed, one must be selected. The decision choice is the choice of the most promising of several alternative courses of action. The best culling is one in which the solution best fits the overall goals and values of the organization and achieves the desired results using the fewest resource. Executives try to select the choice with to the lowest degree risk and uncertainty. Choosing among alternatives also depends on director's personal factors and willingness to take gamble and dubiety. Take a chance propensity is the willingness to undertake hazard with the opportunity of gaining increased payoff. Payoff is always shown in a table form known as the payoff matrix, equally shown in Table 2 below. The table shows u.s.a. of nature with their probabilities of occurrence simply uncontrollable by the determination maker, and their corresponding courses of deportment. The decision-maker is expected to choose the all-time alternative from the list that result into the optima value (utility). The level of take chances a manager is willing to accept will influence the analysis of price and benefits to be derived from the decision [6] .

Implementation of Decision Solution (Alternative)―This involves the utilize of managerial, authoritative, and persuasive abilities to ensure that the chosen alternative is carried out. The determination-maker not only has to identify good decision alternative to select but also to select the alternatives that are capable of beingness implemented. It is important to ensure that any decision implemented is continuously reviewed and updated in the lite of changing surroundings. The behavioral aspects of change are exceedingly important to the successful implementation of decision. Studies show that when employees run into that managers follow up on their decisions by tracking implementation success, they are more committed to positive deportment [half-dozen] . In whatever case the decision-maker, who is in the all-time position to implement the decision, must be enlightened of the objective, assumption, omissions and limitation of the conclusion model [fourteen] .

Evaluation and Feedback―In the evaluation stage of the decision process, determination makers gather information that tells them how well the decision was implemented and whether it was constructive in achieving its goals. Feedback is important considering decision making is not a continuous and never-ending process [vi] Feedback provides decision makers with information that tin precipitate a new decision wheel. It is also part of monitoring that assesses whether a new conclusion needs to be made. The dynamic environs and changes have significant implications regarding the continuing validity of models and their solutions. Thus, a control procedure has to be established for detecting significant changes in decision variables of the problem then that the suitable adjustments can exist fabricated in the solution [xiv] .

5. Methodology and Analysis of the Study

The methodology of this study considers existing works of various seminal researchers and theoretical foundations of decisions-making using probabilities as a measure of gamble and uncertainty. The models developed with their respective analyses are carefully examined toward value creation optimization. Globally, decisions are most ofttimes taken under conditions of doubtfulness and take a chance to make it at optima value creation in organizations [five] [half-dozen] [fourteen] . This paper synthesized these models and evaluated how optimization of value creation could be understood and deployed in organizations. In deterministic models, a good conclusion is judged by the outcome solitary, while in probabilistic models, the decision maker is concerned not only with the issue value but also with the corporeality of risk each decision carries. As an example of deterministic versus probabilistic models, consider the past and the future. Nothing can be washed to change the past, merely everything done soon influences and changes the future, although the future has an element of incertitude. Managers are captivated much more by shaping the future than the history of the past [18] .

Probability Theory in Decision Assay

Doubtfulness is the fact of life and concern, whilst probability is the guide for a "good" life and successful busi-

Tabular array 2. General form of Pay-off Matrix Tabular array.

ness. The concept of probability occupies an important place in the determination making procedure, whether the trouble is one faced in business, in government, in the social sciences, or but in i's ain everyday personal life. In very few decisions making situations is perfect information that all the needed facts are available, as nearly decisions are made in the face of uncertainty [vi] [fourteen] [eighteen] . Probability enters into the process by playing the office of a substitute for certainty (i.eastward. complete cognition). Probabilistic modelling is largely based on application of statistics for probability assessment of uncontrollable events (or factors), also as take chances assessment of decisions.

Probability is derived from the verb to probe meaning to "observe out" what is non also easily accessible or understandable. The word "proof" has the same origin that provides necessary details to sympathize what is claimed to be true. Probabilistic models are viewed equally similar to that of a game; actions are based on expected outcomes. The centre of interest moves from the deterministic to probabilistic models using subjective statistical techniques for interpretation, testing and predictions. In probabilistic modelling, take a chance means incertitude for which the probability distribution is known. Therefore risk assessment means a study to determine the outcomes of decisions along with their probabilities [xviii] . As decision makers often confront a severe lack of information, probability assessment quantifies the information gap between what is known, and what needs to exist known for an optimal decision. The probabilistic models are used for protection against adverse uncertainty, and exploitation of propitious uncertainty. Difficulty in probability cess arises from data that is scarce, vague, inconsistent or incomplete. Business concern determination making as earlier posited is almost always accompanied by atmospheric condition of dubiousness.

Conspicuously, the more data the determination maker has, the ameliorate the decision volition exist. Treating decisions as if they were gambles is the footing of determination theory. This means that we have to trade off the value of a certain outcome against its probability. To operate co-ordinate to the canons of conclusion theory, nosotros must compute the value of a certain outcome and its probabilities; hence, determining the consequences of our choices. The origin of decision theory is derived from economics by using the utility function of payoffs. It suggests that decisions exist made by computing the utility and probability, the ranges of options, and too prescribes strategies for good decisions. Probability assessment is nada more than the quantification of uncertainty. In other words, quantification of uncertainty allows for the communication of uncertainty betwixt persons. At that place can be uncertainties regarding events, states of the globe, beliefs and and then on. Probability is the tool for both communicating uncertainty and managing it.

Lastly, every bit succinctly positioned by [eighteen] , there are unlike types of probabilistic or stochastic decision models that help to analyse the unlike scenarios for optima value cosmos in the existent business earth. In their view,the three most widely used types decision-making depending on the corporeality and degree of noesis are:

one) Decision making under pure uncertainty;

2) Determination making nether risk;

three) Decision making by buying information.

half dozen. Decision-making nether Dubiousness

In decision making under pure doubt, the decision maker has absolutely no knowledge, not fifty-fifty nigh the likelihood of occurrence for any state of nature. In such situations, the decision maker'southward behaviour is purely based on his/her attitude toward the unknown [18] . Conclusion making under uncertainty places the decision- maker in the position of being unable to specify the probabilities of occurrence of the diverse states of nature (hereafter), though the possibilities of states of nature are known. Thus, decisions under dubiousness are carefully taken fifty-fifty with less information. In absence of knowledge about the probability of whatsoever country of nature (future) occurring, the decision-maker must go far at a decision simply on the actual payoff values, together with policy (attitude). There are several theoretical criteria of decision making in this state of affairs of doubtfulness. Some of these theoretical criteria include [xiv] -[16] :

1) Optimism (Maximax or Minimim) benchmark;

2) Pessimism (Maximin or Minimax) benchmark;

iii) Equal probabilities (Laplace) benchmark;

4) Coefficient of optimism (Hurwiez) criterion;

5) Regret (Salvage) criterion.

Optimism (Maximum and Minimum) Criterion―The decision-maker ensures that the opportunity to miss the largest possible profit (maximax) or the everyman possible price (minimin). This is called the "best of best" benchmark [16] . Thus, he selects the alternative (decision selection or form of action) that represents the maximum of the maxima (or minimum of the minima) payoffs (consequences or outcomes). This is called optimistic decision theory since the benchmark requires the conclusion-maker to select an alternative that has the largest (or lowest) possible payoff.

Pessimism (Maximin or Minimax) Criterion―The decision-maker requires in this theoretical criterion to earn no less (or pay no more) than some specified amount. This is achieved by option of the alternative that represents the maximum of the minima (or minimum of the maxima in example of loss) payoffs in case of profits. Information technology is chosen the "best of worst" criterion [sixteen] . This theoretical criteria is known as Wald's criterion and popularly called pessimistic determination theory every bit the decision-maker is conservative about the hereafter and always anticipates the worst possible outcome (minimum for turn a profit and maximum for price or loss).

Equal Probabilities (Laplace) Criterion―This is a situation that the probabilities of the states of nature are non known, and then, an supposition is generated that all united states of america of nature volition have equal probability of occurrence (that is assigning equal probability to each state of nature). Given that states of nature are mutually sectional and collectively sectional, the probability of each of the state must exist i/(number of states of nature). This theoretical benchmark is known as the theory of bereft reason, because except in few cases, some information almost the likelihood of occurrence of states of nature is available.

Coefficient of Optimism (Hurwiecz) Criterion―This benchmark is based on the theoretical premise that a rational decision-maker should be neither optimistic nor pessimistic and, therefore, must display a mixture of both. Hurwiecz in this theory, introduced a coefficient of optimism (denoted past α) to measure the conclusion-makers degree of optimism. This coefficient takes values betwixt 0 and 1, where; 0 represents complete pessimistic attitude about the futurity, and 1 a complete optimistic attitude about the future. Thus, α is a coefficient of optimism, and then (1?α) will be the coefficient of pessimism. Hurwiecz suggested that the decision maker must select an culling that maximizes, thus;

.

In other words, Hurwiecz theory is based on the weighted average of the best and worst payoffs of each action and is calculated thus: Weighted payoff = α × worst payoff + (ane ? α) × all-time payoff.

The Regret (Savage) Criterion―This is the opportunity loss decision criterion or minimax regret conclusion criterion because the conclusion-maker regrets the fact that he/she has adopted a wrong course of activity (or alternative) resulting in an opportunity loss of payoff. Thus, he/she always intend to minimize this regret.

7. Conclusion-Making nether Risk

Decision-making nether risk is a probabilistic decision situation in which more than than one state of nature exists and the decision-maker has sufficient data to assign probability values to the probable occurrence of each of the states. Knowing the probability distribution of us of nature, the best determination is to select that course of activeness which has the largest expected payoff value. The expected (average) payoff of an alternative is the sum of all possible payoffs of that alternative, weighted by the probabilities of the occurrence of those payoffs. Some of the well-nigh widely used criteria for evaluating various courses of action (alternatives) under take chances are [14] -[16] :

1) Expected Monetary Value (EMV);

two) Expected Opportunity Loss (EOL);

three) Expected Value of Perfect Data (EVPI);

4) Posterior Probabilities and Bayesian Analysis.

Expected Monetary Value (EMV)―The expected monetary value (EMV) for a given grade of action is the weighted sum of all possible payoffs for each culling. The expected (or mean) value is the long-run average value that would outcome if the decision were repeated a large number of time. Mathematically EMV is stated as follows:

EMV (Grade of action, S_j) =

where yard = number of possible states of nature;

P_i = probability of occurrence of states of nature, Northward_i;

P_ij = payoff associated with country of nature N_i and course of activity, Southward_j.

Expected Opportunity Loss (EOL)-An alternative approach to maximizing expected monetary value (EMV) is to maximize the expected opportunity loss (EOL), also called expected value of regret. The EOL is defined as the difference between the highest turn a profit (OR payoff) of a state of nature and the actual profit obtained by the particular form of activity taken. In order words, EOL is the amount of payoff that is lost by not selecting that course of action which has the greatest payoff for the state of nature that actually occurs. The course of activity due to which EOL is minimum is recommended. Since EOL is an alternative decision criterion for decision making under risk, therefore, the results volition always be the same as these obtained past EMV criterion discussed earlier. Thus, only i of the two methods should be applied to reach a decision. Mathematically, it is stated equally follows.

EOL (state of nature, N_i) =

where I_ij = opportunity loss due to country of nature, N_i and course of action, S_j;

P_i = probability of occurrence of state of nature, N_i.

Expected Value of Perfect Information (EVPI)―In the decision-making nether risk, each state of nature is associated with the probability of its occurrence. However, if the decision maker tin acquire perfect (complete and accurate) information nearly the occurrence of various states of nature, and so he would exist able to select a course of action that yields the desired payoff for whatever state of nature that actually occurs. It has been seen that the EMV or EOl criterion helps the conclusion maker select a particular form of activeness that optimizes the expected payoff, without the need of any boosted information. Expected value of perfect information (EVPI) represents the maximum corporeality of coin the decision maker has to pay in order to get this additional data about the occurrence of various states of nature, before a decision has to be fabricated. Mathematically it is stated as:

EVPI = (Expected profit with perfect data)-(Expected profit without perfect information).

EVPI =

where, P_ij = best payoff when activity, S_j is taken in the presence of state of nature, N_i;

P_i = probability of state of nature, N_i;

EMV^* = maximum expected budgetary value.

Posterior Probabilities and Bayesian Analysis―The search and evaluation of decision alternatives oftentimes reveal new data. If such data is regarding the furnishings of alternatives, the consequences are restated. When the uncontrollable factors are involved, either the states of nature themselves are reconsidered or their likelihoods are revised. The value of new information is evaluated in terms of its bear on on the expected payoff. The expected value and the cost of the new information are compared to decide whether it is worth acquiring. An initial probability statement to evaluate expected payoff is called a prior probability distribution, the 1 which has, been revised in the light of new information called a posterior probability distribution. Information technology volition be evident that what is a posterior to one sequence of state of nature becomes the prior to others that are withal to happen. The method of computing posterior probabilities from prior probabilities is past using a called Bayes' theorem. A farther analysis of bug using these probabilities, with respect to new expected payoffs with additional information, is called prior-posterior analysis. The Bayes' theorem, in general terms, can be stated every bit follows: Allow be mutually exclusive and collectively exhaustive outcomes. Their probabilities are known. Given the information that result B has occurred, the revised conditional probabilities of outcomes A_i, i.e., are determined by using the post-obit conditional probability relationship:

where;.

Since each joint probability can exist expressed as the production of a known marginal (prior) and conditional probability, , thus;

Finally, the big advantage of Bayes' determination rule is that it incorporates all the bachelor data including all the payoffs and the best available estimates of the probabilities of the corresponding states of nature [15] .

8. Discussions, Determination and Recommendations

All organizations face the challenge of controlling about outcomes that maximizes their expected utilities respectively. But these decisions hardly are taken with perfect knowledge and information by the decision- maker, considering the business organization environs is fluid and portends seismic sea wave changes. These changes create controlling environments of doubt, risk and ambiguity whilst, the organizations objective sign posts for the optima value creation (utility maximization) accept not shifted. In order to achieve the decision objective function optimally, organizations deploy decision theory and assay approach equally a premise for taking informed decisions within the confine of utility function maximization. The absence of effective decision theory and analysis makes many faults and failures in controlling and consequent failure in organizational performance leading to subsequent survival challenges. The emergence of decision theory as a systematic report and a multidimensional field of cognition in the late 20th century make information technology a definite precursor for decision-making effectiveness in organizations.

Decision theory and analysis show that based on the objective function maximization quest and the fast changing environment of the decision-maker, programmed decisions are not obvious business solutions but not- programmed determination types. And because nonprogrammed decisions are often characterized by uncertainty, take chances and ambiguity, the decision maker takes on approaches of administrative (to satisfice) and sometimes political (to build alliances for coalition and scenario assay) approaches. The classical and normative approaches are not applicable due to problems of boundary rationality in the determination-making process. By and large, intuition based on executive's experiential surveys plays a better function in the objective utility optimization. This humanistic decision approach to controlling holds the sway for an optimum value cosmos in presence of Simon'due south theorem on conclusion-making.

Decision theory and analysis also argue that since there is no 1 way and hit method managers codify to take all decisions, it is very of import to clone a determination-making model contingent to the conclusion problems at hand. The models and then abstract the existent world situations of the decision variables. Top on the list of these models are the deterministic and probabilistic (or stochastic) models. The dynamic models put the decision-maker in the position of perfect knowledge and data, but not prevalent in real earth. Whilst, the stochastic models use probability as a substitute for knowledge and information (i.east. risks and uncertainties) to mould optima value creation decisions, representing the real world.

This study analyses decision models under pure uncertainty and run a risk and find that for organizations to realize the optima value creation in pure uncertainty environments, conclusion theory and analysis must exist their forerunner for conclusion-making process and must deploy either of the following models for effective decision-making:

a) Optimism (Maximax or Minimim) criterion;

b) Pessimism (Maximin or Minimax) criterion;

c) Equal probabilities (Laplace) criterion;

d) Coefficient of optimism (Hurwiez) criterion;

eastward) Regret (Salvage) criterion.

Concomitantly, in risky environments, this study succinctly recommends either of the following models for effective determination-making process if organizations crave optima value creation:

1) Expected Monetary Value (EMV);

ii) Expected Opportunity Loss (EOL);

3) Expected Value of Perfect Information (EVPI);

4) Posterior Probabilities and Bayesian Analysis.

Finally, this paper wonders why with the availability of these real earth models for dubiousness and risky environments of decision-making, organizations still strives difficult with issues of decisions-making. Information technology is too more often than not held that entrepreneurs are risk aversive in concern venturing and avoids uncertainties. However, risks and uncertainties are the real earth business environment that is the domain of entrepreneurship venturing. Is entrepreneurship excluded in decision theory and analysis? In other words, are entrepreneurs not needed to deploy conclusion theory and analysis in their business venturing? Therefore, this paper recommends specific decision theory and analysis research in entrepreneurship venturing to answer the question directed at entrepreneurship decision-making. Besides, a cross-sectional research to be conducted on executives and managers regarding their attitudinal chapters to appoint in decision theory and analysis, if not, they should engage decision-making services from consultants and specialists.

Cite this paper

Cephas A.Gbande,Paul T.Akuhwa, (2015) Decision Theory and Assay: An Optima Value Creation Forerunner for Organizations. Open Journal of Applied Sciences,05,355-367. doi: 10.4236/ojapps.2015.57036

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