PREVNEXTSUBMITTable of ContentsResourcesGlossaryTranscriptClear and return to MenuSearch ResultsFilterSlide TextNotesSearch in:Check to includeSearch...TermsDefinitiondefinitionresourcessearchpauseplayreplaysubmitnextpreviousvolumesystems-thinking and decision-making part 2 introduction part one: defined and classified decision problems discussed the importance of a systems thinking approach explored multiple frameworks in this section, we will: understand the role of stakeholders in decision-making processes introduce and examine the concept of decision support systems explore the role and application of decision support systems in organizational decision-making discuss knowledge management systems from a decision-support context stakeholder involvement in decision-making the “stakeholders” can be categorized as follows: problem owners problem users problem customers problem analysts or solvers the roles are always defined with respect to the specific system under consideration. definite identification of who the various stakeholders of a problem situation are becomes fully clear only once the relevant system has been defined, although the analyst may have a fair idea about who most of them are very early in the analysis. click each of the categories to read more. problem owners are the persons exercising control over certain aspects of the problem situation, in particular over the choice of action to be taken. most often, they are also the decision makers. problem users use the solution and/or execute the decisions approved by the problem owners or decision-makers. they have no authority to change the decision or initiate new action. problem customers are the beneficiaries or victims of the consequences of using the solution. in many instances, they may be given no voice or have no means to affect the analysis or its outcomes. they may also include future generations or even other species. problem analysts or solvers analyze the problem and develop a solution for approval by the problem owners. stakeholder roles the terms refer to the roles that people assume, and not to the people themselves. car buyer = the problem owner, the problem user, and the problem customer, as well as the problem analyst - all rolled into one! all stakeholders critical to a decision-making process are identified and involved in the process right from the outset. importance clear stakeholder role definition why is clear stakeholder role definition important? clear stakeholder role definition is important: if initiated by the decision-maker, the project will usually be of a substantive nature, leading to real change. if initiated by any of the other roles, the nature of the project is usually more descriptive than substantive. unless the problem analyst is fairly clear about the roles of the various participants in the problem situation to be studied, the project may head off in the wrong direction from the very start. importance of a clear stakeholder role identification of the problem users is essential for effective implementation. users need to perceive implementation as being in their personal interest or they may easily be tempted to sabotage the project regardless of how successful the technical aspects of the project turn out to be. a classic case of “the operation was successful, but the patient died!” stakeholder role if the results of the study affect future generations or non-human species, it is essential that somebody takes up the defense of their interests - thus giving them a voice. one difficulty sometimes encountered is that the existing assignment of stakeholder roles may be inappropriate. proper problem-structuring may thus be useful in highlighting the needed changes within the organizational structure. brief introduction decision support systems decision support systems = dss what is a decision support system? sprague's definition ekbia postulates alternative school view click each tab to learn more. sprague's definition people from different backgrounds and contexts, a manager and a computer scientist, for instance, tend to view dss quite differently. for example, one definition of dss, attributed to sprague is “a class of information system that draws on transaction processing systems and interacts with other parts of the overall information system to support the decision-making activities of managers and other knowledge workers in the organization”. from this definition, it can be seen that early dss were aimed at decision-makers understood as experts, who were many times assumed to be computer and systems literate. these were highly technocratic systems which did not explicitly take into account the social context in which decisions were taking place. instead, they deployed mainly quantitative scientific information and required skilled users to undertake the analysis. ekbia postulates ekbia postulates, “an overview of the development of dss reveals the dominance of a techno-centric view that portrays technology as the panacea and silver bullet to all social and organizational problems. challenging this view might be the key to our understanding of the shortcomings of current dss and a first step toward an alternative framework”. alternative school view the alternative school of thought views dss, not as technological tools, but rather as any context or platform for helping all those involved in decision-making processes to access the necessary information for a useful debate to take place. introduction to decision support systems tools to inform debates, dialogues & deliberations = tiddd holistically = a “soft dss” outlook technological tools = “hard dss” decision analysis techniques wide range of decision techniques include: multiple criteria decision analysis (mcda) paired comparison analysis analytic hierarchy process (ahp) decision trees influence diagrams (id) game theory linear programming affinity diagrams heuristic methods etc majority of these techniques are applied in the "choice" phase and a few are designed to assist the "design" phase. application of dss organizational decision-making decision-making engagement and social learning lets consider an example that aims at moving decision-making from a traditional command-and-control approach to a more sustainable, inclusive and dynamic decision making process. two central tenets of adaptive management: social learning engagement adaptive management: incorporation of evolving knowledge and understanding of what, how and why decisions are made. social learning: a dynamic process that incorporates learning through inclusion, interaction and engagement, which enables individuals to engage in new ways of thinking together to address difficult decision problems. engagement and social learning decision-making processes group decision support systems aims to facilitate the exchange of ideas among participants through brainstorming and organizing collective thought into a workable consensus. development of: group decision support systems collaborative decision support systems enables groups to work together and participate in decision-making by providing a set of generic tools. group decision support systems compare individuals vs. groups in the decision-making process better because of the greater amount of knowledge available has a wider range of viewpoints result in the acceptance of more risk modern decision support systems enable stakeholders to collaborate in decision-making processes with no limitation of space and time efforts encourage a broad search in various functional areas of the organization generally willing to accept less risk usually who implements a decision can contribute at any time and from any location uncertainty decision-making so far, the systems approaches to decision-making discussed implicitly assumed that if we take a given action, we can predict the resulting effect on the system with absolute certainty. uncertain, risky, stochastic, or probabilistic = uncertain situations where we cannot predict which particular outcome will occur. deterministic situations: problem situations where our knowledge may actually be good enough to satisfy the assumption, at least in a practical sense, if not in theory. uncertainty reasons decision-making uncertainty in decision-making may be caused by one of the following reasons: the process or issue in question may not be known or understood in sufficient detail. statements about a phenomenon or process may be based on incomplete information. it may not be possible to predict what moves other actors in the real world will make. there may be measurement errors about a phenomenon due to mistakes made by the observer or improper application of measuring instruments, both unknown to the observer. numerical value = quantity, size, etc. qualitative attribute = on, off; for, against; etc knowledge management systems we shall discuss the various ways the concept of knowledge management is viewed under different paradigms, and how this affects systems that may be established in organizations. many contemporary authors distinguish among: data information knowledge click each term to read more. data are raw facts or simple observations about the state of the world. information is data in some context or with some kind of human interpretation applied. knowledge is information with guidance for action, for example; knowing how to act given the information. different typologies knowledge management tacit knowledge explicit knowledge declarative knowledge procedural knowledge esoteric knowledge exoteric knowledge click each tab to learn more. deep knowledge shallow knowledge human knowledge: tacit vs. explicit declarative vs. procedural esoteric vs. exoteric deep vs. shallow tacit knowledge contained within a person’s head, and is difficult or impossible to express, write down and codify. examples of tacit knowledge: how to close a deal with a particular type of client how to develop an effective advertising campaign tacit knowledge is of great interest to organizations because it involves knowledge that leads to effective policies, practices and procedures. a good deal of the knowledge management literature deals with the creation of organizational policies and cultures that encourage sharing of tacit knowledge. explicit knowledge can be readily articulated, written down, codified and shared. standard operating procedures are examples of explicit knowledge. referred to as data or information in the knowledge management literature, consists of facts or observations about the state of the world, such as a patient’s temperature, or a business firm’s current ratio. declarative knowledge what most authors would consider knowledge, as it involves directions on “how to” do something, such as how to diagnose hepatitis b, or how to analyze a financial statement. procedural knowledge esoteric knowledge highly specialized, formalized, and applicable to narrow domains; in short, that which is found in most scientific disciplines. science is designed to produce knowledge of this variety. it is of limited value in solving unstructured, complex management problems. exoteric knowledge applicable to broad domains, and in some cases, might be considered “common sense”. it is applicable to complex, unstructured problems. deep knowledge usually related to relatively well-structured scientific and technical domains, and consists of formal theories of behavior of phenomena in those domains. often in social domains where theories and understanding are usually less well organized and codified than in scientific domains. shallow knowledge different perspectives knowledge management there are three perspectives on knowledge management. functional interpretive critical click each tab to learn more. functional perspective most often adopted by those in practice especially in the dss software industry. here knowledge management is defined as the way that organizations create, capture, store, re-use and protect knowledge to achieve organizational objectives. this reflects “realist” ontology, the belief that the world is factual, and that facts and knowledge are out there, waiting to be discovered. from this perspective, it is believed that knowledge can be captured, codified and shared. interpretive perspective founded on the belief that reality is socially constructed, and attention is directed to interpretation, distributed cognition, communications, and social processes. knowing and knowledge are inseparable from action. knowledge is viewed both as action and object; that is, as both procedural and declarative. organizational knowledge is viewed as existing in a “collective mind”, developed through interpretation, communication, and shared meanings, and is in a constant state of flux as new experiences are evaluated and shared. critical perspective concerned primarily with social conflict and antagonistic relationships. various stakeholders and special interest groups take positions and form strategies that produce differences and conflict among them. marxist labor processes, class struggles, and radical humanist perspectives are aspects of the critical perspective. different perspectives knowledge management the most prevalent form of knowledge management is a repository of one form or another. three types of repositories are recognized: structured unstructured tacit repositories in people’s minds in the interpretive environment, it consists of fostering communications between individuals, sharing and enriching interpretations, and coordinating actions. a collective culture must be created to permit effective communication and sharing of knowledge. in the critical perspective it is viewed in a rather pejorative way as the exploitation of workers by owners. inquiring organizations knowledge management inquiring organizations are learning organizations modeled on the theories of inquiring systems. output of an inquiring system is "true" knowledge, or knowledge that is believed not to be false. the "guarantor" in scientific inquiry, for example, is based on use of the "scientific method. scientists include many checks and balances along with a great deal of time and effort to ensure that the results of their inquiries are acceptable to the scientific community. an inquiring organization should take reasonable and prudent steps to ensure that its actions are based on valid knowledge. inquiring organizations must develop core competencies, improve continuously, and fundamentally renew or revitalize themselves. an inquiring organization involves: building a community of minds fostering effective dialogue avoiding bureaucracy occasionally rocking the boat along with reinventing the organization building a storehouse of knowledge, that is, a knowledge management system inquiring organizations km in inquiring organizations summary explored the role of systems thinking looked at what constitutes decision problems classified decision problems considered different models of decision-making reviewed the challenges and complexities of dealing with “wicked” problems discussed various frameworks clarified and emphasized the importance of stakeholder involvement in decision-making reviewed the function and application of modeling tools covered a brief introduction to decision support systems highlighted the importance of engagement and social learning through decision-making looked at decision support tools touched briefly on uncertainty decision-making discussed knowledge management summary “an expert is someone who has succeeded in making decisions and judgments simpler through knowing what to pay attention to and what to ignore.” -edward de bono consider this click to edit master text styles 1 content/short explanation/step goes here. text/content for quote goes here. or for small pieces of content that aren't voiced over. definition: goes here. use as "info cards" for examples or content that require an image and short description, explanation or points of content. image goes here; resize and align. add reference under image inside blue frame, when needed. --> use to highlight a focal area of the content presented --> use to point out focal points of the content presented --> use to connect shapes to build graphics. connector. --> use as a divider. --> use shapes to create diagrams. use colors from the palette and keep the same style among the shapes being used. these colors are suggestions, the use of colors will depend on the relationships you are trying to represent as well from the content. --> use as callouts. --> use to show written instructions on screen, specially when there isn't narrated instructions. insert instructions here use as tag --> use when need to create a light box. the size of the box is determined by the content and designer decisions. --> use as closing button. --> use for previous and next button for small interactions. only when appropriate. if other buttons are needed create them based on this style. <html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>In Part 1, we defined and classified decision problems, discussed the importance of a systems thinking approach to decision making, and explored multiple frameworks and modeling approaches to decision making. In this section we are going to • Understand the role of stakeholders in decision-making processes • Introduce and examine the concept of decision support systems • Explore the role and application of decision support systems in organizational decision-making </textformat><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>• Discuss knowledge management systems from a decision-support context</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Any human activity, particularly problem-solving within a systems context, involves people. All those involved in a decision-making process assume various roles. The “stakeholders” can be categorized as follows: • The problem owners • The problem users • The problem customers • The problem analysts or solvers Click on each of the categories to learn more. The roles are always defined with respect to the specific system under consideration. Definite identification of who the various stakeholders of a problem situation are becomes fully clear only once the relevant system has been defined, although the analyst may have a fair idea about who most of them are very early in the analysis.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Note that the terms problem owner, user, customer, and analyst refer to the roles that people assume, and not to the people themselves. In fact, for many situations, the same individual may act in different roles. Assume, for instance, that you are considering replacing your car. You have limited finance and borrowing capacity, a wish list of desirable and absolutely essential features that your new car should meet, and a wide choice of options amongst a range of new cars. In this case, you are the problem owner, the problem user, and the problem customer, as well as the problem analyst - all rolled into one! It is important to ensure that all stakeholders critical to a decision-making process are identified and involved in the process right from the outset. This ensures ownership and stakeholder buy-in for selected solutions, thereby providing a better chance for successful implementation. Moreover, as earlier seen, most problem situations are a social construct, and different individuals or stakeholder groups will tend to perceive a given situation differently. Obtaining a collective understanding and consensus from the outset concerning the nature of the problem is a critical first step to generating appropriate solutions.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Why is clear stakeholder role definition important? One reason is that any one of these roles may in fact be the initiator of a given project. The role of the project initiator or sponsor will inevitably color the nature of the project, by stamping it with a corresponding focus and world view. For example: • If initiated by the decision-maker, the project will usually be of a substantive nature, leading to real change, with a relevant system for the project clearly defined within the span of control of the decision-maker. Here, the relevant world view is the one held by the decision-maker. • If initiated by any of the other roles, the nature of the project is usually more descriptive than substantive. Its initiator will tend to use it as a means for educating or persuading other actors of the problem situation. It will only become substantive if its findings are adopted and acted upon by the real problem owner. For such projects, the appropriate choice of the relevant system and an appropriate world view may be more problematic and possibly even controversial. Unless the problem analyst is fairly clear about the roles of the various participants in the problem situation to be studied, the project may head off in the wrong direction from the very start. It goes without saying that the analyst must attempt to see the problem situation through the relevant world view of the project sponsor, and not his or her own! </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Similarly, identification of the problem users is essential for effective implementation of any recommendations. The recommendations should be appropriate to the training and educational level of the users. Furthermore, unless the users perceive implementation as being in their personal interest, they may easily be tempted to sabotage the project or the implementation of the recommendations, regardless of how successful the technical aspects of the project turn out to be - a classic case of “the operation was successful, but the patient died!”</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Moreover, if the results of the study affect future generations or non-human species, it is essential that somebody takes up the defense of their interests - thus giving them a voice. </textformat><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>One difficulty sometimes encountered is that the existing assignment of stakeholder roles may be inappropriate. For example, the person having control over the decision process may not have access to the information needed for effective decision-making, while the person with the information may have no decision-making authority. For effective decision-making it may first be necessary to change the organization’s structure and to reassign decision-making roles. Proper problem-structuring may thus be useful in highlighting the needed changes within the organizational structure.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Let us now turn to the subject of decision support systems, or DSS. What, in essence, is a decision support system? People from different backgrounds and contexts, a manager and a computer scientist, for instance, tend to view DSS quite differently. For example, one definition of DSS, attributed to Sprague is “a class of information system that draws on transaction processing systems and interacts with other parts of the overall information system to support the decision-making activities of managers and other knowledge workers in the organization”. From this definition, it can be seen that early DSS were aimed at decision-makers understood as experts, who were many times assumed to be computer and systems literate. These were highly technocratic systems which did not explicitly take into account the social context in which decisions were taking place. Instead, they deployed mainly quantitative scientific information and required skilled users to undertake the analysis. However, as Ekbia postulates, “An overview of the development of DSS reveals the dominance of a techno-centric view that portrays technology as the panacea and silver bullet to all social and organizational problems. Challenging this view might be the key to our understanding of the shortcomings of current DSS and a first step toward an alternative framework”. The alternative school of thought views DSS, not as technological tools, but rather as any context or platform for helping all those involved in decision-making processes to access the necessary information for a useful debate to take place. </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>In 2002, Pereira & Quintana portray this way of thinking as representing a passage from DSS to Tools to Inform Debates, Dialogues & Deliberations referred to as TIDDD. Viewing decision support, more holistically rather than focusing on technological tools, expands the landscape to include decision improvement interventions and strategies that may or may not involve a technical computer artifact. Thus, the DSS concept is a wide-ranging one, with the spectrum of possibilities for interpreting the concept ranging from the techno-centric view mentioned above with a focus on computerized tools; through formal or informal group discussions with or without computer aids; to the intriguing notion that “the cup of coffee that a decision maker drinks can be considered to be a decision support system.” References: Sprague (1980) Ekbia (2004) Pereira & Quintana (2002) (Boerboom, 2006)</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>There is a wide range of decision analysis techniques along with associated tools that have evolved over the years. These include techniques like: • Multiple Criteria Decision Analysis (MCDA) • Paired Comparison Analysis • Analytic Hierarchy Process (AHP) • Decision trees • Influence diagrams (ID) • Game theory • Linear programming • Affinity diagrams • Heuristic methods • Etc. The majority of these techniques are applied in the “Choice” phase of the decision-making process, although a few of them are designed to assist the “Design” phase. It is beyond the scope of this presentation to delve into the details of each of these techniques. Students who may be interested in learning more about them or in applying any of them to a specific problem they may be investigating are encouraged to take advantage of the vast literature resources available online.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>In general, it may be said that DSS provide support to “management activities”. According to Anthony in there are three levels of management activities in organizations: Strategic planning, Management control, and Operational control. Strategic planning is the process of deciding on objectives of the organization, on changes in these objectives, on the resources used to attain these objectives, and on the policies that are applied in managing these resources. It typically involves a small number of high-level people who operate in a non-repetitive and often very creative way. Management control is the process by which managers ensure that resources are obtained and used effectively and efficiently in the accomplishment of organizational objectives. It involves interpersonal interaction, takes place within the context of the policies and objectives established during strategic planning, and has as its paramount goal the assurance of efficient and effective performance. Operational control is the process of ensuring that specific tasks are carried out effectively and efficiently. The main difference between management control and operational control is that the latter focuses on tasks whereas the former is more concerned with people. There is less judgment to be exercised in operational control because the tasks, goals and resources are predefined through the management control activity. Because management activities are different, the information requirements to support them are also different. The task orientation of operational control calls for information that is well-defined, narrow in scope, detailed and accurate. It is mainly obtained from sources within the organization, and very frequent use is made of the information. Within strategic planning, the information required is aggregate information, wide and varied in scope, and mainly obtained from sources outside the organization. The requirements for accuracy are not stringent, and the demands for the information occur infrequently. Information requirements for management control fall between the extremes for operational control and strategic planning. It may also be noted that much of the information required for management control is obtained through the process of interpersonal interaction. In the table, we find examples of processes within organizations, grouped with respect to the category of management activity they fall under. We also see how these processes could be classified based on the types of decision problems associated with them. Finally, the nature of decision support system that would be appropriate for application in each of these categories of processes is also proposed. References: Anthony (1965)</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Let us now consider an example of an approach that aims at moving decision-making in urban water systems from traditional command-and-control approaches that tend to focus on an outcome at a point in time to a more sustainable, inclusive and dynamic decision-making process. Sustainable water systems are defined as “water systems that are managed to satisfy changing demands placed on them, both human and environmental, now and into the future, whilst maintaining ecological and environmental integrity of water systems”. To operationalize sustainable water systems, it is necessary to recognize that sustainability is “not a state to be arrived at but a broad evaluative framework for understanding and justifying social practice”. As such, sustainable water management is not about achieving an end point, or even measuring for this end point, but rather it is the process of influencing what people believe and what they do. The principle of adaptive management is the incorporation of evolving knowledge and understanding of what, how and why decisions are made. Adaptive management represents an iterative cycle of critical processes beginning with data and information collection, analysis, plan development, implementation and then monitoring and evaluation. The information gained from the monitoring can then be used to adjust the plan as needed. The two central tenets of adaptive management are social learning and engagement. Social learning is a dynamic process which enables individuals to engage in new ways of thinking together to address difficult decision problems such as the unsustainable use of water. It is a process that incorporates learning through inclusion, interaction and engagement with stakeholders. References: (ASCE/UNESCO 1998) </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>While available models and evaluation techniques provide valuable input to decision-making, the complex nature of urban water systems requires more than just social and economic criteria to be considered as part of decision support frameworks. Instead, current decision support frameworks need to be presented in a way that incorporates adaptive management, and the inclusion of social learning and engagement within the decision train is necessary to achieving this end. The framework shown in the diagram is proposed as a means to support sustainable decision-making that incorporates social learning through effective stakeholder engagement. </textformat><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>For further insights into the adaptive and social learning decision support framework, please refer to the paper by Pearson et. al., “A Sustainable Decision Support Framework for Urban Water Management”, available through Canvas.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Group decision support aims at facilitating the exchange of ideas among participants through brainstorming and organizing collective thought into a workable consensus. It is aimed at facilitating and enhancing the process of engagement and social learning considered in the previous slide. Research and development in this line has led to the development of Group Decision Support Systems or Collaborative Decision Support Systems. Such systems enable groups to work together and participate in decision-making by providing a set of generic tools to facilitate exchange of numerical, textual and graphical information, generation of solutions, group evaluation, consensus building and voting.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Here is a brief comparison of the roles of individuals vs. groups in the decision-making process: • In establishing objectives, groups are better than individuals because of the greater amount of knowledge available within groups. • In identifying alternatives, the individual efforts of group members encourage a broad search in various functional areas of the organization. • In evaluating alternatives, the collective judgment of the group, with its wider range of viewpoints, is better than that of the individual decision maker. • In choosing an alternative, group interaction and the achievement of consensus usually result in the acceptance of more risk than would be accepted by an individual decision maker. • Implementing a decision, whether or not it was made by a group, is usually accomplished by individual managers. Through the advancement of ICT systems, modern group decision support systems enable stakeholders to collaborate in decision-making processes with no limitation of space and time. The stakeholders can contribute to the group decision at any time and from any location.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>The systems approaches to decision-making discussed so far implicitly assumed that if we take a given action, we can predict the resulting effect on the system with absolute certainty. For some problem situations, our knowledge of the situation may actually be good enough to satisfy this assumption, at least in a practical sense, if not in theory. Situations of this sort are called deterministic. More often, however, the exact outcome that results in a given situation or from a given event may not be known in advance - in other words, it may be uncertain. Moreover, there are situations which are unique in the sense that they will not repeat themselves in a similar way, or even if they do, we may have no experience about them or no way to make comparisons. Thus, it may be possible to list some or most of the possible outcomes, but it may prove difficult to assess the likelihood of each. Situations where we cannot predict which particular outcome will occur are referred to as uncertain, risky, stochastic, or probabilistic. The approaches used for decision-making under certainty may then not be appropriate any longer. Even the form or structure of the decision itself may need to be different. Rather than using decisions in the form of open-loop controls, it may be more appropriate to resort to decision rules involving closed-loop or feedback controls. Refer to earlier presentations for the meaning of these. Such decision rules are referred to as strategies.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Uncertainty in decision-making may be caused by one of the following reasons: • The process or issue in question may not be known or understood in sufficient detail. • Statements about a phenomenon or process may be based on incomplete information. • It may not be possible to predict what moves other actors in the real world will make, like competitors, customers, employees, or the Government and which could affect the outcome. • There may be measurement errors about a phenomenon due to mistakes made by the observer or improper application of measuring instruments, both unknown to the observer. Most uncertainties relevant to systems deal either with the numerical value or the qualitative attribute associated with the phenomenon of interest, or with its timing. The degree of uncertainty may vary from knowing almost nothing about the process or phenomenon to knowing almost everything. In the decision literature, we usually talk about decision making under risk if it is possible to list all outcomes and make numerical statements about the likelihood of each. If neither condition is met, then we talk about decision making under complete uncertainty. Although the term “decision making under uncertainty” is often loosely used to denote both types, the approaches used for dealing with these two cases are radically different. Note: For more details on the specific approaches to decision-making under uncertainty, please refer to the book by Daellenbach & McNickle, “Management science: Decision making through systems thinking”. </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>We end our discussion with a brief look at Knowledge Management Systems referred to as KMS. We shall limit the scope of our discussion to the various ways in which the concept of knowledge management is viewed under different paradigms, and how this affects systems that may be established in organizations to harness knowledge required for effective decision-making. Many contemporary authors distinguish among data, information, and knowledge. Click on each to learn more. •<tab></tab>Typical definitions are that data are raw facts or simple observations about the state of the world •<tab></tab>information is data in some context or with some kind of human interpretation applied; •<tab></tab>knowledge is information with guidance for action, for example, knowing how to act given the information.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>There are several different typologies of knowledge recognized in the literature: tacit vs. explicit; declarative vs. procedural; esoteric vs. exoteric; and deep vs. shallow. Click on each to learn more. Tacit knowledge is that which is contained within a person’s head, and is difficult or impossible to express, write down and codify. Examples of tacit knowledge would be how to close a deal with a particular type of client, or how to develop an effective advertising campaign. Tacit knowledge is of great interest to organizations because it involves knowledge that leads to effective policies, practices and procedures. A good deal of the knowledge management literature deals with the creation of organizational policies and cultures that encourage sharing of tacit knowledge. Explicit knowledge, on the other hand, is that which can be readily articulated, written down, codified and shared. Standard operating procedures are examples of explicit knowledge. Declarative knowledge, referred to as data or information in the knowledge management literature, consists of facts or observations about the state of the world, such as a patient’s temperature, or a business firm’s current ratio. Procedural knowledge is closer to what most authors would consider knowledge, as it involves directions on “how to” do something, such as how to diagnose hepatitis B, or how to analyze a financial statement. Esoteric knowledge is that which is highly specialized, formalized, and applicable to narrow domains; in short, that which is found in most scientific disciplines. Science is designed to produce knowledge of this variety. It is of limited value in solving unstructured, complex management problems. Exoteric knowledge is applicable to broad domains, and in some cases, might be considered “common sense”. It is applicable to complex, unstructured problems. Deep knowledge is usually related to relatively well-structured scientific and technical domains, and consists of formal theories of behavior of phenomena in those domains. Shallow knowledge is often that in social domains where theories and understanding are usually less well organized and codified than in scientific domains. </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>We can also describe three different perspectives on knowledge management: the functional, the interpretive, and the critical. The functional perspective is that most often adopted by those in practice especially in the DSS software industry. Here knowledge management is defined as the way that organizations create, capture, store, re-use and protect knowledge to achieve organizational objectives. This reflects “realist” ontology, the belief that the world is factual, and that facts and knowledge re out there, waiting to be discovered. From this perspective, it is believed that knowledge can be captured, codified and shared. The interpretive perspective is founded on the belief that reality is socially constructed, and attention is directed to interpretation, distributed cognition, communications, and social processes. Knowing and knowledge are inseparable from action. Knowledge is viewed both as action and object; that is, as both procedural and declarative. Organizational knowledge is viewed as existing in a “collective mind”, developed through interpretation, communication, and shared meanings, and is in a constant state of flux as new experiences are evaluated and shared. The critical perspective is concerned primarily with social conflict and antagonistic relationships. Various stakeholders and special interest groups take positions and form strategies that produce differences and conflict among them. Marxist labor processes, class struggles, and radical humanist perspectives are aspects of the critical perspective. </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>In the functional paradigm, the most prevalent form of knowledge management is a repository of one form or another. Conventional databases, knowledge bases of best practices, or knowledge bases in expert systems are examples. Three types of repositories are recognized: </textformat><textformat leading='1' tabstops='[0, 48, 96]' leftmargin='12' indent='-12'>·<tab></tab>structured, consisting of databases and knowledge bases; ·<tab></tab>unstructured, consisting of notes, documents, etc.; and ·<tab></tab>the tacit repositories in people’s minds. </textformat><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'> Knowledge management in the interpretive environment consists of fostering communications between individuals, sharing and enriching interpretations, and coordinating actions. A collective culture must be created in such organizations to permit effective communication and sharing of knowledge. In marked contrast, knowledge management in the critical perspective is viewed in a rather pejorative way as the exploitation of workers by owners, who seek to extract knowledge from them and commoditize it. </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Inquiring organizations are learning organizations modeled on the theories of inquiring systems. In an inquiring system, the models of inquiry, being systems, have inputs, processes, and outputs. The output of an inquiring system is "true" knowledge, or at least knowledge that is believed not to be false. One of the most distinctive features of inquiring systems design is the inclusion of elaborate mechanisms for "guaranteeing" that only "valid" knowledge is produced. Such a concept is not new. The "guarantor" in scientific inquiry, for example, is based on use of the "scientific method," and scientists in general include many checks and balances and usually exhaust a great deal of time and effort to ensure that the results of their inquiries are acceptable to the rest of the scientific community. Thus, the primary maxim for inquiring organizations is that their actions should be based on valid knowledge. Yet organizations faced with the dynamic, and often hostile, environments of today may not have the time nor the resources required to validate knowledge in the scientific sense. Moreover, beliefs that were valid yesterday may no longer hold true in rapidly changing organizational environments that are socially constructed and subject to change by many forces. Thus, while an inquiring organization should take reasonable and prudent steps to ensure that its actions are based on valid knowledge, in many cases, the only reasonable guarantor is some form of consensus among its members. Learning is clearly a fundamental aspect of the inquiring organization, and a well-designed learning subsystem is critical to its success. This implies that inquiring organizations must develop core competencies, improve continuously, and fundamentally renew or revitalize themselves.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Developing an inquiring organization involves building a community of minds, fostering effective dialogue, avoiding bureaucracy, occasionally rocking the boat and reinventing the organization, and building a storehouse of knowledge, that is, a knowledge management system. It seems to make sense that organizations should be able to adapt inquiring systems to their needs, especially if artificial knowledge management systems can be developed to make their operation faster and more sustainable.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>Charles West Churchman, a member of the pragmatic school recasts the theories of knowledge of philosophers Leibniz, Locke, Kant, Hegel and Singer in the language and design of inquiring systems, providing a description of “how learning can be designed, and how the design can be justified”. A summary of Churchman’s classification of the characteristics of inquiring organizations under each of the five philosophical models, with respect to decision-making, knowledge management, and supportive information technology systems, is shown in the table. NOTE: For a detailed discourse, students may refer to the paper by Courtney (2001), “Decision-making and knowledge management in inquiring organizations: Towards a new decision-making paradigm for DSS”, which is available through Canvas.</textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>In this presentation, we have explored the role of systems thinking in decision-making. We have looked at what constitutes decision problems, and classified decision problems with respect to both their nature and the stimuli that create them. We then considered different models of decision-making and the elements and approaches related to each. The challenges and complexities of dealing, in particular, with “wicked” problems, have been outlined, and we have seen how a systems approach is useful in creating structure out of unstructured or ill-structured problem situations, thereby facilitating the identification of appropriate solutions. Various frameworks for application of a systems approach have been discussed. The role and importance of stakeholder involvement in decision-making has been clarified and emphasized, as well as the function and application of modelling tools. We have covered a brief introduction to Decision Support Systems and their associated decision analysis techniques, and considered differing schools of thought regarding the DSS concept. The importance of engagement and social learning through decision-making has been highlighted, and we have looked at the kind of decision support tools necessary to facilitate this. We have also touched briefly on decision-making under uncertainty. Finally, we have discussed the concept of Knowledge Management and explored its role in developing “inquiring organizations”. </textformat></html><html><textformat leading='1' tabstops='[48, 96]' leftmargin='0' indent='0'>We close our discussion with a quotation from Edward de Bono, author of the book “Six Thinking Hats” and proponent of the deliberate teaching of thinking as a subject in schools: “An expert is someone who has succeeded in making decisions and judgments simpler through knowing what to pay attention to and what to ignore.” This, I believe, is a very apt and succinct way of regarding the role and importance of systems thinking in decision-making.</textformat></html>