Foundations and technologies for decision making Chapter 2 Foundations and technologies for decision making

Learning Objectives Understand the conceptual foundations of decision making Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation Recognize the concepts of rationality and bounded rationality, and how they relate to decision making

Learning Objectives Differentiate between the concepts of making a choice and establishing a principle of choice Learn how DSS support for decision making can be provided in practice Understand the systems approach

Decision Making: Introduction and Definitions Characteristics of decision making Groupthink Decision makers are interested in evaluating what-if scenarios Experimentation with the real system may result in failure (develop a schedule and try it!) Experimentation with the real system is possible only for one set of conditions at a time and can be disastrous Changes in the decision making environment may occur continuously, leading to invalidating assumptions about the situation (delivery around holidays!)

Decision Making: Introduction and Definitions Characteristics of decision making Changes in the decision making environment may affect decision quality by imposing time pressure on the decision maker Collecting information and analyzing a problem takes time and can be expensive. It is difficult to determine when to stop and make a decision There may not be sufficient information to make an intelligent decision, Or Information overload

Plan of the chapter Dissecting DSS into its main concepts  Building successful DSS requires a thorough understanding of these concepts

Decision Making: Introduction and Definitions The action of selecting among alternatives A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s)

Decision making VS. Problem solving! Phases of the decision process Intelligence Design Choice Problem solving A process in which one starts from an initial state and proceeds to search through a problem space to identify a desired goal. It includes the 4th phase of the decision process Implementation

Decision-Making Disciplines Behavioral: anthropology, law, philosophy, political science, psychology, social psychology, and sociology Scientific: computer science, decision analysis, economics, engineering, the hard sciences (e.g., biology, chemistry, physics), management science/operations research, mathematics, and statistics Each discipline has its own set of assumptions and each contributes a unique, valid view of how people make decisions

Decision Making: Introduction and Definitions Decision making disciplines Behavioral Scientific Successful decision Effectiveness The degree of goal attainment. Doing the right things Efficiency The ratio of output to input. Appropriate use of resources. Doing the things right

Decision-Making Disciplines Better decisions Tradeoff: accuracy versus speed Fast decision may be detrimental Many areas suffer from fast decisions Effectiveness versus Efficiency Effectiveness  “goodness” “accuracy” Efficiency  “speed” “less resources” A fine balance is what is needed!

Decision Making: Introduction and Definitions Decision style and decision makers Decision style The manner in which a decision maker thinks and reacts to problems. It includes - perceptions, - cognitive responses, - values, and - beliefs

Decision Style Personality temperament tests are often used to determine decision styles There are many such tests Meyers/Briggs, True Colors (Birkman), Keirsey Temperament Theory, … Various tests measure somewhat different aspects of personality They cannot be equated!

Decision Style Decision-making styles Heuristic versus Analytic Autocratic versus Democratic Consultative (with individuals or groups) A successful computerized system should fit the decision style and the decision situation Should be flexible and adaptable to different users (individuals vs. groups)

Decision Makers Small organizations Medium-to-large organizations Individuals Conflicting objectives Medium-to-large organizations Groups Different styles, backgrounds, expectations Consensus is often difficult to reach Help: Computer support, GSS, …

Phases of Decision-Making Process Humans consciously or subconsciously follow a systematic decision-making process - Simon (1977) Intelligence Design Choice Implementation (?) Monitoring (a part of intelligence?)

Phases of the Decision-Making Process

Phases of the Decision-Making Process Intelligence phase The initial phase of problem definition in decision making Design phase The second decision-making phase, which involves finding possible alternatives in decision making and assessing their contributions

Phases of the Decision-Making Process Choice phase The third phase in decision making, in which an alternative is selected Implementation phase The fourth decision-making phase, involving actually putting a recommended solution to work

Decision Making: Intelligence Phase Scan the environment, either intermittently or continuously Identify problem situations or opportunities Monitor the results of the implementation Problem is the difference between what people desire (or expect) and what is actually occurring Symptom versus Problem Timely identification of opportunities is as important as identification of problems

Decision Making: The Intelligence Phase Problem (or opportunity) identification: some issues that may arise during data collection Data are not available Obtaining data may be expensive Data may not be accurate or precise enough Data estimation is often subjective Data may be insecure Important data that influence the results may be qualitative Data change over time (time-dependence)

Application Case 2.1 Making Elevators Go Faster!

Making Elevators Go Faster! Application Case 2.1 Making Elevators Go Faster! Background Problem description Proposed solution Results

British bombers armor

Decision Making: Intelligence Phase Problem Classification Classification of problems according to the degree of structuredness Problem Decomposition Often solving the simpler subproblems may help in solving a complex problem. Information/data can improve the structuredness of a problem situation Problem Ownership Outcome of intelligence phase  A Formal Problem Statement

Web and the Decision-Making Process

Decision Making: The Design Phase The design phase involves finding or developing and analyzing possible courses of action Understanding the problem Testing solutions for feasibility A model of the decision-making problem is constructed, tested, and validated

Decision Making: The Design Phase Modeling involves conceptualizing a problem and abstracting it to quantitative and/or qualitative form Models have: Decision variables Principle of choice

Decision Making: The Design Phase Decision variables A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, and so on Principle of choice The criterion for making a choice among alternatives

Decision Making: The Design Phase Selection of a Principle of Choice It is a criterion that describes the acceptability of a solution approach Reflection of decision-making objective(s) In a model, it is the result variable Choosing and validating against High-risk versus low-risk Optimize versus satisfice Criterion is not a constraint!

Decision Making: The Design Phase Normative models (= optimization) the chosen alternative is demonstrably the best of all possible alternatives Assumptions of rational decision makers Humans are economic beings whose objective is to maximize the attainment of goals For a decision-making situation, all alternative courses of action and consequences are known Decision makers have an order or preference that enables them to rank the desirability of all consequences

Decision Making: The Design Phase Heuristic models (= suboptimization) The chosen alternative is the best of only a subset of possible alternatives Often, it is not feasible to optimize realistic (size/complexity) problems Suboptimization may also help relax unrealistic assumptions in models Help reach a good enough solution faster

Decision Making: The Design Phase Descriptive models Describe things as they are or as they are believed to be (mathematically based) They do not provide a solution but information that may lead to a solution Simulation - most common descriptive modeling method (mathematical depiction of systems in a computer environment) Allows experimentation with the descriptive model of a system

Decision Making: The Design Phase Good Enough, or Satisficing “something less than the best” A form of suboptimization Seeking to achieve a desired level of performance as opposed to the “best” Benefit: time saving Simon’s idea of bounded rationality

Decision Making: The Design Phase Good enough or satisficing Reasons for satisficing: Time pressures Ability to achieve optimization Recognition that the marginal benefit of a better solution is not worth the marginal cost to obtain it

Decision Making: The Design Phase Developing (generating) alternatives In optimization models the alternatives may be generated automatically by the model In most MSS situations it is necessary to generate alternatives manually (a lengthy, costly process); issues such as when to stop generating alternatives are very important The search for alternatives usually occurs after the criteria for evaluating the alternatives are determined The outcome of every proposed alternative must be established

Decision Making: The Design Phase Measuring outcomes The value of an alternative is evaluated in terms of goal attainment Risk One important task of a decision maker is to attribute a level of risk to the outcome associated with each potential alternative being considered

Decision Making: The Design Phase Scenario A statement of assumptions about the operating environment of a particular system at a given time; a narrative description of the decision-situation setting Scenarios are especially helpful in simulations and what-if analyses

Decision Making: The Design Phase Scenarios play an important role in MSS because they: Help identify opportunities and problem areas Provide flexibility in planning Identify the leading edges of changes that management should monitor Help validate major modeling assumptions Allow the decision maker to explore the behavior of a system through a model Help to check the sensitivity of proposed solutions to changes in the environment

Decision Making: The Design Phase Possible scenarios The worst possible scenario The best possible scenario The most likely scenario The average scenario

Decision Making: The Design Phase Errors in decision making The model is a critical component in the decision-making process A decision maker may make a number of errors in its development and use Validating the model before it is used is critical Gathering the right amount of information, with the right level of precision and accuracy is also critical

Decision Making: The Choice Phase The actual decision and the commitment to follow a certain course of action are made here The boundary between the design and choice is often unclear (partially overlapping phases) Generate alternatives while performing evaluations Includes the search, evaluation, and recommendation of an appropriate solution to the model Solving the model versus solving the problem!

Decision Making: The Choice Phase Search approaches Analytic techniques (solving with a formula) Algorithms (step-by-step procedures) Heuristics (rule of thumb) Blind search (truly random search) Additional activities Sensitivity analysis What-if analysis Goal seeking

Decision Making: The Choice Phase Analytical techniques Methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems Algorithm A step-by-step search in which improvement is made at every step until the best solution is found

Decision Making: The Choice Phase Heuristics Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth

Decision Making: The Choice Phase Sensitivity analysis A study of the effect of a change in one or more input variables on a proposed solution What-if analysis A process that involves asking a computer what the effect of changing some of the input data or parameters would be

Decision Making: The Implementation Phase “Nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things.” - The Prince, Machiavelli 1500s Solution to a problem  Change Change management ?.. Implementation: putting a recommended solution to work

How Decisions are Supported

How Decisions are Supported Support for the Intelligence Phase Enabling continuous scanning of external and internal information sources to identify problems and/or opportunities Resources/technologies: Web; ES, OLAP, data warehousing, data/text/Web mining, EIS/Dashboards, KMS, GSS, GIS,… Business activity monitoring (BAM) Business process management (BPM) Product life-cycle management (PLM)

How Decisions are Supported Support for the Design Phase Enabling generating alternative courses of action, determining the criteria for choice Generating alternatives Structured/simple problems: standard and/or special models Unstructured/complex problems: human experts, ES, KMS, brainstorming/GSS, OLAP, data/text mining A good “criteria for choice” is critical!

How Decisions are Supported Support for the Choice Phase Enabling selection of the best alternative given a complex constraint structure Use sensitivity analyses, what-if analyses, goal seeking Resources KMS CRM, ERP, and SCM Simulation and other descriptive models

How Decisions are Supported Support for the Implementation Phase Enabling implementation/deployment of the selected solution to the system Decision communication, explanation and justification to reduce resistance to change Resources Corporate portals, Web 2.0/Wikis Brainstorming/GSS KMS, ES

DSS Capabilities DSS early definition: it is a system intended to support managerial decisions in semistructured and unstructured decision situations DSS were meant to be adjuncts to decision makers  extending their capabilities They are computer based and would operate interactively online, and preferably would have graphical output capabilities Nowadays, simplified via Web browsers and mobile devices

DSS Capabilities

DSS Classifications Association for Information Systems Special Interest Group on Decision Support Systems (AIS SIGDSS) Classification Communication-driven and group DSS Data-driven DSS Document-driven DSS Knowledge-driven DSS Model-driven DSS Often DSS is a hybrid of many classes

DSS Classifications Other DSS Categories Institutional and ad-hoc DSS Custom-made systems versus ready-made systems Personal, group, and organizational support Individual support system versus group support system (GSS)…

Components of DSS

Components of DSS Data Management Subsystem Model Management Subsystem Includes the database that contains the data Database management system (DBMS) Can be connected to a data warehouse Model Management Subsystem Model base management system (MBMS) User Interface Subsystem Knowledgebase Management Subsystem Organizational knowledge base

Components of DSS Database management system (DBMS) Software for establishing, updating, and querying (e.g., managing) a database Data warehouse A physical repository where relational data are organized to provide clean, enterprise-wide data in a standardized format Database The organizing of files into related units that are then viewed as a single storage concept. The data in the database are generally made available to a wide range of users

Components of DSS Model management subsystem User interface Model base management system (MBMS) Software for establishing, updating, combining, and so on (e.g., managing) a DSS model base User interface The component of a computer system that allows bidirectional communication between the system and its user

Components of DSS Knowledge-based management subsystem The knowledge-based management subsystem can support any of the other subsystems or act as an independent component Organizational knowledge base An organization’s knowledge repository

DSS Components: Data Management Subsystem DSS database DBMS Data directory Query facility

Data Management Subsystem The Database Internal data come mainly from the organization’s transaction processing system External data include industry data, market research data, census data, regional employment data, government regulations, tax rate schedules, and national economic data Private data can include guidelines used by specific decision makers and assessments of specific data and/or situations

Data Management Subsystem Data organization Data extraction The process of capturing data from several sources, synthesizing them, summarizing them, determining which of them are relevant, and organizing them, resulting in their effective integration

Data Management Subsystem Database management system (DBMS) Software for establishing, updating, and querying (e.g., managing) a database Query Facility The (database) mechanism that accepts requests for data, accesses them, manipulates them, and queries them Directory A catalog of all the data in a database or all the models in a model base

Data Management Subsystem Key database and database management system issues Data quality Data integration Scalability Data security

DSS Components: Model Management Subsystem Model base MBMS Modeling language Model directory Model execution, integration, and command processor

The Model Management Subsystem Model base A collection of preprogrammed quantitative models (e.g., statistical, financial, optimization) organized as a single unit

The Model Management Subsystem Four categories of models with the model base Strategic models Tactical models Operational models Analytical models

The Model Management Subsystem Strategic models Models that represent problems for the strategic level (i.e., executive level) of management Tactical models Models that represent problems for the tactical level (i.e., midlevel) of management

The Model Management Subsystem Operational models Models that represent problems for the operational level of management Analytical models Mathematical models into which data are loaded for analysis

The Model Management Subsystem Model building blocks and routines Model building blocks Preprogrammed software elements that can be used to build computerized models. For example, a random-number generator can be employed in the construction of a simulation model Model components for building DSS Modeling tools

The Model Management Subsystem Model base management system: MBMS software has four main functions Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks Generation of new routines and reports Model updating and changing Model data manipulation

The Model Management Subsystem Model directory Model execution is the process of controlling the actual running of the model Model integration involves combining the operations of several models when needed A model command processor is used to accept and interpret modeling instructions from the user interface component and route them to the MBMS, model execution, or integration functions

DSS Components: User Interface Subsystem Application interface User Interface (GUI?) DSS User Interface Portal Graphical icons Dashboard Color coding Interfacing with PDAs, cell phones, etc. See Technology Insight 2.2 for next gen devices

User Interface (Dialog) Subsystem The component of a computer system that allows bidirectional communication between the system and its user. User interface management system (UIMS) The DSS component that handles all interaction between users and the system

User Interface (Dialog) Subsystem The user interface process Object A person, place, or thing about which information is collected, processed, or stored Graphical user interface (GUI) An interactive, user-friendly interface in which, by using icons and similar objects, the user can control communication with a computer

User Interface (Dialog) Subsystem DSS user interfaces access is provided through Web browsers including: Voice input and output Portable devices Direct sensing devices

End of the Chapter Questions, comments

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