WHY STUDY DECISION MAKING?
Decision making is the most common form of problem solving in our everyday lives. We make countless decisions every day, many without conscious awareness. For example, navigating from your car to the office requires numerous decisions about the route, elevator strategies, and interpersonal interactions. (Should I say “Hi” to that person or pretend I did not see him?) We constantly make decisions in our every- day lives and our professional lives, such as:
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What should I wear to work today?•
Should I move in order to take another job?•
Which benefits package should I select?•
Should I have surgery or try a drug regimen?•
Which automobile should I purchase to meet my needs?•
What will be the most efficient route to work at this hour?•
What will I select to eat from this menu?•
Which candidate(s) should I vote for?•
Should I purchase an extended warranty for my new appliance or car?In our professional lives, we constantly make decisions, such as:
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Determining guilt or innocence of a defendant in a criminal trial;•
Selecting people for employment or organizational membership;•
Selecting the best material for manufacturing industrial parts;48
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Selecting a treatment option for a medical patient;•
Selecting the most effective marketing strategy for our new products;•
Selecting the most effective insurance plan;•
Deciding on proportions of banties, guineas, and turkins in my poultry operation;•
Selecting stocks for an investment portfolio;•
Selecting a new appellate judge.Essentially, decision making involves the selection of one or more beneficial or satisfying options from a larger set of options. Those options may consist of requirements, strategies, events, predictions, and opportunities, but the decision always requires “a commitment to a course of action that is intended to yield results that are satisfying for specified individuals” (Yates, 2003, p. 24). According to Yates and Tschirhart, (2006), there are many different kinds of decisions, including:
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Choices: where you select a subset from a larger set of alternatives (e.g., dinner options from a menu, clothes purchased from depart- ment store, automobiles to purchase from a car lot);•
Acceptances/rejections: a binary choice in which only one specific option is acknowledged and must be accepted or not (admission to graduate school, acceptance by a country club, issuance of automobile driver’s license);•
Evaluations: statements of worth that are backed up with com- mitments to act (e.g., bid on a house, assignment of grade in a course, proposal for new courthouse building);•
Constructions: attempts to create ideal solutions given available resources (e.g., budget plan for fighting a fire).Decisions, regardless of kind, include the following features (Yates &
Tschirhart, 2006):
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Action: Action is taken by the decision maker, typically involving a selection.•
Commitment: Decisions are made as soon as there is a commit- ment to act.•
Intention: Decisions are driven by a purpose or intention (usually thought to be optimization of value, benefit, or utility).•
Satisfying results: Decisions that provide the greatest utility are the most satisfying.•
Specified individuals: Decisions are made for someone by someone.Decision-Making Problems • 49
We all usually hope to make optimal decisions for ourselves and for others. Parents make decisions about school options, health care, and social activities for their children, who are hopefully the beneficiaries of their decision making. Every day is replete with decisions of minor and major importance. If decision making is the most common kind of problem solved in our everyday and professional lives, then we need to better understand the processes involved and to develop methods and strategies of instruction that support coherent decision making. That is the purpose of this chapter.
HOW DOES DECISION MAKING RELATE TO OTHER PROBLEMS?
Another important reason for studying and supporting decision mak- ing is the centrality of decision making to more complex kinds of problem solving. In Chapter 1, I articulated a typology of problems that includes algorithms, story problems, rule-using/rule-induction prob- lems, decision-making problems, troubleshooting, diagnosis-solutions problems, strategic performance problems, policy-analysis problems, design problems, and dilemmas, many of which are described more fully in Part I of this book. The underlying assumption of that typology is that as the nature of problems varies, the nature of the thinking and activity required to solve those problems varies. That is, one does not solve a medical-diagnosis problem the same way that an engineer designs a piece of medical equipment. To some degree, each kind of problem calls on a distinct set of cognitive skills and processes in order to solve it. Although the typology of problems was not intended origin- ally to imply taxonomic relationships, I believe that there exists a taxonomic relationship among some kinds of problems.
As described in Figure 3.1, many kinds of problems (e.g., policy analysis, diagnosis, and design problems) integrate decision making into those more complex kinds of problem-solving processes. That is, these more complex kinds of problem solving may be conceived of as a series of decision-making problems. Decision making is a critical compo- nent within more complex problems such as diagnosis, negotiation, design, situation assessment, and command and control (Means, Salas, Crandall, & Jacobs, 1993). Although some problems require only deci- sion making (e.g., what kind of insurance policy should I select), other more complex problems entail sequential or iterative decision-making processes. For example, Chapter 7 describes design problem solving as an iterative process of decision-making and model building. “The principal role of the designer is to make decisions. Decisions help to 50 • Problem-Specific Design Models
bridge the gaps between idea and reality, decisions serve as markers to identify the progression of the design from initiation to implementa- tion to termination” (Marston & Mistree, 1997, p. 1). Design is an iterative process of decision-making. After articulating an initial set of constraints and functional specifications, designers make decisions about the object or process being designed (materials, functionality, style, medium, etc.). Those decisions are influenced by emergent con- straints and beliefs. With each cycle, the number and complexity of decisions reduce to a point that satisfices the need (Simon, 1957). As design decisions are made, degrees of freedom decrease as the designer approaches the final solution.
HOW ARE DECISIONS SUPPOSED TO BE MADE?
Historically, there have been two basic approaches to studying decision making: how decisions should be made and how decisions are, in fact, made (Skinner, 1999). The former approach is based on prescriptive or normative theories or models of decision making, while the latter repre- sent descriptive theories, sometimes referred to as naturalistic decision making. Normative theories of decision making attempt to identify the optimal choice in any situation under uncertainty. Those theories pro- vide the basis for operations research and decision-theory research. The research in normative theories of decision making examines how people make decisions involving risk or rational choice under different theor- etical constraints. That research has sought to develop theories about how rational, informed people ought to identify the best option, the study of which is usually based on statistical analysis of options. The goal, as described earlier, is to maximize expected value by evaluating options with different probabilities that will result from each course of action.
Figure 3.1 Taxonomic (prerequisite/corequisite) relationship among problems.
Decision-Making Problems • 51
There are two different kinds of normative models of decision mak- ing. In these decision-making problems, a limited set of alternative actions or solutions (e.g., which car should we buy; which monetary instruments should we invest in; which health plan do we select?) are presented. The first is a formal-empiricist model in which decision makers probabilistically analyze options and make the choice that reduces risk or loss or maximizes expected value. This kind of decision normally assumes a gambling metaphor, where the decision maker determines the level of acceptable risk with regard to potential payoffs (Beach & Connelly, 2005).
The other kind of normative theory of decision making assumes that decision makers generate options, determine evaluation criteria, evalu- ate each option in terms of the criteria, calculate weights, and select an alternative based on weighted criteria. This multi-attribute theory of decision making assumes that people rationally analyze decision options in order to maximize expected value. Both approaches have been shown to be unsuccessful, because a one-size-fits-all strategy often fails in specific settings (Klein, 1997) and because they explicitly ignore domain knowledge. Newer conceptions of decision-making, such as naturalistic decision making (Zsambok & Klein, 1997), examine how experienced people make decisions in contextually rich situations. I shall explore each of these conceptions because they each provide dif- ferent perspectives on the decision-making process. Those perspectives may be useful in helping naive learners to develop more sophisticated decision making skills.
How Do People Gamble on Decisions?
Modern theories of decision making emerged from analyzing games of chance rather than a psychological analysis of risk or value (Tversky
& Kahneman, 2000). The probabilistic analysis of different options resulted in research that assumed a gambling metaphor, leading to normative theories of decision making focused on maximizing the expected value from any gamble. The most commonly researched approach to decision making conceives of decision making as risk assessment where people assess the probability of avoiding risk or a process of cost–benefit analysis, where people assess the likelihood of deriving the biggest benefit from any decision. This research employed laboratory studies where subjects were asked to choose between risk alternatives. The most common experiments employed gambling as a metaphor (e.g., lotteries), such as: If you were given a choice, which gamble would you bet on:
52 • Problem-Specific Design Models
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Choice A: A 10% chance of making $100,000 and a 90% chance of making nothing.•
Choice B: A 30% chance of making $50,000 and 70% of making nothing.However, other risk assessment scenarios were used to test subjects’
choices under uncertainty. For example, selecting between alternative therapies for tumors (radical treatments or moderate treatments):
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Treatment A: A 20% chance of imminent death and 80% chance of normal life, with an expected longevity of thirty-five years.•
Treatment B: Certainty of normal life, with an expected longevity of eighteen years.(Tversky & Kahnemen, 2000, p. 217) The assumption of this research paradigm is that people seek to maxi- mize the expected value or utility from any decision, so the best decisions are those that maximize the benefit to the decision maker.
Utility is determined by a simple formula: probability of each option times the value of each option. The option with the highest expected utility or value would be chosen by any rational person.
Decisions are always made under uncertainty and risk (Huber, 1995).
Each alternative has consequences. Whether a consequence occurs is not in the hands of the decision maker but depends on chance, nature, and luck. Decision makers are supposed to calculate which option will produce the greatest expected value or utility. Although many researchers have challenged the assumptions of gambling decisions (discussed later), many decisions (e.g., financial, political, military) do involve some risk, so helping learners to assess and accommodate that risk in their decisions may be useful. Huber (1995) has proposed a model of risk assessment that includes the following steps:
1. Mentally represent the structure of the system (identify variables and relationships among them; e.g., in an investment task, capital is the target variable).
2. Determine goals for target variables (which value or set of values should the target variables reach?).
3. Choose betting strategy for reaching target variable (e.g., if probability of winning is less than 50%, then bet very low).
4. Make choice of stake (amount invested or bet based on perceived risk).
5. Planning and control (evaluation of betting strategies).
Safir (1993) identified a couple of important but tacit gambling Decision-Making Problems • 53
strategies. He found that positive dimensions are weighted more heav- ily when choosing rather than rejecting; negative dimensions are weighted more heavily when rejecting than choosing; and enriched options tend to be chosen and rejected more than impoverished options. While the probabilistic approach to decision making has critics, models such as this do convey the probabilistic nature of uncertainty that does surround many decisions.
How Do People Make Rational Decisions in Everyday and Professional Situations?
The other prominent normative theory of decision making describes alternative methods for making tradeoffs among different goals that are implicit in the options in order to increase expected utility or value. Referred to as multi-attribute utility theory or rational choice, this normative method is about comparing values of different options and rationally selecting the option that accumulates the highest number of points. Numerous rational choice models have been promoted. The method described by Soelberg (1967) includes the following steps:
1. Identify the set of options.
2. Identify the ways or criteria for evaluating the options.
3. Weigh each evaluation dimension.
4. Rate the options.
5. Select option with highest score.
Janis and Mann (1977) elaborated that process slightly by addressing possible risks:
1. Thoroughly canvas a wide range of options.
2. Survey the full range of objectives.
3. Carefully weigh the costs, risks, and benefits of each option.
4. Intensively search for more information in evaluating options.
5. Assimilate all information.
6. Re-examine positive and negative consequences of each option.
7. Carefully plan to include contingencies if various risks occur.
Rational-choice models of decision making were not intended to be used in time-pressured situations. Rather, decisions under pressure are described by naturalistic decision-making models (see Chapter 5).
Rational-choice methods rely on quantitative analysis (assigning weights and summing weighted options), so they are deemed objective and rigorous, resulting in reliable decisions (Klein, 1999). They are also deemed comprehensive because they help novices determine what they 54 • Problem-Specific Design Models
do not know, and they can be used in a wide range of situations.
Therefore, rational-choice methods are more likely to be used when there is a need for justification, or in conflict-resolution situations where optimization is sought (Klein, 1998).
Given the implicit logic of expected utility theory, who can argue with such a rational approach? Over several decades of research, a number of paradoxical research findings have called into question how rational people really are. People often make choices that defy rational- ity, preferring options with lesser value but greater certainty (Plous, 1993). Often, when multiple options are presented in pairs, people do not ascribe value to each option consistently. “There is little doubt that people violate the principles of expected utility theory” (Plous, 1993, p. 95) because information about alternatives is missing, percep- tion is selective, and memory is fraught with biases. Most of the research on expected utility was conducted in laboratory settings with topics that were isolated from reality. When decisions relate to things that really matter among people, they often violate rules of rationality, illustrating once again that motivation and cognition are integrally related.
Decision theorists and analysts have developed a variety of tools to assist in rational decision making. I review only a few that may help novices to undertake more complex decision making.
How Do You Use a Decision Matrix to Make Decisions?
Perhaps the most common decision-making aid is the decision matrix (aka Pugh method) where options are described in rows and criteria based on quantitative or qualitative values (e.g., urgency, cost, effort or time, buy-in, difficulty, etc.) are displayed in columns in a matrix or table (see Table 3.1). For example, if a person were using a decision matrix to select a new car for purchase, the options would be listed in the left column (e.g., Ford Model A, Honda Model B, Toyota Model C,
Table 3.1 Decision matrix
Criterion 1 Criterion 2 . . . Criterion N
Option A Value A1 Value A2 . . . Value AN
Option B Value B1 Value B2 . . . Value BN
. . . . . . . . . . . .
Option N Value N1 Value N2 . . . Value NN
Decision-Making Problems • 55
etc.). Criteria that are critical to the decision might include fuel economy, reliability estimates from consumer reports, hauling capacity, sexiness, and whatever other qualitative or quantitative assessments can be made. Each column is weighted in terms of importance.
For example, fuel economy might be the most important factor and therefore weighted 40%. Each optional decision is rated in terms of each criterion, with the winner being the option with the highest weighted score.
Decision matrices are most commonly used when a single option must be selected from a list of options, often described by multiple criteria. For example, only one car can be afforded, or one new market- ing plan implemented. While rational methods, such as decision matri- ces, have been criticized, they can prove useful for novice learners who possess under-developed mental models about the decisions being made. For example, Jonassen, Tessmer, and Hannum (1999) showed how a decision matrix may be used during analysis phase of design to select the most important shipboard training needs (Table 3.2).
We are currently designing and evaluating a problem-based learn- ing version of a basic materials science course in the mechanical- engineering curriculum. Rather than teaching students about materials, students are presented with decision-making problems (e.g., You have been asked to redesign X-ray film cassettes so that they are lighter but retain the same stiffness to bending loads), and they must make decisions about the most appropriate material and materials pro- cessing required to fulfill the task. We will scaffold students in the first part of the course with decision matrices that include some of the properties in Table 3.3 when comparing alternative materials. Decision matrices are useful for helping novices to rationally evaluate decision options.
How Do You Use a Swot Analysis to Make Decisions?
Another popular business-oriented decision-analysis tool is known as SWOT analysis. SWOT analysis is a two-dimensional analysis that examines both internal and external forces operating on the business that are both positive and negative. The internal forces consist of strengths and weaknesses of the organization, while the external forces describe opportunities and threats to the organization. During a SWOT analysis, the decision makers will ask what the strengths of the business are (What strengths do we have? What resources do we have available?) while also examining internal weaknesses (What weaknesses do we have in process or organization? What weaknesses do others perceive 56 • Problem-Specific Design Models
Table 3.2 Decision matrix for selecting tasks for analysis (Jonassen, Tessmer, & Hannum, 1999) Task Selection Criteria Worksheet TasksCriticality (40)Universality (10)StandardizationFeasibilityDifficultyTotalNotesPriority Swab the deck0102100226 Load machine x30299050Error element of operation5 Recharge CO2 fire extinguisher3051090544 Extinguish a fire4068615753 Identify a threat contact40210824842 Fire a ballistic missile4031083911
we have?). SWOT analysts will also examine external forces, including opportunities (What situations can we capitalize on?) and threats to our ability to address those opportunities (What obstacles will impede progress?). Examples of SWOT options for business expansion include:
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Strengths: name-brand recognition, financial stability, existing patents or copyrights, diversified product line, strong corporate leadership;•
Weaknesses: lack of diversification, poor financial stability, high turnover, poor leadership;•
Opportunities: partnerships, emerging markets, emerging cus- tomer bases, strong economy, new markets, new technologies;•
Threats: laws and regulations, weak economy, increased competi- tion, weak labor markets, supply problems.Conducting a SWOT analysis should enlighten most decisions.
How Do You Use Expert Systems?
A common method for representing procedural knowledge is a production-rule system, which consists of facts and rules in the form: IF (condition/expression), THEN (action/expression). Based on extensive
Table 3.3
Materials properties to be used in decision matrix in materials science Mechanical Properties Physical Properties
Young’s modulus (d) Melting temperature
Poisson’s ratio (d1) Density
Yield and tensile strength (d1) Thermal conductivity
Shear modulus (d) Specific heat
Shear strength (d) Electrical conductivity
Hardness (d2) Glass transition temperature
Ductility (d1)
Work hardening exponent (d) Damping ability (d)
Fatigue & Fracture Properties Other Properties Fatigue strength (f3) Hardenability (i3) Crack growth rate (f2) Processability (d, e, h, i)
Fracture toughness (f1) Price
Creep strength (f3) Recyclability
Impact energy (f) Life cycle energy consumption
Ductile to brittle transition (f)
58 • Problem-Specific Design Models