Decision making in organizations

Organizations are, in a sense, machines for making decisions. Every day, people decide which customers to prioritize, which supplier to choose, whom to hire, whether to report a problem and whether to continue a struggling project. Good decisions compound over time, and poor ones can destroy value or harm people. This lesson examines how individuals and groups actually make decisions, the predictable errors that creep in, techniques for improving group decisions, creativity and the ethical dimension of choice. BUS-120 shows how to present a weighted decision matrix in a report; here the focus is on the psychology behind decisions.

The rational model and its limits

The rational decision-making model describes how decisions would be made if people had complete information and unlimited time: define the problem, identify decision criteria, weight the criteria, generate alternatives, evaluate each alternative against the criteria and choose the one with the highest score. The model is a useful discipline, especially for large, reversible-only-at-great-cost decisions such as choosing a site for a plant. But it assumes that problems are clear, that all alternatives are known, that preferences are stable and that the decision maker can process all the information.

Herbert Simon, who received the Nobel Prize in economics in 1978, argued that real decision makers operate under bounded rationality. Limited information, time and cognitive capacity mean that people construct simplified models of problems. Instead of optimizing, they satisfice: they search until they find an alternative that is good enough and then stop. Satisficing is not irrational; when information is costly, searching forever is a poor use of time. The danger arises when people satisfice on important decisions that deserve a wider search, or when the first acceptable option is chosen simply because it came to mind first.

import random

random.seed(130)
scores = {"A": [8, 5, 7, 6], "B": [6, 9, 6, 8], "C": [9, 4, 8, 5]}
base = [0.35, 0.30, 0.20, 0.15]

def winner(weights):
    totals = {k: sum(w * s for w, s in zip(weights, v)) for k, v in scores.items()}
    return max(totals, key=totals.get)

print("winner at base weights:", winner(base))
wins = {"A": 0, "B": 0, "C": 0}
trials = 10000
for _ in range(trials):
    # perturb each weight by up to +/-50 %, then renormalize
    w = [b * random.uniform(0.5, 1.5) for b in base]
    total = sum(w)
    wins[winner([x / total for x in w])] += 1
for k, v in wins.items():
    print(f"{k}: first in {v / trials:.1%} of trials")

winner at base weights: B
A: first in 0.0% of trials
B: first in 79.6% of trials
C: first in 20.4% of trials

Intuition

Intuitive decision making is an unconscious process created from distilled experience. Experts, such as experienced firefighters, nurses and chess players, often recognize patterns and know what to do without conscious analysis; psychologist Gary Klein called this recognition-primed decision making. Intuition works well when the environment is regular enough to learn from and the decision maker has had extensive practice with timely feedback. It works poorly in unpredictable environments, such as stock picking or long-range forecasting, where people develop confidence without accuracy. The best decision makers use intuition to generate options and analysis to test them.

Heuristics and biases

Psychologists Daniel Kahneman and Amos Tversky showed that people rely on heuristics, mental shortcuts that are usually efficient but produce systematic errors called biases. Kahneman later described two modes of thinking: System 1, which is fast, automatic and intuitive, and System 2, which is slow, effortful and analytical. Most biases arise when System 1 answers a question that deserves System 2 attention.

Prospect theory, Kahneman and Tversky's description of choices under risk, adds two further insights. First, people evaluate outcomes as gains or losses relative to a reference point, not as final states of wealth. Second, losses loom larger than equivalent gains, a pattern called loss aversion, with losses typically felt about twice as strongly. As a result, people tend to be risk-averse when choosing among gains and risk-seeking when choosing among losses. A manager facing a certain loss on a project may gamble on a long shot to avoid admitting it, which feeds escalation of commitment.

Sunk costs and escalation of commitment

Sunk costs are costs already incurred that cannot be recovered. Rational choices depend only on future costs and benefits, yet people find it very hard to ignore what they have already spent. Escalation of commitment is strongest when the original decision maker is personally responsible, when the project is highly visible, when abandoning it would be seen as failure and when progress seems close. Well-known examples include large public infrastructure and IT projects whose budgets grew many times over while decision makers kept funding them.

Organizations can reduce escalation by setting clear milestones and stop rules before a project begins, having someone other than the original sponsor review continuation decisions, rewarding people for good decision processes rather than only good outcomes, and normalizing the idea that stopping a failing project is a success, not a failure.

Group decision making

Groups have real advantages over individuals: more complete information, more diverse views, more alternatives and greater acceptance of the final decision by those who helped make it. They also have disadvantages: they take more time, they face conformity pressure, discussions can be dominated by a few members and responsibility can become ambiguous. Groups tend to be more accurate than the average individual but less accurate than the best individual, and they are especially valuable when the task benefits from diverse knowledge and when acceptance matters.

Groupthink, a term coined by Irving Janis, occurs when the desire for harmony and agreement in a cohesive group overrides realistic evaluation of alternatives. Symptoms include an illusion of invulnerability, rationalizing warnings, belief in the group's inherent morality, stereotyping outsiders, pressure on dissenters, self-censorship, an illusion of unanimity and self-appointed mindguards who shield the group from contrary information. The 1986 Challenger space shuttle disaster is a frequently cited example: engineers' concerns about O-ring performance in cold weather were overridden in a pre-launch teleconference. Groupthink is most likely in highly cohesive groups with a strong, directive leader, insulation from outsiders and high stress.

Group polarization is a related effect: after discussion, groups tend to adopt a more extreme position in the direction members were already leaning. A group of cautious people becomes more cautious; a group of risk-takers becomes riskier. Discussion makes members aware of arguments they had not considered that support their initial view, and they want to be seen as holding the group's values strongly.

Creativity

Creativity is the ability to produce novel and useful ideas. Teresa Amabile's three-component model holds that creativity requires expertise (knowledge in the domain), creative-thinking skills (such as tolerance for ambiguity, combining ideas from different fields and persisting through frustration) and intrinsic task motivation (interest and enjoyment in the work itself). The work environment can support or kill creativity. Encouragement, autonomy, sufficient resources and a culture that tolerates failure help; harsh criticism of new ideas, extreme time pressure, surveillance and controlling rewards hurt. Diverse teams and exposure to other fields provide raw material for creative combinations.

Ethics in decision making

Every significant decision has an ethical dimension. Three classic criteria help. A utilitarian criterion seeks the greatest good for the greatest number. A rights criterion respects fundamental rights such as privacy, free speech and due process, even when violating them would benefit many. A justice criterion requires that rules be applied fairly and impartially and that benefits and costs be distributed equitably. Each has blind spots: utilitarian reasoning can justify harming a minority, while rigid rights reasoning can block beneficial change. Using all three, and asking how a decision would look if published, are practical safeguards. BUS-101 introduces business ethics more broadly.

Decision environments shape ethics as well as individual values. Pressure to hit targets, ambiguous rules, rewards for results regardless of method and a belief that everyone else is cutting corners all increase unethical choices. People also engage in ethical fading, describing decisions in business terms ("optimizing revenue") that hide their ethical content. Organizations can counter this by making ethical considerations an explicit part of decision templates and by protecting people who raise concerns.

AI and decision support

Algorithms can outperform human judgment in many repetitive prediction tasks, such as forecasting demand or flagging likely fraud, because they apply rules consistently and are not tired or moody. But they can encode historical bias, they may fail in situations unlike their training data, and people tend either to distrust algorithms after seeing them make a single error (algorithm aversion) or to accept their outputs without question (automation bias). Good practice is to use AI tools to inform rather than replace judgment on consequential decisions about people, to understand what data a tool learned from, to monitor its outcomes and to keep a clear human owner who can explain and override a decision.