7. Decision Making

1. 7. DecisionMaking DEFINITION OF DECISION MAKING • a decision-making task • number of alternatives • information available to the option • timeframe relatively long • uncertainty • three phases for decision making • acquiring and perceiving info or cues for the decision • generating and selecting hypotheses or situation assessments • planning and selecting choices DECISION-MAKING MODELS • normative model (rational model) – mathematical models; specify what people ideally should do; not how people actually perform DM tasks • descriptive models – cognitive processes associated with DM behavior

2. Normative Decision Models • revolve around the central concept of utility, the overall value of a choice • multiattribute utility theory

3. expected value theory – uncertainty; replaces the concept of utility with expected value; • relatively limited because many choices in life have different values to different people • subjective expected utility (SEU) theory Descriptive Decision Models • people often rely on simplified shortcuts or rules-of-thumb (heuristics) • satisfying (Simon, 1957) – not absolute best or optimal decision but good enough or satisfactory decision • the amount of info exceeds cognitive processing limitations and time is limited – using simplifying heuristics

4. HEURISTICS AND BIASES • usually very powerful and efficient but not guarantee the best solution • occasionally lead to systematic flaws and errors – biases Information Processing Limits in Decision Making • what happens in WM? • cue reception and integration • hypothesis generation and selection • plan generation and action choice

5. Heuristics and Biases in Receiving and Using Cues • attention to a limited number of cues • cueprimacyand anchoring – first impressions are lasting • inattention to later cues • cue salience • overweighting of unreliable cues Heuristics and Biases in Hypothesis Generation, Evaluation and Selection • generation of a limited number of hypotheses • availability heuristic – frequency, recency • representativeness heuristic • overconfidence • cognitive tunneling • confirmation bias

6. Heuristics and Biases in Action Selection • retrieve a small number of actions • availability heuristic for actions – recency, frequency, strong association • availability of possible outcomes -- hindsight bias • framing bias • choose between a certain loss of $50 and an equal chance of losing $100 or breaking even  risky option • choose between a certain gain of $50 and an equal chance of making nothing or $100  conservative option • sunk cost bias – tendency to choose the risky loss over the sure one

7. DEPENDENCY OF DECISION MAKING ON THE DECISION CONTEXT Skill-, Rule-, and Knowledge-Based Behavior

8. Recognition-Primed Decision Making • experts simply recognize a pattern of cues and recall a single course of action, which is then implemented • representativeness heuristic, rule-based behavior • 3 critical assumptions of the RPD model • experience to generate a plausible option • time pressure not critical because of rapid pattern matching • know how to respond from past experience

9. FACTORS AFFECTING DM PERFORMANCE: AN INTEGRATED DESCRIPTION OF DECISION MAKING Mental simulation Poor learning Inaccurate mental models

10. IMPROVING HUMAN DECISION MAKING Task Redesign Decision-Support Systems • cognitive prosthesis – person in a subservient role to the computer – good for anticipated conditions • cognitive tools – support rather than replace the decision maker Decision Matrices and Trees • cognitive process of weighting alternative actions– normative multiattribute utility theory Spreadsheets • one of the most common decision-support tools – large number of errors contained in spreadsheets (24% to 91%) Simulation • not always enhance decision quality but increase confidence • simulations are incomplete and can be inaccurate

11. Expert Systems • cognitive prostheses – most effective with routine and well-defined situations • cognitive tools – provide feedback to the dicision maker to improve DM Displays • reduce the cognitive load of info seeking and integration • configural displays Training • improving analytical DM – train to overcome heuristics/biases normative utility methods for DM • training to do a better job at metacognition • development of mental models and management of uncertainty and time pressure • at the rule-based level, enhance perceptual and pattern-recognition skills • at the automatic level, focus on relevant cues in raw data form