Decision Making PSY 311

Types of Decisions

  • Examples of decisions in daily life include:

    • What clothes to wear today

    • What clothes to wear for an interview

    • Activity for a first date

    • Where to go for vacation

    • Where to live

    • What to eat tonight

    • What to get mom for the holidays

    • Where to go to college

    • Who to marry

    • How many kids to have

Example Decision

  • Introspective Decision-Making Strategy:

    • Analyze the decision-making process involved in choosing an apartment.

    • Write down thoughts and considerations while weighing options.

  • Alternatives for Apartment Choices:

    • Types of Alternatives: APPLE, BIRCH, CEDAR, DOVER STREET, LANE, ROAD, COURT

    • Attributes of Alternatives:

    • Distance to school:

      • APPLE: 15 min.

      • BIRCH: 25 min.

      • CEDAR: 45 min.

      • DOVER STREET: 20 min.

    • Size of rooms:

      • APPLE: Average

      • BIRCH: Tiny

      • CEDAR: Large

      • DOVER STREET: Tiny

    • Rent per month:

      • APPLE: $480

      • BIRCH: $425

      • CEDAR: $600

      • DOVER STREET: $440

    • Cleanliness:

      • APPLE: Filthy

      • BIRCH: Average

      • CEDAR: Spotless

      • DOVER STREET: Average

    • Noise level:

      • APPLE: Noisy

      • BIRCH: Quiet

      • CEDAR: Very quiet

      • DOVER STREET: Noisy

    • Nearest laundry:

      • APPLE: On site

      • BIRCH: 5 blocks

      • CEDAR: On site

      • DOVER STREET: 1 block

Models of Decision Making

  • Normative Models:

    • Define ideal solutions and optimal outcomes for decision making.

  • Descriptive Models:

    • Describe how people actually make decisions in reality.

Compensatory Models

  • Definition: Allows positive attributes to compensate for negative attributes in decision making.

    • Additive Model:

    • Assigns values for each attribute considered.

    • Sums values for each option to identify the most favorable choice.

  • Additive-Difference Model:

    • Definition: Adds the difference in values of attributes to facilitate comparisons between options.

Noncompensatory Models

  • Definition: Rejects alternatives based on negative attributes, indicating a stricter set of decision criteria.

  • Elimination by Aspects:

    • Evaluates one attribute at a time and eliminates alternatives that do not meet a minimum requirement.

  • Conjunctive Model:

    • Evaluates each alternative one at a time, rejecting any that fail to meet the minimum threshold for any attribute.

  • Satisficing Search:

    • Chooses the first alternative that meets all minimum standards rather than searching for the optimal choice.

Strategy Selection

  • Cognitive Demand of Strategies:

    • Some decision-making strategies are more cognitively taxing than others.

    • Strategies may change based on information processing requirements.

  • Research by Payne (1976):

    • Conducted experiments observing how people adjusted strategies with varying numbers of alternatives (2, 4, 8, or 12) and attributes.

    • Observed a tendency to use noncompensatory models followed by compensatory ones based on the complexity of decisions.

Availability Heuristic

  • Definition: Bases predictions on information that is easy to think about or recall, which may lead to distorted judgments about probabilities.

  • Example Question: Are there more words in the English language that begin with K or have K as their third letter?

    • a. More words begin with K

    • b. More words have K as their third letter

    • c. Both are about the same.

  • Results from Tversky & Kahneman (1973) illustrate that people instinctively choose a due to recall ease, despite actual frequencies being different.

Limited Information

  • Implication: No guarantee of the probabilities for future events.

  • Availability Heuristic Applications:

    • Works well for everyday events but fails due to imperfect encoding of experiences.

Representativeness Heuristic

  • Definition: Bases predictions on the similarity to existing prototypes or events rather than actual probabilities.

  • Example: Predict outcomes based on an example of family births and associated probabilities.

  • Problem Statement Example: Given 100 families with the birth order G-B-G-B-B-G, estimate patterns of similar birth orders.

  • Stereotyping: Use of representativeness can lead to incorrect assumptions about real probabilities, demonstrated in the Steve example where descriptions influence probability judgments despite actual counts being different.

Risk Aversion

  • Definition: Tendency to prefer sure wins over gambles and gambles over sure losses, even when statistically equal.

  • Example Situations:

    • Choosing between a sure prize of $85 and an 85% chance of winning $100. Most people prefer the sure prize.

    • Choosing between a sure fine of $85 and an 85% chance to incur a $100 fine. People often gamble on potential better outcomes here, indicating risk-seeking behavior in potential losses.

Decision Frames

  • Impact of Different Framing:

    • Affects how decisions are made based on perceptions of success or failure (e.g. focusing on success rates vs failure rates). More favorable outcomes are favored when framed positively.

Economic Theory of Decision Making

  • Normative Model - Expected Value:

    • Formulated as:

    • extExpectedValue=p(extwin)imesv(extwin)+p(extloss)imesv(extloss)ext{Expected Value} = p( ext{win}) imes v( ext{win}) + p( ext{loss}) imes v( ext{loss})

    • Example Calculation:

    • Rolling a "2" on a die:

      • rac16imes4+rac56imes(−1)=−rac16rac{1}{6} imes 4 + rac{5}{6} imes (-1) = - rac{1}{6}

Descriptive Models

  • Expected Utility Theory:

    • Recognizes subjective values in decisions considering risk aversion.

  • Expected Utility Formula:

    • extExpectedUtility=p(extwin)imesu(extwin)+p(extloss)imesu(extloss)ext{Expected Utility} = p( ext{win}) imes u( ext{win}) + p( ext{loss}) imes u( ext{loss})

    • Example Calculation:

    • Rolling a "2":

      • rac16imes6+rac56imes(−1)=+rac16rac{1}{6} imes 6 + rac{5}{6} imes (-1) = + rac{1}{6}

Behavioral Economics

  • Study of Human Decision-Making:

    • Highlights that humans do not always act rationally.

    • Investigates true decision behaviors through empirical studies, such as cheating examples described by Ariely.

Dual Process Model

  • Components of the Decision Process:

    • Recognition-Primed Decision: Utilized in time-constrained situations, focusing on utilizing previous experiences.

    • Designed by Kahneman & Frederick (2005):

    • Hot System:

      • Involves associative reasoning and emotions, influencing quick judgments.

    • Cold System:

      • Employs rule-based reasoning that can monitor and override the "Hot" system when necessary.

Media Truth Discernment

  • Research Findings:

    • Distribution of truth discernment scores illustrating biases in distinguishing between real and fake news based on reasoning abilities.

    • Results demonstrate the complexity of decision-making influenced by cognitive processes in media consumption.