Decision Making and Reasoning

Evolutionary Approach to Wason Four-Card Problem

• The evolutionary approach suggests that the Wason four-card problem is best understood through cheating detection.
• People perform well in tasks like the cholera task because they can identify cheating, such as entering a country without required vaccinations.
• Cosmides and Tooby (1992) designed scenarios in unfamiliar cultures to test if cheating, rather than learned permission schemas, is the key variable.
• In a hypothetical culture called the Kulwane, participants were told: "If a man eats cassava root, then he must have a tattoo on his face."
• Participants had to determine which cards to turn over: (1) Eats cassava roots, (2) Eats molo nuts, (3) Tattoo, and (4) No tattoo.
• High performance was observed even with unfamiliar rules, suggesting cheating detection plays a significant role.
• Other experiments showed better performance on statements involving cheating compared to those that didn't.
• Manktelow and Over (1990) tested people with no medical background using the rule: "If you clean up spit blood, you must wear gloves."
• This unfamiliar permission statement improved performance compared to the abstract version of the Wason task.
• The context within which conditional reasoning occurs is critical.
• Familiar situations can improve reasoning, but familiarity is not always necessary or sufficient.
• Controversies in this area highlight the complexity of the human mind.

Decision Making: Choosing Among Alternatives

• Decision making involves choices between different courses of action.
• Decisions range from unimportant (e.g., what to wear) to impactful (e.g., career choices).
• The focus is on how people make choices, whether personal or professional.
• Decisions involve both benefits and costs.

The Utility Approach to Decisions

• Expected utility theory assumes people are rational and aim to maximize utility.

• Utility refers to outcomes that achieve a person's goals.

• The theory is rooted in economics, where utility is linked to monetary value.

• Good decision making maximizes monetary payoff.

• Example: Deciding whether to drive or take the train based on traffic reports.

• Expected Utility Calculation: The expected utility (EU) is calculated by multiplying the assigned value and probability of each possible outcome. The option with the highest EU will be the one that the person making the decision will go for.

  EU = \sum (Value \times Probability)

Problems with the Expected Utility Theory

• People's decisions often don't maximize the probability of a good outcome.
• Denes-Raj and Epstein (1994) found people chose a bowl with a smaller proportion of red jellybeans but more red beans overall.
• Participants felt they had a better chance with more red beans, even knowing the probabilities were against them.
• Contestants in "Deal or No Deal" are influenced by emotions and previous events, not just probabilities.
• In "Deal or No Deal," contestants choose between a guaranteed amount from the bank or continuing the game.
• Post et al. (2008) found that contestants' choices depend on what has happened leading up to their decision.
• Contestants are more cautious if things are going well and more risky when doing poorly to avoid feeling like a loser.
• Decisions are swayed by emotions triggered by preceding events.

How Emotions Affect Decisions

• People with damage to the prefrontal lobe show impaired decision making despite preserved intellectual abilities.
• Flattened emotions may explain their difficulty in evaluating emotional outcomes.
• Damasio's somatic marker hypothesis suggests emotion-related signals bias choices.
• The ventromedial and orbitofrontal regions trigger somatic markers based on memories and knowledge.
• These markers are indexed changes in heart rate, blood pressure, gut motility, etc., related to a previously experienced consequence of a particular choice.
• Somatic states allow emotional anticipation of outcomes.
• Anxious people tend to avoid decisions with large negative consequences (risk avoidance).
• Optimistic people may ignore negative information, leading to poor decisions.

People Inaccurately Predict Their Emotions

• Expected emotions (predicted feelings for a particular outcome) influence decision making.
• Expected emotions determine risk aversion (avoiding risks).
• Risk aversion increases when people believe a loss will have a greater impact than an equivalent gain.
• Prospect theory (Kahneman and Tversky, 1979) suggests choices are based on values assigned to gains and losses.
• Losing 100$ Euros feels worse than winning 100$$, leading people to decline a 50-50 bet even if the win is larger.
• Kermer et al. found people overestimate the negative effect of losing.
• People don't account for coping mechanisms when predicting emotional reactions.
• Inability to predict emotional outcomes accurately leads to inefficient decision making.

Incidental Emotions Affect Decisions

• Incidental emotions are unrelated to the decision itself (e.g., general mood, events earlier in the day, environment).
• These emotions can still affect decisions.
• Simonsohn (2007) found university admissions reviewers weighted academic attributes more on cloudy days and non-academic attributes on sunny days.
• Prospective students were more likely to enroll at a university visited on a cloudy day (Simonsohn, 2009).
• People experience different emotions in different weather conditions.
• Happy feelings take away attention from academic achievement, while sadness has the opposite effect.
• The sadder-but-wiser hypothesis suggests sadness is associated with careful decision making.
• Sadness reduces biases from heuristics, reputation, and stereotypes.
• Lerner, Small, and Lowenstein (2004) found sadness and disgust affected buying and selling prices.
• Disgust is associated with a need to expel things, and sadness with a need for change.
• Sad participants were willing to pay more for a set of pens, reflecting a need for reward replacement.

Decisions Can Depend on the Context Within Which They Are Made

• Increasing choice alternatives can influence decisions.
• Redelmeier & Shafir (1995) found that physicians were less likely to prescribe arthritis medication when given two medication options compared to one.
• Faced with a more difficult decision, people sometimes make no decision at all.

Too Much Choice

• Consumer research shows the too-much-choice effect or choice overload.
• Iyengar and Lepper (2000) found that more choice leads to less purchasing and less satisfaction.
• More alternatives mean more have to be turned down, increasing search costs, time, and uncertainty.

Preceding Decisions

• Context affects medical decision making.
• Shen, Rabinowitz, Geist, & Shafir (2010) found that physicians' decisions to perform a caesarean section were influenced by preceding cases.
• More physicians recommended caesarean sections after reviewing serious cases compared to routine cases.

How Choices Are Stated

• Opt-in vs. opt-out procedures affect organ donation rates.
• Opt-in (informed consent): active registration required.
• Opt-out (presumed consent): everyone is a donor unless they opt out.
• Organ donations are higher in opt-out countries.
• Celebrity endorsement and coordinators also influence organ donation rates.
• The status quo bias is the tendency to do nothing when faced with a decision.
• People stick with their current providers even when better options exist.
• Slovic, Monahan, and MacGregor (2000) showed that the way information is presented affects decisions.
• Statements framed as frequencies (e.g., 20 out of 100) led to different choices than statements framed as probabilities (e.g., 20 per cent chance).

Framing

• Tversky and Kahneman (1981) found that choices are influenced by how they are stated or framed.
• When choices are framed in terms of gains, people use a risk aversion strategy.
• When choices are framed in terms of losses, people use a risk-taking strategy.
• Framing highlights some features and de-emphasizes others.

Neuroeconomics: The Neural Basis of Decision Making

• Neuroeconomics combines psychology, neuroscience, and economics to study brain activation during decisions.
• This research identifies brain areas associated with affective experiences.
• Sanfey et al. (2003) used the ultimatum game to measure brain activity during decision making.
• The ultimatum game involves a proposer and a responder splitting money.
• Responders reject unfair offers, even though it means getting nothing.
• Emotions, not just rational calculations, drive decisions in the ultimatum game.
• The right anterior insula is activated more strongly when responders reject an offer, indicating negative emotional states.
• The prefrontal cortex (PFC) is activated during the decision task, but activation is the same for accepted and rejected offers.
• The PFC handles the cognitive demands of the task, such as accumulating money.
• The anterior insula handles the emotional goal of resenting unfairness.