MGMT 230 - 1 Decision Making

These flashcards cover key concepts related to decision making, cognitive biases, and heuristics, essential for understanding how people make choices.

Heuristic

A shortcut or rule of thumb used to simplify decision making, contrasting with an algorithm.

Algorithm

An exhaustive step-by-step procedure that guarantees a correct solution but may be time-consuming.

System $1$ Thinking

Fast and intuitive decision-making process, often relying on heuristics.

System $2$ Thinking

Slow, deliberate, and logical decision-making process that requires mental effort.

Survivorship Bias

The logical error of focusing on successful entities while ignoring those that did not succeed.

Cheater-detection module

A specialized cognitive mechanism evolved for identifying dishonest behavior in social interactions.

Default Effect

The tendency for people to make a particular choice simply because it is the default option.

Anchoring Effect

The cognitive bias where individuals rely heavily on the first piece of information encountered when making decisions.

Availability Heuristic

A mental shortcut that relies on immediate examples that come to mind when evaluating a specific decision.

Representativeness Heuristic

A mental shortcut where people judge the probability of an event based on how similar it is to a prototype.

Confirmation Bias

The cognitive bias where people favor information that confirms their existing beliefs or hypotheses.

Loss Aversion

The psychological phenomenon where losses are felt more severely than equivalent gains.

Sunk Cost Fallacy

The tendency to continue an endeavor once an investment in money, effort, or time has been made, regardless of future returns.

Maximizer vs. Satisficer

Maximizers seek the absolute best option, while satisficers settle for an option that is "good enough."

Exponential Growth Bias

The underestimation of the impact of exponential growth over time in various contexts.

Peak End Rule

The psychological heuristic where people judge experiences largely based on how they were at their peak and how they ended.

Gambler's Fallacy

The erroneous belief that past events can influence the probability of future independent events.

Thick-tail distribution

Statistical distributions in which extreme events occur more frequently than predicted by normal distributions.