🧠Business Cognitive Bias Unit 2 – Heuristics and Biases in Decision Making

Key Concepts and Definitions

• Heuristics mental shortcuts or rules of thumb used to simplify complex decision-making processes and reduce cognitive effort
• Cognitive biases systematic errors in thinking that can lead to irrational judgments and decisions
  • Stem from heuristics, emotions, social influences, and other psychological factors
• Bounded rationality the idea that human decision-making is limited by available information, cognitive constraints, and time pressures
• Dual-process theory proposes two distinct systems of thinking: System 1 (fast, intuitive, and automatic) and System 2 (slow, deliberate, and controlled)
• Anchoring the tendency to rely heavily on the first piece of information encountered (the "anchor") when making decisions or estimates
• Availability heuristic the tendency to overestimate the likelihood of events that are easily remembered or come readily to mind
• Confirmation bias the inclination to seek out, interpret, and recall information in a way that confirms one's preexisting beliefs or hypotheses

Types of Heuristics

• Representativeness heuristic judging the probability of an event based on how closely it resembles a typical case or stereotype (base rate neglect)
• Affect heuristic making decisions based on emotional responses rather than objective assessments of risks and benefits
• Recognition heuristic choosing the option that is most familiar or recognizable, assuming it is the best choice
• Take-the-best heuristic selecting the option that outperforms others on the most important attribute, ignoring other factors
• Satisficing choosing the first option that meets a minimum threshold of acceptability, rather than seeking the optimal solution
  • Differs from maximizing, which involves exhaustively searching for the best possible option
• Elimination-by-aspects heuristic progressively eliminating options that fail to meet certain criteria, until only one option remains
• Fluency heuristic judging the ease with which information is processed as an indicator of its accuracy, familiarity, or importance

Common Cognitive Biases

• Hindsight bias the tendency to perceive past events as having been more predictable than they actually were at the time
• Overconfidence bias the tendency to overestimate one's own abilities, knowledge, or chances of success
• Framing effect the way in which a problem or decision is presented (the "frame") can significantly influence the choices made
• Sunk cost fallacy the tendency to continue investing time, money, or effort into a project or decision because of past investments, even when it is no longer rational to do so
• Endowment effect the tendency to value an object more highly when one owns it compared to when one does not
• Fundamental attribution error the tendency to overemphasize dispositional (personality-based) explanations for others' behavior while underestimating situational influences
• In-group bias the tendency to favor and treat members of one's own group preferentially compared to those outside the group
  • Can lead to discrimination, stereotyping, and intergroup conflict

Decision-Making Models

• Expected utility theory a normative model that suggests people make decisions by choosing the option with the highest expected utility (probability-weighted average of all possible outcomes)
• Prospect theory a descriptive model that accounts for how people actually make decisions under risk and uncertainty
  • Proposes that people evaluate outcomes relative to a reference point and are more sensitive to losses than gains (loss aversion)
• Bounded rationality model emphasizes the limitations of human cognitive capacities and the use of heuristics to make satisfactory, rather than optimal, decisions
• Recognition-primed decision model describes how experts make rapid decisions in complex, time-pressured situations by recognizing patterns and applying appropriate action scripts
• Naturalistic decision making framework studies how people make decisions in real-world settings characterized by ill-structured problems, dynamic environments, and competing goals
• Multi-attribute utility theory a model for making decisions when there are multiple, often conflicting, objectives or criteria to consider
• Analytic hierarchy process a structured technique for organizing and analyzing complex decisions by breaking them down into pairwise comparisons of alternatives

Real-World Applications

• Behavioral economics applies insights from psychology and other social sciences to understand and influence economic decision-making (nudging)
• Neuromarketing uses neuroscience techniques (fMRI, EEG) to study consumer behavior and optimize marketing strategies
• Risk perception and communication understanding how people perceive and respond to risks can inform public policy, health interventions, and risk management
• Hiring and personnel selection awareness of biases (stereotyping, halo effect) can help organizations make more objective and equitable hiring decisions
  • Structured interviews, work sample tests, and multiple raters can reduce bias
• Medical decision making recognizing cognitive biases (overconfidence, anchoring) can improve diagnostic accuracy and treatment choices
• Negotiation and conflict resolution understanding the role of biases (framing, reactive devaluation) can facilitate more effective and mutually beneficial outcomes
• Investment and financial planning knowledge of behavioral finance concepts (mental accounting, herding) can help individuals make more rational and disciplined investment decisions

Overcoming Biases

• Debiasing techniques strategies designed to reduce the impact of cognitive biases on decision-making
  • Includes considering alternative perspectives, seeking disconfirming evidence, and using decision aids (checklists, algorithms)
• Metacognition the ability to think about and monitor one's own thought processes, which can help identify and correct biased reasoning
• Perspective-taking actively considering the viewpoints and experiences of others, especially those from different backgrounds or with opposing views
• Premortem technique imagining that a project or decision has failed and working backwards to identify potential causes and mitigate risks
• Calibration training exercises that help individuals align their subjective confidence with their objective accuracy, reducing overconfidence
• Diversity and inclusion fostering a diverse range of perspectives and experiences within a group can help challenge assumptions and reduce the impact of shared biases
• Accountability making decision-makers answerable for their choices can motivate more thorough and unbiased reasoning, as well as the consideration of alternative viewpoints

Ethical Considerations

• Fairness and non-discrimination ensuring that decision-making processes and outcomes do not unfairly disadvantage certain groups or individuals based on protected characteristics
• Privacy and data protection safeguarding the collection, use, and storage of personal information used in decision-making systems, especially those involving machine learning and AI
• Transparency and explainability making the reasoning behind decisions, particularly those made by algorithms or AI, understandable and accessible to stakeholders
• Responsibility and accountability determining who is liable for the consequences of biased or flawed decisions, especially when multiple parties (humans and machines) are involved
• Informed consent ensuring that individuals are aware of and agree to how their data is being used in decision-making processes that affect them
• Algorithmic bias recognizing and mitigating the potential for machine learning models to perpetuate or amplify societal biases present in training data
• Value alignment ensuring that the goals and priorities of decision-making systems align with human values and ethical principles

Future Research and Trends

• Debiasing interventions developing and testing new strategies for reducing cognitive biases in various domains (healthcare, finance, policymaking)
• Cognitive computing and AI exploring how advances in artificial intelligence can augment human decision-making and reduce bias
  • Challenges include ensuring transparency, fairness, and robustness of AI systems
• Personalized decision support tailoring decision aids and interventions to individual differences in cognitive style, expertise, and susceptibility to specific biases
• Neuroimaging and decision neuroscience using brain imaging techniques to better understand the neural basis of heuristics, biases, and decision-making processes
• Behavioral insights and public policy applying findings from behavioral science to design more effective and equitable policies and interventions (choice architecture, nudging)
• Cross-cultural research investigating how cultural differences in values, norms, and cognitive styles influence the prevalence and impact of heuristics and biases
• Longitudinal studies examining how heuristics and biases develop and change over the lifespan, and how early interventions can promote more rational decision-making