Individual Decision-Making: Common Models in Psychology

Models of individual decision-making in psychology identify psychological factors that shape individual decision-making. Decision governance will influence these factors, which makes it necessary to at the very least be aware of them, as a basis of thinking about how they may interplay with guidelines and processes introduced through governance.

This text is part of the series on decision governance. Decision Governance is concerned with how to improve the quality of decisions by changing the context, process, data, and tools (including AI) used to make decisions. Understanding decision governance empowers decision makers and decision stakeholders to improve how they make decisions with others. Start with “What is Decision Governance?” and find all texts on decision governance here.

1. Dual-Process Theory

Dual-Process Theory posits that decision-making involves two distinct cognitive systems operating in tandem:

• System 1: Fast, automatic, and intuitive. It relies on mental shortcuts or heuristics and is often influenced by emotions and inherent biases. This system facilitates rapid decisions, especially in familiar or routine contexts, but can also lead to errors due to oversimplification (Kahneman, 2011).
• System 2: Slow, deliberate, and analytical. It involves conscious reasoning, careful evaluation of information, and logical deduction. While more accurate, it is resource-intensive and employed when decisions are complex or unfamiliar (Stanovich & West, 2000).

Stanovich and West expanded this framework, identifying individual differences in the ability to utilize System 2 effectively. Their research emphasizes the impact of intelligence, experience, and education on decision-making quality. Intelligence influences the ability to process information systematically, ensuring that decisions align with long-term goals. Experience enriches the decision-maker’s repository of patterns and heuristics, allowing for faster and more contextually appropriate decisions. Education shapes critical thinking and analytical skills, enhancing the ability to engage System 2 processes effectively (Stanovich & West, 2000).

Applications include structured training programs designed to reduce reliance on biases and foster more analytical approaches in organizational decision-making. Other research supports the distinction between System 1 and System 2 by demonstrating their role in diverse contexts. For example, Evans (2008) discusses how System 1 dominates in high-pressure environments due to its speed, while System 2 is more effective in analytical tasks requiring deliberate reasoning. Neuroimaging studies also highlight distinct neural pathways associated with intuitive and analytical processes, providing physiological evidence for this dual-system framework (Goel, 2007). These findings reinforce the importance of designing interventions that balance intuitive and reflective decision-making to enhance overall effectiveness.

Applications include structured training programs designed to reduce reliance on biases and foster more analytical approaches in organizational decision-making.

2. The Recognition-Primed Decision Model

Proposed by Gary Klein, the Recognition-Primed Decision (RPD) Model highlights how individuals, particularly those with expertise, make decisions under time constraints and uncertain conditions. The model focuses on:

• Pattern Recognition: Experienced decision-makers draw upon their knowledge base to recognize scenarios similar to past situations (Klein, 1999).
• Mental Simulation: Once a course of action is identified, individuals mentally simulate its implementation to assess feasibility and anticipate potential outcomes (Klein, 1999).

The RPD model posits that experts rarely compare multiple alternatives. Instead, they quickly identify a viable option and proceed after minimal evaluation. This model is particularly relevant in high-stakes environments such as emergency response, military operations, and aviation, where decisions must be made swiftly and effectively (Klein, 1999).

3. The Theory of Planned Behavior

The Theory of Planned Behavior (TPB), developed by Icek Ajzen, provides a framework for understanding how behavioral intentions are formed and executed. It is built upon three core constructs:

• Attitudes: This construct reflects the individual’s overall evaluation of the behavior in question, whether they perceive it positively or negatively. These evaluations are influenced by underlying beliefs about the outcomes of the behavior and their desirability. For example, an employee’s attitude toward adopting a new technology will depend on whether they believe it will improve their efficiency and if they value such improvements (Ajzen, 1991).
  
• Subjective Norms: This dimension captures the perceived social pressures to engage in or avoid a behavior. It considers the expectations of significant others, such as colleagues, supervisors, or cultural norms within the organization. For instance, an individual may feel compelled to comply with team standards to avoid disapproval, even if their personal preference differs (Ajzen, 1991).
  
• Perceived Behavioral Control: This aspect refers to the individual’s perception of their ability to perform the behavior, taking into account both internal factors (e.g., skills and confidence) and external factors (e.g., availability of resources or obstacles). For example, an employee might intend to complete a project early but perceive limited time or support as barriers (Ajzen, 1991).
  

TPB is widely applied in designing interventions for behavioral change, leveraging its constructs to shape intentions and behaviors effectively.

• Encouraging Pro-Environmental Actions: TPB is used to develop campaigns that address individuals’ attitudes toward sustainability, emphasizing the benefits of environmentally friendly practices, such as energy conservation or recycling. These campaigns also engage subjective norms by highlighting social approval and collective responsibility, while addressing perceived barriers to action, such as convenience or cost.
  
• Improving Workplace Compliance: Organizations utilize TPB to increase adherence to policies and regulations. For instance, enhancing employee attitudes toward safety measures, reinforcing social norms through leadership and peer examples, and ensuring that employees feel equipped to comply with workplace requirements can improve compliance.
  
• Promoting Public Health Initiatives: TPB informs the design of interventions to encourage healthy behaviors, such as vaccination or physical activity. Public health campaigns often focus on shaping positive attitudes by providing evidence of health benefits, leveraging social norms through testimonials or endorsements, and addressing perceived control by making resources accessible and reducing logistical barriers (Ajzen, 1991).
  

Additional research further underscores the robustness of TPB. Studies have validated its application across various fields, including education and transportation planning. For example, Armitage and Conner (2001) conducted a meta-analysis demonstrating that TPB effectively predicts behaviors like physical activity and dietary choices. Similarly, Bamberg and Schmidt (2003) applied TPB to understand and promote public transport use, finding that interventions targeting attitudes, social norms, and perceived control can significantly influence commuter behavior.

4. The Elimination by Aspects Model

Developed by Amos Tversky, the Elimination by Aspects Model explains how individuals simplify complex decision-making processes by sequentially focusing on specific criteria. Key features of the model include:

• Attribute Prioritization: Decision-makers evaluate options based on a ranked list of attributes, beginning with the most critical. This process involves identifying the most important features that a decision option must fulfill. For instance, when hiring a candidate, an employer might prioritize attributes such as relevant experience or technical skills over other considerations like location or availability. Prioritization helps in focusing efforts on criteria that matter most to the decision’s success (Tversky, 1972).
  
• Sequential Elimination: Options failing to meet the threshold for an attribute are excluded from consideration, progressively narrowing the pool of alternatives. This step-by-step elimination simplifies complex decisions by reducing cognitive load. For example, in product selection, a consumer might first eliminate items outside their budget, then those lacking a key feature, such as durability or warranty. By systematically discarding unsuitable options, the decision-maker streamlines the process and avoids overwhelming comparisons (Tversky, 1972).
  

This approach reduces cognitive load and allows individuals to navigate decisions with multiple alternatives more efficiently.

• Product Selection: Consumers often face a vast array of choices, such as selecting a smartphone or household appliance. Using this model, they might prioritize features like price, brand reputation, or durability as primary criteria. For example, a buyer might first eliminate options beyond their budget, then narrow their choices further based on specific desired features like camera quality or energy efficiency.
  
• Hiring Processes: Employers frequently need to sift through numerous applications to identify the best candidate. They might start by eliminating applicants who do not meet minimum qualifications, such as educational requirements or years of experience. Subsequently, they may evaluate shortlisted candidates based on increasingly specific aspects, such as cultural fit or technical proficiency, to make the final decision.
  
• Prioritization Tasks in Management Contexts: Managers regularly deal with competing priorities in resource allocation or project selection. By employing this model, they can systematically exclude less critical projects or options that fail to meet essential benchmarks, such as strategic alignment or cost-effectiveness, eventually identifying the most impactful choices (Tversky, 1972).
  

5. Heuristics and Biases Framework

Kahneman and Tversky’s research on heuristics and biases underscores the systematic ways in which intuitive decision-making can deviate from rational standards. The framework identifies several heuristics and their associated biases:

• Availability Heuristic: Judgments are influenced by the ease with which examples come to mind, leading to overestimation of recent or salient events (Tversky & Kahneman, 1974).
• Representativeness Heuristic: Probability assessments are made based on similarity to stereotypes or prototypes, often ignoring base rates (Tversky & Kahneman, 1974).
• Anchoring Bias: Initial information serves as a reference point, disproportionately influencing subsequent judgments and estimates (Tversky & Kahneman, 1974).

This framework has profound implications for managerial decision-making, particularly in risk assessment and strategic planning. Awareness of these biases enables organizations to design processes that mitigate their effects, such as structured decision aids and checklists (Tversky & Kahneman, 1974).

6. Emotional Decision-Making Models

Antonio Damasio’s Somatic Marker Hypothesis emphasizes the critical role of emotions in guiding decisions, particularly under conditions of uncertainty. Key aspects include:

• Somatic Markers: Emotional signals associated with prior experiences are activated when evaluating options, helping to prioritize alternatives based on anticipated outcomes. For example, a manager deciding whether to approve a high-risk project may experience a gut feeling of unease based on prior failures in similar scenarios. This emotional response acts as a somatic marker, signaling potential negative consequences and guiding the decision toward safer options (Damasio, 1994).
  
• Integration of Emotion and Cognition: While traditional models often dichotomize emotion and logic, this hypothesis highlights their interdependence in decision-making processes. For instance, in negotiations, a leader’s emotional awareness can influence their cognitive strategies, such as recognizing when to concede or stand firm. Emotional insights complement analytical reasoning by providing a deeper understanding of interpersonal dynamics and potential outcomes (Damasio, 1994).
  

This model is instrumental in contexts requiring interpersonal judgment, such as leadership decisions, negotiation strategies, and customer relationship management. It underscores the need for managers to understand and harness emotional cues effectively (Damasio, 1994). Further research supports the Somatic Marker Hypothesis, such as Bechara et al. (1997), who demonstrated that patients with damage to the ventromedial prefrontal cortex, a region critical for processing emotional signals, show impaired decision-making despite intact cognitive abilities. This finding underscores the importance of emotional integration in effective decision-making. Similarly, studies in neuroeconomics have linked somatic markers to risk-taking behaviors, highlighting their role in shaping both individual and group-level decisions under uncertainty.

Conclusion

Psychological models of individual decision-making provide essential insights into the cognitive, emotional, and contextual factors that influence choices. By integrating theories such as Dual-Process, Recognition-Primed Decisions, and Emotional Decision-Making into organizational frameworks, managers can enhance decision quality, reduce errors, and achieve better outcomes.

The application of these models to decision governance practices offers a structured approach to diagnosing weaknesses, implementing targeted interventions, and monitoring improvements in decision-making performance.

References

• Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
• Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499.
• Bamberg, S., & Schmidt, P. (2003). Incentives, morality, or habit? Predicting students’ car use for university routes with the models of Ajzen, Schwartz, and Triandis. Environment and Behavior, 35(2), 264-285.
• Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding Advantageously Before Knowing the Advantageous Strategy. Science, 275(5304), 1293-1295.
• Damasio, A. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. Penguin Books.
• Evans, J. St. B. T. (2008). Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition. Annual Review of Psychology, 59(1), 255-278.
• Goel, V. (2007). Anatomy of deductive reasoning. Trends in Cognitive Sciences, 11(10), 435-441.
• Kahneman, D., & Tversky, A. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124-1131.
• Klein, G. (1999). Sources of Power: How People Make Decisions. MIT Press.
• Stanovich, K. E., & West, R. F. (2000). Individual Differences in Reasoning: Implications for the Rationality Debate. Behavioral and Brain Sciences, 23(5), 645-665.
• Tversky, A. (1972). Elimination by Aspects: A Theory of Choice. Psychological Review, 79(4), 281-299.

Definitions

1. Bounded Rationality: The concept that decision-makers operate within cognitive and informational constraints (Simon, 1955).
2. Heuristic: A mental shortcut used to simplify decision-making under uncertainty (Tversky & Kahneman, 1974).
3. Somatic Marker Hypothesis: A theory positing that emotional signals guide decision-making, especially under uncertainty (Damasio, 1994).
4. System 1 and System 2: Distinct cognitive processes characterized by automatic, intuitive responses versus deliberate, analytical reasoning (Kahneman, 2011).