Perceptiveness & Experience Shape Rapid Choices

If the time to decide is short, decision governance needs to improve how the decision maker identifies cues, matches them to experience, what they experience they match them to, and the quality of their prediction of action outcomes.

This text is part of the series on the design of decision governance. Decision governance are guidelines, rules, processes designed to improve how people make decisions. It can help ensure that the right information is used, that that information is correctly analyzed, that participants in decision making understand it, and that they use it before they make a decision. Find all texts on decision governance here.

What is the topic of this text?

If a decision process is designed according to the Recognition-Primed Decision (RPD) model, then, one, How does it explain decisions? and two, How to influence that process through governance?

Why is this topic relevant for decision governance?

RPD is an influential decision model for decision situations which require rapid reaction, or, in which the time between the moment the need for a decision is detected, and the decision, is short.

Background: What is Recognition-Primed Decision Making?

In the Recognition-Primed Decision model (Klein, 1993), a decision maker chooses the action they will take based on matching the cues in the decision situations to past experiences, assessing (mentally simulating) possible outcomes of actions informed by experience, and choosing those that seem most likely to yield the desired outcome. The model has been suggested as a description of how individuals choose in decision situations that they have considerable experience in, and when decisions need to be made quickly.

Examples: Decision making using the Recognition-Primed Decision model

Consider the following example.

In The Iliad by Homer, Achilles, the Greek hero, decides to withdraw from battle after a dispute with Agamemnon, the leader of the Greek forces during the Trojan War. Agamemnon, having been forced to return his war prize, Briseis, to appease the gods, demands Achilles’ prize as compensation. In response, Achilles, feeling dishonored and enraged by Agamemnon’s actions, chooses to withdraw himself and his troops from the fighting, despite the consequences for the Greek army.

Let’s imagine how Achilles’ decision would be explained by the Recognition-Primed Decision model.

• Pattern Recognition: Achilles quickly identifies the situation as one in which his honor has been challenged. His past experiences as a warrior guide this recognition, reinforcing the importance of a decisive response.
• Mental Simulation: He mentally simulates the outcomes of his withdrawal; such as the impact on his reputation and how Agamemnon and the Greek forces might react. Achilles predicts that withdrawing would force Agamemnon to acknowledge his value, even if it means the Greeks suffer in the short term.
• Experience-Based Action: Achilles’ course of action is not based on a detailed comparison of alternatives but on an instinctive response drawn from his warrior ethos. His experience leads him to conclude that taking a stand by withdrawing is the most appropriate way to address the slight to his status.

Additional examples are below.

Example: A firefighter arriving at the scene of a building fire immediately notices the color and movement of the smoke, the sound of crackling, and the heat intensity. These are cues that indicate how the fire is spreading and its potential danger to trapped occupants. The firefighter does not need to analyze these signals separately; they instinctively know these are critical indicators for deciding the next steps in firefighting tactics.

Example: A physician diagnosing a patient with chest pain matches the symptoms (sharp pain, radiating to the left arm, accompanied by shortness of breath) to a mental template of a myocardial infarction (heart attack). Based on this pattern, the physician immediately classifies the case as a high-priority emergency, even before all diagnostic tests are complete. The match to this template guides the decision to initiate emergency treatment without further deliberation.

Example: An air traffic controller observes two aircraft on a collision course interprets the context, considering factors such as the altitude, speed, and direction of the planes, as well as the current weather conditions and the volume of nearby air traffic. Even if the current situation partially matches a known pattern of a potential collision, the controller adapts their mental model to include these contextual factors, adjusting the recommended actions accordingly.

Example: A military commander in the field, recognizing the signs of an ambush based on the positioning of enemy forces and the sudden halt of communication, is prompted to take evasive maneuvers. The decision to execute a specific countermeasure arises from past experiences of similar situations, where the same pattern indicated an imminent threat. The commander quickly implements the pre-learned response, bypassing a detailed analysis of other possible strategies.

What is the explanation of a decision in this decision model?

In the Recognition-Primed Decision model, the explanation for a decision is that the decision maker sees it as the appropriate course of action based on the cues in the decision situation and their past experience. 

Both of these are in practice, and especially when decisions are quickly needed, black boxes. There is no need in this model to explain a decision, and the model is structured in such a way – there is no requirement in it for the decision maker to take any specific steps to collect information, consider if they accounted for all relevant cues, check if (soms) cues may be misleading, and so on.

The figure below summarizes the model.

How to influence this decision process through governance?

If the RPD is a good explanation for how a decision maker is choosing, then to steer them to better decisions, we need to influence how they identify cues, match them to experience, their experience, and prediction of outcomes of actions.

• Training decision makers to be more sensitive to cues, as well as helping them learn factors which influence the quality of decisions in the decision situation, are both relevant.
• Cues can be emphasized or deemphasized in the decision situation, which will as a result influence what the decision maker observes and matches to experience.
• Practice decision making repeatedly in simulated or real decision situations, and train on specific strategies that have been demonstrated to yield desirable outcomes. Gigerenzer (1996) found that familiarity with specific patterns and outcomes can significantly influence the heuristics used in decision-making. When a decision-maker frequently encounters a particular strategy or solution, it becomes part of their mental template, increasing the probability that they will adopt it when a similar situation arises.
• Prompting the decision-maker to reflect on previous decisions, especially when they have resulted in less-than-optimal outcomes, can help reshape their mental templates. Research by Kahneman (2011) on decision-making highlights the value of reflection in counteracting cognitive biases and improving future choices. Reflection helps decision-makers identify gaps in their existing patterns and modify their mental models to avoid repeating past mistakes.
• Introducing a trusted advisor or influencer into the decision-making process can help the decision-maker consider new cues and decision patterns. Research by Cialdini (2001) on social influence suggests that people are more likely to be influenced by individuals they trust or see as experts. In the context of the RPD model, advisors can act as external sources of experience, shaping how the decision-maker interprets the cues they receive.

References and Further Reading

• Klein, Gary A. “A recognition-primed decision (RPD) model of rapid decision making.” Decision making in action: Models and methods 5.4 (1993): 138-147.
• Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99-118.
• Cialdini, R. B. (2001). Influence: Science and Practice. Allyn & Bacon.
• Gigerenzer, G. (1996). On Narrow Norms and Vague Heuristics: A Reply to Kahneman and Tversky. Psychological Review, 103(3), 592-596.
• Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.