Level of Detail Can Influence Probability Estimates

The level of detail of information about events described in decision options should not influence probability estimates of these same events. Research suggests otherwise, highlighting two notable effects of detail on probability estimation:

1. “reading a detailed, as opposed to more general, description of a future event increased the estimated probability that the event would actually occur” Wakslak et al. (2006) citing Sherman et al. (1983), and
2. “symptoms of a disease in either a more concrete (e.g., low energy level, muscle aches, severe headaches) or abstract (e.g., disorientation, malfunctioning nervous system) manner and asked participants to imagine contracting the disease. Results indicated that participants who imagined concrete symptoms estimated the likelihood of actually contracting the disease to be higher than those who imagined abstract symptoms” Wakslak et al. (2006) citing Sherman et al. (1985).

In both cases, the perceived probability of an event is shaped by the level of detail provided, regardless of the event’s actual likelihood.

This text is part of the series on the design of decision governance. Decision Governance refers to values, principles, practices designed to improve the quality of decisions. Find all texts on decision governance here, including “What is Decision Governance?” here.

Implications for Decision Governance

These findings suggest that the level of detail provided during decision-making can significantly influence perceived probabilities and, consequently, the decisions made. Decision governance should consider this effect and address it systematically:

1. Standardizing Detail Levels: Decision processes should include guidelines or mandates on the required level of detail when describing decision contexts, available options, and potential outcomes. This can help mitigate the bias introduced by varying levels of detail on probability estimates. For example, in a corporate investment decision, a guideline might specify that each option’s financial projections must include detailed assumptions about market growth rates and competitive responses. By standardizing this level of detail, decision-makers are provided with consistent criteria for the amount and type of information required. This reduces the likelihood of overestimating an outcome based on speculative or overly vague details, ensuring that decisions are based on comparable and reliable information.
2. Using Detail to Influence Estimates: If a decision process seeks to steer probability estimates intentionally, increasing the level of detail could be employed as a tactic. However, this must be approached cautiously, as providing speculative or unwarranted detail risks misleading decision-makers.
   • Example of Good Use: In a risk assessment for a public infrastructure project, providing detailed data on historical flood patterns, projected climate trends, and specific engineering designs ensures decision-makers have a clear understanding of potential risks. This level of detail improves the quality of the probability estimates and supports informed decision-making.
   • Example of Bad Use: In a product launch scenario, adding speculative details about market reception based on vague or unverified trends (e.g., “customers will likely prefer this color scheme due to recent fashion trends”) may inflate the perceived success probability. This could lead to overconfidence and poor investment decisions based on unreliable assumptions.
3. Impact of Additional Assumptions: Adding detail about future events often requires introducing more assumptions about uncertain outcomes. This has two critical consequences:
   • Fragility of Assumptions: As detail increases, predictions become more susceptible to errors, as each assumption could be invalidated by new information over time. This makes detailed predictions more prone to failure. For example, in project planning for a large infrastructure initiative, adding highly specific details about future labor costs, material availability, and weather conditions might create an overly rigid plan. If any of these assumptions proves inaccurate due to unexpected economic shifts or climate anomalies, the entire project timeline or budget could be compromised. On the other hand, providing moderate detail, with contingency ranges for uncertain factors, allows for more robust planning while maintaining adaptability to new information.
   • Proliferation of Options: Increased detail can unintentionally generate new options. For example, specifying that “Option A leads to travel to Vancouver directly” might introduce a new option, such as “Option C: travel to Vancouver with a layover.” This increases complexity and may overwhelm the decision-making process.

Cases Where Detail Affects Probability Estimation

The statement “A detailed description of a future event increases the estimated probability of the event occurring, as compared to a general description” is supported by empirical research. For instance, Wakslak et al. (2006) conducted experiments where participants were asked to estimate the probability of future events, such as a person getting into a prestigious graduate school. When these events were described with detailed scenarios, including specifics about the person’s qualifications, the participants’ probability estimates were significantly higher compared to when the descriptions were more general. This suggests that detailed scenarios enhance cognitive accessibility, making events feel more plausible and thus more likely. Similarly, Sherman et al. (1985) showed that concrete descriptions, such as vivid symptoms of a disease, heightened perceived probabilities compared to abstract representations. This phenomenon aligns with findings in cognitive psychology that more vivid or concrete information is often perceived as more plausible or likely.

However, this effect is not universal. Cases where detailed descriptions do not increase perceived probability often involve highly skeptical audiences or situations where the additional detail introduces contradictions or implausible assumptions. For example, if a business scenario includes excessive and conflicting detail about market trends, stakeholders may begin to question the overall coherence of the forecast, leading to reduced confidence in its likelihood. Similarly, when the detail appears irrelevant or speculative—such as an overly specific narrative about future geopolitical changes in an investment report—it may be dismissed as noise, weakening its influence on probability estimates.

References and Further Reading

1. Wakslak, Cheryl J., et al. “Seeing the forest when entry is unlikely: probability and the mental representation of events.” Journal of Experimental Psychology: General 135.4 (2006): 641.
2. Sherman, Steven J., et al. “Social explanation: The role of timing, set, and recall on subjective likelihood estimates.” Journal of Personality and Social Psychology 44.6 (1983): 1127.
3. Sherman, Steven J., et al. “Imagining can heighten or lower the perceived likelihood of contracting a disease: The mediating effect of ease of imagery.” Personality and Social Psychology Bulletin 11.1 (1985): 118-127.

Definitions of Key Terms

• Cognitive Accessibility: The ease with which information or an idea can be brought to mind. Highly detailed or vivid information is often more accessible, influencing judgment and decision-making (Wakslak et al., 2006).
• Concrete Representations: Descriptions that are specific, detailed, and vivid, often including sensory or experiential information. These make events more imaginable and can heighten their perceived likelihood (Sherman et al., 1985).
• Abstract Representations: Descriptions that are general, vague, or conceptual, lacking specific details. These are less likely to enhance perceived probability due to reduced cognitive accessibility (Sherman et al., 1985).
• Fragility of Assumptions: The susceptibility of detailed predictions to errors when underlying assumptions are invalidated by new information. More detailed assumptions increase the likelihood of predictive failure (Wakslak et al., 2006).
• Proliferation of Options: The unintended creation of additional decision alternatives due to increased detail, which can complicate and overwhelm the decision-making process (Wakslak et al., 2006).