Executive Decision-Making Using Intelligence

By Matthew Lehman, Ph.D.

Two executives talking.

The evolving climate of crime and other public safety issues presents law enforcement executives with a level of persistent uncertainty that pushes decision-making to an unprecedented level of complexity. Demand to operate at a high cognitive level is most relevant in the current environment, where there are more complex issues, compressed budgets (requiring creative solutions), and rapid changes in technology used in investigations.

Usually, decision-making is considered a dynamic process, influenced by factors that lead to massive difficulties. This is compounded for law enforcement administrators during critical incidents and rapidly evolving situations where there is a requirement and an expectation to make the right decisions. Integrating intelligence analysis into an agency’s administration can help solve this issue.

Decision-Making Theory

Common sense dictates what it means to decide, but we can take for granted the cognitive investment it takes to get to the point that the correct action is clear (i.e., decision threshold). The term decision can be interpreted as “conscious deliberation.”1 Therefore, decision-making would be the irreversible commitment to that conscious debate.

To make the concept even more frustrating, a unifying theory of decision-making among the sciences does not exist. Many science disciplines describe and measure decision-making, including economics, mathematics, statistics, neurology, psychology, sociology, and political science. The science of decision-making is extraordinarily complex and entails many different theories that surpass the level of review in this article.

Matthew Lehman

Dr. Lehman is a supervisory intelligence analyst for the FBI’s Office of Partner Engagement, an adjunct faculty member at the University of Virginia, and an instructor with the FBI National Academy.

Logically, strong leaders make effective decisions, but what tools are available to make decisions with the least amount of cognitive effort? In this article, I argue that an appropriately leveraged intelligence unit can help administrators make better decisions.

Uncertainty, Risk, and Ambiguity

The difficulty in making decisions stems from the ability to identify uncertainty and risk. The Society for Risk Analysis Glossary defines uncertainty as “not knowing the true value of a quantity or the future consequences of an activity” and risk as “the effect of uncertainty on objectives.”2 In academic literature, the variability of each option’s possible outcome influences risk, meaning that options get riskier as the distribution of potential results spreads out.3 The range of outcomes can include a value tied to danger, but risk does not always mean danger.

Consider a horizontal, linear representation of time flowing from left to right, with the present time in the center. The potential event, or “boom,” is to the right of the present time, and the decision to be made is to the left. Some practitioners refer to the furthest point from an event on this time horizon as “left of the boom.” This is an example of when a decision becomes difficult because of the uncertainty and variability of the many outcomes available between now and the event. Option selection this far out from the event is too risky; we do not want to be wrong or waste resources, so this decreases the likelihood of committing to a decision.

Knowing that making decisions involves uncertainty and risk, what happens when we are doubtful of the information gathered to make our inference of uncertainty? In other words, what if we are uncertain about uncertainty? This is something we deal with every day, and it is called ambiguity.

The dictionary defines ambiguous as “doubtful or uncertain especially from obscurity or indistinctness.”4 Ambiguity means that collected information is not always obvious, clearly distinguished, or structured enough to categorize, so we must interpret it. We are constantly examining unclear information and seeking to find a pattern or structure that will make it less confusing, but many times errors occur. This can have a detrimental effect if the ambiguous information misinforms our determination of uncertainty, resulting in a riskier decision.

It is interesting how we handle ambiguity in our decision-making process. Evidence suggests that we all have differing levels of tolerance to ambiguity. “Intolerance of ambiguity may be defined as ‘the tendency to perceive (i.e., interpret) ambiguous situations as sources of threat’ [and] tolerance of ambiguity as ‘the tendency to perceive ambiguous situations as desirable.”5

The main takeaway from this discussion about uncertainty and risk is that decision-making is based on our ability to process the unknown, so having experience is helpful.

Recognition-Primed Decision-Making

In 1985, researchers developed the recognition-primed decision (RPD) model, a principle for naturalistic decision-making, after studying urban fireground commander decisions. The commanders used their previous experiences as a guide for rapid decision-making in command and control.6 The RPD model suggests that a decision maker’s past experience has a causal effect on the current situation. Researchers of this model reported that these patterns revealed cues, provided expectations, identified plausible goals, and suggested typical reactions in a certain situation. The commanders rapidly matched the current emergency to learned patterns recalled from similar occurrences in the past and carried out the most typical course of action without comparing options.7 In other words, commanders were using similar experiences to “prime” the decisional process. Notably, the study identified that the commanders rapidly formed decisions, monitored the situation for changes, and modified plans according to those changes.8

In previous decision models, evaluation was used to select options. In contrast, the research on RPD strategies indicated that the fireground commanders rarely deliberated over advantages or disadvantages of their options.9 The researchers proposed that the key to the fireground commander’s evaluation of a decision was to use mental simulation, or imagery, to watch their decision being implemented and to test for any unwanted outcomes.

Rarely deliberating on options means that skill level influences a person’s ability to recognize previous experiences.10 Most likely, this is because an expert in a particular field has more knowledge to recall, which results in better decision-making. The RPD model suggests that a person who makes rapid decisions with reliable accuracy and without debating is an expert. This is significant for developing law enforcement decision-making because training11 and intelligence products can help build experience.12 These tools will elevate a person’s recall capability to visualize courses of action despite never having “lived” the situation.



In the criminal context, intelligence is formally defined as “information compiled, analyzed, and/or disseminated in an effort to anticipate, prevent, or monitor criminal activity.”13 The key word in this definition is analyzed because there would be no intelligence without analysis.

“ ... collected information is not always obvious, clearly distinguished, or structured enough to categorize, so we must interpret it.”

Typically, intelligence is characterized in three ways.

  1. Process: The act of converting information into intelligence through analysis.14
  2. Product:  A written or verbal distribution of the results of the intelligence process in the form of actionable information, sometimes referred to as a knowledge product because it informs decision makers on a suitable strategy.15
  3. Entity:  A person or group of people who collect, process, analyze, and create an actionable intelligence product.

In a slightly deeper context, the U.S. Department of Defense says that:

Intelligence tells JFCs [joint force commanders] what their adversaries or potential adversaries are doing, what they are capable of doing, and what they may do in the future. Intelligence assists JFCs and their staffs in visualizing the battlespace and in achieving information superiority. Intelligence also contributes to information superiority by attempting to discern the adversary’s probable intent and future course of action.16

This means that the intelligence unit is taking raw information and applying it to a prospective future that is simulated through complex cognitive analysis, similar to recognition-primed decision-making.

To be clear, intelligence does not predict the future — it is the intersection of many different science disciplines, used to reduce ambiguity and the unknown to produce something to react upon.

Intelligence Products

There are three types of intelligence products.

  1. Tactical: Case-driven and assist decision-making at the investigator level.
  2. Operational: Created through analysis so that operational units can understand the issues and develop 30- to 60-day mitigation strategies.
  3. Strategic: The most challenging to create, comprise “information concerning existing patterns or emerging trends of criminal activity designed to assist in criminal apprehension and crime control strategies, for both short- and long-term investigative goals.”17

Intelligence products can build decision makers’ repertoire of knowledge by using recognition- primed decision-making, thereby preparing them with information and assessments about adversarial operations. This primed data would prompt them to conduct mental simulations, creating a shortcut for future decision options when they recognize a familiar situation. Analysts use their experience, specialized knowledge, and a multitude of tools and tradecraft procedures to create these products.

Intelligence-Led Decisions

Regarding the improbability of making decisions far left of the boom because of the discomfort associated with risk, we can become proactive through intelligence. We can collect information and use the semiscientific methodology of analysis to gather the knowledge a manager needs for proactive strategy. To be proactive, we must narrow down the possible outcomes to increase assurance in making risky decisions. Executives must have confidence in their personnel to produce information that consists of inferential analysis that determines “the best explanation for uncertain, contradictory, and incomplete data.”18 Ambiguous data affects decisions and risk perception, so by using intelligence we can reduce the unknown to focus on carrying out a decisive action and conserving cognitive ability.

“The RPD [Recognition-Primed Decision] model suggests that a decision maker’s past experience has a causal effect on the current situation.”

Law enforcement administrators who have expert-level intuitions driven from high-volume experiences are likely to dismiss low-probability, high-impact events. It is imperative that decision makers use intelligence to explore the dark corners of the problems and develop a concept of operations that may depart from biased common sense. Following information to an actionable assessment saves a lot of time if the unexpected occurs. However, the dogmatic culture of law enforcement typically does not embrace left of the boom explorations given all the problematic events that are happening. When an executive’s actions are too focused on the response effort, it can present an unexpected outcome that could have been anticipated.

An intelligence unit that is not converting information so that it is actionable is not supporting operations. By creating useable, tactical, operational, and strategic products, the unit is priming managers for future decision thresholds. Using specific verbiage when creating products, such as “likelihood,” “confidence level,” and “assumptions,” accomplishes that goal.19 Each of these terms describes probability, utility, value, and certainty and helps to prepare a course of action.


Despite its length, this article has only grazed the surface of the complex nature of decision-making, especially in difficult and uncertain situations that law enforcement encounters every day. Decision-making permeates every aspect of our personal and professional lives. A constant expansion of how to leverage its power is essential for everyone in an organization, from academy cadet to chief.

The purpose of this article was to describe how decision theory and methodology connect to intelligence and can function best within a law enforcement organization. In the current climate of change, agencies will benefit from expanding the science of decision-making at all levels by incorporating intelligence, which offers an evidence-based approach to mitigation through knowledge development.

The FBI Office of Partner Engagement has several courses available for analysts, officers, and managers. If you are interested in attending an intelligence course, contact OPE-EEU@fbi.gov for more information.

“ ... by using intelligence we can reduce the unknown to focus on carrying out a decisive action and conserving cognitive ability.”

Dr. Lehman can be contacted at mlehman2@fbi.gov.


1 Herbert A. Simon, “A Theory of Administrative Decision” (Ph.D. diss., University of Chicago, 1943), 3, accessed March 18, 2021, https://search.proquest.com/docview/301881354/citation/8E90AF2660D14588PQ/1.
2 Society for Risk Analysis, Society for Risk Analysis Glossary (2018), 4, accessed March 12, 2021, https://www.sra.org/wp-content/uploads/2020/04/SRA-Glossary-FINAL.pdf.
3 Elke Weber, Sharoni Shafir, and Ann-Renée Blais, “Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation,” Psychological Review 111, no. 2 (2004), accessed March 12, 2021, https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/391/391b.pdf.
4 Merriam-Webster Dictionary, s.v. “ambiguous,” accessed March 12, 2021, https://www.merriam-webster.com/dictionary/ambiguous.
5 Stanley Budner, “Intolerance of Ambiguity as a Personality Variable,” Journal of Personality 30, no. 1 (March 1962): 29, accessed March 12, 2021, https://doi.org/10.1111/j.1467-6494.1962.tb02303.x.
6 Gary Klein, Roberta Calderwood, and Anne Clinton-Cirocco, “Rapid Decision Making on the Fire Ground,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting 30, no. 6 (September 1986), accessed March 30, 2021, https://doi.org/10.1177/154193128603000616.
7 Gary Klein, “Naturalistic Decision Making,” Human Factors 50, no. 3 (June 2008), accessed March 12, 2021, https://doi.org/10.1518/001872008X288385.
8 U.S. Army Research Institute for the Behavioral and Social Sciences, Recognition-Primed Decision Strategies, Gary Klein and Beth Crandall (Alexandria, VA, 1996), accessed March 12, 2021, https://www.researchgate.net/publication/235112159_Recognition-Primed_Decision_Strategies.
9 Ibid.
10 Gary Klein, Roberta Calderwood, and Donald MacGregor, “Critical Decision Method for Eliciting Knowledge,” IEEE Transactions on Systems, Man, and Cybernetics 19, no. 3 (May 1989), accessed March 12, 2021, https://doi.org/10.1109/21.31053.
11 Michelle Ridlehoover, “Need for Critical Thinking in Police Training,” FBI Law Enforcement Bulletin, May 7, 2020, accessed March 12, 2021, https://leb.fbi.gov/articles/perspective/perspective-need-for-critical-thinking-in-police-training.
12 Nate Huber, “Intelligence-Led Policing for Law Enforcement Managers,” FBI Law Enforcement Bulletin, October 10, 2019, accessed March 12, 2021, https://leb.fbi.gov/articles/featured-articles/intelligence-led-policing-for-law-enforcement-managers.
13 International Association of Chiefs of Police, National Law Enforcement Policy Center, Criminal Intelligence (Alexandria, VA: IACP National Law Enforcement Policy Center, 1998), 11, accessed March 12, 2021, https://it.ojp.gov/documents/CriminalIntelligencePaper0703.pdf.
14 Huber.
15 Jerry Ratcliffe, “The Effectiveness of Police Intelligence Management: A New Zealand Case Study,” Police Practice and Research 6, no. 5 (December 2005), accessed March 12, 2021, https://doi.org/10.1080/15614260500433038.
16 Joint Chiefs of Staff, Doctrine for Intelligence Support to Joint Operations, Joint Publication 2-0 (2000), v, accessed March 12, 2021, https://www.hsdl.org/?abstract&did=3735.
17 IACP National Law Enforcement Policy Center, 3.
18 Emily Patterson, Emilie Roth, and David Woods, “Predicting Vulnerabilities in Computer-Supported Inferential Analysis Under Data Overload,” Cognition, Technology, and Work 3, no. 4 (2001): 225, accessed March 12, 2021, http://csel.eng.ohio-state.edu/productions/intelligence/2_Studies/Ariane_501_Analysis/PattersonEtAl2001_PredictingVulnerabilitiesInInferentialAnalysis.pdf.
19 Office of the Director of National Intelligence, Analytic Standards, Intelligence Community Directive 203 (2015), accessed March 12, 2021, https://www.dni.gov/files/documents/ICD/ICD%20203%20Analytic%20Standards.pdf.