The ultimate guide to decision-making: a managerial approach
- Alberto Carniel
- Jan 19, 2020
- 11 min read
Updated: Dec 31, 2025
As a marketer, I’ve always been attracted by the psychological aspects that influence people in decision-making.
They help me understand what happens in a prospect’s mind when they’re deciding whether to buy a product or service—and they also guide me to make better strategic decisions.
If you want to learn how to effectively evaluate alternatives, discover what disruptive elements come into play during the decision-making process, and the best tools to use, keep reading.
Table of contents
THE DECISION-MAKING FRAMEWORK
Decision-making is the process that enables people to choose between two or more alternatives.
Each alternative comes with:
An expected value (what you think you’ll get), and
A degree of uncertainty (what you can’t fully control).
Because of uncertainty, decision-making requires real attention when evaluating the alternatives.
Decision-making framework
The “triangle” of decision-making can be summarized in three components:
Problem management
Decision analysis
Risk management
Kepner and Tregoe explain that problem management should happen before making a decision, because the information collected there becomes the foundation for decision analysis.
Risk evaluation, instead, becomes critical when the decision has implications for future objectives and strategies.
A useful reminder:
A problem is about something that already happened (and you want to fix).
A decision is about an action that still has to happen.
That’s why decision-making needs risk analysis across alternatives.
Defining decision analysis
Decision analysis is the process used to determine the pros and cons of all possible outcomes.
It also requires a finite number of alternatives.
Sometimes, less information is better.
Duncan’s work on perceived environmental uncertainty highlights that decision makers seek information to reduce uncertainty—but that doesn’t mean “more info” automatically leads to better decisions.
In fact, information overload happens when there’s a gap between:
The volume of information, and
Our capacity to absorb and use it.
Kutty, Shee, and Pathak describe how excessive information can negatively affect decision-making.
A simplified step-by-step process for decision analysis
List all the possible alternatives.
Set parameters to evaluate each alternative.
Prioritize alternatives based on your value system and attractiveness criteria.
Make the decision.
That last step may sound obvious… but analysis paralysis is extremely common in organizations.
Over-analyzing a situation can freeze action, and a decision is never made.
7 steps of the decision-making process
According to Phil Higson and Anthony Sturgess (Uncommon Leadership, 2014), the decision-making process can be divided into 7 steps.
7 steps to effective decision making:
Identify the decision. Realize what you need to decide and define its nature.
Gather information. Get relevant insights: what matters and what doesn’t? Who can influence the outcome?
Identify the alternatives. What different courses of action exist? What other interpretations of the data are possible?
Weight the evidence. List pros/cons, imagine outcomes, then rank alternatives based on your value system.
Choose among alternatives.
Take action.
Review the decision and its consequences. Did the result solve the original need? If not, revisit earlier steps.
This is a rational decision-making model: it brings order and logic into decisions, starting from a problem/opportunity and ending with action.
Why rational models matter (even for busy managers)
Paul C. Nutt analyzed hundreds of strategic decisions and found that a large share of decisions rely on error-prone tactics.
One of the reasons?
Managers often believe that recommended decision-making practices take too much time and require excessive cash outlays.
Nutt also identified three common blunders:
Rush to judgment: patching an issue with the first “acceptable” solution due to pressure and urgency.
Misuse of resources: spending heavily to justify a hurried decision, instead of investing in the right steps (problem definition, expectations, blockers, etc.).
Failure-prone tactics: using the wrong methodology—especially skipping participation. Nutt highlights that participation massively increases success rates, yet it’s often absent.
Bottom line: good decision-making practices often cost very little compared to the cost of cleaning up the consequences of a bad decision.
Defining problem management
Problem management is a process that manages the lifecycle of problems inside an organization.
It includes:
Identifying the root cause,
Resolving the problem,
Implementing the resolution,
Preventing the same incident from repeating.
It also involves documenting issues and workarounds to reduce the future number and impact of incidents.
It activates when something negatively affects the organization.
How can you diagnose and fix problems in your business?
You should craft a service blueprint: a detailed process flow chart.
Defining risk management
The International Organization for Standardization (ISO) defines risk management as:
“Coordinated activities to direct and control an organization with regard to risk.”
And risk as:
“The effect of uncertainty on objectives.”
In other words, risk management aims to decrease or eliminate risk.
Risk is made of two elements:
The probability something goes wrong;
The costs / negative consequences if it does.
Risk analysis (the step before risk management)
Risk analysis includes identifying threats and estimating how likely they are to occur.
Start by listing threats (brainstorming helps).
Tools like SWOT analysis and Porter’s Five Forces can also support this step.
Then estimate risk using:
Risk value = probability of event × cost of eventProbability: single random event (mutually exclusive outcomes)
For a single random event with mutually exclusive outcomes:
Probability = event ÷ outcomesExample: a 6-sided die. Probability of rolling a 3 is:
1 ÷ 6 ≅ 16.7%
Example: a jar with 5 red, 4 green, 11 blue balls (20 total). Probability of drawing a red ball:
5 ÷ 20 = 25%
Probability: multiple events
To calculate probability of multiple events, multiply their individual probabilities:
P(event 1) × P(event 2) × … = probability of multiple eventsUsing the same jar: probability of drawing red first, green second, blue third (dependent events):
5/20 × 4/19 × 11/18 = 44/1368 = 0.032 = 3.2%
For independent events (like rolling a die twice), probability of rolling the same number twice:
1/6 × 1/6 = 0.0277 ≅ 2.8%Risk management strategies
Once you’ve estimated risk value, you can choose a strategy:
Avoid the risk if there’s no meaningful upside.
Share the risk (partners, teams, third parties).
Example: insurance—your deductible is the risk you’re willing to carry.
Accept the risk (last resort), when the protection cost is higher than potential losses or the upside is worth it.
Even when accepting risk, implement preventive actions to reduce weak points and lower the probability of unfavorable events.
ROLE OF EMOTIONS IN DECISION-MAKING
Decisions can be divided into two categories:
Programmable decisions: routines/repetitive processes governed by standard procedures (lower risk).
Non-programmable decisions: new/unexpected situations requiring specific tactics (higher risk).
In both cases, a rational approach helps (cost/benefit, maximizing gains, minimizing losses).
But emotions will always influence the quality of logic and can generate irrational decisions.
Emotion as the disruptive factor
Emotions are inevitable.
They can mislead and alter perception:
Fear
Contempt
Anger
Delight
Joy
Panic
Anxiety
Frustration
Enthusiasm
Excitement
Emotions are also information: they guide attention toward certain aspects rather than others and are often involved in heuristic decision-making.
Defining heuristic decision-making
Heuristic decision-making relies on unconscious “rules of thumb,” focusing on some variables and ignoring others.
It’s faster and cheaper than step-by-step analysis.
But because it drops evaluative elements, it can lead to inaccurate outcomes.
A classic example is the gambler’s fallacy (Monte Carlo fallacy): the wrong belief that if an event happens more frequently than usual, its probability of happening again decreases (or vice versa).
Another example is the compromise effect: people often prefer the “middle” option over extremes—especially when information is incomplete.
That’s why many offers come in three tiers (small/medium/large).
The middle option becomes the default for a big chunk of buyers.
Prospect Theory
Kahneman and Tversky’s prospect theory (1979) challenged purely rational models and provided an empirical framework for decision-making under risk.
Lottery dilemma, case A
Imagine being $300 richer.
Choose between:
A sure earning of $100
50% chance of winning $200, 50% chance of winning nothing
Lottery dilemma, case B
Imagine being $500 richer.
Choose between:
A sure loss of $100
50% chance of losing $0, 50% chance of losing $200
In case A, most people are risk-averse toward profit.
In case B, many become risk-seeking to avoid loss.
Prospect theory explains this with three psychological aspects:
Framing effect: context and wording change perceived starting point (status quo).
Loss aversion: people are more motivated to avoid losses than to achieve gains.
Isolation effect: people isolate consecutive probabilities instead of integrating them into a single probability structure.
Isolation effect example
Dilemma A
Choose between:
20% chance to win $400
25% chance to win $300
Dilemma B
Two phases: 75% chance you win nothing, 25% chance you pass phase one.
If you pass, choose between:
$300 for sure
80% chance to win $400
Even though the outcomes match, the framing changes choices.
This is the isolation effect in action.
Affect heuristic and the “Asian disease problem”
Kahneman and Tversky also showed how affect heuristic influences decision-making: people assign value to alternatives based on emotional response.
Words matter.
The classic Asian disease problem demonstrates how framing shifts risk preferences.
Problem A (positive frame)
200 people are saved
1/3 chance of saving everyone, 2/3 chance of saving no one
Problem B (negative frame)
400 people die
1/3 chance that no one dies, 2/3 chance that everyone dies
Same situation, different language, different decisions.
The endowment effect
The loss aversion described in prospect theory is closely related to the endowment effect, shown in experiments by Kahneman, Knetsch, and Thaler (1990).
People attribute higher value to what they own versus the same item they don’t own yet.
That’s why:
Willingness to accept (to give up an item) is often higher than
Willingness to pay (to acquire it).
This is also why some sales tactics work frighteningly well: “Try it on… keep it… it’s yours!”
Once you feel ownership, letting go becomes harder.
Beliefs bias in decision-making
We perceive reality through representations shaped by emotions, information, and experience.
Over time, those representations become beliefs.
Beliefs help us decide faster—but they can also generate bias.
Here are some common ones:
Illusory truth effect: repeated statements feel more true over time.
Illusion of control: we overestimate our ability to influence outcomes.
Excessive optimism: we overestimate positive outcomes and underestimate negative ones.
Selective perception / bias blind spot: we spot bias in others more than in ourselves.
Attribution bias: we credit ourselves for success and blame others for failure.
Authority bias: we accept predictions because we respect the source.
Home bias: we prefer what’s familiar or geographically close.
Status quo bias: we prefer outcomes that keep us close to our current state.
Partial information: we treat incomplete info as if it’s comprehensive.
Scope neglect: we ignore scale and underestimate the total impact of “many small costs.”
Bounded (or limited) rationality
Herbert A. Simon argued that human rationality is bounded: we can’t process everything, so we aim for decisions that are good enough rather than optimal.
This approach is often called satisficing: searching through options until a satisfying threshold is met.
Gigerenzer and Selten describe bounded rationality through the idea of an adaptive toolbox: people use “fast and frugal” heuristics to make decisions under uncertainty.
TOOLS FOR BETTER DECISION-MAKING
If you’ve arrived here, you now know:
What decision-making is,
What influences it,
And why rationality alone isn’t enough.
Now it’s time to learn how to design a decision-making process.
The six thinking hats
The six thinking hats system by Edward de Bono, 1985.
The hats represent six “modes”:
White hat: facts, data, intelligence gathering, analogies, past events
Red hat: emotions, intuitions, gut reactions
Black hat: devil’s advocate (risks, downsides, what can go wrong)
Yellow hat: angel’s advocate (benefits, value, opportunities)
Green hat: creativity (new ideas and perspectives)
Blue hat: process control (priorities, rules, structure, coordination)
If you want structure, the blue hat works beautifully with the 7 steps model.
Decision tree
In decision analysis, you can use a tree-like model to map:
Alternatives
Probability
Outcomes
Costs
Utility
A decision tree is great when you need to visualize how different paths branch out—and what each path is likely to produce.
Decision matrix
A decision matrix is a grid that helps you compare multiple alternatives across multiple criteria.
Three reasons why you should use a decision matrix:
It reduces time to decision-making
It increases objectivity
It clarifies and prioritizes alternatives
To build one, you need:
Alternatives (usually top row or first column)
Criteria (the other axis)
A scoring system (e.g., 1–5)
To build my decision matrices, I use Google Sheets: free, reliable, and easy to share.
How to craft a decision matrix
Set up your table (alternatives + criteria).
Brainstorm the criteria with stakeholders.
In the example: quality, cost, ease of use, support.
Choose a ranking method.
Common: 1–5 rating scale
Alternative: compare to a baseline as worse (-1), same (0), better (1)
That baseline-comparison approach is known as the Pugh matrix method, introduced by Stuart Pugh.
(Optional) Weight the variables.
This is useful when criteria don’t have equal importance.
Score your options by multiplying weights × scores.
Pros and cons list
The classic pros and cons list (also called a decisional balance sheet) is still useful.
It helps you quickly highlight:
Options that meet necessary criteria
Options that are too risky to support
It’s simple—but don’t confuse “simple” with “weak.”
It’s often the fastest way to get unstuck.
CONCLUSIONS
After discovering that a big chunk of organizational decisions are driven by flawed tactics, will you introduce some decision-making best practices in your company?
What tool will you try first: six thinking hats, decision matrix, or a decision tree?
Comment below.
I can’t wait to know your progress in decision-making.
References
Duncan, R. B. (1972). Characteristics of organizational environments and perceived environmental uncertainty. Administrative Science Quarterly, 17(3), 313–327.
Hasher, L., Goldstein, D., & Toppino, T. (1977). Frequency and the conference of referential validity. Journal of Verbal Learning and Verbal Behavior, 16, 107–112.
Higson, P., & Sturgess, A. (2014). Uncommon Leadership: How to Build Competitive Advantage by Thinking Differently. Kogan Page.
ISO. (2018). ISO 31000: Risk management — Guidelines. (Definitions of “risk” and “risk management”.)
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990). Experimental tests of the endowment effect and the Coase theorem. Journal of Political Economy, 98(6), 1325–1348.
Kepner, C. H., & Tregoe, B. B. (1997). The New Rational Manager: An Updated Edition for a New World. Princeton Research Press.
Kutty, A. D., Shee, H. K., & Pathak, R. D. (2007). Decision-making: too much info!. Monash Business Review, 3(3), 8–9.
Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67, 371–378.
Nutt, P. C. (2002). Why Decisions Fail: Avoiding the Blunders and Traps That Lead to Debacles. Berrett-Koehler.
Ohio State News. (2002). Why decisions fail (coverage and excerpt of Nutt’s findings).
Pool, V. K., Stoffman, N., & Yonker, S. E. (2012). No Place Like Home: Familiarity in mutual fund manager portfolio choice. The Review of Financial Studies, 25(8).
Pronin, E. (2007). Perception and misperception of bias in human judgment. Trends in Cognitive Sciences, 11(1), 37–43.
Pugh, S. (1981). Concept selection: A method that works. In V. Hubka (Ed.), Review of Design Methodology (Proceedings of ICED). Zürich: Heurista.
Sharot, T., Korn, C. W., & Dolan, R. J. (2011). How unrealistic optimism is maintained in the face of reality. Nature Neuroscience.
Thompson, S. C. (1999). Illusions of control: How we overestimate our personal influence. Current Directions in Psychological Science, 8(6).
Zuckerman, M. (1979). Attribution of success and failure revisited, or: The motivational bias is alive and well in attribution theory. Journal of Personality.
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