It is a Tuesday morning. You are staring at two job offers. One pays more. The other excites you more. You weigh them for days. You finally choose. And then — almost immediately — a quiet voice starts: “But what if the other one…?”

Now imagine that same Tuesday morning — but instead of two job offers, you have fourteen. Eight are on LinkedIn. Three came through your network. Two are from companies you have never heard of, found through an app that learned your preferences. And there is one freelance path that does not fit any category at all.

This is not a hypothetical. This is the modern condition of choice. Kierkegaard had an Either/Or problem. We have an Everything/Or problem.

The Danish philosopher Søren Kierkegaard diagnosed the wound of choice nearly two centuries ago. He warned that we are trapped inside it, no matter which way we go. What he could not have imagined was that the wound would multiply — that the problem would not be choosing between two difficult paths, but choosing between hundreds of paths, all arriving simultaneously, all demanding a decision right now.

The good news? Modern decision science — particularly the field of Multi-Criteria Decision Making (MCDM) — has developed precise, practical tools to navigate both the ancient Either/Or trap and its modern cousin, choice overload. Not to eliminate regret entirely, but to make decisions we can actually stand behind.

Kierkegaard’s Uncomfortable Gift

Søren Kierkegaard (1813–1855) was a philosopher who spent his life making people uncomfortable with their own lives. In Either/Or (1843), he presented a radical idea: you will regret it either way.

He was not being cynical. He was pointing to something deeply real: every choice we make simultaneously closes off another possibility. When you choose A, you lose B — not just in practice, but in imagination. And imagination, as any anxious mind knows, is merciless.

Kierkegaard described three stages of human existence:

For Kierkegaard, the tragedy was not making wrong choices. The tragedy is making choices at all — because choice implies loss. The only exit is what he called the “leap of faith”: a total, committed choice made not from certainty, but from courage and self-knowledge.

In plain words: You choose Noodle at a restaurant. The pasta looks delicious. You taste your Noodle — it is good. But your eye keeps drifting to your neighbour’s plate. What if…? This is Kierkegaard’s Either/Or over lunch. Multiply it across careers, partnerships, and investments — and you understand the existential weight he was describing.

From Either/Or to Everything/Or — The Modern Choice Overload

Kierkegaard lived in 19th-century Copenhagen. His world was local, slow, and bounded. A person might choose between two or three careers in a lifetime. Marriage had a small social pool. The news arrived by post. The options, while still agonising to choose between, were countable.

That world is gone.

The Numbers Tell the Story

Consider what the modern decision-maker faces every day:

The philosopher was right that choice causes regret. He could not have known that choice would also cause paralysis.

The Paradox of Choice

Psychologist Barry Schwartz named this phenomenon in his landmark 2004 book The Paradox of Choice.^[8] His central, counter-intuitive finding: more options do not make us more free or more satisfied. They make us more anxious, more likely to regret, and less likely to decide at all.

Schwartz identified two personality types in the face of choice:

The cruel irony of the modern world is that it turns satisficers into maximisers by force. When options are few, settling is easy. When options are infinite, the fear that you settled haunts you. The internet has made maximisers of all of us — presenting comparison tables, review aggregators, and “people also considered” lists that make every decision feel incomplete.

Decision Fatigue — The Hidden Cost

The cognitive load of too many choices has a clinical name: decision fatigue.^[9] Research by Roy Baumeister and colleagues shows that the quality of human decisions deteriorates sharply after sustained periods of choosing. This is why:

• Judges grant more lenient paroles in the morning than in the afternoon
• Doctors prescribe more default treatments as the clinic day wears on
• Consumers buy more impulsively at the end of a shopping trip

In the modern world, by the time you face your most important decisions of the day, you have often already spent your cognitive budget on dozens of trivial ones — which streaming show to watch, which commute route to take, which email to answer first.

The Either/Or problem Kierkegaard described assumed two paths. The modern problem is not two paths — it is a forest with no paths at all, only options stretching to the horizon, each looking equally plausible, equally regrettable.

The New Layer of Regret

This abundance does not just add more options. It adds a new category of regret that Kierkegaard never named: Regret of Incomplete Search — the nagging feeling that you did not look hard enough. That the perfect option was out there. That someone else found it. That you settled.

This regret is uniquely modern, and uniquely corrosive, because it is unfalsifiable. With two options, you can at least evaluate the road not taken. With ten thousand options, the unevaluated remainder is infinite. The imagination has unlimited material to work with.

The Science of Regret — Kierkegaard Was Right

Behavioural economists have since confirmed what Kierkegaard suspected. Daniel Kahneman and Amos Tversky demonstrated through Prospect Theory (1979) that we do not experience gains and losses symmetrically.^[1] Losses hurt approximately twice as much as equivalent gains feel good. The path not taken looms larger in our minds than it deserves.

Research identifies two types of regret:

Thomas Gilovich and Victoria Medvec (1995) found in a landmark study that in the long run, people regret inaction more than action.^[2] We regret the businesses we never started, the conversations we never had, the roads we never took — far more than the ones we did and stumbled on.

The Regret Landscape — Four Scenarios

What is MCDM — and Why Does It Help?

Multi-Criteria Decision Making (MCDM) is a branch of operations research and management science that provides structured methods for making choices when multiple, often conflicting, criteria must be considered simultaneously.^[3] It was developed to solve exactly the problem described in Part Two: too many options, too many criteria, too much cognitive load for unaided human judgment.

Most decisions feel paralysing because we are trying to optimise for everything at once, in our heads, without structure. MCDM gives that chaos a skeleton. It does four things that unaided human judgment cannot:

We will walk through three MCDM tools with worked templates you can use immediately.

The Weighted Scorecard

What It Is

The simplest and most widely used MCDM method. You list your options, name your criteria, assign weights based on their importance to you, score each option against each criterion, and compute a weighted total. The highest score wins — but more importantly, you have made your values explicit.

The magic is not the final number. It is the conversation you have with yourself while filling it out.

Step-by-Step

1. List all realistic options (rows)
2. List all criteria that matter to you (columns)
3. Assign a weight to each criterion (must add up to 100%)
4. Score each option on each criterion (1 = very poor, 5 = excellent)
5. Multiply each score by its criterion weight
6. Sum across all criteria for each option
7. The option with the highest weighted total is your analytical recommendation

Template — Career Choice Example

Scenario: You are choosing between three job offers.

Step 1: Define Criteria and Weights

Step 2: Score Each Option (1–5 scale)

Step 3: Compute Weighted Scores

TOPSIS

What It Is

TOPSIS — Technique for Order Preference by Similarity to Ideal Solution — was developed by Hwang and Yoon (1981).^[4] It asks: imagine the perfect option — it scores best on every single criterion. Now imagine the worst option. Which of your real choices is closest to the ideal and farthest from the worst?

TOPSIS is more mathematically rigorous than the weighted scorecard and is widely used in engineering, supply chain management, healthcare resource allocation, and public policy.^[5] It handles situations with many options and criteria where simple scoring becomes insufficient.

The Core Idea (Simply Explained)

Think of each option as a point in a multi-dimensional space, where each dimension is one criterion. TOPSIS calculates:

• The distance of each option from the Ideal Best (best score on every criterion)
• The distance of each option from the Ideal Worst (worst score on every criterion)

The “closeness coefficient” for each option = distance from worst ÷ (distance from worst + distance from best). The closer this coefficient is to 1, the better the option.

Step-by-Step

1. Build the decision matrix (same as weighted scorecard)
2. Normalise scores so different scales become comparable
3. Apply weights to the normalised scores
4. Identify the Ideal Best and Ideal Worst per criterion
5. Calculate Euclidean distance from Ideal Best and Ideal Worst for each option
6. Compute the closeness coefficient
7. Rank options by closeness coefficient (higher = better)

Template — Infrastructure Project Selection

Scenario: A district administration must select one of three rural road projects to fund.

Step 1: Decision Matrix (raw scores)

Step 2: Assign Weights

Step 3: Normalise Each Column (Divide each value by the square root of the sum of squared values in that column)

Step 4: Apply Weights → Weighted Normalised Matrix

Step 5: Identify Ideal Best (V+) and Ideal Worst (V−)

Step 6: Distances and Closeness Coefficient

AHP — Analytic Hierarchy Process

What It Is

The Analytic Hierarchy Process was developed by mathematician Thomas L. Saaty at the University of Pittsburgh in 1977 and published formally in 1980.^[6] It is used by governments, the World Bank, multinational corporations, and military planners for strategic decisions. AHP is arguably the most sophisticated and widely validated of all MCDM methods.^[7]

AHP works through pairwise comparisons — instead of directly assigning weights (which is surprisingly difficult and inconsistent), you compare criteria two at a time: “Is Growth Potential more important than Salary? How much more — a little, moderately, or a lot?”

The genius of AHP is its Consistency Ratio (CR): it mathematically tests whether your preferences are logically consistent. If you say A>B, and B>C, but then say C>A — AHP catches this contradiction. It forces you to be coherent in your own values, which is harder than it sounds and enormously clarifying.

Saaty’s Comparison Scale

Reciprocals apply automatically: if A is rated 3 over B, then B is rated 1/3 over A.

Step-by-Step

1. Define goal, criteria, and options in a 3-level hierarchy
2. Build pairwise comparison matrices for criteria
3. Calculate priority weights from the matrices
4. Compute the Consistency Ratio (CR) — should be < 0.10
5. Synthesise: multiply option scores by criterion weights, sum for final ranking

Template — Policy Decision Example

Scenario: A government department must choose between three digital transformation strategies.