🔥The Art of Choosing Quickly — Without Choosing Wrong
I Want the Best Choice — But I Can’t Compare Everything
A tale of temptations, time, and strategy — where a crowded forest marketplace becomes the first lesson in Greedy Algorithms.
Characters: Arjun, Guru Bodhi, Market Vendors · Arc: Beginner · Pattern: Greedy Strategy & Interval Scheduling
📜 Sutra of Arrival — The Market of a Thousand Temptations
The Gurukul forest had transformed. Lanterns hung from branches, incense curled in the air, and rows of stalls lined the stone path. It was the Market of a Thousand Offerings — a festival day where every teacher, trader, and mystic displayed their finest work.
Arjun walked in with a small pouch of coins and a single afternoon of free time.
One stall sold scrolls on architecture. Another offered potions for focus. A third promised to teach “everything about startups in one hour”. Every vendor shouted the same line:
Arjun clutched his pouch tighter. “I want to spend my time and coins wisely,” he muttered. “But how do I choose when everything looks important?”
📜 Sutra of Anxiety — When “Best” Becomes a Trap
Arjun unfolded a small parchment he had brought from his room. On it, he had listed all the sessions and stalls he wanted to visit:
- A morning talk on building clear mental models.
- A workshop on memory techniques.
- A session on negotiation and influence.
- A practical class on writing better code.
- And at least three stalls promising “life-changing scrolls”.
Each session had its own start and end time, and many overlapped. If he picked one, he would miss two others.
“This is exactly like my real life,” Arjun groaned. “I bookmark ten courses, five books, and three side-project ideas. I want the best use of my time, but comparing everything seems impossible. So I end up… scrolling instead of choosing.”
The crowd parted, and Guru Bodhi appeared by his side, robes quietly shimmering in the lantern light.
📜 Sutra of Strategy — The Rule That Cuts Through Noise
Guru Bodhi knelt by a patch of smooth earth and began to draw small rectangles representing sessions along a line of time.
“Each session,” he said, “starts at some hour and ends at another. You cannot be in two at once. But you wish to attend as many good ones as possible before sunset.”
Arjun nodded eagerly. “So I should pick the highest-rated talk first? Or the longest one? Or the one with the most buzz?”
Always choose the session that finishes earliest among those you can still attend.”
“That sounds… too simple,” Arjun said. “How can such a small rule be wise when the market is this complex?”
He circled a set of rectangles on the earth — none overlapping, all ending as early as possible — and Arjun saw how many fit into the day.
🧭 Real-World Scroll — Where Greedy Thinking Actually Helps
The Gurukul market is just a mirror. In real life, you face similar scheduling puzzles almost daily:
- Choosing which meetings to attend to protect focus time.
- Planning a conference day to attend as many talks as possible.
- Structuring an evening of study blocks around work and family.
- Designing a release plan: which small features fit into a sprint without overlaps.
In all of these, you have a fixed time window and several candidate intervals. Your goal is to pack in “as many good things as possible” without overlap. For such problems, the greedy rule “always pick the earliest finishing option that still fits” works beautifully.
🧠 The Scroll of Sessions — Algorithm Scroll
We’ll represent each session as a small dictionary with a
name,
start, and
end time (measured in hours from sunrise).
Session Data
sessions = [
{'name': 'Mental Models', 'start': 1, 'end': 3},
{'name': 'Memory Workshop', 'start': 0, 'end': 2},
{'name': 'Negotiation Class','start': 4, 'end': 7},
{'name': 'Better Code Lab', 'start': 1, 'end': 5},
{'name': 'Writing Circle', 'start': 8, 'end': 9},
]Our goal: choose a subset of sessions with no overlaps that gives Arjun the maximum number of sessions before sunset.
Greedy Rule
1. Sort all sessions by their end time (earliest finishing first).
2. Start from the earliest finishing session and keep picking the next session that starts after the last one ended.
def select_sessions(sessions):
"""Return a greedy set of non-overlapping sessions."""
# 1. Sort by end time (earliest finishing first)
sessions_sorted = sorted(
sessions,
key=lambda s: s['end']
)
chosen = []
current_end = 0
# 2. Keep any session that starts after the last one ends
for s in sessions_sorted:
if s['start'] >= current_end:
chosen.append(s)
current_end = s['end']
return chosenComplexity
-
Sorting:
O(n log n). -
Single pass selection:
O(n).
Guru: “For this kind of problem, greed is not foolish. The earliest finishing choice frees your future time, letting you fit more wisdom into one day.”
📉 When Greedy Thinking Is Dangerous
Arjun’s eyes shone. “So I should always follow greedy rules in life? Just pick what looks best right now?”
Greedy works only when choosing the best local step also leads to a strong global result. That is true for this scheduling puzzle — but not for everything.
- Good fits: picking non-overlapping events, simple routing with obvious costs, grabbing highest interest debt to pay off first.
- Bad fits: long-term career moves, deep research directions, or anything where early sacrifices create much bigger gains later.
“Greedy algorithms,” Guru Bodhi concluded, “are like a sharp knife. Extremely useful — as long as you know which problems deserve such directness.”
📜 Sutra of Action — How Arjun Uses the Rule
With Guru Bodhi’s rule in mind, Arjun rewrote his schedule. He sorted the day’s sessions by when they ended, then circled only those he could attend without overlap.
The parchment looked lighter now. Not perfect — but workable. A small set of good sessions instead of a fog of infinite choices.
As the sun began to set, Arjun walked from terrace to terrace, present and unhurried. He had not compared every possible future — and yet, his day was full.
