โมเดลการตัดสินใจแฟร์มาต์และปาสกาล

Stop debating "which path feels right" and start multiplying. The Fermat-Pascal method demands you assign numbers, however rough. Estimate: staying with SMBs gives you a 60% chance of modest traction (say, $50k MRR in 9 months) and a 40% chance of stagnation ($10k). Expected value: 0.6×50 + 0.4×10 = $34k. Pivoting to enterprise: maybe 20% chance of a big win ($200k) but 80% chance of burning 6 months with zero deals ($0). Expected value: 0.2×200 + 0.8×0 = $40k. The pivot looks better on paper—but that's before you discount for your burn rate and the cost of switching.

Now ask the brutal question: what's your real probability of closing enterprise deals? If you've never sold there, be honest—20% is generous. Drop it to 10%, and the pivot's EV falls to $20k. That's the trap: gut feelings inflate probability. Also factor in runway. Six months of pivot burn at $80k/month is $480k. If you can't survive the sales cycle, the probability of success collapses to zero regardless of the number.

My recommendation: run a Bayesian update. In the next 30 days, get 10 enterprise prospect conversations. If you book 3 serious pilots, revise your probability upward and pivot. If you get zero, stay with SMBs and cut burn. Don't decide on hope—decide on evidence. The math doesn't lie, but only if you feed it real numbers.

Stop asking "is it worth it" and start asking "what is the expected value of each shift over a year." You already track drained days—good. Estimate the probability of a serious care error on a drained day versus a normal one, then multiply by the cost of that error (patient harm, license risk, your own guilt). That's the downside you're pricing, not just "feeling tired."

Now do the math. Say night shift adds $6,000/year. If it raises your error probability from 1% to 3% per shift, and one error costs you $50,000 in career damage or worse, that's an expected loss of $1,000 per shift × 50 shifts—$50,000. The 15% bump is a rounding error against that.

But don't stop there—apply Bayes. Track your personal data for two months: actual drained frequency, commute incidents, sleep hours. Update your prior. If you find you're resilient on nights, the probability shifts and the deal flips. The point isn't to guess—it's to make the probabilities explicit and let the expectation decide, not the paycheck. Run the numbers with your real costs, not your hopes.

You are already thinking like Pascal and Fermat — good. Stop debating in adjectives and start multiplying. Pull your past grades and define the outcomes: for each format, list the probability of a "low score" (say, below C) and the probability of a complaint, plus the typical grade penalty or workload cost.

Build a simple expected value table. Example: Individual essays — 70% chance of solid work (value +3), 30% chance of low scores (value −2). Teams with roles — 55% chance of solid work (+3), 25% low scores (−2), 20% complaints (−1). Multiply: individual = 0.7×3 + 0.3×(−2) = 1.5. Teams = 0.55×3 + 0.25×(−2) + 0.20×(−1) = 0.95. Individual wins on expectation.

But don't stop there. Bayesian update: if you pilot teams with stricter roles for one project, treat the observed complaint rate as new evidence and revise your prior. Also think long-term — over 10 projects, a small edge compounds. Choose the format with higher expected value, then measure and adjust. Do not let one bad team project dictate policy; let the math, not the memory, decide.