Economics & Markets
Section 1
Core Idea
In many games, the link between strategy and outcome is probabilistic: you choose an action, but the result depends on chance (nature’s move) or mixed strategies. Analysing such games means working with expected payoffs, not single payoffs. Founders see this in R&D (success probability), launches (market response), and partnerships (counterparty behaviour). The lens: identify what’s random, assign (or bound) probabilities, then optimise expected value; avoid confusing one realisation with the quality of the decision.
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Section 2
How to See It
Product & R&D
You're seeing it when outcomes are uncertain (will the feature work? will users adopt?). Strategy is a portfolio of bets; evaluation is by expected value over many such bets.
Go-to-market
You're seeing it when launch results vary (channel performance, conversion). You’re playing against nature and competition; plan for distributions, not point forecasts.
Deals
You're seeing it when closing or partnership success is probabilistic. Optimal play is to size bets and sequence so that expected value is positive and variance is tolerable.
Section 3
How to Use It
Model key moves as having a probability distribution over outcomes. Choose strategies that maximise expected payoff (or expected utility if risk matters). Use decision trees or simple scenarios to make the randomness explicit. After the fact, judge the process, not the single outcome — good decisions can lose; bad ones can win.
Decision filter
"Is the outcome of this move random or contingent on others? If so, what’s the distribution, and what’s the expected value of our strategy?"
As a founder
Treat major bets (hires, product bets, channels) as stochastic. Size and diversify so that expected value is positive and you can survive variance. Avoid doubling down on a single draw as if it were the expectation.
Section 5
Founders & Leaders
Ed ThorpMathematician; author, Beat the Dealer; quantitative investor
Thorp applied probability and expected value to games and investing. Founders can adopt the lens: in uncertain, repeatable situations, design strategy for expected value and manage risk (position size, optionality) so that the law of large numbers works for you.
Section 7
Connected Models
Reinforces
Expected Utility Theory
Stochastic outcomes are evaluated via expected utility (or expected value when risk-neutral). Same idea: weight outcomes by probability and choose the action that maximises that expectation.
Reinforces
Probabilistic Thinking
Thinking in probabilities — rather than single-point forecasts — is how you model and use stochastic games. Probabilistic thinking is the mental habit; stochastic games are the structure.
Leads-to
Kelly Criterion
When outcomes are stochastic and repeatable, Kelly sizing tells you how much to bet to maximise long-run growth. It’s the natural bridge from stochastic payoffs to action.
Section 8
One Key Quote
— Daniel Kahneman, Thinking, Fast and Slow"A good decision is one that is optimal given what is known at the time. Outcomes are partly determined by luck."
Section 11
Summary & Further Reading
Stochastic games: strategy leads to a probability distribution over outcomes. Optimise expected value (or utility); judge decisions by process, not one draw. Use the lens for R&D, launch, and deal-making.
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