Thinking In Bets Pdf Github Hot! May 2026

import numpy as np

expected_value = evaluate_bet(probability, payoff, risk_free_rate) print(f"Expected value of the bet: {expected_value}") This code defines a function evaluate_bet to calculate the expected value of a bet, given its probability, payoff, and risk-free rate. The example usage demonstrates how to use the function to evaluate a bet with a 70% chance of winning, a payoff of 100, and a risk-free rate of 10.

Parameters: probability (float): Probability of winning the bet. payoff (float): Payoff of the bet. risk_free_rate (float): Risk-free rate of return. thinking in bets pdf github

In an uncertain world, decision-making is a crucial aspect of our personal and professional lives. However, humans are prone to cognitive biases and often rely on intuition rather than probabilistic thinking. "Thinking in Bets" is a concept popularized by Annie Duke, a professional poker player, which involves making decisions by thinking in probabilities rather than certainties. This paper explores the concept of Thinking in Bets, its application in decision-making, and its relevance to uncertainty and probabilistic thinking. We also provide a GitHub repository with Python code examples to illustrate the concepts discussed in the paper.

# Example usage probability = 0.7 payoff = 100 risk_free_rate = 10 payoff (float): Payoff of the bet

Returns: float: Expected value of the bet. """ expected_value = probability * payoff - (1 - probability) * risk_free_rate return expected_value

def evaluate_bet(probability, payoff, risk_free_rate): """ Evaluate a bet by calculating its expected value. However, humans are prone to cognitive biases and

Probabilistic thinking is essential in decision-making under uncertainty. It involves understanding and working with probabilities to evaluate risks and opportunities. Probabilistic thinking can be applied to various domains, including finance, engineering, and medicine.