When faced with a big decision and lots of unknowns, what kind of thinking helps you guess how likely different results are, even without full information?

The kind of thinking that helps you guess how likely different results are, even without full information, is probabilistic thinking. This approach focuses on quantifying uncertainty by assigning probabilities to different potential outcomes. Probability is a numerical measure, typically between 0 and 1 (or 0% and 100%), representing how likely an event is to occur. Even with limited information, probabilistic thinking allows for estimation and inference. Estimation is the process of forming an approximate judgment or calculation of an outcome's likelihood. Inference is drawing conclusions about unknown facts based on available evidence and logical reasoning. A particularly powerful framework within probabilistic thinking for dealing with evolving information and uncertainty is Bayesian thinking, often referred to as Bayesian inference. This method involves starting with an initial belief about the likelihood of a particular outcome, known as a prior probability. As new, even incomplete, information or evidence becomes available, this prior belief is systematically updated and adjusted to form a new, more refined understanding of the outcome's likelihood, called a posterior probability. This iterative updating process allows decision-makers to continually improve their assessment of how likely different results are, moving from an initial 'guess' to a progressively more informed estimation, without ever requiring complete certainty. For example, if considering a new investment, an initial estimate of its profitability (prior) can be updated based on early market trends or economic reports (new evidence), leading to a revised likelihood of success (posterior).