Modeling interactions that hinge on others' choices, then solving for equilibrium to predict the outcome.
Game theory turns situations where several people shape each other's outcomes into formal models you can solve. You fix who plays, what options each one has, and what payoff every combination of choices produces. Then you look for the state where nobody gains by switching alone. This guide covers moving a situation with an opponent into a model and judging it by equilibrium. It doesn't cover setting direction by matching goals to resources, or correcting the biases in your own judgment.
You have just stepped into game theory. You separate situations your choice alone settles from situations the other side's choice helps settle. In a given example you point to the players, the options each one holds, and the payoff attached to every combination. You read a table where two people each pick between two options. You don't build tables yet. You read models other people made and get used to the shape of an interaction.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.2 Static Game Solving, where you turn real situations into tables yourself. Gick & Holyoak(1983) report that one case alone yields no structure, and that a shared structure sticks as a schema only when you compare cases that look different. Say the players, options, and payoffs of one situation out loud, then add a case from another field in the same 2x2 frame. What you left out shows up beside what they share.
The graduate course sequence runs from static-game solution concepts (rationalizability, Nash equilibrium) to extensive-form games (backward induction, subgame perfection) to advanced topics (repeated games, reputation, global games, cooperative solutions). It is the primary basis for the level boundaries.
A university module description that states learning outcomes for strategy, best response, and equilibrium. It cross-validates the level expected at the undergraduate to graduate boundary.
Research that splits the depth of strategic reasoning into level-k tiers. It turns "how many moves ahead the other side reads" into an observable measure, which grounds the checklist items about reasoning depth.
Defines Nash equilibrium as a state where no one can improve their payoff by changing only their own strategy, and a subgame perfect strategy as one that picks the highest-payoff path of the relevant subgame at every node reached. Independent cross-validation for the solution concept progression.
Reports that transfer to a new situation happens only after a shared structure is pulled out of different cases as a schema. Grounds the transfer mechanism of the modeling role axis and the training prescriptions in the transition hints.
