Abstract
We introduce the family of Conjectural Learning procedures, in which agents form conjectures about what the opponent will play, as a function of their action or some state variable. We prove general properties of these procedures, comparing them to two dynamic systems generated by nearsighted agents solving repeatedly a static game, either cooperatively, or noncooperatively. We then specify three simple functional forms of conjectures and analyze the five resulting dynamic systems, in terms of steady states and convergence conditions. We next further specify these procedures to the case of a game of groundwater management, using a quadratic functional form for the profit function of agents. We obtain then explicit expressions for steady states and convergence conditions for the dynamic systems. Finally, we conduct numerical experiments in order to assess the “performance” of pairs of agents adopting each of the five behaviors.