  An underlying assumption of Stackelberg Games (SGs) is perfect rationality of
the players. However, in real-life situations (which are often modeled by SGs)
the followers (terrorists, thieves, poachers or smugglers) -- as humans in
general -- may act not in a perfectly rational way, as their decisions may be
affected by biases of various kinds which bound rationality of their decisions.
One of the popular models of bounded rationality (BR) is Anchoring Theory (AT)
which claims that humans have a tendency to flatten probabilities of available
options, i.e. they perceive a distribution of these probabilities as being
closer to the uniform distribution than it really is. This paper proposes an
efficient formulation of AT in sequential extensive-form SGs (named ATSG),
suitable for Mixed-Integer Linear Program (MILP) solution methods. ATSG is
implemented in three MILP/LP-based state-of-the-art methods for solving
sequential SGs and two recently introduced non-MILP approaches: one relying on
Monte Carlo sampling (O2UCT) and the other one (EASG) employing Evolutionary
Algorithms. Experimental evaluation indicates that both non-MILP heuristic
approaches scale better in time than MILP solutions while providing optimal or
close-to-optimal solutions. Except for competitive time scalability, an
additional asset of non-MILP methods is flexibility of potential BR
formulations they are able to incorporate. While MILP approaches accept BR
formulations with linear constraints only, no restrictions on the BR form are
imposed in either of the two non-MILP methods.
