docs: def K_to_F(self, K, method='doubling'):
          """
          Compute agent 1's best value-maximizing response F, given K.
  
          Parameters
          ----------
          K : array_like(float, ndim=2)
              A j x n array
          method : str, optional(default='doubling')
              Solution method used in solving the associated Riccati
              equation, str in {'doubling', 'qz'}.
  
          Returns
          -------
          F : array_like(float, ndim=2)
              The policy function for a given K
          P : array_like(float, ndim=2)
              The value function for a given K
  
          """
          A1 = self.A + dot(self.C, K)
          B1 = self.B
          Q1 = self.Q
          R1 = self.R - self.beta * self.theta * dot(K.T, K)
          lq = LQ(Q1, R1, A1, B1, beta=self.beta)
          P, F, d = lq.stationary_values(method=method)
  
          return F, P