Update page_2.md

pages/page_2.md

@@ -6,14 +6,14 @@ $$ P(A | B) = \frac{P(B | A) \cdot P(A)}{P(B)} $$
 
 Incorporate feedback with Bayes' rule:
 
-\[ P(A | B_1, B_2, ..., B_n) \propto P(A) \cdot \prod_{i=1}^{n} P(B_i | A) \]
+$$ P(A | B_1, B_2, ..., B_n) \propto P(A) \cdot \prod_{i=1}^{n} P(B_i | A) $$
 
 For game theory, Nash equilibrium guides strategic choices:
 
-\[ \text{NE:} \quad \pi_i(s_i^*, s_{-i}^*) \geq \pi_i(s_i, s_{-i}^*) \]
+$$ \text{NE:} \quad \pi_i(s_i^*, s_{-i}^*) \geq \pi_i(s_i, s_{-i}^*) $$
 
 Explore meta-induction by adapting strategies based on opponents' past behaviors:
 
-\[ s_i(t+1) = f(s_i(t), h_{-i}(t)) \]
+$$ s_i(t+1) = f(s_i(t), h_{-i}(t)) $$
 
 Iteratively update strategies considering historical outcomes and opponents' strategies.