diff --git a/README.md b/README.md
index dfac7907a50d4bbde3dae3b3ffcdbe0a2a059d0c..24b2c541058c8f6cfe9116c8e25f9539cfe50478 100644
--- a/README.md
+++ b/README.md
@@ -433,6 +433,10 @@ Figures present the average points earned and prediction per round (95% confiden
 
 Against Constant behavior, <tt>GPT-4.5</tt> and <tt>Qwen3</tt> ...
 
+The models exhibit varied approaches to decision-making in the MP game.
+<tt>GPT-4.5</tt> follows a fixed alternating pattern, switching between "Head" and "Tail" each turn, assuming the opponent behaves similarly.
+<tt>Mistral-Small<tt> adopts a reactive strategy, analyzing the  frequency of the  opponent’s past moves and selecting the less common one. On the other hand, <tt>Qwen3</tt>, relies on randomness — choosing moves unpredictably while  presuming the opponent will mimic its choice. <tt>LLaMA3</tt> does not  implement  a functioning strategy. Overall, these approaches demonstrate simplistic heuristics that may lack credibility and efficiency.
+
 ![Prediction Accuracy per Round by Actions Against Constant Behaviour (with 95% Confidence Interval)](figures/mp/mp_prediction_ConstHT.svg)
 ![Points Earned per Round by Actions Against Constant Behaviour (with 95% Confidence Interval)](figures/mp/mp_payoff_ConstHT.svg)
 ![Prediction Accuracy per Round by Actions Against Alternate Behaviour (with 95% Confidence Interval)](figures/mp/mp_prediction_Altern.svg)