From 7b8f9f3379b123c6d8eace9dfec81757ad8eedd4 Mon Sep 17 00:00:00 2001
From: stephanebonnevay <stephane.bonnevay@lizeo-group.com>
Date: Fri, 6 Jun 2025 07:44:05 +0200
Subject: [PATCH] Readme

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 24b2c54..9ce2aa1 100644
--- a/README.md
+++ b/README.md
@@ -429,9 +429,10 @@ For our experiments, we consider two simple models for the opponent where:
 
 We evaluate the models' ability to identify these behavioural patterns by calculating the average number of points earned per round.
 
-Figures present the average points earned and prediction per round (95% confidence interval) for each LLM against the two opponent behavior (constant and alternate) models in the matching pennies game. 
+Figures present the average points earned and prediction per round (95% confidence interval) for each LLM against the two opponent behavior models (constant and alternate) in the matching pennies game. 
 
-Against Constant behavior, <tt>GPT-4.5</tt> and <tt>Qwen3</tt> ...
+Against Constant behavior, <tt>GPT-4.5</tt> and <tt>Qwen3</tt> were able to generate a valid strategy. The charts show that they are able to correctly predict their opponent's strategy after just a few rounds. They perfectly identify the fact that their opponent always plays the same move.
+The predictions made by <tt>Mistral-Small<tt>, <tt>LLaMA3</tt>, and <tt>DeepSeek-R1</tt> are not incorrect, but the moves played are not in line with these predictions, which leads to a fairly low expected gain.
 
 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.
-- 
GitLab