Improve synthesis

README.md

@@ -430,26 +430,6 @@ into their own decisions. Despite some being able to identify patterns,
 most fail to translate these beliefs into optimal responses. Only <tt>Llama3.3:latest</tt> shows any reliable ability to 
 infer and act on opponents’ simple behaviour
 
-Our findings reveal notable differences in the cognitive capabilities of LLMs 
-across multiple dimensions of decision-making.
-<tt>Mistral-Small</tt> demonstrates the highest level of consistency in economic decision-making, 
-with <tt>Llama3</tt> showing moderate adherence and </tt>DeepSeek-R1</tt> displaying considerable inconsistency.
-
-<tt>GPT-4.5</tt>, <tt>Llama3</tt>, and <tt>Mistral-Small</tt> generally align well with declared preferences, 
-particularly when generating algorithmic strategies rather than isolated one-shot actions. 
-These models tend to struggle more with one-shot decision-making, where responses are less structured and 
-more prone to inconsistency. In contrast, <tt>DeepSeek-R1</tt> fails to generate valid strategies and 
-performs poorly in aligning actions with specified preferences.
-<tt>GPT-4.5</tt> and <tt>Mistral-Small</tt> consistently display rational behavior at both first- and second-order levels.
-<tt>Llama3</tt>, although prone to random behavior when generating strategies, adapts more effectively in one-shot 
-decision-making tasks. <tt>DeepSeek-R1</tt> underperforms significantly in both strategic and one-shot formats, rarely
-exhibiting  coherent rationality.
-
-All models—regardless of size or architecture—struggle to anticipate or incorporate the behaviors of other agents 
-into their own decisions. Despite some being able to identify patterns, 
-most fail to translate these beliefs into optimal responses. Only <tt>Llama3.3:latest</tt> shows any reliable ability to 
-infer and act on opponents’ simple behaviour
-
 ## Authors
 
 Maxime MORGE