PyGAAMAS: minor corrections of README.md

README.md

@@ -26,8 +26,8 @@ erratically to changes in the game’s parameters.
 In this game, an investor allocates a basket $x_t=(x^A_t, x^B_t)$ of $100$ points between 
 two assets: Asset A and Asset B. The value of these points depends on random prices $p_t=(p_{t}^A, p_t^B)$, 
 which determine the monetary return per allocated point. For example, if $p_t^A= 0.8$ and $p_t^B = 0.8$, 
-each point assigned to Asset A is worth $\$0.8$, while each point allocated to Asset B yields $\$0.5$. T
-he game is played $25$ times to assess the consistency of the investor’s decisions.
+each point assigned to Asset A is worth $\$0.8$, while each point allocated to Asset B yields $\$0.5$. 
+T he game is played $25$ times to assess the consistency of the investor’s decisions.
 
 To evaluate the rationality of the decisions, we use Afriat's
 critical cost efficiency index (CCEI), i.e. a widely used measure in
@@ -274,22 +274,6 @@ informed decision-making.
 Table below evaluates the models' ability to generate second-order  rational behaviour for player 1. The configurations 
 where CR improves second-order rationality are in bold, and those where CR degrades this rationality are in italics.
 
-When the models generate strategies, <tt>GPT-4.5</tt> exhibits second-order
-rational behaviour in configurations (a), (c), and (d), but fails in
-configuration (b) to distinguish the optimal action from a nearly optimal one.
-Llama3 makes its decision randomly. Mistral-Small shows strong
-capabilities in generating second-order rational behaviour. DeepSeek-R1
-does not produce valid responses.
-
-When generating actions, <tt>Llama3</tt> adapts to different types of beliefs
-and adjustments in the payoff matrix. <tt>GPT-4.5</tt> performs well in the
-initial configuration (a), but encounters significant difficulties when the
-payoff structure changes (b, c, d), particularly with implicit beliefs. Although
-Mistral-Small works well with given or explicit beliefs, it faces
-difficulties with implicit beliefs, especially in variant (d).
-<tt>DeepSeek-R1</tt> does not appear to be a good candidate for simulating
-second-order rationality.
-
 When generating strategies, <tt>GPT-4.5</tt> consistently exhibits second-order rational behavior in all configurations 
 except (b), where it fails to distinguish the optimal action from a nearly optimal one. Llama3 makes decisions randomly, 
 showing no strong pattern of rational behavior. In contrast, <tt>Mistral-Small</tt> and <tt>Mixtral-8x7B</tt> 
@@ -297,7 +281,7 @@ demonstrate strong  capabilities across all conditions, consistently generating
 <tt>Llama3.3:latest</tt> performs well with given and explicit beliefs but struggles with implicit beliefs.
 <tt>Qwen3</tt> generate irrational strategies. <tt>DeepSeek-R1</tt> does not produce valid responses in strategy generation.
 
-When generating actions, Llama3.3:latest adapts well to different types of beliefs and adjustments in the payoff matrix 
+When generating actions, <tt>Llama3.3:latest</tt> adapts well to different types of beliefs and adjustments in the payoff matrix 
   but struggles with implicit beliefs, particularly in configuration (d). <tt>GPT-4.5</tt> performs well in the initial 
 configuration (a) but encounters significant difficulties when the payoff structure changes in (b), (c), and (d), 
 especially with implicit beliefs. <tt>Mixtral-8x7B</tt> generally performs well but shows reduced accuracy for implicit beliefs 
@@ -336,7 +320,7 @@ particularly in less confident or under-specified contexts.
 |                     | actions + CR   | *0.90*    | *0.90*       | *0.86*       | *0.50*    | *0.50*       | *0.50*       | *0.76*    | 0.96         | *0.70*       | *0.67*    | *0.83*       | 0.67         |
 | **Mixtral:8x7b**    | actions        | 1.00      | 1.00         | 1.00         | 1.00      | 1.00         | 0.50         | 1.0       | 1.0          | 1.0          | 1.00      | 1.00         | 0.73         |
 |                     | actions + CR   | 1.00      | *0.96*       | 1.00         | 1.00      | 1.00         | **1.0**      | 1.0       | 1.0          | 1.0          | 1.00      | 1.00         | *0.28*       |
-| **Listral-Small**   | actions        | 0.93      | 0.97         | 1.00         | 0.87      | 0.77         | 0.60         | 0.77      | 0.60         | 0.70         | 0.73      | 0.57         | 0.37         |
+| **Mistral-Small**   | actions        | 0.93      | 0.97         | 1.00         | 0.87      | 0.77         | 0.60         | 0.77      | 0.60         | 0.70         | 0.73      | 0.57         | 0.37         |
 |                     | actions + CR   | **1.00**  | *0.93*       | 1.00         | **0.95**  | **0.96**     | **0.90**     | **0.90**  | **0.76**     | *0.43*       | *0.67*    | *0.40*       | 0.37         |
 | **Deepseek-R1:7b**  | actions        | 1.00      | 0.96         | 1.00         | 1.00      | 1.00         | 0.93         | 0.96      | 1.00         | 0.92         | 0.96      | 1.00         | 0.79         |
 |                     | actions + CR   | 1.00      | **1.00**     | 1.00         | 1.00      | 1.00         | **1.00**     | *0.90*    | 1.00         | **1.00**     | **1.00**  | 1.00         | **1.00**     |
@@ -422,11 +406,11 @@ move into their decision-making, we analyse their performance of each generative
 agent in the RPS game. In this setup, a victory awards 2 points, a draw 1 point,
 and a loss 0 points.
 
-Figures below illustrates the average points earned per round along with
+Figure below illustrates the average points earned per round along with
 the 95 % confidence interval for each LLM when facing constant strategies,
 when the model generates one-shot actions. 
-Even if <tt>Mixtral:8x7b</tt>, <tt>Mistral-Small</tt>, and <tt><Qwen3/tt>  accurately predict its 
-opponent’s move, they fails to integrate this belief into
+Even if <tt>Mixtral:8x7b</tt>, <tt>Mistral-Small</tt>, and <tt>Qwen3</tt> accurately predict its 
+opponent’s move, they fail to integrate this belief into
 its decision-making process. Only <tt>Llama3.3:latest</tt> is capable of inferring
 the opponent’s behavior to choose the winning move.