PyGAAMAS: Add Qwen3 in the dictator game

README.md

@@ -84,7 +84,7 @@ is set to $0.7$.
 Newt Figure presents a violin plot illustrating the share of the
 total amount (\$100) that the dictator allocates to themselves for each model.
 The median share taken by <tt>GPT-4.5</tt>, <tt>Llama3</tt>,
-<tt>Mistral-Small</tt>, and <tt>DeepSeek-R1</tt> through one-shot decisions is
+<tt>Mistral-Small</tt>, <tt>DeepSeek-R1</tt> and <tt>Qwen3</tt> through one-shot decisions is
 \$50, likely due to a corpus-based biases like term frequency. 
 The median share taken by <tt>mixtral:8x7b</tt>, <tt>Llama3.3:latest</tt>,
 is \$60. When we ask the

data/dictator/dictator.csv

@@ -209,3 +209,33 @@ iteration,model,temperature,amount,my_share,other_share,motivation
 28,deepseek-r1:7b,0.7,100,50,50,"I chose to split the points equally because fairness often leads to mutual satisfaction in such situations. By allocating 50% each, we ensure that neither party feels exploited or undervalued."
 29,deepseek-r1:7b,0.7,100,50,50,"Equal allocation promotes fairness and mutual satisfaction."
 30,deepseek-r1:7b,0.7,100,50,50,"The decision to allocate equally was made as a fair choice without knowledge of specific returns, aiming for balance and avoiding strategic advantage."
+1,qwen3,0.7,100,50,50,"I believe in fair distribution, so I choose to split the points equally."
+2,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and maximize overall returns."
+3,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and maximize overall returns."
+4,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and mutual benefit."
+5,qwen3,0.7,100,50,50,"I want to split the points evenly to ensure fairness and mutual benefit."
+6,qwen3,0.7,100,50,50,"I want to split the points equally to show fairness and ensure both parties benefit."
+7,qwen3,0.7,100,60,40,"I aim to be fair but also consider my own needs."
+8,qwen3,0.7,100,50,50,"I want to split the points equally because fairness is important to me."
+9,qwen3,0.7,100,50,50,"I want to split the points equally because fairness is important to me."
+10,qwen3,0.7,100,50,50,"I want to split the points equally to be fair."
+11,qwen3,0.7,100,50,50,"I want to be fair and ensure both parties get equal returns."
+12,qwen3,0.7,100,60,40,"I want to allocate 60 points to myself and 40 to the other person. My motivation is to ensure a fair split while also maximizing my own benefit."
+13,qwen3,0.7,100,60,40,"I want to be fair but also ensure I get a reasonable share."
+14,qwen3,0.7,100,50,50,"I want to split the points equally to show fairness and maximize the total return."
+15,qwen3,0.7,100,70,30,"I want to allocate 70 points to myself and 30 points to the other person. My motivation is to ensure a fair split while also taking a larger share since I believe in contributing more."
+16,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and mutual benefit."
+17,qwen3,0.7,100,50,50,"I want to split the points evenly to ensure fairness and maximize mutual benefit."
+18,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and mutual benefit."
+19,qwen3,0.7,100,50,50,"I want to be fair and allocate equal points to both myself and the other person."
+20,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness."
+21,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and maximize overall return."
+22,qwen3,0.7,100,50,50,"splitting equally seems fair"
+23,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and mutual benefit."
+24,qwen3,0.7,100,50,50,"I want to be fair and ensure both parties receive equal returns."
+25,qwen3,0.7,100,50,50,"I want to split the points equally to show fairness and ensure both parties benefit."
+26,qwen3,0.7,100,50,50,"I want to be fair and distribute the points equally between myself and the other person."
+27,qwen3,0.7,100,50,50,"I want to split the points equally to be fair."
+28,qwen3,0.7,100,50,50,"I want to be fair and ensure both parties get equal returns."
+29,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and maintain a balanced outcome for both parties."
+30,qwen3,0.7,100,50,50,"I want to split the points equally to ensure fairness and mutual benefit."

src/dictator/dictator.py

@@ -71,6 +71,7 @@ class Dictator:
             response_format=AgentResponse
         )
 
+
     async def run(self) -> Dict:
         """Runs the model if strategy is False, otherwise uses a classical method."""
         if self.strategy:
@@ -104,30 +105,35 @@ class Dictator:
             )
 
             try:
-                # Debug: print the raw response to check if fields are missing or named differently
-                print(f"Raw response (Attempt {attempt + 1}): {response}")
-
-                # Extract the JSON portion from the response string using regex
-                response_json_str = re.search(r'```json\n(.*?)\n```', response['response'], re.DOTALL)
-
-                if response_json_str:
-                    # Parse the extracted JSON string into a dictionary
-                    response_json = json.loads(response_json_str.group(1))
+                # Correct: get the content from the chat message
+                raw_text = response.chat_message.content
+
+                # Debug: show the raw content
+                print(f"Raw content (Attempt {attempt + 1}): {raw_text}")
+
+                # Try to load JSON directly
+                try:
+                    response_json = json.loads(raw_text)
+                except json.JSONDecodeError:
+                    # If it's wrapped in ```json ... ```, extract it
+                    match = re.search(r'```json\s*(.*?)\s*```', raw_text, re.DOTALL)
+                    if match:
+                        response_json = json.loads(match.group(1))
+                    else:
+                        print(f"Could not parse JSON from response (Attempt {attempt + 1})")
+                        continue
 
-                    # Create the AgentResponse object from the parsed response JSON
-                    agent_response = AgentResponse(**response_json)
-                    my_share, other_share = agent_response.my_share, agent_response.other_share
+                agent_response = AgentResponse(**response_json)
+                my_share, other_share = agent_response.my_share, agent_response.other_share
 
-                    # Validate the response
-                    if 0 <= my_share <= self.amount and 0 <= other_share <= self.amount and my_share + other_share <= self.amount:
-                        return agent_response.dict()
-                    else:
-                        print(f"Invalid response detected (Attempt {attempt + 1}): {response_json}")
+                # Validate shares
+                if 0 <= my_share <= self.amount and 0 <= other_share <= self.amount and my_share + other_share <= self.amount:
+                    return agent_response.dict()
                 else:
-                    print(f"JSON not found in response (Attempt {attempt + 1})")
+                    print(f"Invalid values in response (Attempt {attempt + 1}): {response_json}")
 
             except Exception as e:
-                print(f"Error in OpenAI request (Attempt {attempt + 1}): {e}")
+                print(f"Error in OpenAI response handling (Attempt {attempt + 1}): {e}")
 
         raise ValueError("Model failed to provide a valid response after multiple attempts.")
 
@@ -253,6 +259,6 @@ class Dictator:
 
 # Run the async function and return the response
 if __name__ == "__main__":
-    agent = Dictator(amount=100, model="deepseek-r1:7b", temperature=0.7, strategy=False)  #  "llama3.3:latest", "mixtral:8x7b"
+    agent = Dictator(amount=100, model="qwen3", temperature=0.7, strategy=False)  #  "llama3.3:latest", "mixtral:8x7b"
     response_json = asyncio.run(agent.run())
     print(response_json)
\ No newline at end of file

src/dictator/dictator_draw_violin.py

@@ -4,13 +4,15 @@ import seaborn as sns
 
 # Definition of the color palette
 color_palette = {
+    'random': '#333333',  # Black
     'gpt-4.5-preview-2025-02-27': '#7abaff',  # BlueEscape
     'llama3': '#32a68c',  # GreenFuture
     'llama3.3:latest': '#4b9f7d',  # GreenLlama3.3
     'mistral-small': '#ff6941',  # WarmOrange
     'mixtral:8x7b': '#f1a61a',  # YellowMixtral
     'deepseek-r1': '#5862ed',  # InclusiveIndigo
-    'deepseek-r1:7b': '#9a7bff'  # PurpleDeepseek-r1:7b
+    'deepseek-r1:7b': '#9a7bff',  # PurpleDeepseek-r1:7b
+    'qwen3': '#c02942'
 }
 
 # Load the data
@@ -21,7 +23,8 @@ model_order = [
     'gpt-4.5-preview-2025-02-27',
     'llama3', 'llama3.3:latest',  # Place llama3 and llama3.3:latest together
     'mistral-small', 'mixtral:8x7b',  # Bring mistral-small and mixtral:8x7b closer
-    'deepseek-r1', 'deepseek-r1:7b'
+    'deepseek-r1', 'deepseek-r1:7b',
+    'qwen3'
 ]
 
 # Create the violin plot

src/dictator/dictator_experiments.py

@@ -41,7 +41,7 @@ class DictatorExperiment:
 
 # Running the experiment
 if __name__ == "__main__":
-    models = ["mixtral:8x7b", "llama3.3:latest", "deepseek-r1:7b"] # "gpt-4.5-preview-2025-02-27" "llama3", "mistral-small", "deepseek-r1"
+    models = ["qwen3"] # "gpt-4.5-preview-2025-02-27" "llama3", "mistral-small", "deepseek-r1", "mixtral:8x7b", "llama3.3:latest", "deepseek-r1:7b"
     temperature = 0.7
     amount = 100
     iterations = 30

src/investment/investment.py

@@ -281,6 +281,6 @@ class Investment:
 
 # Run the async function and return the response
 if __name__ == "__main__":
-    game_agent = Investment(model="mistral-small", temperature=0.0)
+    game_agent = Investment(model="deepseek-r1", temperature=0.0)
     response = asyncio.run(game_agent.run_rounds(30))
     print(response)