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diff --git a/src/dictator/dictator_consistency.py b/src/dictator/dictator_consistency.py
index 59ada6b8edeeda6d01d905104f680e377e40569c..04282d2769768918ec90176c481d73b978db1e4e 100644
--- a/src/dictator/dictator_consistency.py
+++ b/src/dictator/dictator_consistency.py
@@ -1,6 +1,9 @@
import os
import asyncio
from typing import Dict, List
+
+from gpytorch.settings import debug
+from ollama import generate
from pydantic import BaseModel
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.messages import TextMessage
@@ -9,7 +12,7 @@ from autogen_ext.models.openai import OpenAIChatCompletionClient
import json
import random
import numpy as np
-from scipy.optimize import minimize
+from scipy.optimize import linprog
# Load API key from environment variable
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
@@ -18,19 +21,20 @@ if not OPENAI_API_KEY:
# Define the expected response format as a Pydantic model
class AgentResponse(BaseModel):
- my_share: float
- other_share: float
+ my_share: int
+ other_share: int
reasoning: str
# The dictator game simulation class
class DictatorConsistency:
- def __init__(self, model: str, temperature: float, random=False, max_retries: int = 3):
+ def __init__(self, model: str, temperature: float, max_retries: int = 3):
+ self.debug = False
self.model = model
self.temperature = temperature
self.strategy = random
self.max_retries = max_retries # Maximum retry attempts in case of hallucinations
- if not random:
+ if not model == "random":
is_openai_model = model.startswith("gpt")
base_url = "https://api.openai.com/v1" if is_openai_model else "http://localhost:11434/v1"
@@ -53,25 +57,14 @@ class DictatorConsistency:
async def run(self, m: float, n: float) -> Dict:
"""Runs the model if strategy is False, otherwise uses a classical method."""
- if self.strategy:
+ if self.model == "random":
return self.apply_random(m, n)
instruction = f"""
- You have $100 to allocate between yourself and an anonymous person.
- - Allocating 1 point for yourself is worth {m} dollars.
- - Allocating 1 point for the other person is worth {n} dollars.
- Your goal is to decide how to allocate these $100.
-
- Example:
- - If `m = 1` and `n = 2`, then for every point you allocate to yourself, you receive $1, and for every point you allocate to the other person, they receive $2.
- - If you choose to allocate 30 points to yourself, then you would receive 30 * 1 = $30, and the other person would receive $100 - $30 = $70 .
- - Thus, your total allocation would be 30 points for yourself and 70/2 = 35 points for the other person, totaling $100.
-
-
- Please provide:
- - How many points you want to allocate to yourself (out of $100),
- - How many points you want to allocate to the other person.
-
+ You have 100 points to allocate between yourself and an anonymous person.
+ In this round, allocating every 1 point for yourself returns {m} dollars for yourself,
+ and allocating every 1 point for the other one returns {n} dollars for him/her.
+ What is your allocation?
Your response should be in JSON format with `my_share`, `other_share`, and `reasoning`.
"""
@@ -91,82 +84,97 @@ class DictatorConsistency:
response_data = response.chat_message.content
agent_response = AgentResponse.model_validate_json(response_data) # Parse JSON
my_share, other_share = agent_response.my_share, agent_response.other_share
- print(f"Response (Attempt {attempt+1}): {response_data}")
+ if self.debug:
+ print(f"Response (Attempt {attempt+1}): {response_data}")
# Validate values: ensure they sum to $100 considering the values M and N
- if 0 <= my_share and 0 <= other_share :
- total_value = my_share * m + other_share * n
- if abs(total_value - 100.0) < 1e-6: # Use a tolerance for floating-point comparison
- return agent_response.model_dump()
- else:
- print(f"Invalid response detected (Attempt {attempt+1}): Total value {total_value} != 100.")
+ if 0 <= my_share and my_share <= 100 and 0 <= other_share and other_share <= 100 and my_share + other_share == 100:
+ return agent_response.model_dump()
else:
- print(f"Invalid response detected (Attempt {attempt+1}): {response_data}")
+ if self.debug :
+ print(f"Invalid response detected (Attempt {attempt+1}): {response_data}")
except Exception as e:
print(f"Error parsing response (Attempt {attempt+1}): {e}")
-
raise ValueError("Model failed to provide a valid response after multiple attempts.")
def apply_random(self, m:int, n:int) -> Dict:
"""Generates a response."""
- my_share = (random.uniform(0, 10))
- other_share = (100 - my_share * m) / n
- # Validate values: ensure they sum to $100 considering the values M and N
- if 0 <= my_share and 0 <= other_share:
- total_value = my_share * m + other_share * n
- if abs(total_value - 100.0) < 1e-6: # Use a tolerance for floating-point comparison
- return {
- "my_share": my_share,
- "other_share": other_share,
- "reasoning": "Random choice"
- }
- else:
- return {
- "my_share": my_share,
- "other_share": other_share,
- "reasoning": "choices outside budget"
-
- }
+ my_share = random.randint(0, 100)
+ other_share = 100 - my_share
+ return {
+ "my_share": my_share,
+ "other_share": other_share,
+ "reasoning": "Random choice"
+ }
+
+
+ def generate_M_N(self):
+ while True:
+ M = random.uniform(0.1, 1) # Random value in [0.1, 1]
+ N = random.uniform(0.1, 1) # Random value in [0.1, 1]
+ if max(M, N) >= 0.5: # Ensure max(M, N) is at least 0.5
+ return round(M, 1), round(N, 1)
+
async def run_rounds(self, nb_rounds: int) -> List[Dict]:
"""Runs the dictator game for n rounds and returns the results."""
results = []
prices = []
choices = []
+ budgets = []
for _ in range(nb_rounds):
- m = round(random.uniform(0.1, 1.0), 1) # Random price for self
- n = round(random.uniform(0.1, 1.0), 1) # Random price for other
- print(f"m, n: {m}, {n}")
+ m, n = self.generate_M_N()
+ if self.debug:
+ print(f"m, n: {m}, {n}")
result = await self.run(m, n)
- print(f"result: {result}")
+ if self.debug:
+ print(f"result: {result}")
results.append(result)
prices.append([m, n])
+ budgets.append(m * result['my_share'] + n * result['other_share'])
choices.append([result['my_share'], result['other_share']])
+ ccei_value = self.compute_ccei(prices, choices,budgets)
+ if self.debug:
+ print(f"prices: {prices}")
+ print(f"choices: {choices}")
+ print(f"budgets: {budgets}")
+ print(f"CCEI: {ccei_value}")
+ return ccei_value
+
+
+ def compute_ccei(self, prices, choices, budgets):
+ """
+ Computes the Critical Cost Efficiency Index (CCEI).
+ :param prices: List of price vectors (list of lists)
+ :param choices: List of chosen consumption bundles (list of lists)
+ :param budgets: List of budget values (list)
+ :return: CCEI value (between 0 and 1)
+ """
+ n = len(prices) # Number of observations
+ # Objective: maximize lambda (equivalent to minimizing -lambda)
+ c = [-1] # We minimize -lambda to maximize lambda
+ A_ub = [] # Constraint matrix
+ b_ub = [] # Right-hand side values
+ for t in range(n):
+ for s in range(n):
+ lhs = np.dot(prices[t], choices[s]) # p_t * x_s
+ rhs = budgets[t] # I_t
+ # Corrected constraint: p_t * x_s - lambda * I_t <= 0
+ A_ub.append([rhs]) # Correctly include I_t as coefficient of lambda
+ b_ub.append(lhs) # p_t * x_s moved to RHS
+ # Bounds for lambda: between 0 and 1
+ bounds = [(0, 1)]
+ # Solve the linear program
+ result = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method="highs")
+ if result.success:
+ return round(-result.fun, 4) # Extract the optimized lambda
+ else:
+ if self.debug:
+ print("CCEI computation failed. Check constraints.")
+ return 0 # Return 0 instead of None to indicate irrationality
- ccei_value = self.compute_ccei(prices, choices)
- print(f"prices: {prices}")
- print(f"choices: {choices}")
- print(f"CCEI: {ccei_value}")
- return results
-
- def compute_ccei(self, prices: List[List[float]], choices: List[List[float]]) -> float:
- """Computes the Critical Cost Efficiency Index (CCEI)."""
- def objective(lambda_val):
- return lambda_val[0]
-
- constraints = []
- for t, (p_t, x_t) in enumerate(zip(prices, choices)):
- for s, x_s in enumerate(choices):
- if t != s:
- constraints.append({
- 'type': 'ineq',
- 'fun': lambda lambda_val, p_t=p_t, x_s=x_s, x_t=x_t: lambda_val[0] * np.dot(p_t, x_t) - np.dot(p_t, x_s)
- })
-
- result = minimize(objective, [1], constraints=constraints, bounds=[(0, 1)])
- return result.x[0] if result.success else 0
# Run the async function and return the response
if __name__ == "__main__":
- game_agent = DictatorConsistency(model="mistral-small", temperature=0.7, random=True) # Toggle strategy here
- responses = asyncio.run(game_agent.run_rounds(10))
- print(json.dumps(responses, indent=2))
+ game_agent = DictatorConsistency(model="mistral-small", temperature=0.7) # Toggle strategy here
+ response = asyncio.run(game_agent.run_rounds(25))
+ print(response)
diff --git a/src/dictator/dictator_consistency_experiments.py b/src/dictator/dictator_consistency_experiments.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac425e7d68f79887e12417b6800bf8a58f9d4bdc
--- /dev/null
+++ b/src/dictator/dictator_consistency_experiments.py
@@ -0,0 +1,37 @@
+import os
+import asyncio
+import csv
+from typing import List
+import random
+from dictator_consistency import DictatorConsistency # Assuming this is in a separate file
+
+# Define models, temperature, and iterations
+models = ["random", "llama3", "mistral-small", "deepseek-r1"] # "gpt-4.5-preview-2025-02-27"
+temperature = 0.0
+iterations = 30
+output_file = "../../data/dictator/dictator_consistency.csv"
+
+async def run_experiment():
+ # Open CSV file to store results
+ with open(output_file, mode="w", newline="") as file:
+ writer = csv.writer(file)
+ writer.writerow(["iteration", "model", "temperature", "ccei"]) # CSV header
+
+ # Run each model for multiple iterations
+ for model in models:
+ print(f"Running experiments for model: {model}")
+ for iteration in range(1, iterations + 1):
+ print(f"Iteration {iteration}/{iterations} for {model}")
+
+ # Run DictatorConsistency experiment
+ game_agent = DictatorConsistency(model=model, temperature=temperature)
+ ccei_value = await game_agent.run_rounds(25) # Run 25 rounds
+
+ # Write results to CSV
+ writer.writerow([iteration, model, temperature, ccei_value])
+ file.flush() # Ensure data is written immediately
+ print(f"Saved iteration {iteration} for {model} -> CCEI: {ccei_value}")
+
+# Run the async function
+if __name__ == "__main__":
+ asyncio.run(run_experiment())
\ No newline at end of file
diff --git a/src/dictator/draw_consistency_draw.py b/src/dictator/draw_consistency_draw.py
new file mode 100644
index 0000000000000000000000000000000000000000..849e73da577b644bcb1fc4fa7bacf83d725bca2e
--- /dev/null
+++ b/src/dictator/draw_consistency_draw.py
@@ -0,0 +1,44 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Custom color palette
+color_palette = {
+ 'random': '#333333', #
+ 'gpt-4.5-preview-2025-02-27': '#7abaff', # Blue
+ 'llama3': '#32a68c', # Green
+ 'mistral-small': '#ff6941', # Orange
+ 'deepseek-r1': '#5862ed' # Indigo
+}
+
+# Load CSV file
+file_path = "../../data/dictator/dictator_consistency.csv" # Update path
+df = pd.read_csv(file_path)
+
+# Clean column names
+df.columns = df.columns.str.strip()
+
+# Ensure required columns exist
+if "ccei" not in df.columns or "model" not in df.columns:
+ raise ValueError("Missing required columns ('ccei' or 'model') in the dataset!")
+
+# Set Seaborn style
+sns.set(style="whitegrid")
+
+# Create figure
+plt.figure(figsize=(10, 6))
+
+# Draw boxplot
+sns.boxplot(data=df, x="model", y="ccei", palette=color_palette, width=0.6)
+
+# Add plot labels
+plt.title("CCEI Distribution by Model", fontsize=14)
+plt.xlabel("Model", fontsize=12)
+plt.ylabel("CCEI Value", fontsize=12)
+
+# Rotate x-axis labels for better readability
+plt.xticks(rotation=20)
+
+# Save the figure
+output_path = "../../figures/dictator/dictator_consistency.svg"
+plt.savefig(output_path, format="svg")