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Commit 559b042e authored by Maxime MORGE's avatar Maxime MORGE
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Investment game

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.idea/csv-editor.xml
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......
README.md
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......@@ -13,6 +13,49 @@ simple contexts but struggle with more complex scenarios requiring
anticipation of other agents' behaviour. Our study outlines research
directions to overcome the current limitations of LLMs.
## Consistency
To assess the decision-making consistency of various LLMs, we introduce an
investment game that allows us to observe whether LLMs follow stable
decision-making patterns or react erratically to variations in the game’s
parameters.
An investor must allocate a basket (p_t^A, p_t^B) of 100 points between Asset A
and Asset B. The value of these points depends on two random parameters (a_t,
b_t), which determine how much money is received per allocated point.
For example, if a = 0.8 and b = 0.5, this means that each point allocated to
Asset A is worth $0.8, while each point allocated to Asset B yields $0.5. The
game is played 25 times to analyze the consistency of the investor’s
decision-making.
The investor’s decisions are then evaluated using an index called the Critical
Cost Efficiency Index (CCEI), which measures whether decisions adhere to a
certain level of economic rationality. This index is commonly used in
experimental economics and behavioral sciences to determine whether an
individual’s choices comply with the Generalized Axiom of Revealed Preference
(GARP). If an individual violates rational choice consistency, it is possible to
make a minimal budget adjustment (reducing resources) to align their decisions
with rationality. The CCEI quantifies the smallest budget reduction necessary to
make the choices consistent with rational principles.
Each basket’s budget is calculated as follows:
I_t = p_t^A \times a_t + p_t^B \times b_t
The CCEI index is derived from observed decisions. It is based on a linear
optimization problem that seeks the smallest necessary adjustment to budget
constraints to make the decisions conform to a rational model.
We aim to find the largest \lambda (0 ≤ λ ≤ 1) such that, for every observation,
the adjusted decisions satisfy the rationality constraint: p^_t \cdot x_s \leq
\lambda I_t This means that if we slightly reduce the individual’s budget (by
multiplying it by \lambda), their choices will become consistent with
rationality.
A CCEI close to 1 indicates that the choices are rational and consistent with
rational choice theory, whereas a low CCEI suggests irrational behavior. For
each model, 30 iterations of the game are conducted with a fixed temperature of
0.0 to maximize response consistency.
## Preferences
......@@ -72,6 +115,7 @@ retained by the dictator remains stable. However, the decisions become more
deterministic at low temperatures, whereas allocation diversity increases at
high temperatures, reflecting a more random exploration of available options.
### Preference alignment
We define four preferences for the dictator, each corresponding to a distinct form of social welfare:
......
data/dictator/dictator_consistency.csv deleted 100644 → 0
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iteration,model,temperature,ccei
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2,random,0.0,0.3338
3,random,0.0,0.1623
4,random,0.0,0.3429
5,random,0.0,0.171
6,random,0.0,0.2451
7,random,0.0,0.4368
8,random,0.0,0.2843
9,random,0.0,0.1131
10,random,0.0,0.197
11,random,0.0,0.1562
12,random,0.0,0.1362
13,random,0.0,0.1057
14,random,0.0,0.281
15,random,0.0,0.224
16,random,0.0,0.1506
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19,random,0.0,0.3756
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25,random,0.0,0.3
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1,deepseek-r1,0.0,0.3913
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data/investment/investment.csv 0 → 100644
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iteration,model,temperature,ccei
1,optimal,0.0,1.0
2,optimal,0.0,1.0
3,optimal,0.0,1.0
4,optimal,0.0,1.0
5,optimal,0.0,1.0
6,optimal,0.0,1.0
7,optimal,0.0,1.0
8,optimal,0.0,1.0
9,optimal,0.0,1.0
10,optimal,0.0,1.0
1,random,0.0,1.0
2,random,0.0,1.0
3,random,0.0,1.0
4,random,0.0,1.0
5,random,0.0,1.0
6,random,0.0,1.0
7,random,0.0,1.0
8,random,0.0,1.0
9,random,0.0,1.0
10,random,0.0,1.0
data/investment/investment.old.csv 0 → 100644
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2,random,0.0,0.2841
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13,random,0.0,0.2523
14,random,0.0,0.2014
15,random,0.0,0.1676
16,random,0.0,0.2469
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19,random,0.0,0.1818
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figures/dictator/dictator_consistency.svg → figures/investment/investment.svg
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......@@ -6,7 +6,7 @@
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......
src/investment/__init__.py 0 → 100644
+ 0
− 0
View file @ 559b042e
src/dictator/dictator_consistency.py → src/investment/investment.py
+ 69
− 52
View file @ 559b042e
import os
import asyncio
import os
import random
from typing import Dict, List
from gpytorch.settings import debug
from ollama import generate
from pydantic import BaseModel
import numpy as np
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.messages import TextMessage
from autogen_core import CancellationToken
from autogen_ext.models.openai import OpenAIChatCompletionClient
import json
import random
import numpy as np
from pydantic import BaseModel
from scipy.optimize import linprog
# Load API key from environment variable
......@@ -21,20 +17,20 @@ if not OPENAI_API_KEY:
# Define the expected response format as a Pydantic model
class AgentResponse(BaseModel):
my_share: int
other_share: int
assetA: int
assetB: int
reasoning: str
# The dictator game simulation class
class DictatorConsistency:
# The investment game simulation class
class Investment:
def __init__(self, model: str, temperature: float, max_retries: int = 3):
self.debug = False
self.debug = True
self.model = model
self.temperature = temperature
self.strategy = random
self.max_retries = max_retries # Maximum retry attempts in case of hallucinations
if not model == "random":
if not model == "random" and not model == "optimal":
is_openai_model = model.startswith("gpt")
base_url = "https://api.openai.com/v1" if is_openai_model else "http://localhost:11434/v1"
......@@ -59,18 +55,20 @@ class DictatorConsistency:
"""Runs the model if strategy is False, otherwise uses a classical method."""
if self.model == "random":
return self.apply_random(m, n)
if self.model == "optimal":
return self.apply_optimal(m, n)
instruction = f"""
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.
You have 100 points to allocate between Asset A and Asset B.
In this round, allocating 1 point to Asset A yields {m} dollars,
while allocating 1 point to Asset B yields {n} dollars.
What is your allocation?
Your response should be in JSON format with `my_share`, `other_share`, and `reasoning`.
Your response should be in JSON format with `assetA`, `assetB`, and `reasoning`.
"""
for attempt in range(self.max_retries):
agent = AssistantAgent(
name="Dictator",
name="Investor",
model_client=self.model_client,
system_message="You are a helpful assistant. You will be given 25 rounds of decision-making tasks and will be responsible for making decisions. You should use your best judgment to come up with solutions that you like most."
)
......@@ -83,29 +81,42 @@ class DictatorConsistency:
try:
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
assetA, assetB = agent_response.assetA, agent_response.assetB
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 my_share <= 100 and 0 <= other_share and other_share <= 100 and my_share + other_share == 100:
if 0 <= assetA and assetA <= 100 and 0 <= assetB and assetB <= 100 and assetA + assetB == 100:
return agent_response.model_dump()
else:
if self.debug :
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:
def apply_random(self, m: int, n: int) -> Dict:
"""Generates a response."""
my_share = random.randint(0, 100)
other_share = 100 - my_share
assetA = random.randint(0, 100)
assetB = 100 - assetA
return {
"my_share": my_share,
"other_share": other_share,
"assetA": assetA,
"assetB": assetB,
"reasoning": "Random choice"
}
}
def apply_optimal(self, m: int, n: int) -> Dict:
"""Generates a response."""
if m > n:
assetA = 100
assetB = 0
else:
assetA = 0
assetB = 100
return {
"assetA": assetA,
"assetB": assetB,
"reasoning": "Optimal choice"
}
def generate_M_N(self):
while True:
......@@ -114,9 +125,8 @@ class DictatorConsistency:
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."""
async def run_rounds(self, nb_rounds: int) -> float:
"""Runs the investment game for n rounds and computes the CCEI."""
results = []
prices = []
choices = []
......@@ -130,51 +140,58 @@ class DictatorConsistency:
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)
budget = round(m * result['assetA'] + n * result['assetB'], 4)
budgets.append(budget)
choices.append([result['assetA'], result['assetB']])
# Compute CCEI
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
c = [1] # Minimize theta
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)]
rhs = np.dot(prices[t], choices[t]) # p_t * x_t
if lhs > rhs: # Only add constraints where direct revealed preference exists
A_ub.append([-budgets[t]]) # -theta * I_t
b_ub.append(lhs - rhs)
# Ensure A_ub is a valid 2D array
if A_ub:
A_ub = np.array(A_ub, dtype=float)
if A_ub.ndim == 1: # Ensure it's 2D
A_ub = A_ub.reshape(-1, 1)
else:
A_ub = np.zeros((1, 1), dtype=float) # If no constraints, use a trivial 1x1 matrix
# Ensure b_ub is also valid
b_ub = np.array(b_ub, dtype=float) if b_ub else np.zeros(1, dtype=float)
bounds = [(0, 1)] # Theta bounds
# 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
return round(result.x[0], 4) # Return optimized theta value
else:
if self.debug:
print("CCEI computation failed. Check constraints.")
return 0 # Return 0 instead of None to indicate irrationality
print("CCEI computation failed. Check constraints.")
return 0 # Return 0 if LP fails
# Run the async function and return the response
if __name__ == "__main__":
game_agent = DictatorConsistency(model="mistral-small", temperature=0.7) # Toggle strategy here
response = asyncio.run(game_agent.run_rounds(25))
game_agent = Investment(model="mistral-small", temperature=0.0) # Toggle strategy here
response = asyncio.run(game_agent.run_rounds(10))
print(response)
src/dictator/draw_consistency_draw.py → src/investment/investment_draw.py
+ 2
− 2
View file @ 559b042e
......@@ -12,7 +12,7 @@ color_palette = {
}
# Load CSV file
file_path = "../../data/dictator/dictator_consistency.csv" # Update path
file_path = "../../data/investment/investment.csv" # Update path
df = pd.read_csv(file_path)
# Clean column names
......@@ -40,5 +40,5 @@ plt.ylabel("CCEI Value", fontsize=12)
plt.xticks(rotation=20)
# Save the figure
output_path = "../../figures/dictator/dictator_consistency.svg"
output_path = ("../../figures/investment/investment.svg")
plt.savefig(output_path, format="svg")
src/dictator/dictator_consistency_experiments.py → src/investment/investment_experiments.py
+ 6
− 9
View file @ 559b042e
import os
import asyncio
import csv
from typing import List
import random
from dictator_consistency import DictatorConsistency # Assuming this is in a separate file
from investment import Investment # 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"
models = ["optimal", "random"] # "gpt-4.5-preview-2025-02-27" "random", "llama3", "mistral-small", "deepseek-r1"
temperature = 0.0
iterations = 30
output_file = "../../data/dictator/dictator_consistency.csv"
iterations = 10
output_file = "../../data/investment/investment.csv"
async def run_experiment():
# Open CSV file to store results
......@@ -24,8 +21,8 @@ async def run_experiment():
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
game_agent = Investment(model=model, temperature=temperature)
ccei_value = await game_agent.run_rounds(10) # Run 25 rounds
# Write results to CSV
writer.writerow([iteration, model, temperature, ccei_value])
......
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