chore: add chart of supra competive pricing

This commit is contained in:
2026-02-02 12:03:30 +01:00
parent c4fd1352c9
commit 08c0afb55a
7 changed files with 4248 additions and 1 deletions

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@@ -317,7 +317,7 @@ We also need to think about a policy like taxation to the agents Strategy-Proof
\subsubsection{Pricing Mechanism Summary} \subsubsection{Pricing Mechanism Summary}
We now present the complete pricing mechanism that integrates the behavioral separability, contamination estimation, and robust optimization components developed in the preceding sections. Algorithm~\ref{alg:phantom_pricing_loop} formalizes the defensive pricing loop as a Stackelberg game where the platform (leader) sets prices and the aggregate demand (follower) responds through observed session trajectories. We now present the complete pricing mechanism that integrates the behavioral separability, contamination estimation, and robust optimization components developed in the preceding sections. Algorithm~\ref{alg:phantom_loop_clean} formalizes the defensive pricing loop as a Stackelberg game where the platform (leader) sets prices and the aggregate demand (follower) responds through observed session trajectories.
\begin{algorithm}[t] \begin{algorithm}[t]
\caption{PHANTOM defensive pricing loop (bachelor-thesis level)} \caption{PHANTOM defensive pricing loop (bachelor-thesis level)}

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@@ -1,4 +1,10 @@
\section{Results} \section{Results}
\begin{figure}[ht]
\centering
\input{chapters/figures/supra.tex}
\caption{Evolution of price distributions over experiment steps. The heatmap illustrates the density of price offerings. This is an early baseline simulation which demonstrates supra-competitive price-setting in deep learning agents such as SAC as can be clearly seen by the high density at the highest available price.}
\label{fig:supra_heatmap}
\end{figure}
\subsection{Behavioral Analysis} \subsection{Behavioral Analysis}

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@@ -0,0 +1,131 @@
import pandas as pd
import json
import numpy as np
import sys
import os
def process_supra(input_file, output_file):
print(f"Processing {input_file} -> {output_file}")
# Read the CSV
try:
# The CSV has a weird format: "Step","giddy-deluge-6 - distributions/prices"
# The header is on line 1.
# Let's verify the file content format first effectively.
# The previous read showed standard CSV with quoted fields.
df = pd.read_csv(input_file, quotechar='"', skipinitialspace=True)
except Exception as e:
print(f"Error reading CSV: {e}")
return
# Prepare for re-binning
# We need a common set of bins to plot a heatmap (surface)
# First, let's collect all data to determine range
all_min = float("inf")
all_max = float("-inf")
parsed_data = []
# The column names might be dynamic, so let's rely on indices
# Column 0: Step
# Column 1: JSON blob
for index, row in df.iterrows():
try:
step = int(row.iloc[0])
json_str = row.iloc[1]
# Cleaning potential double quotes issue if pandas didn't catch it perfect
# but pandas read_csv usually handles standard CSV escaping well.
data = json.loads(json_str)
bins = np.array(data["bins"])
values = np.array(data["values"])
# Update global range
if bins.min() < all_min:
all_min = bins.min()
if bins.max() > all_max:
all_max = bins.max()
parsed_data.append({"step": step, "bins": bins, "values": values})
except Exception as e:
print(f"Skipping row {index} due to error: {e}")
continue
if not parsed_data:
print("No data parsed.")
return
print(f"Found {len(parsed_data)} steps. Range: {all_min} to {all_max}")
# Define common grid
# Y-axis (Price)
# Using 100 bins for resolution
y_bins_edges = np.linspace(all_min, all_max, 101)
y_bin_centers = (y_bins_edges[:-1] + y_bins_edges[1:]) / 2
# Open output file
with open(output_file, "w") as f:
# PGFPlots 3D format often prefers no header or a specific header.
# We will use named columns.
f.write("step,price,density\n")
# Sort by step to ensure correct mesh ordering
parsed_data.sort(key=lambda x: x["step"])
for item in parsed_data:
step = item["step"]
original_bins = item["bins"]
original_values = item["values"]
# Re-binning logic
current_new_hist = np.zeros(len(y_bin_centers))
for i, (new_start, new_end) in enumerate(
zip(y_bins_edges[:-1], y_bins_edges[1:])
):
val = 0.0
# This inner loop is slightly inefficient O(N*M) but N~3000, M~100 -> 300k ops, totally fine.
for j in range(len(original_values)):
b_start = original_bins[j]
# Handle cases where values array might be 1 shorter than bins (histogram edges vs centers)
# The provided JSON has "bins" array larger than "values" by 1 usually for edges.
if j + 1 >= len(original_bins):
break
b_end = original_bins[j + 1]
b_width = b_end - b_start
if b_width <= 0:
continue
# Calculate overlap
overlap_start = max(new_start, b_start)
overlap_end = min(new_end, b_end)
overlap = max(0, overlap_end - overlap_start)
if overlap > 0:
# Add proportional count
val += original_values[j] * (overlap / b_width)
current_new_hist[i] = val
# Write row to file for this step
for price, density in zip(y_bin_centers, current_new_hist):
# PGFPlots expects x y z
f.write(f"{step},{price},{density}\n")
# Add a blank line for PGFPlots matrix format (essential for 'mesh' or 'surf')
f.write("\n")
if __name__ == "__main__":
# Resolve relative paths relative to where script is run, or use absolute
base_dir = os.path.dirname(os.path.abspath(__file__))
input_path = os.path.join(base_dir, "supra.csv")
output_path = os.path.join(base_dir, "supra_data.csv")
process_supra(input_path, output_path)

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@@ -0,0 +1,41 @@
"Step","giddy-deluge-6 - distributions/prices"
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1 Step giddy-deluge-6 - distributions/prices
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View File

@@ -0,0 +1,26 @@
\begin{tikzpicture}
\begin{axis}[
view={0}{90}, % Top-down view for heatmap
xlabel={Step},
ylabel={Price},
colorbar,
colorbar style={
title={Density},
ylabel={},
},
colormap/viridis,
% Adjust these axis limits if necessary based on data
enlargelimits=false,
axis on top,
width=0.9\columnwidth,
height=0.6\columnwidth,
]
\addplot3[
surf,
shader=flat,
mesh/check=false % Disable check to rely on empty lines
] table [col sep=comma, x=step, y=price, z=density] {chapters/figures/supra_data.csv};
\end{axis}
\end{tikzpicture}

File diff suppressed because it is too large Load Diff

View File

@@ -29,6 +29,8 @@
\usepackage{subcaption} \usepackage{subcaption}
\usepackage{siunitx} \usepackage{siunitx}
\usepackage{tikz} \usepackage{tikz}
\usepackage{pgfplots}
\pgfplotsset{compat=1.18}
\usepackage{listings} \usepackage{listings}
\usepackage{xcolor} \usepackage{xcolor}
\usepackage[ruled,vlined]{algorithm2e} \usepackage[ruled,vlined]{algorithm2e}