From 08c0afb55aa602af5b0ef09430baa01908465232 Mon Sep 17 00:00:00 2001 From: Daniel Rosel Date: Mon, 2 Feb 2026 12:03:30 +0100 Subject: [PATCH] chore: add chart of supra competive pricing --- paper/src/chapters/03-methodology.tex | 2 +- paper/src/chapters/04-results.tex | 6 + paper/src/chapters/figures/process_supra.py | 131 + paper/src/chapters/figures/supra.csv | 41 + paper/src/chapters/figures/supra.tex | 26 + paper/src/chapters/figures/supra_data.csv | 4041 +++++++++++++++++++ paper/src/preamble.tex | 2 + 7 files changed, 4248 insertions(+), 1 deletion(-) create mode 100644 paper/src/chapters/figures/process_supra.py create mode 100644 paper/src/chapters/figures/supra.csv create mode 100644 paper/src/chapters/figures/supra.tex create mode 100644 paper/src/chapters/figures/supra_data.csv diff --git a/paper/src/chapters/03-methodology.tex b/paper/src/chapters/03-methodology.tex index 0c68240..540ae68 100644 --- a/paper/src/chapters/03-methodology.tex +++ b/paper/src/chapters/03-methodology.tex @@ -317,7 +317,7 @@ We also need to think about a policy like taxation to the agents Strategy-Proof \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] \caption{PHANTOM defensive pricing loop (bachelor-thesis level)} diff --git a/paper/src/chapters/04-results.tex b/paper/src/chapters/04-results.tex index ca57292..e2a1735 100644 --- a/paper/src/chapters/04-results.tex +++ b/paper/src/chapters/04-results.tex @@ -1,4 +1,10 @@ \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} diff --git a/paper/src/chapters/figures/process_supra.py b/paper/src/chapters/figures/process_supra.py new file mode 100644 index 0000000..a7b01e6 --- /dev/null +++ b/paper/src/chapters/figures/process_supra.py @@ -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) diff --git a/paper/src/chapters/figures/supra.csv b/paper/src/chapters/figures/supra.csv new file mode 100644 index 0000000..9a2caf5 --- /dev/null +++ b/paper/src/chapters/figures/supra.csv @@ -0,0 +1,41 @@ +"Step","giddy-deluge-6 - distributions/prices" 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diff --git a/paper/src/chapters/figures/supra.tex b/paper/src/chapters/figures/supra.tex new file mode 100644 index 0000000..439f22e --- /dev/null +++ b/paper/src/chapters/figures/supra.tex @@ -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} diff --git a/paper/src/chapters/figures/supra_data.csv b/paper/src/chapters/figures/supra_data.csv new file mode 100644 index 0000000..f005217 --- /dev/null +++ b/paper/src/chapters/figures/supra_data.csv @@ -0,0 +1,4041 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ad633de..d8f9876 100644 --- a/paper/src/preamble.tex +++ b/paper/src/preamble.tex @@ -29,6 +29,8 @@ \usepackage{subcaption} \usepackage{siunitx} \usepackage{tikz} +\usepackage{pgfplots} +\pgfplotsset{compat=1.18} \usepackage{listings} \usepackage{xcolor} \usepackage[ruled,vlined]{algorithm2e}