10.1 自发行 vs 代理发行的决策

selfNetFen  = grossFen × platformNetPermille / 1000 − selfUaFen
pubGrossFen = grossFen × upliftPermille / 1000
pubNetFen   = max( minGuaranteeFen,
                   pubGrossFen × platformNetPermille / 1000 × (1000 − publisherSharePermille) / 1000 )
deltaFen    = pubNetFen − selfNetFen
decision    = "publisher" if deltaFen > 0 else "self"

#!/usr/bin/env python3
# file: sim/o10_decision.py
# tier: S
# verify: /opt/homebrew/bin/python3 sim/o10_decision.py --config config/ --out out/
"""自发行 vs 代理发行净收入决策仿真：把「分成比例 × 流水情景」铺成一张净收入对照表。

读 config/o10_decision.json。对每个（情景 scenario, 报价 offer）组合：

    selfNetFen  = grossFen * platformNetPermille // 1000 - selfUaFen
    pubGrossFen = grossFen * upliftPermille // 1000
    pubNetFen   = max(minGuaranteeFen,
                      pubGrossFen * platformNetPermille // 1000
                      * (1000 - publisherSharePermille) // 1000)
    deltaFen    = pubNetFen - selfNetFen
    decision    = "publisher" if deltaFen > 0 else "self"

自发行要自己掏买量预算（selfUaFen），代理发行由发行商承担买量（故不扣 UA），
但要把可分成流水的 publisherSharePermille 让给发行商；minGuaranteeFen 是保底，
兜住下限。全程整数、地板除，无浮点。产出 out/o10_decision.json + .csv + .svg。
分成比例、发行商买量放大倍数、保底额都是「行业惯例」口径的建模假设；真实报价与
真实流水需真实业务环境，命令行不可复现（X）。本文不提供任何法律意见。
"""
from __future__ import annotations

import argparse
import csv
import json
import os
import xml.etree.ElementTree as ET


def load(d: str) -> dict:
    with open(os.path.join(d, "o10_decision.json"), encoding="utf-8") as f:
        return json.load(f)


def evaluate(cfg: dict, scenario: dict, offer: dict) -> dict:
    """一个（情景, 报价）组合的净收入与决策。"""
    plat = cfg["platformNetPermille"]
    self_net = scenario["grossFen"] * plat // 1000 - cfg["selfUaFen"]
    pub_gross = scenario["grossFen"] * offer["upliftPermille"] // 1000
    pub_net_raw = pub_gross * plat // 1000 * (1000 - offer["publisherSharePermille"]) // 1000
    pub_net = max(offer["minGuaranteeFen"], pub_net_raw)
    delta = pub_net - self_net
    return {
        "scenarioId": scenario["id"], "scenarioName": scenario["name"], "grossFen": scenario["grossFen"],
        "offerId": offer["id"], "offerName": offer["name"],
        "publisherSharePermille": offer["publisherSharePermille"], "upliftPermille": offer["upliftPermille"],
        "selfNetFen": self_net, "pubGrossFen": pub_gross, "pubNetRawFen": pub_net_raw,
        "guaranteeBound": offer["minGuaranteeFen"] > pub_net_raw,
        "pubNetFen": pub_net, "deltaFen": delta, "decision": "publisher" if delta > 0 else "self",
    }


def evaluate_all(cfg: dict) -> list[dict]:
    return [evaluate(cfg, s, o) for s in cfg["scenarios"] for o in cfg["offers"]]


def draw_svg(path: str, rows: list[dict], scenarios: list[dict], offers: list[dict]) -> int:
    """纯 Python SVG：每个情景一组，每个报价两根条——自发行净收入 vs 代理发行净收入（分）。"""
    W, ROW, PAD = 760, 96, 150
    H = 80 + ROW * len(scenarios)
    root = ET.Element("svg", {"xmlns": "http://www.w3.org/2000/svg", "width": str(W),
                              "height": str(H), "viewBox": f"0 0 {W} {H}"})
    ET.SubElement(root, "rect", {"width": str(W), "height": str(H), "fill": "#ffffff"})
    ET.SubElement(root, "text", {"x": "16", "y": "28", "font-size": "15",
                                 "fill": "#111827"}).text = "《合成小岛》自发行 vs 代理发行净收入（分）"
    hi = max([max(r["selfNetFen"], r["pubNetFen"]) for r in rows] + [1])
    span = W - PAD - 24
    for i, s in enumerate(scenarios):
        y = 56 + i * ROW
        ET.SubElement(root, "text", {"x": "16", "y": str(y + 12), "font-size": "12",
                                     "fill": "#374151"}).text = f'{s["name"]}（流水 {s["grossFen"]} 分）'
        for j, o in enumerate(offers):
            r = next(x for x in rows if x["scenarioId"] == s["id"] and x["offerId"] == o["id"])
            yb = y + 18 + j * 24
            for k, (val, color, tag) in enumerate([(r["selfNetFen"], "#94a3b8", "自"),
                                                   (r["pubNetFen"], "#2563eb" if r["decision"] == "publisher" else "#f59e0b", "代")]):
                w = span * max(val, 0) // hi
                ET.SubElement(root, "rect", {"x": str(PAD), "y": str(yb + k * 10), "width": str(w),
                                             "height": "9", "rx": "2", "fill": color})
            ET.SubElement(root, "text", {"x": str(PAD + span + 2), "y": str(yb + 14), "font-size": "10",
                                         "fill": "#6b7280"}).text = f'{o["id"]} Δ{r["deltaFen"]} {r["decision"][:3]}'
    os.makedirs(os.path.dirname(path), exist_ok=True)
    ET.ElementTree(root).write(path, encoding="utf-8", xml_declaration=True)
    return len(ET.tostring(root))


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--config", default="config/")
    ap.add_argument("--out", default="out/")
    a = ap.parse_args()
    cfg = load(a.config)
    rows = evaluate_all(cfg)

    pub_count = sum(1 for r in rows if r["decision"] == "publisher")
    # 跨语言对拍摘要：core 侧复算同一串数字，逐位比对（本节 S 层；10.3 给出 P 层对拍示例）。
    digest = ",".join(f'{r["scenarioId"]}:{r["offerId"]}:{r["selfNetFen"]}:{r["pubNetFen"]}:{r["decision"]}'
                      for r in rows)

    os.makedirs(a.out, exist_ok=True)
    svg = draw_svg(os.path.join(a.out, "o10_decision.svg"), rows, cfg["scenarios"], cfg["offers"])
    with open(os.path.join(a.out, "o10_decision.csv"), "w", encoding="utf-8", newline="") as f:
        w = csv.writer(f)
        cols = ("scenarioId", "scenarioName", "grossFen", "offerId", "offerName",
                "publisherSharePermille", "upliftPermille", "selfNetFen", "pubGrossFen",
                "pubNetRawFen", "guaranteeBound", "pubNetFen", "deltaFen", "decision")
        w.writerow(cols)
        for r in rows:
            w.writerow([r[k] for k in cols])
    report = {"version": cfg["version"], "game": cfg["game"], "observedAt": cfg["observedAt"],
              "platformNetPermille": cfg["platformNetPermille"], "selfUaFen": cfg["selfUaFen"],
              "scenarioCount": len(cfg["scenarios"]), "offerCount": len(cfg["offers"]),
              "publisherWins": pub_count, "selfWins": len(rows) - pub_count,
              "rows": rows, "digest": digest,
              "svg": os.path.join(a.out, "o10_decision.svg"), "svgBytes": svg}
    with open(os.path.join(a.out, "o10_decision.json"), "w", encoding="utf-8") as f:
        json.dump(report, f, ensure_ascii=False, indent=2)

    print(json.dumps({
        "publisherWins": pub_count, "selfWins": len(rows) - pub_count,
        "cells": [{"s": r["scenarioId"], "o": r["offerId"], "selfNet": r["selfNetFen"],
                   "pubNet": r["pubNetFen"], "delta": r["deltaFen"], "decision": r["decision"],
                   "guaranteeBound": r["guaranteeBound"]} for r in rows],
        "digest": digest}, ensure_ascii=False, indent=2))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())

{
  "version": 1,
  "game": "merge-island",
  "observedAt": "2026-10",
  "note": "自发行 vs 代理发行的净收入决策建模假设，口径是「行业惯例」的公开可观察机制，不是任何平台或发行商后台的实测，也不构成法律意见。platformNetPermille=平台扣成后开发者/发行商可分成收入占比（千分比）；selfUaFen=自发行必须自己掏的买量预算（分）；upliftPermille=发行商买量把流水放大到的千分比；publisherSharePermille=发行商拿走可分成收入的比例（千分比）；minGuaranteeFen=保底金额（分）。真实报价与真实流水需真实业务环境，命令行不可复现（X）。全整数，无浮点。",
  "platformNetPermille": 700,
  "selfUaFen": 1200000,
  "scenarios": [
    { "id": "bear", "name": "悲观流水", "grossFen": 4000000 },
    { "id": "base", "name": "中性流水", "grossFen": 8000000 },
    { "id": "bull", "name": "乐观流水", "grossFen": 14000000 }
  ],
  "offers": [
    { "id": "p60", "name": "发行C·分成60%·弱放大", "publisherSharePermille": 600, "upliftPermille": 1150, "minGuaranteeFen": 1700000 },
    { "id": "p30", "name": "发行A·分成30%·中放大", "publisherSharePermille": 300, "upliftPermille": 1200, "minGuaranteeFen": 1000000 },
    { "id": "p50", "name": "发行B·分成50%·强放大", "publisherSharePermille": 500, "upliftPermille": 1800, "minGuaranteeFen": 2500000 }
  ]
}