机制设计理论
简要概述：我正在探索一种简单的、现实世界的电子商务定价机制，该机制将确定性的折扣路径与微小概率的“免费购买”选项相结合。每次购票都会略微提高所有人的公共折扣，因此该系统试图实现正和而非零和。我希望对该机制提出批评意见，并寻求关于证明（个体理性、预算平衡、无亏损）的反馈以及实施方面的建议。
### 机制简介
这是一个应用机制，设定了（i）随时间/销售变化的折扣计划，以及（ii）买家以极小概率p获得免费商品（或全额退款）的机会。买家可以选择：
1. 以当前折扣价立即购买，或
2. 通过购买低价票尝试运气，获得商品的微小免费机会。
每次购票都会将商品的公共折扣降低一个档次（对价格的外部性），这即使对风险厌恶的买家也能提高转化率。
### 重要性
- 将“促销/赠品”转变为可调节的、预算有限的机制。
- 鼓励网络效应：寻求风险的用户为风险中性/厌恶的用户提供折扣。
- 在合理的需求/弹性假设下，可能同时提高福利和收入。
### 设计草图（希望获得反馈）
- 约束条件：个体理性（买家应期望获得非负的剩余）、期望中的近似预算平衡，以及平台风险上限（p·价格 ≤ 利润范围）。
- 调节因素：p(t)、票价τ、折扣步长Δ、冷却时间/限制以防止滥用、反Sybil规则。
- 建模：使用蒙特卡洛方法进行校准；采用马尔可夫风格的保留模型进行重复互动；对p和Δ进行A/B测试。
### 待解问题
- 这里是否有清晰的IC/IR/BB的证明/条件？
- 福利与经典销售+优惠券的比较？
- 各个司法管辖区的监管态度（促销 vs. 彩票 vs. 抽奖）？
### 状态与请求
我正在打包一个兼容Shopify的模块和一份简短的白皮书。我希望获得严谨的批评意见、类似机制的指引，或对机制设计和实际工程感兴趣的合作者。
### 联系方式
Mert — beyazpiyon54@gmail.com
查看原文
TL;DR: I’m exploring a simple, real-world mechanism design for e-commerce pricing that mixes a deterministic discount path with a tiny-probability “free purchase” option. Each ticket purchase slightly increases the public discount for everyone, so the system tries to be positive-sum rather than zero-sum. Looking for critique on the mechanism, proofs (IC/IR/BB), and implementation feedback.<p>What it is
An applied mechanism that sets (i) a discount schedule over time/sales and (ii) a very small probability p that a buyer gets the item for free (or a full rebate). Buyers choose between:
1. Buy Now at the current discounted price, or
2. Try Your Luck by buying a low-cost ticket with a tiny chance to get the item free.<p>Every ticket purchase nudges the item’s public discount down a notch for everyone (externality on price), which increases conversion even for risk-averse buyers.<p>Why this might matter
• Converts “promotion/giveaway” into a tunable, budget-bounded mechanism.
• Encourages network effects: risk-seeking users fund discounts enjoyed by risk-neutral/averse users.
• Potentially increases welfare and revenue simultaneously (under reasonable demand/elasticity assumptions).<p>Design sketch (feedback wanted)
• Constraints: Individual Rationality (buyers should expect non-negative surplus), approximate Budget Balance in expectation, and platform risk caps (p·price ≤ margin envelope).
• Knobs: p(t), ticket price τ, discount step Δ, cooldowns/limits to prevent abuse, anti-sybil rules.
• Modeling: Monte Carlo for calibration; Markov-style retention for repeated interaction; A/B on p and Δ.<p>Open questions
• Clean proofs/conditions for IC/IR/BB here?
• Welfare vs. classic sales + coupons?
• Regulatory posture across jurisdictions (promotion vs. lottery vs. sweepstakes)?<p>Status & Ask
I’m packaging a Shopify-compatible module and a short whitepaper. I’d love rigorous critique, pointers to similar mechanisms, or collaborators who enjoy mechanism design + practical engineering.<p>Contact: Mert — beyazpiyon54@gmail.com