机制设计理论
1 分•作者: mertbirlik•7 个月前
TL;DR：我正在探索一种简单的、现实的电子商务定价机制设计，它结合了确定性折扣路径和极小概率的“免费购买”选项。每次购买门票都会略微增加所有人的公开折扣，因此该系统试图实现正和而非零和。 寻求对该机制的批评、证明（IC/IR/BB）和实施反馈。
是什么
一种应用机制，用于设置 (i) 随时间/销售额变化的折扣计划，以及 (ii) 买家以极小概率 p 免费获得商品（或全额退款）。买家可以选择：
1. 立即以当前折扣价购买，或
2. 试试运气，购买一张低价门票，有机会免费获得该商品。
每次购买门票都会为所有人略微降低商品的公开折扣（价格上的外部性），这会提高转化率，即使对于厌恶风险的买家也是如此。
为什么这可能很重要
• 将“促销/赠品”转化为可调整的、预算受限的机制。
• 鼓励网络效应：寻求风险的用户为风险中性/厌恶风险的用户享受的折扣提供资金。
• 潜在地同时增加福利和收入（在合理的供需/弹性假设下）。
设计草图（需要反馈）
• 约束条件：个体理性（买家应期望非负盈余）、期望中的近似预算平衡以及平台风险上限（p·价格 ≤ 利润包络）。
• 旋钮：p(t)、门票价格 τ、折扣步长 Δ、冷却/限制以防止滥用、反女巫攻击规则。
• 建模：用于校准的蒙特卡罗模拟；用于重复交互的马尔可夫式留存；A/B 测试 p 和 Δ。
未决问题
• 在这里找到 IC/IR/BB 的清晰证明/条件？
• 福利与经典的销售 + 优惠券相比如何？
• 在不同司法管辖区内的监管立场（促销 vs. 抽奖 vs. 抽奖活动）？
状态和请求
我正在打包一个与 Shopify 兼容的模块和一份简短的白皮书。 我很乐意接受严格的批评、指向类似机制的指针，或者喜欢机制设计 + 实际工程的合作者。
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