# Chapter 15. Sponsored Search Markets [TOC] ## 15.1 Advertising Tied to Search Behavior Just an introduction to **keyword-based advertising** - Conventions of the search industry: 1. Paying per click 2. Setting prices through an auction - This chapter would focus on **how to design such an auction**. :::info **3 stages:** 1. If the search engine knew all the advertisers’ valuations for clicks - Apply matching market model _(Section 15.2)_ 2. If we assume that the advertisers’ valuations are not known - Apply VCG principle to encourage truth-telling _(Section 15.3, 15.4)_ 3. It is not clear that the VCG procedure is the best way to generate revenue for the search engine - The industry has adopted GSP auction in practice _(Section 15.5, 15.6)_ ::: ## 15.2 Advertising as a Matching Market ### Definition - **Clickthrough rates:** the number of clicks per hour that an ad placed in that slot will receive - **Revenues per click:** the expected amount of revenue the advertiser receives per user who clicks on the ad :::info Let $r_i$ denote the **clickthrough rate** of slot $i$, and $v_j$ denote the **revenue per click** of advertiser $j$: $\Rightarrow r_iv_j$ = **Expected benefit** of $j$ received from being shown in slot $i$ ::: ### Relate to matching market :::info **Quick review** - Each buyer $j$ has a valuation for the item offered by each seller $i$, and we denote it $v_{ij}$ - The goal is to find a perfect matching between sellers and buyers ::: - Slots $\leftrightarrow$ Sellers - Advertisers $\leftrightarrow$ Buyers - The problem of assigning slots to advertisers $\leftrightarrow$ The problem of assigning sellers to buyers - The advertiser's expected benefit $r_iv_j$ $\leftrightarrow$ The buyers’ valuations $v_{ij}$ With the connection to matching markets, we can use the framework from Chapter 10 to determine **market-clearing prices**. ![](https://i.imgur.com/TJgZJpQ.jpg) :::danger **What if the search engine doesn’t know the advertisers’ valuations?** - We need to define a price-setting procedure for matching markets to encourage truthful reporting of valuations ::: ## 15.3 Encouraging Truthful Bidding in Matching Markets: The VCG Princlple ### Definition - **Vickrey-Clarke-Groves (VCG) principle:** Each individual is charged an amount equal to the **"harm"** they cause to the other bidders - **Harm:** the total amount better off everyone else would be if this individual weren’t there - Example: |With advertiser $x$|Without advertiser $x$| |---|---| |![](https://i.imgur.com/nYIo0rU.jpg)|![](https://i.imgur.com/92R9dB1.jpg)| |Total evaluation of $y$ and $z$ = 12|Total evaluation of $y$ and $z$ = 25| **The harm caused by $x$ is $25-12=13$** ### Notation - $S$: the set of sellers - $S−i$: the set of sellers with seller $i$ removed - $B$: the set of buyers - $B-j$: the set of buyers with buyer $j$ removed - $V^S_B$: the **maximum total valuation** over all possible perfect matchings of sellers and buyers :::info 1. If we give item $i$ to buyer $j$, then the best total valuation the rest of the buyers could get is $V^{S−i}_{B−j}$ 2. If buyer $j$ simply didn’t exist, but item $i$ were still an option for everyone else, then the best total valuation the rest of the buyers could get is $V^{S}_{B−j}$ 3. The VCG price $p_{ij}$ that we charge to buyer $j$ for item $i$ = the harm caused by $j$ $$ p_{ij} = V^{S}_{B−j} - V^{S−i}_{B−j} $$ ::: ### Price-setting procedure 1. Ask buyers to announce valuations for the items 2. Choose an optimal assignment of items to buyers 3. **Charge each buyer the appropriate VCG price** Example: - **Step 1:** Collect valuations and choose and optimal assignment ![](https://i.imgur.com/eZur8wUm.jpg) - $V^S_B = 30+10+2 = 42$ - $V^{S-a}_{B-x} = 10+2 = 12$ - $V^{S-b}_{B-y} = 30+2 = 32$ - $V^{S-c}_{B-z} = 30+10 = 40$ - **Step 2:** Charge $x$ its harm as the VCG price |Assume $x$ doesn't exist|Evaluate $x$'s harm (VCG price)| |---|---| |$V^S_{B-x} = 20+5 = 25$|$p_{ax} = V^{S}_{B−x} - V^{S−a}_{B−x} = 25-12 = 13$| |![](https://i.imgur.com/92R9dB1.jpg)|![](https://i.imgur.com/t3zbhIA.jpg)| - **Step 3:** Charge $y$ its harm as the VCG price |Assume $y$ doesn't exist|Evaluate $y$'s harm (VCG price)| |---|---| |$V^S_{B-y} = 30+5 = 35$|$p_{by} = V^{S}_{B−y} - V^{S−b}_{B−y} = 35-32 = 3$| |![](https://i.imgur.com/32IZbvK.jpg)|![](https://i.imgur.com/gnLXkJ2.jpg)| - **Step 4:** Charge $z$ its harm as the VCG price |Assume $z$ doesn't exist|Evaluate $z$'s harm (VCG price)| |---|---| |$V^S_{B-z} = 30+10 = 40$|$p_{cz} = V^{S}_{B−z} - V^{S−c}_{B−z} = 40-40 = 0$| |![](https://i.imgur.com/csiYfn2.jpg)|![](https://i.imgur.com/n9rJAdJ.jpg)| ## 15.4 Analyzing the VCG Mechanism: Truth-Telling as a Dominant Strategy The following contexts show that the VCG procedure encourages truth-telling in a matching market. :::info **Claim** If items are assigned and prices computed according to the VCG procedure, then truthfully announcing valuations is a dominant strategy for each buyer ::: :::success **Proof** - The main idea is to show that a buyer will **NOT** get better payoff if she lies about her valuations - If buyer $j$ decides to lie about her valuations, then one of two things can happen: 1. Buyer $j$ still gets the same item $i$ - Her payoff remains $v_{ij} - p_{ij}$ 2. Buyer $j$ gets another item $h$ - We need to show that $v_{ij} - p_{ij} \geq v_{hj} - p_{hj}$ $$ \begin{aligned} & v_{ij} - p_{ij} \geq v_{hj} - p_{hj} \\ \Rightarrow \ & v_{ij} - (V^{S}_{B−j} - V^{S−i}_{B−j}) \geq v_{hj} - (V^{S}_{B−j} - V^{S−h}_{B−j}) \\ \Rightarrow \ & v_{ij} + V^{S−i}_{B−j} \geq v_{hj} + V^{S−h}_{B−j} \\ \Rightarrow \ & V^{S}_{B} \geq v_{hj} + V^{S−h}_{B−j} \end{aligned} $$ - The LHS is the maximum total valuation **over all possible perfect matchings**, while the RHS the maximum total valuation **with a restriction** _(only over those matchings that pair $j$ with $h$)_, so the inequality must hold ::: ## 15.5 The General Second-Price Auction(GSP) ### Definition - $n$ ad slots with clickthrough rate $r_i$ - $r_i$ stands for the probability of being clicked - WLOG, say $r_i\geq r_{i+1}$ - $n$ advertisers with true value $v_j$, bid in price $b_i$ - $v_i$ stands for the revenue per click - WLOG, say $b_i\geq b_{i+1}$ - bidder $i$ wins the $i$th slot, in charge of $r_ib_{i+1}$ - payoff of $i$th bidder is $r_i(v_i-b_{i+1})$ ![](https://i.imgur.com/gPSQuBX.png) ### Remark - if there are fewer ad slot, we add virtual slot with rate $r=0$ - if there is only one slot, then it is equivalent to Second-Price Auction - truth-telling may not be an equilibrium - consider $x$ change from $7$ to $5$ - there may be more than one equilibrium - $(5,4,2)$ and $(3,5,1)$ - the revenue of GSP can be either higher or lower than VCG - GSP - $(5,4,2)\rightarrow48$, $(3,5,1)\rightarrow34$ - VCG - $(40,4,0)\rightarrow 44$ ![](https://i.imgur.com/he7Ek5e.png) - in practice, search engine impose a minimum bid(reserve price) $p_i$ ## 15.6 Equilibria of the Generalized Second-Price Auction :::info **Theorem.** Let $G$ be a GSP graph, then $G$ has a Nash Equilibrium **Proof.** Let $p_i$ be the market-clearing prices of slot $i$ and $p^\star_j=p_j/r_j$. Claim that $$ p^\star_1\geq p^\star_2\geq\ldots\geq p^\star_n $$ Given $j\geq k$, advertiser $k$ perfer slot $k$ to $j$, that is $$ r_k(v_k-p_k^\star)\geq r_j(v_k-p_j^\star) $$ since $r_k\leq r_j$, thus $p_j^\star\geq p_k^\star$ Then define advertiser $1$ bid $p_1^\star+1$ and advertiser $j$ bid $p_{j-1}^\star$, then it is an equilibrium ::: ## 15.7 Ad Quality - We assume an ad slot has a fixed click rate $r_i$ - Yahoo! - In reality, the click rate depends on the advertiser - Google improved model - add the *quality factor* $q_j$ for advertiser $j$ - if advertiser $j$ appears at the slot $i$, then the click rate is $q_jr_i$ - assign the order by $q_jb_j$ - Google don't reveal how they compute $q_j$ ## 15.8 Complex Queries and Interaction among Keywords - keyword-based advertising market is complicated ## 15.9 Advanced Material: VCG Prices and the Market-Clearing Property All number are integers. :::info **Theorem.** the VCG prices is the unique set of market-clearing prices of minimum total sum of price. Recall the VCG price is defined by $$ p_i=V_{B-j}^S-V_{B-j}^{S-i} $$ ::: :::info **Lemma 1.** Let $G$ be a preferred-seller graph, $M$ be a perfect matching on $G$ with minimum sum of price, $i$ be an item that $v_i>0$, then there is an item $j$ with $v_j=0$ and an alternating path from $i$ to $j$, begin with non-matching edge. **Proof.** Suppose $i$ has no non-matching edge, then we can reduce the price. Let $X$ be set that $i$ can reach by alternating path starting with non-matching edge, then - $X\setminus\{i\}\neq\emptyset$ - if buyer $k\in X$, then the item $k$ matchs is also in $X$ - if item $h\in X$, then buyers connect to $h$ with non-matching edge are in $X$ - assume for item $h\in X$, $p_h\neq 0$ Now claim if we substract price of all items in $X$ by $1$, it is still market-clearing. Assume buyer $n$ was matched with item $e$, and an item $f$ has less payoff after that, then - price of $f$ is reduced, $f\in X$ - price of $e$ is not changed, $e\not\in X$ - $nf\in EG$ - $n\in X$, then $e\in X$, contradiction Thus, it yields that matching edges are not changes, thus it is still market-clearing after subtraction. Then contradiction to the minimum price. ::: ![](https://i.imgur.com/9UvreO0.png) :::info **Lemma 2.** Let $G$ be a matching market with minimum market-clearing prices $p$, $M$ be a perfect matching on $G$, and a buyer $j$, then - the prices $p$ is also market-clearing when $j$ is zeroed out - $j$ recieves a zero-priced item - the payoffs of buyers not $j$ stay the same **Proof.** Let $i$ be the item matched by $j$, then there exist an alternating path $P$ from $i$ to a zero-priced item $i^\star$, then after zero out $j$, buyers on $P$ change their matching item along $P$, and $j$ matches $i^\star$. ::: ![](https://i.imgur.com/cj96D1S.png) :::info **Theorem.** the VCG prices is the unique set of market-clearing prices of minimum total sum, that is $$ p_i=V_{B-j}^S-V_{B-j}^{S-i} $$ **Proof.** Let - $v_{ij}$ be the valuaion of buyer $j$ for item $i$ - $p_i$ be the price of item $i$ - if $j$ recieve $i$, the payoff $z_j=v_{ij}-p_i$ - $P$ be the sum of all price of items - $Z$ be the sum of all payoff Note that $$ \mbox{total payoff on }M=\mbox{total valuation on }M-\mbox{total price} $$ That is $$ Z=V_B^S-P $$ On the other hand, if $j$ matchs $i$, we have $$ v_{ij}+V_{B-j}^{S-i}=V_B^S $$ Now, if we zero out $j$ - the total valuation is $V_{B-j}^S$ - the total price stays the same by Lemma 2, thus $P$ - the payoff stay the same except $j$, thus the new total payoff is $Z-z_i$ thus we have $$ Z-z_j=V_{B-j}^S-P $$ implies that $$ z_j=V_B^S-V_{B-j}^S $$ thus $$ v_{ij}-p_i=z_j=V_B^S-V_{B-j}^S=(v_{ij}+V_{B-j}^{S-i}-V_{B-j}^S) $$ concludes that $$ p_i=V_{B-j}^S-V_{B-j}^{S-i} $$ :::