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HKEJ Column

Home Research Thought Leadership HKEJ Column
Potential anti-competitive effects of algorithmic pricing

Potential anti-competitive effects of algorithmic pricing

12 Nov 2020
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算法定價對競爭的潛在負面影響

疫情肆虐加速普羅大眾把購物習慣從線下轉向線上。根據IBM美國零售指數的最新數據,冠狀病毒大流行讓零售業從實體店轉向網上購物的進程提早了約5年;經濟合作暨發展組織(OECD)的報告也指出:疫情正在加快電子商務平台向新的客戶和產品擴展速度。毋庸置疑,進程產生好處:企業從實體經營轉向線上業務時,降低了成本,同時提高了生產效率。但若要確保消費者能從中受益,線上平台所處的經營環境應該保持競爭性。能否維持這一點,未來仍存在不少變數,其中讓人擔心的是,以機器學習為核心的定價算法可能對營商環境的競爭帶來潛在負面影響。

價格串通更易維持

近年來,在太平洋兩岸的線上平台都已被普遍使用自動定價算法。威爾遜及其合作者於2016年的研究指出:在亞馬遜1600家最熱門的供貨商中,三分之一使用定價軟件設定價格;搜狐網(Sohu.com)亦稱,京東(JD)等在線零售商使用算法來自動調整八成非促銷商品的價格。這類定價軟件使供貨商可以在幾毫秒內實時優化價格,例如響應市場環境的變化或競爭對手的改變來調整價格。自動定價算法也可以用於實體店。隨着電子貨架標籤和無人超市普及,動態定價在這一類實體店中也可能變得愈來愈常見。自動定價算法普及,也引發競爭法學者、經濟學學者,以及競爭法執法者對競爭法/反壟斷法的反思和探討。討論關於自動定價算法是否會引發企業之間的價格串通,以及現有的競爭法/反壟斷法是否足以確保一個競爭充分的線上營商環境。

首先要考慮的是定價算法能夠接近即時響應競爭對手的價格能力。我們可以一種簡單的競爭策略作為價格串通的例子:市場中的企業首先維持高價,直至觀察到競爭對手降低價格;倘若競爭對手降低價格,企業則瞬間立即降價,與競爭對手的價格處於同一水平作為還擊。假設每個企業都使用自動定價算法執行上述策略,那麼任何試圖通過降價來獲取更大市場份額的手段都會失敗。因此每家企業都很清楚,只要市場上的企業數量不多,便沒有公司有動機去降低價格,從而使企業間的價格串通更容易維持。

胡蘿蔔加大棒方案

第二個值得關注的問題是人工智能或機器學習算法的複雜性和自主性。一個價格同盟要有可持續性,競爭者必須採用「胡蘿蔔加大棒方案」。根據該方案,如果企業能共同維持高價格水平,則大家共享高價格帶來的利潤,即「胡蘿蔔」;反之,如果任何企業偏離原有的高價,想通過降低價格奪取更多的市場份額,其他企業會與該企業進行價格戰,懲罰該企業,即「大棒」。基於該邏輯,賓夕法尼亞大學沃頓商學院的經濟學教授約瑟夫.哈靈頓在2019年指出,監管者應將內置於定價算法中的「胡蘿蔔加大棒方案」定為違反反壟斷法。確實,如果企業在計算價格的時候,直接在算法中加入「胡蘿蔔加大棒方案」的元素,將該算法明確禁止可防止價格串通。

另一方面,編寫程式的人可能並沒有直接將該算法寫入程式中。但是,程式通過自身學習所獲得的定價策略,可能與「胡蘿蔔加大棒方案」別無二致。這種情況下,上一條建議可能難以防範價格串謀。令人擔憂的是,已經有研究指出,機器學習算法可以自行演化出類似「胡蘿蔔加大棒的方案」:在《美國經濟評論》上發表的論文中,歐洲大學學院(EUI)和經濟與政策研究中心(CEPR)研究人員Emilio Calvano、Giacomo Calzolari、Vincenzo Denicolo和Sergio Pastorello使用模擬的市場環境,來考察機器學習算法所習得的定價策略。每家公司均使用強化學習算法(Q-learning)來作為核心的定價算法。結果發現,在毋須借助企業間溝通的情況下,算法能夠通過自身演化,將價格維持在較高水平。如【圖】所見,即使研究員將一家公司在第一回合中(在圖中以圓形為標記)強行調低其產品定價時,另一家公司(以三角形為標記)也會降價。他們最終將收取相同價格並逐漸回復到原有的價格水平。


【圖】記錄研究員强制降低一個算法的定價以偏離最佳定價水平,一個周期後,後續兩種算法所收取的價格。該圖像取自於1,000次實驗所獲得的平均結果。 對於那些由於定價偏離,而出現了價格周期性變化的情況,研究員在計算平均價格的時候選取第一個周期的價格,並取所有價格的平均值。 在計算總體的平均值的時候,研究員會將其視爲一個觀測值。
(圖片來源:EUI與CEPR研究員發表於《美國經濟評論》的論文)

使消費者蒙受損失

以上作者認為,採用這些定價策略的公司,現階段難以被確認是否違反了反壟斷法:因為儘管事實上它們達到了價格串通的結果,但監管機構卻無法找到它們之間合謀的意圖,也沒有明確的協議和公司之間關於串謀定價的溝通紀錄。因此難以對其以違反反壟斷法進行起訴。

第三個潛在的問題是同一套定價算法可能會被廣泛使用,從而使企業在產品定價上更容易串通。當一家提供定價算法的公司為同一市場的多個競爭者提供服務,那麼它會有動機將程式寫得更易於價格串聯,幫助其客戶群體獲得更高的利潤。不排除在極端情況下,某些市場所有公司如果都使用同一套定價算法時,算法會自然地將所有公司的價格都設置在壟斷價格的水平,從而讓消費者蒙受損失。從另一個角度來講,即使市場有幾個提供定價算法的公司,競爭者也可能通過採用同一間公司提供的算法,協助實現串聯價格。最後,定價算法公司之間的合併存在對競爭的負面影響,可能更甚於一般的企業合併。

一般而言,公司的合併會使新組成的公司提高其產品或服務價格獲取更大利潤。

倘若兩家定價算法公司決定合併時,很可能會導致市場上可選的定價算法方案數量減少,讓更多公司使用同樣的定價算法,也會進一步鼓勵這些使用算法定價的公司之間的價格串通。

綜合以上所述,算法定價的崛起為競爭法學者、經濟學學者、競爭法執法者,以及從業人員提出了新挑戰。透過這篇文章,筆者希望引起更多普羅大眾關注算法定價可能對市場競爭所帶來的影響。

房育輝教授
港大經管學院教授(管理及商業策略、經濟學)

(本文同時於二零二零年十一月十一日載於《信報》「龍虎山下」專欄)

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