用抽獎為預測市場提供誘因抵抗操弄：一個經濟學實驗

Just Run Sweepstakes—Designing Prediction Markets to Counter Manipulation

蘇孟謙(Meng-Chien Su)

指導教授 ： 王道一

國立臺灣大學/社會科學院/經濟學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201700347

摘要

我們在有、無潛在市場操弄者的環境之下，跑了相同的預測市場實驗，並考慮四種誘因機制：真錢、假錢、交易績效競賽、交易點數兌換彩券抽獎。我們發現在沒有市場操弄的狀況下，所有的市場都運作良好，且市場觀察者會隨著市場價格、買單賣單數量差異來調整他們對真實狀況的預測。然而，當存在市場操弄時，市場觀察者會較為仰賴市場價格變化，較少考慮買單賣單數量差異（因為市場操弄者可以輕易提出大量買單、賣單）。市場操弄者在假錢、交易績效競賽兩種誘因機制下，成功誤導了市場價格，因為其他交易員對操弄做出反應的誘因不高。相對地，真錢與彩券抽獎的誘因機制能提供交易員套利動機，藉以抵抗市場操弄，幫助市場價格（與預測）來收集資訊。我們的結果建議預測市場使用彩券抽獎作為合法的誘因機制，來替代真錢誘因。

關鍵字

預測市場 ； 市場操弄 ； 誘因 ； 支付方案 ； 資訊收集 ； 抽獎

並列摘要

We run the same prediction market with potential manipulation under four different incentive schemes: Real money, play money, contests and lotteries based on points earned. We find all markets work well when there is no manipulation, and bystanders adjust their forecasts according to evolving market price and excess bids/asks. However, when there is manipulation, forecasters rely more on evolving market price, but less on excess bids/asks (since manipulators can easily tamper them). Nonetheless, manipulators successfully mislead market prices under play money and contests because other traders have little incentives to react. In contrast, real money and lotteries provide incentive for arbitragers to counter manipulation, helping market prices (and forecasts) to aggregate information. This suggests using lotteries as legal alternatives to real money prediction markets.

並列關鍵字

Prediction Markets ； Manipulation ； Incentives ； Payment Schemes ； Information Aggregation ； Sweepstakes

參考文獻

Rhode, P. W., and Strumpf, K. S. (2004), “Historical Prediction Markets: Wagering on Presidential Elections,” Working paper.

Berg, J. E., Nelson, F., and Rietz, T. A. (2008), “Prediction Market Accuracy in the Long Run,” International Journal of Forecasting, 24 (2), 285-300.

Camerer, C. F. (1998), “Can Asset Markets Be Manipulated? A Field Experiment With Racetrack Betting,” Journal of Political Economy, 106(3), 457-482.

Camerer, C. F., Ho, T.-H., Chong, J.-K. (2004), “A Cognitive Hierarchy Model of Games,” Quarterly Journal of Economics, 119(3), 861-898.

Cowgill, B., Wolfers, J., and Zitzewitz, E. (2009), “Using Prediction Markets to Track Information Flows: Evidence from Google,” University of Pennsylvania Working Paper.