過去預測市場的交易機制大多都是利用雙方喊價市場進行交易，本文採用 Hanson (2003) 提出的預測方法—市場計分法來進行預測，實驗背景為2012年與2008年的臺灣總統大選，並參考真實世界的預測市場—臺灣的「未來事件交易所」中，各縣市對於總統候選人得票率預測合約的資訊，再加入族群的群聚效果以及反執政黨效果作為參數，最後設計實驗在不同的群聚效果之下，比較模擬市場計分法的預測結果，與預測市場中利用雙方喊價市場機制的預測結果之差異。
　　研究結果發現，在全體民眾共識一致的社會狀態之下，市場計分法再加入高的群聚效果，會最接近雙方喊價市場機制的預測結果。反之在全體民眾共識較不一致的狀態下，市場計分法加入低的群聚效果，即能使預測結果接近雙方喊價市場機制的預測結果。在2012年與2008年實驗之下，研究還發現高群聚效果主要分布在偏藍的縣市，低群聚效果主要分布在偏綠的縣市，此與當時的社會背景以及不同政黨的選民結構有關。

Instead of using double auction market mechanism, we apply market scoring rule (MSR) to overcome potential liquidity problem (Hanson, 2003). Year 2008 and 2012 Taiwan Presidential Election results have been adopted in agent-based model (ABM). We use ABM to explore the possible belief distributions behind the prediction market in Taiwan, known as xFuture. We assume that the initial belief distribution come from the results of actual vote shares. Then this initial belief distribution will evolve through social networking, controlled by degree of segregation and information radius. In addition, we also add anti-ruling party effect to approximate excess demand of opposition party as evidenced in the trading volume of prediction market. Our goal is to find the best fit setting for the prediction market. We find that under high social consensus, MSR with a higher degree of segregation setting tends to fit xFuture better. In addition, we find that higher degree of segregation settings fit most KMT ruling cities, while lower degree settings tend to fit DPP ruling cities. The results may reflect different society opinion between these two periods.

第一章　緒論	1
1.1　前言	1 
1.2　研究動機與目的	3 
1.3　本文架構	4 

第二章　文獻回顧	5
2.1　預測市場	5 
2.2　預測市場交易機制	9 
2.3　代理人基模型	13 
2.4　種族分離模型	14 

第三章　模型介紹	16
3.1　NetLogo	16 
3.2　代理人、空間、觀察者	17 
3.3　群聚效果	19 
3.4　代理人的政治信念	21 
3.5　反執政黨效果	22 

第四章　實驗設計與流程	25
4.1　未來事件交易所合約介紹	25 
4.2　交易流程	30 
4.3　交易流程圖	31 

第五章　實驗結果與分析	32
5.1　2008年選舉背景	32 
5.2　2008年模擬結果	33 
5.3　2012年選舉背景	37 
5.4　2012年模擬結果	37 
5.5　檢定實驗結果	42 
5.6　研究結果分析	44 

第六章　結論與未來展望	49
6.1　結論	49 
6.2　未來研究與發展	51 

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