本文選題：沖突預(yù)測 + 機(jī)器學(xué)習(xí)��； 參考：《世界經(jīng)濟(jì)與政治》2017年07期

【摘要】：通過機(jī)器學(xué)習(xí)來預(yù)測沖突正在成為當(dāng)前國際關(guān)系研究領(lǐng)域的一個熱議話題。但是從跨學(xué)科交叉研究的視角來看,計算機(jī)介入政治分析和國際關(guān)系研究并不是一個新現(xiàn)象,其間經(jīng)歷了從計算機(jī)模擬沖突場景到機(jī)器學(xué)習(xí)自動識別沖突模式的復(fù)雜變革歷程。二者的共同點是都重視仿真社會互動情景與政治復(fù)雜演進(jìn)過程,反對有關(guān)政治沖突現(xiàn)象的簡單線性解釋;但二者在研究取向上還是有著本質(zhì)的不同,計算機(jī)模擬提倡基于特定社會理論的情景建模與邏輯推演,而機(jī)器學(xué)習(xí)則強(qiáng)調(diào)無特定社會理論支撐的特征識別與關(guān)聯(lián)預(yù)測。有鑒于此,本文首先分析了計算機(jī)模擬與機(jī)器學(xué)習(xí)在沖突預(yù)測中的研究路徑差異,然后重點闡述了無理論支撐下將機(jī)器學(xué)習(xí)應(yīng)用于沖突預(yù)測之可能,并以2010—2016年印度恐怖襲擊預(yù)測為例,實證檢驗了基于BP神經(jīng)網(wǎng)絡(luò)的機(jī)器學(xué)習(xí)在真實社會情景中的實際沖突預(yù)測效力,結(jié)果發(fā)現(xiàn)基于機(jī)器學(xué)習(xí)的沖突預(yù)測范式即使在沒有特定社會理論支撐下,也具備一定沖突預(yù)測能力,并可產(chǎn)生新的沖突知識發(fā)現(xiàn)。但即便如此,作為一種跨學(xué)科交叉研究范式,機(jī)器學(xué)習(xí)介入沖突預(yù)測仍然面臨重重困難。
[Abstract]:Prediction of conflict through machine learning is becoming a hot topic in the field of international relations.However, from the perspective of interdisciplinary research, computer involvement in political analysis and international relations research is not a new phenomenon, which has undergone a complex transformation from computer simulation conflict scene to machine learning automatic identification of conflict pattern.Their common point is that they attach importance to the simulation of social interaction scenarios and the complicated evolution process of politics, and oppose the simple linear explanation of the phenomenon of political conflict, but they still have essential differences in research orientation.Computer simulation advocates scenario modeling and logical deduction based on specific social theory, while machine learning emphasizes feature recognition and correlation prediction without the support of specific social theory.In view of this, this paper first analyzes the research path difference between computer simulation and machine learning in conflict prediction, and then focuses on the possibility of applying machine learning to conflict prediction without theoretical support.Taking the prediction of terrorist attacks in India 2010-2016 as an example, the paper empirically tests the effectiveness of machine learning based on BP neural network in actual conflict prediction in real social situations.The results show that the conflict prediction paradigm based on machine learning has a certain ability of conflict prediction even without the support of specific social theory and can produce new conflict knowledge discovery.But even so, as a cross-disciplinary research paradigm, machine learning involved in conflict prediction still faces a lot of difficulties.
【作者單位】： 對外經(jīng)濟(jì)貿(mào)易大學(xué)國際關(guān)系學(xué)院;
【基金】：國家社科基金青年項目“基于大數(shù)據(jù)驅(qū)動的外交決策模式創(chuàng)新與我國實踐路徑研究”(項目編號:15CGJ034) 對外經(jīng)濟(jì)貿(mào)易大學(xué)中央高�；究蒲袠I(yè)務(wù)費專項資金資助(批準(zhǔn)號:CXTD8-05)之階段性成果
【分類號】：D80
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本文編號：1748718