scispace - formally typeset
Search or ask a question

Can AI predict geopolitical events? 


Best insight from top research papers

AI has shown promise in predicting geopolitical events. The combination of machine learning models with human forecasters has been successful in improving forecasting accuracy . Crowdsourcing and hybridizing human and machine models have been effective in increasing performance . Deep siamese neural networks have been used to aggregate forecasts from volunteer analysts, resulting in more accurate predictions . Multitask learning algorithms have been employed to recommend suitable questions to forecasters, increasing prediction accuracy . These advancements in AI have demonstrated the potential for predicting geopolitical events with greater accuracy than traditional methods . However, it is important to note that while AI can improve forecasting, it is still a challenging task and experts often struggle to outperform random baselines .

Answers from top 5 papers

More filters
Papers (5)Insight
The paper discusses a deep siamese neural network model for aggregating predictions from volunteer analysts in geopolitical event forecasting, but it does not explicitly mention whether AI can predict geopolitical events.
The provided paper does not specifically mention AI's ability to predict geopolitical events.
The paper proposes a crowdsourcing triage algorithm for geopolitical event forecasting, but it does not specifically mention AI predicting geopolitical events.
The paper does not directly answer the question of whether AI can predict geopolitical events. However, it discusses the use of machine models in combination with human input to improve forecasting accuracy.
Yes, the paper proposes that machine learning and artificial intelligence techniques can be used to forecast geopolitical events.

Related Questions

How artificial intelligence influence development on contemporary ideological geopolitics warfare?8 answersArtificial Intelligence (AI) is reshaping the landscape of contemporary ideological geopolitics and warfare, influencing power dynamics, military capabilities, and the global balance of power. The rapid development of AI technologies has become a pivotal instrument of power, with significant implications for both hard power, such as military applications, and soft power, including economic impact and political influence. The United States and China, in particular, have emerged as dominant forces in the AI arena, thereby influencing global geopolitics through their technological prowess and strategic deployments of AI capabilities. This technological race has not only heightened the rivalry between these superpowers but has also led to a realist interpretation of their competition, affecting international policy and governance of AI. AI's influence extends to the military domain, where its capabilities are expected to transform warfare and strategic competition. The integration of AI into military systems suggests a deterministic shift towards more autonomous forms of warfare, potentially altering the nature of military power and strategic calculations. This shift is further complicated by the geopolitical implications of AI-enabled weapons technologies, which are reshaping security policies and the conceptualization of future battlefields. Moreover, AI's role in ideological geopolitics is not limited to state actors. Non-state actors and alliances, such as NATO, are also adapting to the challenges and opportunities presented by AI, developing strategies to maintain a competitive advantage in an increasingly uncertain security environment. The governance and ethical considerations surrounding AI, including issues of patent eligibility and the impact on social order, highlight the complex interplay between technological innovation and legal frameworks. In essence, AI is accelerating existing trends in geopolitics and warfare, exacerbating power imbalances, and necessitating a reevaluation of traditional geopolitical strategies and alliances. Its pervasive influence underscores the need for comprehensive international dialogue and cooperation to navigate the challenges and harness the opportunities presented by this transformative technology.
Theoretical basis of geopolitical risk?5 answersThe theoretical basis of geopolitical risk encompasses a multifaceted approach that integrates insights from international relations theory, economic impacts, and the evolving nature of global conflicts and investments. Joseph Simonian highlights the importance of a theoretically grounded framework for analyzing geopolitical risk within investment portfolios, emphasizing the application of international relations theory and quantitative methods such as game theory and machine learning for a more rigorous analysis. Osama Sweidan extends this discussion by examining the influence of US macroeconomic indicators on international geopolitical risk, suggesting that economic and political dominance can significantly affect global geopolitical dynamics. Robert F. Engle and colleagues propose a definition of geopolitical risk based on volatility shocks across financial markets, introducing a statistical model to measure these risks and suggesting a new criterion for portfolio optimality to mitigate exposure. Xinjie Wang and co-authors document the negative relationship between corporate investment and geopolitical risk, indicating how firms' investment decisions are influenced by the uncertainty associated with geopolitical tensions. Philipp Harms traces the interest in political risk analysis back to the hostile policies of developing countries in the seventies, highlighting the significance of cross-border investment decisions in the face of geopolitical risks. Dario Caldara and Matteo Iacoviello present a monthly indicator of geopolitical risk based on newspaper coverage, showing how geopolitical tensions have historically led to declines in real activity and shifts in capital flows, with the threat of adverse events playing a significant role. Yong Wang and colleagues introduce a metric of geoeconomical distance to assess the risk of interstate conflict, revealing how geopolitical alignments and distances influence conflict risks. A news-based measure of adverse geopolitical events further underscores the impact of geopolitical risk on investment and employment, highlighting the role of both the threat and realization of such events. Lastly, Daniel Deudney's work on reformulating geopolitics through a Marxian historical materialism lens provides a conceptual foundation for understanding the interaction between geography, technology, and security politics. Together, these studies offer a comprehensive theoretical basis for understanding geopolitical risk, emphasizing the importance of integrating economic, political, and historical perspectives to grasp the complexities of global geopolitical dynamics.
Can artificial intelligence be used to analyze and predict the spread of political ideologies on social media platforms?5 answersArtificial intelligence (AI) can indeed be utilized to analyze and predict the spread of political ideologies on social media platforms. Various studies have demonstrated the effectiveness of machine learning models, such as Naïve Bayes classifiers and Long Short Term Memory (LSTM) algorithms, in identifying political biasnessand sentiment analysis. These AI techniques can process vast amounts of social media data to extract subjective information, opinions, and attitudes, enabling the classification of users based on their political orientation with high accuracy. Additionally, employing graph neural networks can further enhance the understanding of political polarization by jointly modeling user characteristics, post contents, and user-item relations without requiring ideology labels, leading to improved detection of political ideologies and polarization. AI's capabilities in analyzing social media content make it a valuable tool for studying and predicting the spread of political ideologies.
What are the strategies of USA in the AI race from a geopolitical perspective?5 answersThe United States is employing a "terrain strategy" to compete against authoritarian regimes in the AI race. This strategy aims to leverage the malleability of AI to offset authoritarians' structural advantages in engineering and deploying AI. The US recognizes the risks posed by foreign-based network platforms and the vulnerabilities of free and democratic societies in the face of AI advancements. It acknowledges that AI technologies can enhance national security threats and change the dynamics of security relationships among rivals. The US is also focused on preserving its global leadership advantages in both the economy and security. It aims to maintain collaboration with China in areas of mutual interest while selectively decoupling in other areas. The US is investing in AI innovation and diffusion to solidify its lead and control the future of global power and security. While the competition between the US and China in AI does not currently constitute a security dilemma, the nascency of the field and the potential for offensive advantages suggest that a security dilemma may emerge in the future.
Can AI be used in designing a political campaign?5 answersAI can be used in designing a political campaign by leveraging advanced tools and algorithms to analyze data, create personalized ads, and target specific voter segments. Recent advances in AI have shown early successes in political campaigns, such as the Trump presidential campaign and the Brexit referendum. However, the use of AI in political campaigns raises ethical concerns and potential threats to democratic principles and human rights. AI-generated messages have been found to be persuasive and can influence human beliefs on policy issues. To ensure the ethical use of AI in political campaigns, there is a need for transparency, accountability, and regulation of algorithms and AI applications. Additionally, educating voters and politicians about AI technologies and their conscious use in political processes is important. International cooperation and the establishment of standards for the ethical use of AI in elections are also crucial.
How does Twitter affect the way people understand geopolitical events?4 answersTwitter has a significant impact on the way people understand geopolitical events. The explosion in social media usage has made the analysis of Twitter data relevant in the political science area. By analyzing large-scale datasets of tweets, researchers can retrieve, analyze, and visualize data to gain actionable insights and knowledge related to political events. Twitter provides a platform for users to share their opinions and engage in discussions about global political events, such as Russia's invasion of Ukraine and the 2022 French Presidential election. Through the analysis of account creation and suspension dynamics on Twitter, patterns of platform abuse and subsequent moderation during major events can be identified. Additionally, Twitter can be used to automatically extract information and understand hashtags related to real-world events, providing insights into the sentiments and emotions expressed by users. Overall, Twitter plays a crucial role in shaping people's understanding of geopolitical events by providing a platform for information sharing, analysis, and discussion.

See what other people are reading

What are some of the methods used for statistics based internet classification and the metrics used for classification evaluation?
5 answers
Statistical methods for internet traffic classification include Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback–Leibler divergences, Support Vector Machines (SVM), Pearson Correlation, Kolmogorov-Smirnov and Chi-Square tests, and Entropy. For evaluating classification systems, metrics like standard and balanced accuracy, error rates, F-beta score, Matthews correlation coefficient (MCC), area under the ROC curve, equal error rate, cross-entropy, Brier score, and Bayes EC are commonly used. These metrics assess the quality of both hard decisions and continuous scores produced by classification systems, providing a comprehensive evaluation framework for internet traffic classification algorithms and models.
Are there load forecasting techniques using REFIT dataset?
5 answers
Load forecasting techniques have been developed using various datasets, including the REFIT dataset. The REFIT dataset has been utilized in research to improve load forecasting accuracy. Different methodologies, such as deep learning with feature engineering and the use of recurrent neural networks like LSTM, have been applied to enhance load forecasting using datasets similar to REFIT. Additionally, artificial intelligence techniques like fuzzy logic, artificial neural networks (ANN), and ANFIS have been employed for load forecasting, which can also be adapted to datasets like REFIT. Comparisons between different forecasting methods, including ARIMAX, ANN, and LSTM, have shown that techniques like ARIMAX can outperform others in certain scenarios, indicating the relevance of such methods for datasets like REFIT.
What is the difference of pv production between clear sky and rain?
5 answers
The difference in photovoltaic (PV) production between clear sky and rainy conditions is significant. Clear sky days exhibit higher efficiency in energy collection compared to cloudy or rainy days. Factors such as solar radiation, ambient temperature, and mass flow rate play crucial roles in determining PV performance. Rainy and cloudy days can reduce average power and irradiance by up to 93.77% and 93.32%, respectively, compared to clear days. Additionally, humidity inversely affects voltage, current, and power output, while wind velocity directly impacts PV performance. Forecasting PV production under clear sky conditions is crucial, as it significantly influences the accuracy of PV production forecasts. Overall, clear sky conditions are more favorable for efficient PV energy generation compared to rainy or cloudy days.
What are the limitations of integrating omic and survival data?
5 answers
Integrating omic and survival data poses several limitations. Firstly, the challenge lies in accurately predicting patient outcomes due to the complexity of high-dimensional molecular data and the need for appropriate model building. Additionally, the presence of censored survival outcomes in the data further complicates the inferential process, requiring advanced statistical methods for analysis. Moreover, the lapse between diagnosis and molecular testing can lead to left truncation issues, affecting the validity and interpretation of survival analyses. Despite efforts to develop predictive models using multiomics data, the utility for prediction purposes may be limited, with only specific methods showing slightly better performance compared to traditional clinical models. These challenges highlight the importance of utilizing appropriate statistical techniques and considering the integration of clinical knowledge to maximize the predictive value of omics data in survival analysis.
How do individual products limit the use of historical data for forecasting?
5 answers
Individual products limit the use of historical data for forecasting by posing challenges when there is limited or no historical data available. This limitation is particularly evident in industries like retail and consumer products where new products are frequently introduced, leading to insufficient historical sales data for accurate forecasting. The short life cycles of products and seasonal trends further complicate the forecasting process, especially when dealing with newly launched products that have minimal sales history. To address this issue, various techniques such as identifying similar products, using similarity measures, consistency checks, and feature random search methods have been proposed to improve forecast accuracy for products with limited historical data.
How accurate are machine learning algorithms in predicting thyroid diseases compared to traditional methods?
5 answers
Machine learning algorithms have shown promising accuracy in predicting thyroid diseases compared to traditional methods. For instance, a study by Xu et al. compared Cox regression and machine learning algorithms for predicting the survival of Anaplastic thyroid carcinoma (ATC) patients, where machine learning models, particularly the Logistic algorithm and DeepSurv algorithm, demonstrated good predictive performance. Additionally, another study by Lee et al. developed a machine learning algorithm that classified thyroid cell clusters using correlative imaging contrasts, achieving accuracies of up to 100% when combining color and refractive index images. Furthermore, a study on predicting complications of thyroid damage after radiation therapy found that machine learning algorithms like decision tree, random forest, and support vector machine were effective in identifying predictors of thyroid dysfunction with high accuracy. These findings collectively highlight the superior accuracy of machine learning algorithms in predicting thyroid diseases over traditional methods.
How many ML research about liver transplantation?
4 answers
There are several machine learning (ML) research studies focusing on liver transplantation. One study compared ML techniques like random forests and neural networks with traditional regression models to predict survival post-transplantation. Another paper discussed the emergence of artificial intelligence in various aspects of liver transplantation, including survival prediction analysis and organ allocation. Additionally, a literature review highlighted the application of translational medicine in liver transplantation, covering areas such as ischemia-reperfusion injury, rejection, and ongoing innovations in the field. Furthermore, a proteomics study identified protein biomarkers related to postoperative complications after liver transplantation, emphasizing the role of the immune system in affecting transplant prognosis. Overall, these studies showcase the growing interest and advancements in utilizing ML and artificial intelligence in liver transplantation research.
Can wisdom of crowds improve stock price prediction?
4 answers
The wisdom of crowds can indeed enhance stock price prediction. Research indicates that crowd wisdom extracted from online investment communities positively influences stock predictions. Comparing expert recommendations and crowd sentiment, it was found that crowd wisdom has a greater impact on stock prices, emphasizing its value in creating investment strategies. Additionally, the aggregation of individual estimates in the crowd can lead to significantly more accurate collective predictions, even surpassing those of individual experts. However, it is crucial to consider factors like sentiment diversity and independence within the crowd to enhance prediction accuracy. While sentiment from micro-blogging sites can predict market behavior, diverse and high-volume sentiment is more predictive of price volatility and traded volume rather than price direction. In markets where agents learn from equilibrium prices, a dynamic wisdom of the crowd prevails, leading to more accurate equilibrium prices than the most accurate individual agent.
What are the motivations of crowd science contributors?
5 answers
Crowd science contributors are primarily motivated by intrinsic factors such as learning, fun, and satisfaction, rather than extrinsic factors like monetary rewards. However, monetary incentives can attract a wider audience, especially from developing countries, due to the lower payment threshold in those regions. Different income levels of contributors, based on GDP, can impact the quality and pace of contributions in subjective tasks, with low-income contributors submitting faster but lower quality answers compared to high-income contributors. Additionally, contributors to citizen science projects like Zooniverse prioritize values such as achievement, self-direction, and security, which can guide strategies for motivating contributions based on these value orientations. Overall, the desire to help, learn, and make a difference in science are key motivations for crowd science contributors.
How to measure predictive performance of a risk score?
4 answers
To measure the predictive performance of a risk score, various statistical methods are utilized. These include assessing discrimination, calibration, and risk reclassification abilities. Additionally, the use of polygenic risk scores has been explored in predicting coronary heart disease events, with measures such as the C statistic, net reclassification improvement (NRI), and calibration being crucial in evaluating the predictive accuracy. Converting regression models into integer scores for risk classification may reduce prediction accuracy, highlighting the importance of utilizing the full regression model for accurate predictions. The index of prediction accuracy (IPA) is a valuable measure that combines discrimination and calibration, providing a comprehensive evaluation of prediction accuracy in medical outcome settings. Furthermore, improving the variant coverage of polygenic risk scores is essential for enhancing prediction accuracy in multifactorial human diseases.
What are the benefits of using group discussion as a decision-making tool in various settings?
5 answers
Group discussion as a decision-making tool offers numerous benefits across different settings. It enhances kindness towards group members, increases charity concern, and fosters a stronger feeling of attachment among participants. Additionally, group decision-making taps into the "wisdom of the crowd," leading to wiser choices and promoting social bonds. In educational settings, group guidance with discussion techniques has been proven effective in improving decision-making for high school students, emphasizing the importance of collaborative approaches in career development. Moreover, in healthcare environments like fetal care centers, involving parents in shared decision-making supports informed choices amidst uncertainty, ultimately aligning decisions with the best interest standard. Overall, group discussions facilitate active listening, mutual understanding, and collective responsibility, making them valuable tools for decision-making in diverse contexts.