Purpose: The current research was conducted to imaging governance intelligence and the result of the co-creation of human and machine intelligence in the governance laboratory. In this research, governance intelligence is first introduced, and then its place in the governance laboratory is explained. Not only has the concept of governance recently received the attention of scientific and experimental actors in the scientific literature, but the governance laboratory has also been introduced to identify the system of governance issues and the evidence-based and scientific solution to these issues. In the meantime, due to the high complexity of governance, governance intelligence will help on the one hand to understand and on the other hand to solve problems in the governance laboratory that may occur.Design/ methodology/ approach:  Sandelowski and Barroso's (2007) meta-synthesis qualitative method was used for this research, and during it, 250 related documents were initially examined from among various scientific documents in domestic and foreign reliable scientific databases, and finally, 60 documents were coded as samples. Considering the new and emerging concept of governance, governance laboratory and governance intelligence, with the analysis of scientific texts that refer to various aspects of these phenomena and between the lines of research, the model of governance intelligence has been designed to provide a clearer picture of the concept and reality presented governance intelligence for researchers and practitioners.Research Findings: The results showed that governance intelligence has 10 aspects and dimensions, which indicate its complexity; These funds include rational and problem-solving intelligence, policy-making and regulatory intelligence; Foresight and strategic intelligence, holistic and systemic intelligence, management and leadership intelligence, political and network intelligence, spiritual and moral intelligence, cultural and social intelligence, financial and economic intelligence, emotional and emotional intelligence. Together, these dimensions create a suitable intelligence capacity for governance. The gathering of such dimensions and components of governance intelligence shows the multiplicity of factors and elements of governance, resulting in the need for multiple intelligences in governance. As the complexity of a phenomenon increases, the intelligence required to understand and solve its problems will also become more complex. Human and machine intelligence together can overcome these complexities and provide more suitable solutions for governance issues, which have been able to understand the complexity of governance and provide deeper analyses of it. Finally, governance laboratories provide an approach for the co-creation of human and machine intelligence, in 10 dimensions to identify, understand and solve governance issues, and it is expected that with the continuous development of governance intelligence, it will be possible to provide scientific and reasonable answers for the system of governance issues in the country. He gradually witnessed the maturity of governance intelligence, and as a result, more accurate answers to governance issues.Limitations & Consequences: The most important limitation of the current research is the lack of specific and clear definitions and concepts of the research topic, strengthening the necessity of conducting the research. It is expected that such researches will have scientific and experimental consequences in the thinking of experts and scientists as well as the actions of governance activists who consider governance to be complex and require deep and wide analysis and always seek to develop intellectual and practical capacities in understanding and solving governance issues.Practical Consequences: The present article emphasizes the creation of a governance laboratory in understanding and solving problems and introduces governance intelligence (human and machine intelligence) as a necessity and one of the most important capacities for understanding and solving governance problems, especially in the governance laboratory.Innovation or value of the Article: The introduction of the governance laboratory, governance intelligence, and the perspective of experimental governance that emphasizes evidence-based and scientific methodology for solving governance issues is the initiative and innovation of this article.Paper Type: Original Paper

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82. 6% accuracy in predicting stock price in 1-day ahead. Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock.