Quantitative Analysis of Dynamic Behaviours of Rural Areas at Provincial Level Using Public Data of Gross Domestic Product

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Uncertainty Analysis for Multi-state Weighted Behaviours of Rural Area with Carbon Dioxide Emission Estimation

This paper develops a spatial analysis approach, which incorporates three components and a carbon dioxide (CO 2) emission factor, has been developed to evaluate the multi-state weighted behaviours with CO 2 emission uncertainty of the rural areas at an administrative district level. A Mendel genetic algorithm (Mendel-GA) is applied to the spatial analysis problem, where the Mendel genetic operator implies the random assignment of alleles from parents to their offspring by using the Mendel's principles. A functional region affecting index ? is developed as a fitness function for the Mendel-GA driven evaluation, in which a gross domestic product (GDP) data based method is utilised to estimate the CO 2 emission under uncertainty. A simulation for the city of Chongqing in China has been conducted and the results show that the proposed ? modelling method can work valuably for the spatial analysis of the functional regions and can be taken as a technical tool for the policy makers at the rural area level

African-American High-Tech Enterprises: Agent-Based Modeling and Simulation for Innovation

African-American high-tech enterprises and innovation are underrepresented in the economy and industrial sector, in comparison to other types of ownership. Understanding the causal relationship underlying an enterprise’s interactions with external entities may increase and sustain the innovation capabilities of a high-tech enterprise. Agent-based modeling provides a computational methodology to visualize the iterative processes that lead to innovation output and the aggregate behavior of the simulated entrepreneurial system consisting of multiple interacting entities and factors. It is not possible even with long-term social longitudinal studies. Furthermore, the model can be readily enhanced with newly discovered interactions. This research focused on creating a socio-technological agent-based model (ABM) for assessing the probability of successful innovation by African-American owned businesses in high-tech dominated industries. A set of autonomous agents characterized the ABM: African-American owned high-tech enterprises, funding institutions, government research and development (R&D) services, research universities, and other enterprises. The framework was created using data from interviews with African-American entrepreneurs and implemented as a NetLogo simulation. A genetic algorithm in R was used for the simulation’s evolutionary behavior. By combining variations of agent attributes, the study created seven real-world simulation scenarios to evaluate the impact of initial capital, university R&D collaboration, government policy support, funding institution support, firm networking, and a combination of these factors on an African-American enterprise (AAE). The simulation results were validated with an analytic hierarchy process (AHP) decision model using expert judgments. The key findings of this study confirmed: initial funding is significant for AAE firms, but it can be offset by higher socio-economic status; collaboration assists entrepreneurs strengthen their core competencies and obtain funding support for both starting up and innovation; university collaboration is essential for innovation and start-ups; government support assists in improving the ratio of AAE to non-African-American enterprise (non-AAE) firms; and easy access to bank loans or venture funding assist in creating equal opportunity for firms with different backgrounds, although this does not necessarily lead to higher success rates