A Hierarchical Decision-Making Framework in Social Networks for Efficient Disaster Management

One of the major challenges faced by the current society is developing disaster management strategies to minimize the effects of catastrophic events. Disaster planning and strategy development phases of this urgency require larger amounts of cooperation among communities or individuals in society. Social networks have also been playing a crucial role in the establishment of efficient disaster management planning. This article proposes a hierarchical decision-making framework that would assist in analyzing two imperative information flow processes (innovation diffusion and opinion formation) in social networks under the consideration of community detection. The proposed framework was proven to capture the heterogeneity of individuals using cognitive behavior models and evaluate its impact on diffusion speed and opinion convergence. Moreover, the framework demonstrated the evolution of communities based on their inter-and intracommunication. The simulation results with real social network data suggest that the model can aid in establishing an efficient disaster management policy using social sensing and delivery.

A Decision-Making Model of Technological-Focused Government Agency Selection of Technological Start-Up Businesses

In developing countries, the government has played an important role in supporting startup businesses in various aspects, primarily through tech-focused government agencies. With a limited budget, the government agencies are critical to select plenty of tech startups for funding, leaving only promising tech startups. Consequently, government agencies inevitably face decision-making problems under uncertain circumstances, like private equity investment situations. Reviewing the relevant decision-making frameworks has identified that a classical multiple criteria decision-making (MCDM) approach is currently used, assuming decision-makers acquire complete information that is not realistic. Moreover, both qualitative and quantitative criteria used in evaluating startup businesses cannot represent the uncertainty which is the fundamental nature of the decision-making circumstance. Thus, this article presents a decision-making framework of tech-focused government agencies for selecting startup businesses based on a fuzzy MCDM of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Besides, it identifies selection criteria with mixed research methodologies and determines weights of importance criteria by the Delphi method. Finally, the proposed framework results are fairness, transparency, and eliminating bias in decision-making, including more efficiency when the framework’s ranking orders significantly correspond with actual performances. HIGHLIGHTS Criteria for selecting start-up businesses in technological-focused government agencies A decision-making framework of tech-focused government agencies for selecting startup businesses based on a fuzzy MCDM of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) The performance of the decision-making framework in selecting startup businesses to acquire high potential tech startups to drive the national economy GRAPHICAL ABSTRACT