Due to the price elasticity of demand for secondhand commodities, it is difficult to establish a quantitative model for the auction. This paper proposes an agent-based multiattribute reverse auction model to support multicommodity combinatorial auction. First, this paper establishes an agent-based reverse auction model and introduces the framework, procedures, and protocols of the model in detail. Second, in light of the multicommodity environment, the targets, protocols, auction strategies, and approaches are identified. Finally, by using the proposed agent-based auction model, both buyers and sellers will reach simultaneous agreements on the details of the commodities to complete the auction. 相似文献
Journal of Business Ethics - Widespread unethical corporate misconduct in an industry triggers industry-wide crises. This research investigates how industry misconduct affects consumers’... 相似文献
This paper empirically studies the occurrence and extent of asset stripping via undervaluing public assets during the mass privatization of state-owned and collectively owned enterprises in China. Using three waves of a national survey of private firms, we provide evidence that state-owned and collectively owned assets were substantially underpriced, indicating the presence of corruption during privatization. Further analysis shows that the extent of underpricing is more severe in regions with less market competition or weaker property rights protection, and more pronounced for intangible assets such as intellectual property rights and land use rights. When comparing firm efficiency between privatized firms and de novo private firms, we find that the former group continues to enjoy considerable preferential treatments, yet significantly underperforms the latter, possibly due to continued government control and intervention. Finally, we provide evidence that insider privatization is an important source of corruption during the privatization process. 相似文献
We use topic modeling to study research articles in environmental and resource economics journals in the period 2000–2019. Topic modeling based on machine learning allows us to identify and track latent topics in the literature over time and across journals, and further to study the role of different journals in different topics and the changing emphasis on topics in different journals. The most prevalent topics in environmental and resource economics research in this period are growth and sustainable development and theory and methodology. Topics on climate change and energy economics have emerged with the strongest upward trends. When we look at our results across journals, we see that journals have different topical profiles and that many topics mainly appear in one or a few selected journals. Further investigation reveal latent semantic structures across research themes that only the insider would be aware.