In-Organization Ethics Power-Allocation Mechanisms and Members’ Decision-Making Behavior

This study examines experimental evidence showing how ethics power allocation mechanisms affect an individual’s in-organization resource division and ethical behavior. We used two two-stage lab experiments to explore power seeking and usage; the experiments contained two stages of power contending and power usage. Stage one used two different power-seeking mechanisms in the honesty game. Stage two was based on the dictator game and the ultimatum game to measure an individual’s power usage. The results show that the decisions taken by power-holders could influence the optimization of collective resources, and power-holders who gain power with unethical methods could result in collective resource allocation inequities. With more balanced in-organization power, members tend to be more honest. Subjects also adjust their unethical behavior to adapt to the environment, which could cause the diffusion of unethical behavior. This paper re-designed the dictator game and the ultimatum game by adding an ethically vulnerable power acquisition mechanism. For organizations to prevent the disproportionate dispersion of resources and achieve more public benefits, it is meaningful for managers to create a proper in-organization ethical power allocation mechanism.

Analysis on Decision-Making Changes of Multilevel Governments and Influencing Factors in Watershed Ecological Compensation

Watershed ecological compensation has been widely accepted as a system to promote the cooperation of various stakeholders to solve the problem of transboundary water pollution, but the existing research does not fully consider the impact of compensation fee paid by different governments on stakeholders’ decision-making. Therefore, this paper constructs a tripartite game model between upstream governments, downstream governments, and the central government by using evolutionary game theory and determines the influence of different factors on the decision-making process of each player through simulation. The results show the following: (1) the initial probability significantly affects the decision-making behavior of each player; (2) daily supervision of the central government and the reduction of the environmental protection cost can promote the implementation of watershed ecological compensation; (3) the fine to downstream governments makes the decisions of the central government and downstream governments change periodically; and (4) the increase of ecological compensation fee urges downstream governments to choose noncompensation, and compensation fee paid by the central government has a critical value.

Use of Local Advantages to Promote the Development of MICE Tourism-Take Hunan Province as an Example

Hunan is a major province of tourism resources in China, but the current situation of exhibition tourism is not performing well. In order to study how to use local advantages to promote the development of exhibition tourism, this article deeply studies the factors influencing the decision-making behavior of exhibition tourists, and analyzes the factors and reliability through SPSS22.0 software. Finally, the factors that affect the decision-making behavior of exhibition tourism are summarized into six factors: exhibition organization factors, auxiliary and basic tourism supply factors, personal factors, destination attractive factors, cost factors, and self-improvement factors. And analyze the local advantages of Hunan: rich natural resources, huge development potential, and profound cultural heritage. Finally, four suggestions for the development of the exhibition tourism market are put forward: improving the quality of exhibition tourism products, optimizing the supply system of exhibition tourism destinations, increasing publicity in a specific period, and determine government policy inclination.

Factors Influencing Rural Households’ Decision-Making Behavior on Residential Relocation: Willingness and Destination

All the traditional models of centralized residence based on “building a new socialist countryside” and “maintaining a balance between the increase and the decrease” are top-down in nature and require farmers to make responses and readjustment to all possible policies and changes. Therefore, it’s important to understand farmers’ preferences and take their willingness and needs into account when designing and implementing the relative planning programs of centralized residence. In this paper, with the numerical value 10 as the criterion of Events Per Variable (EPV) and Variance Inflation Factor (VIF), four different types of binary logistic regression were respectively applied to analyze factors that may influence farmer households’ relocation willingness and relocation destination in the following five aspects: Individual characteristics, household characteristics, housing characteristics, farmland characteristics, and implementation environment of centralized residence. As indicated in the results, people would show more willingness to relocate when they were younger, had higher household income, lived in an older building, possessed a bigger building area, owned farmland with higher quality, or lived in an environment with a higher infrastructure match rate. In addition, household income was a common factor influencing households’ choice between nearby relocation sites (NRS) and urban areas as their relocation destinations. The building area and occupancy rate negatively affected households’ choice of NRS, while building age negatively affected that of urban areas. Based on these influencing factors, some policy suggestions are proposed in this paper in terms of job creation, implementation of zoning and classification strategies, improvement of the quality of land transfer services, and reconstruction of the rural landscapes.

Asymmetrical choice-related ensemble activity in direct and indirect-pathway striatal neurons drives perceptual decisions

Action selection during decision-making depends on the basal ganglia circuits that comprise the direct and indirect pathways known to oppositely control movement. However, the mechanism for coordinating these opponent pathways during decision-making remains unclear. We address this by employing deep-brain two-photon imaging and optogenetic manipulations of the direct- and indirect-pathway spiny projection neurons (dSPNs and iSPNs) in the posterior striatum during an auditory decision-making behavior. We show that while dSPNs and iSPNs play opposite causal roles during decision-making, each subtype contains divergent ensembles preferring different choices. The ensembles in dSPNs show stronger contralateral dominance than those in iSPNs manifested by higher-level activation and synchronization. Consistent with this asymmetrical contralateral dominance, optogenetic disinhibition of both pathways promoted contralateral choices. A computational model incorporating the striatal ensemble asymmetry recapitulated the causal behavioral effects. Our results uncover the asymmetry between opponent SPN ensembles as a circuit mechanism for action selection during decision-making.

Big Data-Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.