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Despite the Ethiopian government's commitment to attracting foreign direct investment to its emerging manufacturing sector and its shared interests with Chinese private businesses in building profitable investments, relations between Chinese private businesses and the Ethiopian government are not always amicable. 'Win-win' narratives tend to oversimplify the reality of Chinese investments in Ethiopia's manufacturing sector, despite shared interests. This paper takes the Chinese-financed Eastern Industrial Park in Ethiopia as a case study to explore the dynamics of state-business relations between the host country government agencies (the Ethiopian Investment Commission in particular) and Chinese private businesses. It argues that the institutions led by Chinese government agencies serve only limited purposes and are led by the interests of certain kinds of firms, and that private firms tend to locally improvise and create their own bargaining mechanisms, even when the Chinese state has built official channels for bargaining. Relations between Chinese private businesses and host country governments therefore do not fit simple 'win-win' narratives but are highly complex, context-specific, diverse, and fluid.

High-deductible health plans (HDHPs) have become increasingly prevalent among employer-sponsored health plans and plans offered through the Health Insurance Marketplace in the United States. This study examined the impact of deductible levels on health care experiences in terms of care access, affordability, routine checkup, out-of-pocket cost, and satisfaction using data from the Health Reform Monitoring Survey. The study also tested whether the experiences of Marketplace enrollees differed from off-Marketplace individuals, controlling for deductible levels. Results from multivariable and propensity score weighted regression models showed that many of the outcomes were adversely affected by deductible levels and Marketplace enrollment. These results highlight the importance of efforts to help individuals choose the plan that fits both their medical needs and their budgets. The study also calls for more attention to improving provider acceptance of HDHPs and Marketplace plans as these plans become increasingly common over time.

Intro -- Preface -- ICSS 2020 Committees -- Program Committee -- Conference Organizing Committee -- Best Student Paper Award Committee -- Best Conference Paper Award Committee -- Contents -- Epidemic Informatics and Control: A Review from System Informatics to Epidemic Response and Risk Management in Public Health -- 1 Introduction -- 2 Epidemic Challenges to Our Society -- 2.1 Health System Challenges -- 2.2 Economic Challenges -- 3 Measure the Epidemic Dynamics -- 3.1 Testing and Sampling -- 3.2 Spatiotemporal Surveillance of Epidemic Processes -- 3.3 Data Management and Visualization -- 4 Analyze the Data for Epidemic Insights -- 4.1 Descriptive Analytics -- 4.1.1 Correlation Analysis -- 4.1.2 Regression Modeling -- 4.2 Spatiotemporal Analytics -- 4.3 Privacy-Preserving Data Analytics -- 5 Improve the Resilience of Health Systems -- 5.1 Artificial Intelligence for Smart and Interconnected Health Systems -- 5.2 Healthcare Resource Allocation for Coverage Control -- 5.3 Re-design of Health Systems -- 6 Prescriptive Analytics - Control the Spread -- 6.1 Simulation Modeling and Computer Experiments -- 6.2 Epidemic Simulation in the Spatial Network -- 6.3 Computer Experiments of NPIs -- 7 Conclusions -- References -- Private vs. Pooled Transportation: Customer Preference and Congestion Management -- 1 Introduction -- 2 Data -- 2.1 Descriptive Evidence -- 3 Choice Model -- 4 Estimation -- 4.1 Endogeneity -- 4.2 Instruments -- 4.3 Control Function Approach to Estimation -- 4.4 Results from the Choice Model -- 5 Counterfactuals -- 5.1 Applying Percentage Surcharges to a Congestion Zone -- 5.2 Offering Discounts to Users -- 5.3 Improving Service Features -- 6 Conclusion -- References -- Optimal Dispatch in Emergency Service System via Reinforcement Learning -- 1 Introduction -- 2 Literature Review -- 3 Markov Decision Process Formulation.

Intro -- Contents -- Deep Learning and Prediction of Survival Period for Breast Cancer Patients -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 3.1 Data Collection and Cleaning -- 3.2 Data Preprocessing -- 4 Research Methodology -- 4.1 Deep Learning Architectures -- 4.2 Model Architecture and Parameters -- 4.3 Model Tuning -- 4.4 Models for Comparison with Previous Research -- 4.5 Feature Importance -- 4.6 Experimental Setting -- 5 Results and Discussion -- 5.1 Evaluation Metrics -- 5.2 Classification Model Results -- 5.3 Regression Model Results -- 5.4 Discussion -- 5.5 Feature Importance -- 6 Conclusions -- References -- Should Managers Care About Intra-household Heterogeneity? -- 1 Introduction -- 2 Literature Review -- 3 Data -- 4 Model -- 5 Results -- 6 Managerial Implications -- 7 Conclusion -- References -- Penalizing Neural Network and Autoencoder for the Analysis of Marketing Measurement Scales in Service Marketing Applications -- 1 Introduction -- 2 Background -- 2.1 Autoencoder -- 2.2 Relationship Between Factor Model and Autoencoder -- 3 Proposed Method -- 4 Empirical Analysis -- 4.1 Data Collection -- 4.2 Comparative Models and Estimations -- 4.3 Result -- 5 Discussion -- 6 Concluding Remarks -- References -- Prediction of Gasoline Octane Loss Based on t-SNE and Random Forest -- 1 Introduction -- 2 Research Method -- 3 Experiment -- 3.1 Nonlinear Dimensionaliy Reduction -- 3.2 Linear Dimension Reduction -- 3.3 Prediction Model of Cotane Loss Based on Random Forfest -- 3.4 Analysis of Model Results -- 4 Conclusion -- References -- Introducing AI General Practitioners to Improve Healthcare Services -- 1 Introduction -- 2 Literature Review -- 3 The Model -- 4 Analytical Results -- 5 Numerical Results -- 6 Discussion -- References -- A U-net Architecture Based Model for Precise Air Pollution Concentration Monitoring.

Given its serious impacts on the public's health, air pollution in China is a matter of strong public concern, particularly in reference to malodorous waste gas. Petition letters related to atmospheric pollution accounted for about 40% of the total petition cases. However, scholarly views differ on whether the Chinese government responds to public opinion on the environment and seeks to improve its environmental governance behavior. For this study, data from national surveys on the public's environmental satisfaction administered during the period 2011–2015 were analyzed to determine whether the public's dissatisfaction with the state of the environment in a given year resulted in increased investments by provincial governments in pollution governance during the following year. The study's findings revealed that governmental behavior in response to public opinion on the environment was selective within the field of environmental governance, with provincial governments being inclined to invest more in waste gas pollution control than in water pollution control. Furthermore, results from this study show that the Chinese government tends to put more efforts into the environmental field where it could more easily achieve short-term benefits.

In this study we sought to examine how consumers perceive price unfairness when they learn that they have missed a price promotion after making a purchase, and explore the accompanying negative emotions and behavioral responses. The promotions that consumers learn they have missed
could occur either before purchasing (i.e., prepurchase promotion) or after purchasing (i.e., postpurchase promotion). By conducting a 2 (attribution of responsibility: internal vs. external attribution) × 2 (time of the missed promotion: prepurchase vs. postpurchase promotion) experiment
with 202 participants, we found that greater perceived price unfairness (PPU) is generated when consumers attribute missing the promotion to the sellers' actions. Additionally, PPU is more likely to induce anger than disappointment and regret. The time of the missed promotion alters the impact
of PPU on regret. Lastly, consumers who experience high levels of anger and disappointment tend to complain and spread negative word of mouth while those who experience high regret tend to be inactive.

AbstractIn this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.