水利建设市场主体信用评价指标体系研究

水利建设市场主体信用等级是水利工程建设政府监管的基础,而信用评价指标体系的科学性将对信用等级评价结果的准确性产生重大影响。针对现有信用评价指标体系有利于大型企业而不利于小型企业这一问题,从政府监管视角出发,以施工企业为例,研究并构建了水利工程建设施工单位信用评价指标体系,建立了贝叶斯网络信用评价模型。利用水利部有关施工企业的信用数据,对模型进行了训练及测试,据此得到的信用等级评价准确率达90.91%。基于本文指标体系的信用等级评价方法可为有关部门完善信用评价指标体系提供参考。     

Performance assessment of UK airports: Evidence from a Bayesian dynamic frontier model

This paper analyzes the cost efficiency of UK airports over the period 1998-2008, using a Bayesian dynamic frontier model. This model provides a more structural explanation for the variation in airports inefficiency than has been presented by previous models, and also allows for cost inefficiency effects. On average, the dynamic frontier results, estimated via the Markov Chain Monte-Carlo simulation, indicate that UK airports improved their efficiency over time. Factors found to be important determinants of cost efficiency include airport size, price regulation, price cap variations and airport competition. Policy implications of the results are derived.     

L-M贝叶斯正则化BP神经网络在红外CO_2传感器的应用

针对温度会影响红外CO_2传感器的输出电压,造成对CO_2的浓度检测误差较大的问题,提出了一种基于L-M贝叶斯正则化BP神经网络的温度补偿方法。实验中将传感器输出电压比和温度作为神经网络的输入,CO_2浓度作为神经网络的输出,并通过L-M算法和贝叶斯正则化对神经网络进行优化。经过实验仿真证明,在温度补偿后红外CO_2传感器测量输出的浓度值最大相对误差为4.557 8%,具有较高的精确度。因此L-M贝叶斯正则化BP神经网络能对红外CO_2传感器进行有效的温度补偿,可为相关红外传感器仪器的改进提供参考。     

Setting an exam as an information design problem

We take a teacher's exam-setting task as an information design problem. Specifically, the teacher chooses a conditional distribution of grades given students' types. After observing their exam results, each student updates her belief regarding her type via Bayes' rule and chooses an action. Students' reactions to the same exam result could be different, depending on their heterogeneous prior beliefs. The teacher's objective is to persuade students to take a certain action (e.g., applying to universities), which some may not choose without an exam. The teacher adopts different grade distributions, depending on the teacher's and the students' heterogeneous prior beliefs.     

Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors

We develop an analytical model intended as the first stage in the development of expert systems to improve auditor knowledge in, and assist in the decision process of, Going Concern Opinions (“GCOs”). Our approach is consistent with a design science approach to developing information systems, resulting in an initial artifact, the mathematical model, which can, through iterative design science and behavioral research, inform a technology-based expert system. Based on Bayesian networks, our model provides insights about auditors’ revision, or inflation, of the probability to issue a GCO based on the interrelationship that forms with the incremental existence of one, two, or three publicly observable financial statement risk factors – net operating loss, negative cash flows from operations, and negative working capital. We calculate the revised probabilities using empirical data of GCOs from 2004 to 2015. Results reveal that the incremental relationship (one, two, or three factors present) effectively models expert auditors’ decisions to issue a GCO, and suggests the existence of these measurable inflation factors that represent situational and auditor-specific factors. We also find that Non-Big Four auditors inflate these factors differently than Big Four auditors to arrive at a decision to issue a GCO.     

Understanding how heterogeneous agents affect Principal's returns: Perspectives from short-termism and Bayesian learning

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents with higher moral levels were more likely to choose higher effort and lower manipulation. Simultaneously, the principal offers lower incentives and receives more significant payoff. We also extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general and learning models and find that the learning contract can bring more profit to the principal.     

European systemic credit risk transmission using Bayesian networks

The analysis of systemic credit risk is one of the most important concerns within the financial system. Its complexity lies in adequately measuring how the transmission of systemic default spreads through assets or financial markets. The transmission structure of systemic credit risk across several European sectoral CDS is studied by dynamic Bayesian networks. The new approach allows for a more advanced analysis of systemic risk transmission, including long-term and more complex relationships. The modelling reveals as relevant only relationships between the original series and one- and two-lagged series. Network structure learning displays a robust and stationary underlying risk transmission structure, pointing to a consolidated transmission mechanism of systemic credit risk between CDSs. Between 5 % and 40 % of sectoral CDS series variances are explained by the network relationships. The modelling allows us to ascertain which relationships between the CDS series show positive (amplifier) and negative (reducer) effects of systemic risk transmission.     

Equity-settled share-based payments and their (strategic) use under asymmetric information

Share-based payments are of widespread use in today's economy. Consulting firms are increasingly accepting equity compensation for their services (particularly from startups) and many governments provide fiscal incentives to support this choice. Likewise, profit-sharing licensing is an on-trend business practice by innovative firms and patent holders when transferring their technology to interested adopters. This paper unveils strategic considerations according to which an agent/seller designs its optimal policy in regard to the equity share to request in exchange for its service, technology, or trademark. The model assumes a fringe of interested users/customers differentiated by both the support they need from the seller and the value of the underlying relationship; and also holding an informational disadvantage on their own type. Given the seller's cost configuration, equilibrium outcomes entail entering a profit-sharing relationship either with the high-type customers only or with all customers. Yet, in this case, equity-based payment claims are —for rent extraction purposes— common (i.e., not differentiated) across types.     

Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques

Sustainable economic development in the future is driven by public policy on regional, national and global levels. Therefore a comprehensive policy analysis is needed that provides consistent and effective policy support. However, a general problem facing classical policy analysis is model uncertainty. All actors, those involved in the policy choice and those in the policy analysis, are fundamentally uncertain which of the different models corresponds to the true generative mechanism that represents the natural, economic, or social phenomena on which policy analysis is focused. In this paper, we propose a general framework that explicitly incorporates model uncertainty into the derivation of a policy choice. Incorporating model uncertainty into the analysis is limited by the very high required computational effort. In this regard, we apply metamodeling techniques as a way to reduce computational complexity. We demonstrate the effect of different metamodel types using a reduced model for the case of CAADP in Senegal. Furthermore, we explicitly show that ignoring model uncertainty leads to inefficient policy choices and results in a large waste of public resources.     

A Markov chain model for forecasting results of mixed martial arts contests

In this paper, we present a new methodology for forecasting the results of mixed martial arts contests. Our approach utilises data scraped from freely available websites to estimate fighters’ skills in various key aspects of the sport. With these skill estimates, we simulate the contest as an actual fight using Markov chains, rather than predicting a binary outcome. We compare the model’s accuracy to that of the bookmakers using their historical odds and show that the model can be used as the basis of a successful betting strategy.