Efficiency analysis in the presence of uncertainty

In a stochastic decision environment, differences in information can lead rational decision makers facing the same stochastic technology and the same markets to make different production choices. Efficiency and productivity measurement in such a setting can be seriously and systematically biased by the manner in which the stochastic technology is represented. For example, conventional production frontiers implicitly impose the restriction that information differences have no effect on the way risk-neutral decision makers utilize the same input bundle. The result is that rational and efficient ex ante production choices can be mistakenly characterized as inefficient—informational differences are mistaken for differences in technical efficiency. This paper uses simulation methods to illustrate the type and magnitude of empirical errors that can emerge in efficiency analysis as a result of overly restrictive representations of production technologies.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Adjusting for the environment in DEA: A comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.     

Cardinal versus ordinal criteria in choice under risk with disconnected utility ranges

This paper provides a formal justification for the existence of subjective random components intrinsic to the outcome evaluation process of decision makers and explicitly assumed in the stochastic choice literature. We introduce the concepts of admissible error function and generalized certainty equivalent, which allow us to analyze two different criteria, a cardinal and an ordinal one, when defining suitable approximations to expected utility values. Contrary to the standard literature requirements for irrational preferences, adjustment errors arise in a natural way within our setting, their existence following directly from the disconnectedness of the range of the utility functions. Conditions for the existence of minimal errors are also studied. Our results imply that neither the cardinal nor the ordinal criterion do necessarily provide the same evaluation for two or more different prospects with the same expected utility value. As a consequence, a rational decision maker may define two different generalized certainty equivalents when presented with the same prospect in two different occasions.     

Otra Empanada en la Parilla: Examining the Role of Culture and Information Sharing in Chile and Australia

One of the biggest assets of a firm is its information base. Included in this information base is a knowledge of prior errors and failures. Extant research suggests that while the propensity to share “bad news” (i.e. a prior error) is dependent on the cost of sharing, the perceived value of that cost may be culturally dependent. One area of interest that has received substantial attention in the prior literature has been cross‐cultural differences in negative information sharing in general, as well as the particular context in which the individual's superior is either present or absent during the information‐sharing process. Our study examines the role of the two cultural values (individualism/collectivism and to a lesser extent power distance) in explaining national differences in information sharing. By focusing on a sample from Chile and Australia, we were able to remove the regional cultural dimension of face, which has been inherent in prior studies that used Greater China as the representative of a collectivist society. Results from our quasi experiment show that when a supervisor is present during information sharing, collectivist Chilean decision‐makers are more willing to share negative information with their colleagues than their counterpart and individualist Australian decision‐makers. Our results also show that when a supervisor is absent, both Australian and Chilean decision‐makers are willing to share more negative information but the increase in the Australian propensity is significantly greater than that of the Chileans.     

Too big or too small? A synthetic view of the commitment problem of interregional transfers

There is a growing body of literature on the commitment problem of interregional transfers. The problem occurs because of an ex post bailout by a central government leading to ex ante adverse incentive consequences for a local government. However, different models have yielded different economic consequences. The local government may be too large, overspending and/or overborrowing, or it may be too small, raising less of its own revenue. In the presence of interregional spillovers, the equilibrium may yield a Pareto-efficient outcome. The present paper aims to synthesize these models, developing a simple decentralized leadership model. A critical question concerns what decision is made ex ante by the local government—namely public expenditure or tax collection—with the remaining policy instrument being residual adjusted by ex post transfers. We discuss how different scenarios affect the equilibrium outcome.     

Predicting a recovery date from the economic crisis of 2008

Predicting a recovery from a crisis is always difficult, but it is particularly so with the 2008 crisis in the United States. How could a small segment of the financial markets known as subprime credit bring down the world’s largest economy into the worst recession since WWII? The resulting conflicts in policy responses are so severe that the short-term objective (recovery) clashes with the longer-term and more structural goals (governance, regulations, technology). This and the enormous uncertainties caused by it add to the difficulties to predict the pace of recovery. While the economic turnaround depends on consumers’ decision to spend and business’ decision to invest and hire, in an uncertain situation such decisions can only be taken as a result of market players’ perceptions of opportunity that depend on their emotional state and confidence. When the latter produces spontaneous urge to action (‘animal spirits’), the recovery process accelerates. Thus, the appropriate model to predict recovery should be able to incorporate such perceptions factors. By identifying and prioritizing economic and policy factors, it is shown how such a model, the Analytic Network Process (ANP), can be used to make the prediction of the recovery time of the US economy. The forecast was made during Spring 2009 by the author working with participants in a seminar of “Economics of Financial Crisis” at Cornell University. We used an expert judgment approach within the framework of a decision theory model, based on the ANP structure that captures the interplay between financial market, housing sector, and market confidence, all of which are influenced by a range of policies. It is estimated that a real sustainable recovery will begin around late July or early August 2010. While a quicker recovery is possible given the enormous size of fiscal stimulus, monetary injection and unprecedented measures of qualitative easing, it is our conjecture that the temporary nature of all these measures will make such a quick turn-around unsustainable (a double-dip recession). When sensitivity analysis was performed, it was found that altering the priorities of the policies, and their interactions with the aggregate demand components, would not significantly change the estimated time to recovery. This stability of the prediction is due to the overriding importance of restoring confidence, making the other factors less important.     

A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture

This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.     

A multimodal,multicommodity, and multiperiod planning problem for coal distribution to poor families

Tackling poverty has been one of the greatest global challenges and a prerequisite to sustainable development of countries. Countries implement nationally appropriate social protection systems and measures to address poverty. This paper addresses an aid system adopted by the government in Turkey where significant amounts of coal is distributed to poor families each year. The objective of the coal aid system is to complete the delivery of coal to poor families by the start of winter. However, an analysis of the data from previous years indicates that the distribution to many families cannot be completed on time. This results from the fact that planning is done manually and by trial-and-error as there is no system that can be used for distribution planning. This paper describes the planning problem encountered and develops a mathematical model to solve it. The proposed model is a multimodal, multicommodity, and multiperiod linear programming (LP) model. The model can be used to develop and update a distribution plan as well as to answer several what-if questions with regard to capacities, time constraints, and so forth. The model is solved using CPLEX for several problem instances obtained under different scenarios using data for the year 2012. The results show that at least 9% cost savings and about 40% decrease in distribution completion time can be achieved when the model is used. We analyze scenario results qualitatively and quantitatively and provide several insights to the decision makers. As a part of quantitative analysis, we develop regression models to predict optimal costs based on several factors. Our main contribution is to provide an efficient and effective tool to handle a large-scale real-world problem. The model has also helped to prove that the organization responsible for distribution planning may move from the current planning practice to an all-encompassing top-down approach.     

On the conceptualization and measurement of dynamic capabilities for sustainability: Building theory through a systematic literature review

We performed a systematic literature review to explore and understand how the 23-year long debate about dynamic capabilities has addressed sustainability issues. Based on this, we propose a unifying framework that distinguishes different facets of dynamic capabilities for sustainability (DCsS) based on the different types of performance they are more suitable to predict. The theoretical contribution is twofold. First, we add to the literature by highlighting the dependence of different forms of DCsS on different levels of the centrality of sustainability outcomes in corporate strategies. Second, we shed light on the operationalizations of DCsS by providing guidance on the adoption of pertinent constructs and measurement models. The review concludes with a discussion of the managerial implications of the proposed conceptual framework to help decision makers better understand, which strategic moves to make to achieve their intended sustainability goals.     

Too big or too small? A synthetic view of the commitment problem of interregional transfers

There is a growing body of literature on the commitment problem of interregional transfers. The problem occurs because of an ex post bailout by a central government leading to ex ante adverse incentive consequences for a local government. However, different models have yielded different economic consequences. The local government may be too large, overspending and/or overborrowing, or it may be too small, raising less of its own revenue. In the presence of interregional spillovers, the equilibrium may yield a Pareto-efficient outcome. The present paper aims to synthesize these models, developing a simple decentralized leadership model. A critical question concerns what decision is made ex ante by the local government—namely public expenditure or tax collection—with the remaining policy instrument being residual adjusted by ex post transfers. We discuss how different scenarios affect the equilibrium outcome.     

Traces of the Iron Curtain: A multivariate analysis of regional cohesion in Europe

The present study aims to test relative welfare differences among regions in Europe, so as to examine whether the post-communist era has led to more socio-economic cohesion in Europe. The performance of European regions is analysed, compared, and assessed by using the Regional Competitiveness Index (RCI) and stylised fixed nominal categories. The current status of regional cohesion is tested on the basis of detailed data on 268 NUTS 2 European regions by using a robust methodology oriented towards univariate comparison of location parameters, multivariate classification by the decision tree and CHAID algorithm, and comparison of nominal variables with four values based on density plots.Multivariate classification appears to offer statistically excellent results with an overall correct prediction rate for post-socialist and capitalist regions in Europe of 99.6%. The research results from the Higher education and Innovation pillars, reveal a convergence of capitalist and post-socialist regions with capital cities and a divergence of regions with administrative capitals and other regions. Relatively, the two groups which perform best are both groups with capitals, while the group of capitalist regions with a capital city is significantly better in almost all pillars.The key message is that the transition of post-socialist regions is not yet over. Capitalist regions in Europe perform better than post-socialist regions in eight of the nine pillars of regional competitiveness. Our research results also reveal that the group of post-socialist regions without capital cities are significantly lagging behind the rest of the regions in Europe, and thus form the most vulnerable group of European regions. As there is data continuity in the official RCI classification and measurement, policy makers will be able to compare the performance of their own regions over time and to design appropriate concerted strategies accordingly. From this perspective, our study draws several interesting lessons and results for policy makers at various levels. Place-based regional planning and policy based on our analysis framework may be helpful in developing effective measures to cope with the socio-economic legacy of the “Iron Curtain” and get closer to regional cohesion in Europe.     

System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach

In areas from medicine to climate change to economics, we are faced with huge challenges and a need for accurate forecasts, yet our ability to predict the future has been found wanting. The basic problem is that complex systems such as the atmosphere or the economy can not be reduced to simple mathematical laws and modeled accordingly. The equations in numerical models are therefore only approximations to reality, and are often highly sensitive to external influences and small changes in parameterisation — they can be made to fit past data, but are less good at prediction. Since decisions are usually based on our best models of the future, how can we proceed? This paper draws a comparison between two apparently different fields: biology and economics. In biology, drug development is a highly inefficient and expensive process, which in the past has relied heavily on trial and error. Institutions such as pharmaceutical companies and universities are now radically changing their approach and adopting techniques from the new field of systems biology to integrate information from disparate sources and improve the development process. A similar revolution is required in economics if models are to reflect the nature of human economic activity and provide useful tools for policy makers. We outline the main foundations for a theory of systems economics.     

A multiple criteria sorting method for supporting the maintenance management of medical ventilators: The case of Hospital da Luz Lisboa

Medical equipment is characterized by a constant flow of innovations, which is transforming the delivery of healthcare. This creates the need for healthcare organizations to incorporate methodologies to support the maintenance management of these equipment. Within this context, Luz Saúde, the holding company of one of the largest healthcare groups in Portugal, intends to complement the medical equipment maintenance management program of the biggest hospital in its network, Hospital da Luz Lisboa. To this aim, we develop a decision aiding tool for assessing the maintenance condition of medical ventilators and recommend a plan of action. For that, we apply a multicriteria decision aiding methodology, utilizing the Electre Tri-nC method. In interaction with the decision makers, we construct a model using twelve criteria for assigning ventilators to one of the five ordered categories representing the current condition (excellent, very good, good, adequate, and poor). Most of the medical ventilators in the analysis were considered in adequate or good maintenance conditions, which was consistent with the decision makers’ expectations. A detailed analysis of the results evidenced the robustness of the model. This study constitutes the first step toward the use of multicriteria methodologies for supporting decision processes in Hospital da Luz Lisboa.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

The Manager’s Notepad: Working Memory,Exploration, and Performance

This study builds upon March and Simon’s proposition that individual‐level differences must be considered when explaining decision‐making performance. We extend their discussion on the importance of decision‐makers’ attention to explain heterogeneous patterns of exploration and exploitation within the same uncertain environment. We develop a model of decision‐making under uncertainty in which ‘working memory’ – i.e., the ability to hold multiple elements in mind to actively process them – explains the emergence of heterogeneity in exploration‐exploitation choice patterns. We validated the model in a laboratory study and two replications involving 171 individuals. Our findings show that differences in working memory allow us to identify individuals who are more likely to choose exploration over exploitation appropriately, and thus achieve higher performance. We discuss the implications for management theories, and re‐propose the work of March and Simon as a unifying framework that still can be used to generate and test managerially relevant hypotheses.     

Renegotiation Facilitates Contractual Incompleteness

Attempts to economize on bargaining costs imply that two parties may write a contract which is incomplete in the sense that each party tacitly cedes some decision rights to the other. If decision makers can be disciplined by the threat of ex post renegotiation of decisions initially delegated to them, contracts may be even more incomplete. In the limit, the parties may leave all nonprice decisions out of the contract. By thus arguing that the threat of renegotiation facilitates contractual incompleteness, the paper reverses the direction of causality stressed by the literature.     

Decision making in the multiproduct firm: Adaptability and firm organization

This study presents an explicit model of the choice process by which decision makers within a multiproduct firm would optimally allocate their firm-specific inputs. The first-order conditions of the model indicate the kind of information required for efficient transfer of these inputs. Transfer within the firm can be more efficiently accomplished than contracting over markets. Consequently, one may demonstrate the superior efficiency of the multiproduct firm over a comparable group of single-product firms independent of any production externalities. Further, this superior efficiency results only if the central administration of the firm controls the exchange of these inputs.     

On the positive correlation property in competitive insurance markets

Most competitive models of insurance markets under asymmetric information predict a positive relationship between coverage and the accident probability. This paper derives the conditions under which this prediction holds true in a general setting involving multiple loss levels and fixed administrative costs. If there is one loss level, this prediction holds necessarily true only if all equilibrium contracts offer strictly positive coverage. With multiple loss levels, the positive relationship between coverage and the accident probability may not hold true even if all contracts offer strictly positive coverage and administrative costs are zero. These results have important implications for empirical testing.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.