Mean demand when consumers satisfy the weak axiom of revealed preference

In this paper we consider consumption sectors of individuals who's demand functions are homogeneous of degree zero in prices and income. All individuals hold the same income and satisfy the corresponding budget identity and the weak axiom of revealed preference. It is shown that on any compact set of strictly positive prices and incomes this setup does not impose any restrictions on the class of mean demand functions other than the budget identity, provided the individual consumption sets are unbounded. If they are bounded below the result holds locally.     

The geometry of revealed preference

In this paper, we examine how the geometry underlying revealed preference determines the set of preferences that can be revealed by choices. Specifically, given an arbitrary binary relation defined on a finite set, we ask if and when there exists a data set which can generate the given relation through revealed preference. We show that the dimension of the consumption space affects the set of revealed preference relations. If the consumption space has more goods than observations, any revealed preference relation can arise. Conversely, if the consumption space has low dimension relative to the number of observations, then there exist both rational and irrational preference relations that can never be revealed by choices.     

The relationship between revealed preference and the Slutsky matrix

This paper presents a method of calculating the utility function from a smooth demand function whose Slutsky matrix is negative semi-definite and symmetric. The calculated utility function is the unique upper semi-continuous function corresponding with the demand function. Moreover, we present an axiom for demand functions. We show that under the strong axiom, this new axiom is equivalent to the existence of the corresponding continuous preference relation. If the demand function obeys this axiom, the calculated utility function is also continuous. Further, we show that the mapping from the demand function into a continuous preference relation is continuous, which ensures the applicability of our results for econometrics. Moreover, if this demand function satisfies the rank condition, then our utility function is smooth. Finally, we show that under an additional axiom, the above results hold even if the demand function has corner solutions.     

The Importance of Attributional Complexity for Transformational Leadership Studies

Attributional complexity refers to the capability of discriminating and integrating dimensions related to social judgment in order to understand social behaviour. While previous leadership research has examined the role of leader attributions, it has neglected the role of attributional complexity. We theorize and find support for a relationship between higher attributional complexity and transformational leadership behaviours, based on a sample of 100 leaders and their direct reports. Leaders who were more complex in their social judgments, attributing complex external and internal causes to others' behaviours and actions, were seen as more transformational by their direct reports. Our findings suggest that attributional complexity is a construct that warrants consideration in future research on both transformational leadership and leadership attributions.     

A simple proof of the existence of an equilibrium when the weak axiom holds

An elementary proof of the existence of a competitive equilibrium is given for economies where the weak axiom holds. It is shown by an induction argument on the number of goods that the problem is reduced to the question of the existence of an equilibrium in a two-good economy.     

和谐社会理念的一种解释:集体意识

构建和谐社会的理念具有集体意识的性质。文中从集体意识的含义、特征和形成出发,探讨了作为一种集体意识的和谐社会理念产生的前提、形成机制与对社会现实的反作用。同时强调了对构建和谐社会这种集体意识的正确引导。     

基于复杂性科学的供应链管理

运用复杂性科学的基本原理和方法分析论述了供应链管理的复杂性特征．探讨了基于复杂性科学的供应链管理的基本原理及其对当今企业的重要性，为运用复杂性科学进行供应链管理进行了有益的探索。     

技术复杂度对合作创新绩效的影响分析

论文以上市高科技民营企业为样本,分析合作背景下技术复杂度的两个维度对合作创新绩效的影响,并考察高管团队特征在其中的调节作用。研究结果表明:技术复杂度的两个维度与企业合作创新绩效呈倒U型关系。根据实证结果,我国高科技企业应该充分利用自身的资源和能力,通过更大范围的合作增强创新能力。     

Revealed preference test and shortest path problem; graph theoretic structure of the rationalizability test

This paper presents some substantial relationships between the revealed preference test for a data set and the shortest path problem of a weighted graph. We give a unified perspective of several forms of rationalizability tests based on the shortest path problem and an additional graph theoretic structure, which we call the shortest path problem with weight adjustment. Furthermore, the proposed structure is used to extend the result of Quah (2014), which sharpened classical Afriat’s Theorem-type results.     

Asymptotic results and tests for the choice of approximative models in nonlinear two-phases regression models, heteroscedatic case

This paper is devoted to the study of the least squares estimator of f for the classical, fixed design, nonlinear model X (t _i)=f(t _i)+ε(t _i), i=1,2,…,n, where the (ε(t _i))_i=1,…,n are independent second order r.v.. The estimation of f is based upon a given parametric form. In Brodeau (1993) this subject has been studied in the homoscedastic case. This time we assume that the ε(t _i) have non constant and unknown variances σ²(t _i). Our main goal is to develop two statistical tests, one for testing that f belongs to a given class of functions possibly discontinuous in their first derivative, and another for comparing two such classes. The fundamental tool is an approximation of the elements of these classes by more regular functions, which leads to asymptotic properties of estimators based on the least squares estimator of the unknown parameters. We point out that Neubauer and Zwanzig (1995) have obtained interesting results for connected subjects by using the same technique of approximation. Received: February 1996     

Divergence in alternative Hicksian welfare measures: the case of revealed preference for public amenities

This paper investigates the divergence between the two Hicksian welfare measures of non‐traded amenity improvement associated with housing. First, the Hicksian surplus measures for amenity changes are analytically developed based on explicit specification of utility structures. A hedonic two‐stage approach is then applied to empirically show that, for quantity changes, in contrast to hypothetical markets, divergence in real market is small. The paper also analytically develops expressions for the income and substitution effects and empirically shows that for a given income effect, the greater the substitution effect the smaller the divergence between the two measures. Copyright © 2002 John Wiley & Sons, Ltd.     

Variable markups in the long-run: A generalization of preferences in growth models

This paper introduces variable markups in a horizontal-differentiation growth model by considering a larger class of preferences that nests the classic “CES” specification usually present in the workhorse love-for-variety models. Our first result is to obtain a generalized characterization of the Euler condition for this broader class of utility functions: in our model, the Euler rule features a supplementary term aiming at compensating the consumer for variations in the preference for variety along the consumption level. We are then also able to demonstrate that in our generalized framework, the economy’s balanced growth path displays both endogenous markups and a strictly positive growth rate of the number of available varieties (being the engine of growth). Finally, we show that under endogenous markups, the economy’s growth rate and firms’ market power can display a negative correlation, as opposed to the standard result obtained in the CES framework.     

The Influence of Network Management and Complexity on Network Performance in Taiwan,Spain and the Netherlands

Abstract

Using survey data of respondents involved in spatial planning projects in Taiwan, Spain and the Netherlands (n = 678), this article examines the influence of network management strategies and complexities (examined with regard to differences in perception of problems and solutions, and unexpected events) on perceived network performance. This theory-driven, empirical research shows that the effect of network management strategies on perceived performance is stronger than the impact of unexpected events or actors’ differences in perceptions of problems and solutions. We find this result in all the three countries. Our model explains 19.1 per cent of the variance in the perceived network performance.     

Complexity of Innovation in the public sector: A workgroup-level analysis of related factors and outcomes

Complex innovation incorporates more than one innovation type. Using the number of dimensions of the ‘most significant innovation’ implemented by each public employee’s workgroup as a proxy for innovation complexity, this study explores factors that are associated with complexity and examines how complexity affects innovation outcomes. Employing a sample of 4,369 Australian Government employees, we find that the more complex the innovation, the greater the number of barriers a workgroup has to face in its implementation. A broader (but selective) range of idea sources and a more decentralized workplace where both individual and team creativity is encouraged increase the likelihood of implementing complex innovations. Innovation complexity is positively correlated with the variety of beneficial outcomes, suggesting both policy and management interest in supporting complex innovation in the public sector.     

Lost and Found in Translation: How Firms Use Anisomorphism to Manage the Institutional Complexity of CSR

Prior research on the internationalization of firms from emerging countries has fruitfully invoked institutional theory to emphasize the legitimacy benefits that firms that obtain from showing isomorphism with international norms such as Corporate Social Responsibility (CSR). Without denying the intuitive appeal for these firms to communicate acceptance of CSR, we suggest that firms face a legitimacy trade-off, where the hoped-for legitimacy benefits of isomorphism must be weighed against other home-country institutional considerations. We advance and test this notion that firms will navigate this institutional complexity by engaging in anisomorphism, i.e., espousing general acceptance with international values but with selective ‘translation’ based on home country differences. We test our predictions by analysing firms' communication of CSR, using a unique dataset comprised of 245 firms observed over the period from 2000 to 2018. Consistent with our predictions, we find that firms from countries more reliant on natural resource extraction (e.g., mining and fossil fuel industries) de-emphasize the environmental component of CSR, and firms from more autocratic countries de-emphasize the human rights component of CSR. Additionally, and consistent with our presumption of firms' weighing the international versus home-country legitimacy trade-off, we find that these main effects are sensitive to changes in firms' levels of internationalization.     

Robust estimation for the order of finite mixture models

In this paper, we study a robust and efficient estimation procedure for the order of finite mixture models based on the minimizing a penalized density power divergence estimator. For this task, we use the locally conic parametrization approach developed by Dacunha-Castelle and Gassiate (ESAIM Probab Stat 285–317, 1997a; Ann Stat 27:1178–1209, 1999), and verify that the minimizing a penalized density power divergence estimator is consistent. Simulation results are provided for illustration.     

Effectiveness of Q-learning as a tool for calibrating agent-based supply network models

This paper examines effectiveness of Q-learning as a tool for specifying agent attributes and behaviours in agent-based supply network models. Agent-based modelling (ABM) has been increasingly employed to study supply chain and supply network problems. A challenging task in building agent-based supply network models is to properly specify agent attributes and behaviours. Machine learning techniques, such as Q-learning, can be a useful tool for this purpose. Q-learning is a reinforcement learning technique that has been shown to be an effective adaptation and searching mechanism in distributed settings. In this study, Q-learning is employed by supply network agents to search for ‘optimal’ values for a parameter in their operating policies simultaneously and independently. Methods are designed to identify the ‘optimal’ parameter values against which effectiveness of the learning is evaluated. Robustness of the learning's effectiveness is also examined through consideration of different model settings and scenarios. Results show that Q-learning is very effective in finding the ‘optimal’ parameter values in all model settings and scenarios considered.     

Multilevel IRT models for the analysis of satisfaction for distance learning during the Covid-19 pandemic

The Covid-19 pandemic played a relevant role in the diffusion of distance learning alternatives to “traditional” learning based on classroom activities, to allow university students to continue attending lessons during the most severe phases of the pandemic. In such a context, investigating the students' perspective on distance learning provides useful information to stakeholders to improve effective educational strategies, which could be useful also after the end of the emergency to favor the digital transformation in the higher educational setting.Here we focus on the satisfaction in distance learning for Italian university students. We rely on data comprising students enrolled in various Italian universities, which were inquired about several aspects related to learning distance.We explicitly take into account the hierarchical nature of data (i.e., students nested in universities) and the latent nature of the variable of interest (i.e., students' learning satisfaction) through a multilevel Item Response Theory model with students' and universities' covariates.As the main results of our study, we find out that distance learning satisfaction of students: (i) depends on the University where they study; (ii) is affected by some students' socio-demographic characteristics, among which psychological factors related to Covid-19; (iii) is affected by some observable university characteristics.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.