A combined optimization and agent-based approach to supply chain modelling and performance assessment

The main objective of this paper is to give an example of how expert systems techniques for distributed decision-making can be combined with contemporary numerical optimization techniques for the purposes of supply chain optimization and to describe the resulting software implementation. In this paper, multi-agent modelling techniques are applied to simulate and control a simple demand-driven supply chain network system, with the manufacturing component being optimized through mathematical programming. The system measures supply chain performance and the effect of different parameters in the replenishment control system, and can be used to simulate the behaviour of a system that uses optimization for part of its decision-making. The objective of this supply chain network system is to reduce operating cost, while maintaining a high level of customer order fulfilment.     

Crowding density in urban environment and its effects on emotional responding of pedestrians: Using wearable device technology with sensors capturing proximity and psychophysiological emotion responses while walking in the street

ABSTRACT

We conducted a study to examine the influence of crowding in high-density urban areas on the emotional responses of people. We hypothesized that high density leads to the invasion of a personal space that is surrounding everyone’s body which in turn evokes emotional responses, especially negative emotions like stress and aggression. Subjects (n = 30) walked a predefined, crowded path in a Hong Kong commercial center which consisted of urban parts and a park. Along the way, emotional responses were measured psychophysiologically via a wearable device, capturing electrodermal activity, skin temperature, and ambient temperature, among others. Additionally, we used a movement detection sensor to measure the invasion of personal space by other pedestrians. Significant effects of personal space crossings on aversive and appetitive emotional responses are found. Implications for future research are discussed.     

Alphabet Economics: The link between names and reputation

In economics, most coauthored papers have all coauthors in alphabetical order. It is sometimes argued that this conveys advantages to people whose names start with letters that come early in the alphabet. This paper examines whether the alphabetical ranking of names affects someone's reputation. Overall, we find that faculty members with earlier last name initials are more likely to get employment at high standard research departments. Furthermore, we show that the relationship between alphabetical placement and academic success remains significant if we use as an alternative measure of reputation the number of people showing an interest in the papers of a particular academic. This paper also investigates whether the reported alphabetical effect creates differential incentives for coauthoring. It is found that the reputational advantage of first-authorship motivates people to manipulate their names so as to obtain a more beneficial alphabetical position within the majority of articles.     

Simulating from polynomial-normal distributions

The family of polynomial-normal distributions includes a number of widely used distributions, such as the Gram–Charlier and Edgeworth distributions. In this note, we present three simple algorithms, (i) CDF Inversion, (ii) Acceptance–Rejection, (iii) and Ratio–of–Uniforms, for simulating variates from a polynomial-normal distribution. Details on the efficiency of the Acceptance–Rejection and the Ratio–of–Uniforms algorithms and a comparison across the various implementations are provided.     

Multivariate limited translation empirical Bayes estimators

The paper develops multivariate limited translation empirical Bayes estimators of the normal mean vector which serve as a compromise between the empirical Bayes and the maximum likelihood estimators. These compromise estimators perform better than the regular empirical Bayes estimators, in a frequentist sense, when there is wide departure of an individual observation from the grand average.     

Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables

Pairwise likelihood is a limited information estimation method that has also been used for estimating the parameters of latent variable and structural equation models. Pairwise likelihood is a special case of composite likelihood methods that uses lower-order conditional or marginal log-likelihoods instead of the full log-likelihood. The composite likelihood to be maximized is a weighted sum of marginal or conditional log-likelihoods. Weighting has been proposed for increasing efficiency, but the choice of weights is not straightforward in most applications. Furthermore, the importance of leaving out higher-order scores to avoid duplicating lower-order marginal information has been pointed out. In this paper, we approach the problem of weighting from a sampling perspective. More specifically, we propose a sampling method for selecting pairs based on their contribution to the total variance from all pairs. The sampling approach does not aim to increase efficiency but to decrease the estimation time, especially in models with a large number of observed categorical variables. We demonstrate the performance of the proposed methodology using simulated examples and a real application.

     

Corporate Governance and Tobin's Q as a Measure of Organizational Performance

This empirical study examines the relationship between corporate governance and organizational performance (OP), measured using Tobin's Q (TQ) in the context of an emerging economy for which, as yet, only a handful of studies have been conducted. We employ a system generalized method of moments approach controlling for endogeneity and test it on a newly created dataset comprising 324 listed firms in Pakistan. We find that board size, number of board committees and ownership concentration are positively linked with high TQ ratio, whilst board independence and CEO duality display a negative relationship. In terms of moderating effects, we find that ownership concentration negatively moderates the relationship between board independence and OP, as well as that of CEO duality and OP. The relationship between the number of board committees and OP is positively moderated by ownership concentration. Our findings contribute towards a better articulation and application of a more concrete measure of OP ? that of the TQ ratio ? whilst, at the same time, testing the board composition–performance relationship in the context of an upcoming and increasingly important emerging market. Wider applicability of results and policy implications are discussed.