FAIRness of research information

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Social Preference, Incomplete Information, and the Evolution of Ultimatum Game in the Small World Networks: An Agent-Based Approach

Certain social preference models have been proposed to explain fairness behavior in experimental games. Existing bodies of research on evolutionary games, however, explain the evolution of fairness merely through the self-interest agents. This paper attempts to analyze the ultimatum game's evolution on complex networks when a number of agents display social preference. Agents' social preference is modeled in three forms: fairness consideration or maintaining a minimum acceptable money level, inequality aversion, and social welfare preference. Different from other spatial ultimatum game models, the model in this study assumes that agents have incomplete information on other agents' strategies, so the agents need to learn and develop their own strategies in this unknown environment. Genetic Algorithm Learning Classifier System algorithm is employed to address the agents' learning issue. Simulation results reveal that raising the minimum acceptable level or including fairness consideration in a game does not always promote fairness level in ultimatum games in a complex network. If the minimum acceptable money level is high and not all agents possess a social preference, the fairness level attained may be considerably lower. However, the inequality aversion social preference has negligible effect on the results of evolutionary ultimatum games in a complex network. Social welfare preference promotes the fairness level in the ultimatum game. This paper demonstrates that agents' social preference is an important factor in the spatial ultimatum game, and different social preferences create different effects on fairness emergence in the spatial ultimatum game.Spatial Ultimatum Game, Complex Network, Social Preference, Agent Based Modeling

The relative importance of comprehensive performance measurement systems and financial performance measures on employees’ perceptions of informational fairness

Research on how performance measurement systems affect employees' perceptions of workplace fairness is important. As organizations often rely on their performance measurement systems to communicate information to their employees, it is useful to ascertain if and how the developments of performance measurement systems that are far more comprehensive than traditional financial systems affect employees' perceptions of informational fairness through the information communicated to employees. Informational fairness refers to employees' perceptions of workplace fairness that is based on the amount and the truthfulness of information that organizations provide to their employees. Based on a sample of managers from manufacturing organizations, the Partial Least Square results indicate that comprehensive performance measurement systems (comprehensive PMS) have a significant direct effect on jobrelevant information. They also indicate that comprehensive PMS have an indirect effect on informational fairness via job-relevant information. In contrast, systems that are based on financial measures have no significant effects on job-relevant information and informational fairness. These results demonstrate how comprehensive PMS (through the communication of a greater amount of job-relevant information) can be used to engender employees' perceptions of high workplace fairnes

How Fair Is Income Taxation in the View of the German Public?

It is not possible to provide a scientific answer to questions regarding the 'fairness' of taxation. Nonetheless, empirical economics and social science research can still yield helpful information for legislators charged with deciding on the level and structure of the tax system. Such research also includes the public's assessment of the fairness of the tax system. At the beginning of 2005, DIW Berlin incorporated questions concerning the fairness of income taxation in its 'Socio-Economic Panel' longitudinal study. Preliminary findings of this research are now available. The tax burden on unskilled workers was considered excessively high by almost two-thirds of respondents. By contrast, the burden on the board members of large enterprises was considered too low by three-quarters of those surveyed, and this view was actually shared by two-thirds of the executive staff in the sample. However, it is not possible to draw any direct conclusions for economic and fiscal policy from these findings. The evaluation of such data is a wholly political act lying in the exclusive domain of the parliament. However, these results at least demonstrate unambiguously that when it comes to the issue of income and fiscal equity, people belonging to all sections of the population think in a more egalitarian manner than is assumed by large segments of public opinion and politics.

Roles of Information Security Awareness and Perceived Fairness in Information Security Policy Compliance

Drawing on the Theory of Planned Behavior (TPB), this research investigates two factors that drive an employee to comply with requirements of the information security policy (ISP) of her organization with regards to protecting information and technology resources: an employee’s information security awareness (ISA) and her perceived fairness of the requirements of the ISP. Our results, which is based on the PLS analysis of data collected from 464 participants, show that ISA and perceived fairness positively affect attitude, and in turn attitude positively affects intention to comply. ISA also has an indirect impact on attitude since it positively influences perceived fairness. As organizations strive to get their employees to follow their information security rules and regulations, our study sheds light on the role of an employee’s ISA and procedural fairness with regards to security rules and regulations in the workplace

Effect of Tax Rate, Taxation Technology and Information, Possibility of Fraud Detection, and Tax Fairnesson Taxpayer Perception of Tax Fraud Ethics (Empirical Study on KPP Pekanbaru, Dumai, Rokan Hilir)

This research is aimed to examine effect of tax rate, taxation technology and information, possibility of fraud detection, and tax fairness on taxpayer perception of tax fraud ethics. This research uses primary data by questioners. Data are analyzed by using multiple regression with 105 respondents of individual taxpayers (wajib pajak orang pribadi) listed in Kantor Pelayanan Pajak  (KPP) Pratama Senapelan Pekanbaru, KPP Dumai Kota, KPP Rokan Hilir, Riau, Indonesia. Results show that tax rate has effect on taxpayer perception of tax fraud ethics, taxation technology and information has effect on taxpayer perception of tax fraud ethics, possibility of fraud detection has effect on taxpayer perception of tax fraud ethics, tax fairness has effect on taxpayer perception of tax fraud ethics.Keywords: tax rate, taxation technology and information, fraud, fairness, purposive sampling

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.Comment: First two authors contributed equally. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 202