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Are people algorithm averse, as some previous literature indicates? If so, can the retention of human oversight increase the uptake of algorithmic recommendations, and does keeping a human in the loop improve accuracy? Answers to these questions are of utmost importance given the fast-growing availability of algorithmic recommendations and current intense discussions about regulation of automated decision-making. In an online experiment, we find that 66% of participants prefer algorithmic to equally accurate human recommendations if the decision is delegated fully. This preference for algorithms increases by further 7 percentage points if participants are able to monitor and adjust the recommendations before the decision is made. In line with automation bias, participants adjust the recommendations that stem from an algorithm by less than those from another human. Importantly, participants are less likely to intervene with the least accurate recommendations and adjust them by less, raising concerns about the monitoring ability of a human in a Human-in-the-Loop system. Our results document a trade-off: while allowing people to adjust algorithmic recommendations increases their uptake, the adjustments made by the human monitors reduce the quality of final decisions.

We study academic consequences of non-academic misconduct for accused researchers at US universities. Focusing on allegations of sexual misconduct, we find detrimental effects on scientific impact, productivity and career. Other researchers are less likely to cite the perpetrator’s prior work after allegations surface. The effect is absent in male-dominated fields and weakens with distance in the co-authorship network, indicating that researchers learn about allegations via their peers. Although we find that alleged perpetrators tend to remain active researchers, they are less likely to be affiliated with a university and publish fewer articles following the incident.

As technology-assisted decision-making is becoming more widespread, it is important to understand how the algorithmic nature of the decision-maker affects how decisions are perceived by the affected people. We use a laboratory experiment to study the preference for human or algorithmic decision makers in re-distributive decisions. In particular, we consider whether algorithmic decision maker will be preferred because of its unbiasedness. Contrary to previous findings, the majority of participants (over 60%) prefer the algorithm as a decision maker over a human—but this is not driven by concerns over biased decisions. Yet, despite this preference, the decisions made by humans are regarded more favorably. Participants judge the decisions to be equally fair, but are nonetheless less satisfied with the AI decisions. Subjective ratings of the decisions are mainly driven by own material interests and fairness ideals. For the latter, players display remarkable flexibility: they tolerate any explainable deviation between the actual decision and their ideals, but react very strongly and negatively to redistribution decisions that do not fit any fairness ideals. Our results suggest that even in the realm of moral decisions algorithmic decision-makers might be preferred, but actual performance of the algorithm plays an important role in how the decisions are rated.

Using a survey with 57 German firms, we evaluate the level of digitalization of the HR management function and document perceived benefits and barriers of technology adoption from organizational and individual users’ perspectives. The results give a reason for optimism. Most of the companies report that the core HR processes are digitized. We do not observe adverse effects of the digital HRM tools on users’ job satisfaction and work stress. Still, more than half of companies do not yet use digital tools for strategic HRM decisions. Respondents appreciate the increased speed and cost-efficiency of digital HRM processes and associate it with a competitive advantage in talent acquisition. The most prominent adoption barriers are lack of qualified professionals, high costs, and uncertainty regarding the legal framework. Moreover, we test if small and medium-sized enterprises differ systematically from larger organizations in how they use digital HRM tools.

What is the effect of labour market adjustment to automation on political participation? We study the consequences of the introduction of industrial robots across US commuting zones on voter turnout in US counties between 2000 and 2016. We first replicate prior results showing negative effects of exposure to robots on employment and household incomes at local labour markets and then show that an increase in the exposure by one robot per thousand workers leads to a 0.64 percentage point lower voter turnout at US presidential elections. We contrast this result with the effect of the exposure to Chinese imports, for which we do not find a negative effect on political participation. Using individual level data we document that people at risk of automation are 15% percent more likely to abstain. To understand why the effect is not uniform, we conduct an online survey experiment. We find that the nature of the shock matters beyond the mere economic consequences. While the government is seen as instrumental in addressing the trade shock, it is perceived less effective in the case of automation. Our finding highlight an important behavioral aspect of the political economy of technological change.

Today, humans interact with automation frequently and in a variety of settings ranging from private to professional. Their behavior in these interactions has attracted considerable research interest across several fields, with sometimes little exchange among them and seemingly inconsistent findings. In this article, we review 138 experimental studies on how people interact with automated agents, that can assume different roles. We synthesize the evidence, suggest ways to reconcile inconsistencies between studies and disciplines, and discuss organizational and societal implications. The reviewed studies show that people react to automated agents differently than they do to humans: In general, they behave more rationally, and seem less prone to emotional and social responses, though this may be mediated by the agents’ design. Task context, performance expectations and the distribution of decision authority between humans and automated agents are all factors that systematically impact the willingness to accept automated agents in decision-making - that is, humans seem willing to (over-)rely on algorithmic support, yet averse to fully ceding their decision authority. The impact of these behavioral regularities for the deliberation of the benefits and risks of automation in organizations and society is discussed.

Although taxation distorts work incentives both for taxpayers and transfer recipients, its net effect on labor provision is shown to be less severe than predicted by the theory. It is likely that the reciprocity between taxpayers and transfer recipients plays an important role in mitigating the negative consequences of redistribution and maintaining a high level of effort. To check it we run a series of real-effort experiments exploring the production effects of taxation in the environment with unilateral monitoring: Taxpayers can continuously monitor the effort of the transfer recipient, which is designed to trigger reciprocity. Surprisingly, we find that monitoring decreases the total labor provision: recipients produce significantly less under monitoring, while the production of the taxpayers remains unchanged.

A large body of literature demonstrates that redistribution leads to inefficiencies due to distorted work incentives. Yet, this result is obtained under the assumption that people are absolutely free in their labor-leisure allocation decisions and that taxation is merely a wage cut. We challenge this assumption and study labor supply decisions in a framework with the subsistence income constraint and a redistribution system which supports disadvantaged players. The results of the real-effort experiment show that the introduction of the moderate subsistence income requirement causes a substantial increase in productivity among taxpayers, with slight additional boost if tax returns are transferred to recipients and not wasted unproductively. As for recipients, the prospect of receiving a transfer significantly enhances their productivity and spurs the overall efficiency leading to a self-sorting of recipients according to their skills.

Spectral properties of linear operators are very important in stability analysis of dynamical systems. The paper studies the non-selfadjoint second order differential operator that originated from a steady state stability problem in dynamic of viscous Newtonian fluid on the inner surface of horizontally rotating cylinder in the presence of gravitational field. The linearization of the thin liquid film flow in the lubrication limit about the uniform coating steady state results into the operator which domain couples two subspaces spanned by positive and negative Fourier exponents which are not invariant subspaces of the operator. We prove that the operator admits factorization and use this new representation of the operator to prove compactness of its resolvent and to find its domain. Спектральные свойства линейных операторов играют важную роль в анализе устойчивости динамических систем. В заметке исследуются свойства несамосопряженного дифференциального оператора второго порядка, связанного с исследованием проблемы устойчивости стационарного динамического состояния тонкой пленки, образованной вязкой ньютоновской жидкостью и расположенной на внутренней поверхности вращающегося цилиндра, при наличии гравитационного поля. Линеаризация по малому параметру (отношению толщины потока к размеру цилиндра) в этом случае порождает дифференциальный оператор с областью определения, вложенной в прямую сумму двух подпространств, натянутых, соответственно, на базисы {einx} и {e-inx} (n > 0), причем указанные подпространства не являются инвариантными по отношению к оператору, и одномерного подпространства констант. Доказывается, что этот оператор допускает представление в виде произведения двух дифференциальных операторов первого порядка. Полученное представление используется для доказательства компактности резольвенты исследуемого оператора и непосредственного описания его области определения. M. Chugunova, Institute of Mathematical Sciences, Claremont Graduate University, Claremont, USA, marina.chugunova@cgu.edu,V. Strauss, Department of Pure & Applied Mathematics, Sim´on Bol´ıvar University, Caracas,Venezuela, str@usb.ve Марина Васильевна Чугунова, PhD, профессор, математический институт, Кларемонтский университет, г. Кларемонт, США, marina.chugunova@cgu.edu. Владимир Абрамович Штраус, доктор физико-математических наук, профессор, университет Симон Боливар, г. Каракас, Венесуэла, str@usb.ve.