The reshoring decision under uncertainty in the post-COVID-19 era

Purpose Using insights from the supply chain resilience perspective and the international business literature, this study aims to investigate the determinants of firms’ decisions to reshore manufacturing under the high levels of uncertainty brought about by the ongoing US–China trade war and COVID-19 pandemic. Design/methodology/approach The proposed conceptual framework is tested using survey data collected from 702 Taiwanese firms with manufacturing in China. The firms were drawn from a database compiled by Taiwan’s Ministry of Economic Affairs. Findings The results show that two supply chain factors (tariffs and supply chain completeness) and two non-location-bound factors (labor cost and material cost) are critical determinants of the decision to reshore under uncertainty. Originality/value This research elucidates and empirically validates several factors that influence the reshoring decision in uncertain environments. The findings provide valuable theoretical, practical and strategic insights into how firms should manage their value chains in the post-COVID-19 era.

Optimal Risk Sharing in Society

We consider risk sharing among individuals in a one-period setting under uncertainty that will result in payoffs to be shared among the members. We start with optimal risk sharing in an Arrow–Debreu economy, or equivalently, in a Borch-style reinsurance market. From the results of this model we can infer how risk is optimally distributed between individuals according to their preferences and initial endowments, under some idealized conditions. A main message in this theory is the mutuality principle, of interest related to the economic effects of pandemics. From this we point out some elements of a more general theory of syndicates, where in addition, a group of people are to make a common decision under uncertainty. We extend to a competitive market as a special case of such a syndicate.

Behavioral Decision Making in Normative and Descriptive Views: A Critical Review of Literature

Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM—an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision—theory, experiments, and applications).

Financial Decision under Uncertainty: Lessons from Indonesian Millennial

Financial decision under uncertain event is relatively tough not only for individual but also government and business owner. It impacts the individual welfare and other critical aspects. This paper attempts to examine millennial financial behavior in order to promote better understanding in facing uncertain event and conduct better risk mitigation. This study analyzed millennial financial decision by involving 2270 respondents. The result reveals that social influence, personality traits, financial literacy, and perceived risk provide strong relationship on millennial financial decision which present important implication for both academics and decision makers.

Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain

The installation of wind power technology is growing steadily and the trend can be expected to continue if the objectives proposed by the European Commission are to be achieved. In some countries a considerable percentage of installed wind power capacity is near the end of its useful lifetime. In the case of Spain, the figure is 50% within five years. Over the last 20 years, wind energy technology has evolved considerably and the expected capacity factor has improved, thus increasing annual energy production, and capital expenditure and operational expenditure have decreased substantially. This paper studies the optimal decision under uncertainty between life extension and full repowering for a generic wind farm installed in the Iberian Peninsula when the future hourly electricity prices and the capacity factor evolve stochastically and seasonally. The results show that in economic terms, full repowering is the best option, with a net present value of €702,093 per MW installed, while reblading is the second best option. The methodology can be transferred to other specific wind farms in different electricity markets and can be used to develop national wind energy policy recommendations to achieve projected shares in the electricity mix.

EU’s Ordering of COVID-19 Vaccine Doses: Political Decision-Making under Uncertainty

Worldwide, politicians, scientists, and entrepreneurs are operating under high uncertainty and incomplete information regarding the adequacy of measures to deal with the COVID-19 pandemic. It seems indisputable that only widespread and global immunity can bring normalization to social life. In this respect, the development of a vaccine was a milestone in pandemic control. However, within the EU, especially in Germany, the vaccination plan is increasingly faltering, and criticism is growing louder. This paper considers the EU’s political decision in general and the decisions of the German government to procure vaccine doses against the background of modern economics as a decision under uncertainty and critically analyzes the process.

Expecting social punishment facilitates control over a decision under uncertainty by recruiting medial prefrontal cortex

In many decision-making situations, sub-optimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing sub-optimal choices, which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional magnetic resonance imaging (fMRI) study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants’ mood. Model-based fMRI analyses and dynamic causal modelling indicate that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centred on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed.

Base rate neglect and neural computations for subjective weight in decision under uncertainty

Base rate neglect, an important bias in estimating probability of uncertain events, describes humans’ tendency to underweight base rate (prior) relative to individuating information (likelihood). However, the neural mechanisms that give rise to this bias remain elusive. In this study, subjects chose between uncertain prospects where estimating reward probability was essential. We found that when the variability of prior and likelihood information about reward probability were systematically manipulated, prior variability significantly affected the degree to which subjects underweight the base rate of reward probability. Activity in the orbitofrontal cortex, medial prefrontal cortex, and putamen represented the relative subjective weight that reflected such bias. Further, sensitivity to likelihood relative to prior variability in the putamen correlated with individuals’ overall tendency to underweight base rate. These findings suggest that in combining prior and likelihood, relative sensitivity to information variability and subjective-weight computations critically contribute to the individual heterogeneity in base rate neglect.

Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

In automated parking systems, a path planner generates a path to reach the vacant parking space detected by a perception system. To generate a safe parking path, accurate detection performance is required. However, the perception system always includes perception uncertainty, such as detection errors due to sensor noise and imperfect algorithms. If the parking path planner generates the parking path under uncertainty, problems may arise that cause the vehicle to collide due to the automated parking system. To avoid these problems, it is a challenging problem to generate the parking path from the erroneous parking space. To solve this conundrum, it is important to estimate the perception uncertainty and adapt the detection error in the planning process. This paper proposes a robust parking path planning that combines an error-adaptive sampling of generating possible path candidates with a utility-based method of making an optimal decision under uncertainty. By integrating the sampling-based method and the utility-based method, the proposed algorithm continuously generates an adaptable path considering the detection errors. As a result, the proposed algorithm ensures that the vehicle is safely located in the true position and orientation of the parking space under perception uncertainty.

Information processing in tax decisions: A MouselabWEB study on the Allingham and Sandmo model of income tax evasion

The highly influential Allingham and Sandmo model of income tax evasion framed the decision whether to comply or to evade taxes as a decision under uncertainty, assuming that taxpayers are driven by utility-maximization. Accordingly, they should choose evasion over compliance if it yields a higher expected profit. We test the main assumptions of this model considering both compliance decisions and the process of information acquisition applying MouselabWEB. In an incentivized experiment, 109 participants made 24 compliance decisions with varying information presented for four within-subject factors (income, tax rate, audit probability, and fine level). Additional explicit expected value information was manipulated between-subjects. The results reveal that participants attended to all relevant information, a prerequisite for expected value like calculations. As predicted by the Allingham and Sandmo model, choices were clearly influenced by deterrence parameters. Against the assumptions, these parameters were not integrated adequately, as evasion did not increase with rising expected rate of return. More transitions between information necessary for calculating expected values did not result in higher model conformity, just as presenting explicit information on expected values. We conclude that deterrence information clearly influences tax compliance decisions in our setting, but observed deviations from the model can be attributed to failure to integrate all relevant parameters.