Subjective Uncertainty Research Articles

Effects of Macro Uncertainty on Mean Expectation and Subjective Uncertainty: Evidence from Households and Professional Forecasters

Effects of Macro Uncertainty on Mean Expectation and Subjective Uncertainty: Evidence from Households and Professional Forecasters

Entrepreneurial Specialization: How Subjective ('Knightian') Uncertainty Matters

Entrepreneurial Specialization: How Subjective ('Knightian') Uncertainty Matters

Analytical and numerical assessment of a preliminary support design – a case study

Underground tunneling results in redistribution of the in-situ stress state. This process can create unstable underground conditions. Tunnel instability can result in fatalities, property damages, project delays, high rehabilitation cost, and can compromise the overall functionality of the excavation. This study assesses the efficiency of the empirically recommended support design of the Pahang-Selangor Water Transfer Tunnel in Peninsular, Malaysia using convergence-confinement (CV-CF) and 2-Dimensional two-dimensional Finite Element Method (FEM). To reduce the effect of subjective uncertainties associated with the rock mass rating system, the lump rating system was replaced with the continuous rating system to classify the rock masses and provide empirical support design for the tunnel. The maximum capacity and stiffness of the support systems were analytically determined using the CV-CF method. The results of the maximum elastic deformation of the support systems were compared to the actual critical internal pressures which show that the empirically recommended support systems can satisfactorily stabilize the underground excavation. The Reliability of the support design, the plastic zones, and total deformations were determined using Phase2. The radius of plastic zones and the maximum total deformations were significantly reduced after the supports were installed. The plastic zones that remained were restricted within the reinforced zones, signifying the effectiveness of the recommended support systems.

A review on fuzzy preference modeling methods for group decision-making

With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling.

On the role of interdisciplinary, transdiscliprinary, and philosophical knowledge in modern higher education

The article traces the substantial and functional evolution of interdisciplinary and transdisciplinary knowledge and its integration into the theory and practice of higher education. The method of distinguishing between classical, non-classical, and post-non-classical rationality is used to disclose the specifics of university transdisciplinarity. The proposed hypothesis suggests that in non-classical and post-non-classical education, different types of studied objectivity exist while when the subject boundaries are fixed, various forms of subject uncertainty are observed. Difficulties in the practice of non-classical education are associated with the objective of overcoming double uncertainty. In one case, the onedimensionality of the study depends on the choice of ontological conditions that are only sufficient for a given monodiscipline. In another case, the task of combining the intervals of studying a subject in the framework of multidisciplinary knowledge is being solved. Transdisciplinarity manifests primarily through educational modeling technologies. What can be attributed to the specifics of post-non-classical education is the study of two types of objectivity: the system-level reality in cases of severe disequilibrium and the system-operational reality in cases of mild disequilibrium. Thus, the subject area demonstrates substantial and systemic uncertainty. It is concluded that the study of systemic objects as a part of the educational process requires interdisciplinary efforts and is carried out in line with the following scheme: problem – project – concept – practical solution.

Experiencing subjective uncertainty and being ready for change

Experiencing subjective uncertainty and being ready for change

Aversion amplification in the emerging COVID‐19 pandemic: The impact of political trust and subjective uncertainty on perceived threat

AbstractHealth psychology shows that responses to risk and threat depend on perceptions as much as objective factors. The present study focuses on the precursors of perceived threat of COVID‐19. We draw on political and social psychology and use the aversion amplification hypothesis to propose that subjective uncertainty and political trust should interactively impact perceived threat. We conducted a cross‐sectional survey amongst the general population of Scotland (N = 188) in the early period of the COVID‐19 pandemic in the UK. We hypothesised that high political trust should ameliorate the threat‐elevating impact of uncertainty, thereby reducing the perceived threat from a high to moderate level. This hypothesis was supported, even after accounting for demographic differences. The discussion addresses the implications of the interactive role of trust and uncertainty for strategies to manage public behaviour as the pandemic progresses.

Designing, Not Checking, for Policy Robustness: An Example with Optimal Taxation

Economists typically check the robustness of their results by comparing them across plausible ranges of parameter values and model structures. A preferable approach to robustness—for the purposes of policymaking and evaluation—is to design policy that takes these ranges into account. We modify the standard optimal income tax model to include the policymaker’s subjective uncertainty over parameter values, and we characterize robust optimal policy as that which maximizes expected social welfare. After calibrating uncertainty over the elasticity of taxable income from past empirical work and novel survey data on economists’ beliefs, we compare the implied robust optimal marginal tax rates to the alternative benchmark policy based on the best point estimates of relevant parameters. Our results suggest that robust optimal marginal tax rates are typically more progressive than in benchmark analyses, raising top marginal tax rates by between 5 and 7 percentage points, and generating modest expected welfare gains.

Incorporating human factors into LCM using fuzzy TCI model

ABSTRACT Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal Control Model (LCM); the requirement for a more desirable underlying CF model for the Task Capability Interface (TCI) model; the ignorance of the fuzziness of human brains when modeling Task Difficulty (TD). Specifically, in order to imitate driver’s natural or subjective uncertainty and ambiguity of his TD, the fuzzy logic approach is introduced, and the TD is then incorporated into the LCM. Thereafter, both numerical simulation and field-data validation have been performed. Results indicate that our proposed model is more capable of accommodating HF and exhibits better performance than its predecessor.

Calculation of hybrid reliability of turbine disk based on self-evolutionary game model with few shot learning

A turbine disk design based on uncertainty quantifies the risks and improves the structural reliability. An optimized design issue related to turbine disk fatigue life reliability with subjective and objective multi-source uncertainties is a three-layer nested analysis process, where each iterative step requires a double-layer nested analysis containing probability and improbability. A self-evolution game model is proposed to solve the efficiency problem of the optimized design issue under a specific accuracy requirement. In the initial state, only a few sample points are needed to start the game. In the process of its evolution, the most valuable points are searched automatically, and performance functions are identified only at the points where the game diverges. The maximum failure probability is obtained after game consistency is achieved. Accuracy and efficiency are verified with typical numerical examples. The method is adopted to analyze the hybrid low cycle fatigue reliability of a real turbine disk. It is found that, owing to the elimination of an unnecessary artificial distribution hypothesis, the reliability is lower under the same safety life, and the results are more in line with engineering practice.

Surveying business uncertainty

We elicit subjective probability distributions from business executives about their own-firm outcomes at a one-year look-ahead horizon. In terms of question design, our key innovation is to let survey respondents freely select support points and probabilities in five-point distributions over future sales growth, employment, and investment. In terms of data collection, we develop and field a new monthly panel Survey of Business Uncertainty. The SBU began in 2014 and now covers about 1,750 firms drawn from all 50 states, every major nonfarm industry, and a range of firm sizes. We find three key results. First, firm-level growth expectations are highly predictive of realized growth rates. Second, firm-level subjective uncertainty predicts the magnitudes of future forecast errors and future forecast revisions. Third, subjective uncertainty rises with the firm’s absolute growth rate in the previous year and with the magnitude of recent revisions to its expected growth rate. We aggregate over firm-level forecast distributions to construct monthly indices of business expectations (first moment) and uncertainty (second moment) for the U.S. private sector.

Economic uncertainty before and during the COVID-19 pandemic

We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based policy uncertainty, Twitter chatter about economic uncertainty, subjective uncertainty about business growth, forecaster disagreement about future GDP growth, and a model-based measure of macro uncertainty. Four results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly – from a 35% rise for the model-based measure of US economic uncertainty (relative to January 2020) to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting differences between Wall Street and Main Street uncertainty measures. Fourth, in Cholesky-identified VAR models fit to monthly U.S. data, a COVID-size uncertainty shock foreshadows peak drops in industrial production of 12–19%.

Venturing into the Unknown: A Meta-Analytic Assessment of Uncertainty in Entrepreneurship Research

Uncertainty exists within a small constellation of core constructs that are central to the study of entrepreneurship. And yet, despite the common agreement and consensus that uncertainty plays a central role in entrepreneurship theory and research, there are important unresolved debates around several key questions including conflicting views on the nature of uncertainty (i.e., objective versus subjective uncertainty), the relationship between uncertainty and different types of entrepreneurial action (i.e., innovation, adaptation, and uncertainty bearing), and the complex relationships among uncertainty, entrepreneurial action, and organizational performance. The purpose of this study is to examine the body of empirical evidence in the field of entrepreneurship through the use of meta-analysis and meta-structural equation modeling in order to take steps towards disentangling the multilevel and multidimensional relationship among uncertainty, entrepreneurial action, and the fate of entrepreneurial ventures, thereby contributing to future theory development in the field of entrepreneurship.

Hybrid fuzzy system dynamics model for analyzing the impacts of interrelated risk and opportunity events on project contingency

Traditional risk analysis techniques are ineffective for capturing the dynamic causal interactions and subjective uncertainties involved in assessing risk and opportunity events since they treat risks independently and rely on the availability of sufficient historical data. In this paper, a hybrid fuzzy system dynamics (FSD) model is developed to analyze the impacts of interrelated and interacting risk and opportunity events on work package cost to determine work package and project contingencies using expert judgement and subjective assessment. A fuzzy decision-making trial and evaluation laboratory (DEMATEL) method is employed to structure and analyze the causal interactions among risk and opportunity events. This paper provides the following contributions: (1) a systematic risk assessment and prioritization procedure; (2) a structured method for defining the dynamic causal relationships among risk and opportunity events and quantifying their impact on work package and project contingencies using FSD; and (3) a method for representing linguistic variables and applying fuzzy arithmetic in FSD.

Uncertainty aversion in game theory: Experimental evidence

This paper studies, using a laboratory experiment, the effects of uncertainty aversion (the union of risk aversion and ambiguity aversion) on behavior in a normal form game. We isolate and identify two components of uncertainty aversion in games: the effect of an agent’s own uncertainty preferences and effect of the agent’s beliefs regarding their opponent’s uncertainty preferences. Uncertainty preferences are correlated with behavior in games. Induced beliefs about the risk preferences of an opponent have a larger effect on strategic behavior than induced beliefs about an opponent’s ambiguity preferences, although both components have a significant effect on behavior. The results support the hypothesis that strategic uncertainty is an important determinant of strategic behavior, and that the response to strategic uncertainty is modulated by subject uncertainty attitudes.

«...Мимо меня прошел я сам»: субъектная неопределенность в поэзии Олега Григорьева

«...Мимо меня прошел я сам»: субъектная неопределенность в поэзии Олега Григорьева

«Песни и слезы» в рассказе А. Весёлого «Вольница»: образ «простонародной революции»

«Песни и слезы» в рассказе А. Весёлого «Вольница»: образ «простонародной революции»

Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase.

Does Social Similarity Pay Off? Homophily and Venture Capitalists’ Deal Valuation, Downside Risk Protection, and Financial Returns in India

We ask how social similarity between start-up founders and venture capitalists (VCs) influences VCs’ pricing decisions and returns on investments. We conceptualize how regional and caste similarity, two salient aspects of social similarity in India, affect two distinct aspects of deal pricing: premoney valuation and investors’ downside risk protection in the Indian venture capital market. We theorize that VCs reflect the benefits and costs of social similarity by setting higher premoney valuation when investing in companies led by socially similar founders while also minimizing their downside risks in these investments. We expect that social similarity’s impact on pricing is amplified when VCs face greater subjective uncertainty, such as for early-stage deals or if the VCs lack expertise in the start-up company’s product market. Finally, we claim that VCs achieve superior returns on investments when their deal pricing accurately reflects the impact of social similarity. We tested our conceptual model using both parametric and nonparametric methods on a hand-collected data set of all deals that occurred during 2005–2012, and we supplemented our analyses with in-depth, qualitative interviews that contextualize our findings. The pattern of findings on regional similarity are consistent with our model, but the effects of caste in our data are theoretically anomalous. Post hoc analyses to resolve the anomaly suggest an “intrinsic quality” mechanism, whereby higher-caste VCs set higher valuations when matching with lower-caste founders that signal high quality. Overall, our findings offer evidence that VCs incorporate social attributes into deal pricing in nuanced yet boundedly rational ways.

The multiple priors of the open-minded decision maker

A multiple-prior decision maker is open-minded if he/she can describe, as subjective uncertainty, all convex sets of distributions over payoff relevant consequences. Theorem 1: open-mindedness is equivalent to the ability to subjectively describe both the uniform distribution on an interval and the set of all distributions on an interval. Theorem 2: sets of priors that fail either condition cannot describe a dense class of problems. The use of open-minded sets of priors to model decision makers allows the objective and the subjective approaches to uncertainty to inform each other. It also changes the implications of previously used axioms for multi-prior preferences because the axioms must apply to a larger set of problems.