Institutions, uncertainty, and entrepreneurial judgment

Entrepreneurial action takes place in a context of Knightian uncertainty. In order to overcome this uncertainty, entrepreneurs engage in a process of judgment resulting in a decision about the course of action. Institutions arise mainly to reduce economic friction by providing structure to human interaction and thus reducing uncertainty. However, institutions may also introduce further uncertainty and thus disrupt the judgment process preceding entrepreneurial action. The present paper builds upon recent efforts to integrate the concepts of uncertainty and institutions within the entrepreneurial context. Drawing on Frank H. Knight's seminal insight, the judgment-based view of entrepreneurship, and relevant concepts of entrepreneurial outcomes, the main contribution of the paper lies in the development of a model offering a coherent description of the way institutions affect uncertainty and the entrepreneurial process.

Confidence in complex risk judgments: the roles of uncertainty, experience, and affect

Disagreement persists about the origin of confidence and the internal signals that influence its formation. Using combined individual participant data from four studies (N = 181), we examined confidence in relation to the perceived source of uncertainty for a risk judgment and explored the roles of domain-specific experience and affective evaluations in the formation of confidence. In each study, participants with domain-specific experience (backcountry skiers) performed complex risk judgments (judging avalanche risk) for multiple highly uncertain contexts (hypothetical scenarios in avalanche terrain). We examined whether more experienced participants could better recognize the inherent uncertainty of the decision environment, and if they did so with greater confidence. For complex tasks such as judging avalanche risk, experience should increase a person’s understanding of the probabilistic, unpredictable nature of that environment. Yet our findings suggests that participants of all levels of experience attributed uncertainty to their own judgment process rather than to the limitations and inherent uncertainty of the environment. We also examined whether participants’ affective evaluations influenced confidence in their risk judgments. Affective evaluations are understood to play a crucial orienting role in the risk judgment process. We found evidence of an interplay between affective and cognitive judgments in the formation of confidence. Participants were more confident when their affective evaluation matched their risk judgment, and less confident when there was a mismatch between the two. Our research illustrates a troubling limitation in the development of confidence with experience and the potential (dis)advantageous effect of affective evaluations on confidence in certain contexts.

Connecting Judgment Process and Accuracy of Student Teachers: Differences in Observation and Student Engagement Cues to Assess Student Characteristics

Teachers' ability to assess student cognitive and motivational-affective characteristics is a requirement to support individual students with adaptive teaching. However, teachers have difficulty in assessing the diversity among their students in terms of the intra-individual combinations of these characteristics in student profiles. Reasons for this challenge are assumed to lie in the behavioral and cognitive activities behind judgment processes. Particularly, the observation and utilization of diagnostic student cues, such as student engagement, might be an important factor. Hence, we investigated how student teachers with high and low judgment accuracy differ with regard to their eye movements as a behavioral and utilization of student cues as a cognitive activity. Forty-three participating student teachers observed a video vignette showing parts of a mathematics lesson to assess student characteristics of five target students, and reported which cues they used to form their judgment. Meanwhile, eye movements were tracked. Student teachers showed substantial diversity in their judgment accuracy. Those with a high judgment accuracy showed slight tendencies toward a more “experienced” pattern of eye movements with a higher number of fixations and shorter average fixation duration. Although all participants favored diagnostic student cues for their assessments, an epistemic network analysis indicated that student teachers with a high judgment accuracy utilized combinations of diagnostic student cues that clearly pointed to specific student profiles. Those with a low judgment accuracy had difficulty using distinct combinations of diagnostic cues. Findings highlight the power of behavioral and cognitive activities in judgment processes for explaining teacher performance of judgment accuracy.

Quantifying machine influence over human forecasters

Crowdsourcing human forecasts and machine learning models each show promise in predicting future geopolitical outcomes. Crowdsourcing increases accuracy by pooling knowledge, which mitigates individual errors. On the other hand, advances in machine learning have led to machine models that increase accuracy due to their ability to parameterize and adapt to changing environments. To capitalize on the unique advantages of each method, recent efforts have shown improvements by “hybridizing” forecasts—pairing human forecasters with machine models. This study analyzes the effectiveness of such a hybrid system. In a perfect world, independent reasoning by the forecasters combined with the analytic capabilities of the machine models should complement each other to arrive at an ultimately more accurate forecast. However, well-documented biases describe how humans often mistrust and under-utilize such models in their forecasts. In this work, we present a model that can be used to estimate the trust that humans assign to a machine. We use forecasts made in the absence of machine models as prior beliefs to quantify the weights placed on the models. Our model can be used to uncover other aspects of forecasters’ decision-making processes. We find that forecasters trust the model rarely, in a pattern that suggests they treat machine models similarly to expert advisors, but only the best forecasters trust the models when they can be expected to perform well. We also find that forecasters tend to choose models that conform to their prior beliefs as opposed to anchoring on the model forecast. Our results suggest machine models can improve the judgment of a human pool but highlight the importance of accounting for trust and cognitive biases involved in the human judgment process.

The Accuracy of Personality Trait Judgments

How accurately can we judge the personality traits of ourselves and others? What are the factors that influence our ability to make correct judgments? How can we use this information to improve our social interactions and relationships? In this introduction to the Oxford Handbook of Accurate Personality Judgment, the reader is introduced to the study of personality trait accuracy. Foundations of this research are reviewed and an overview of the volume is provided. Chapters explore current judgment models and review empirical work on moderators of accuracy, including characteristics of judges, targets, traits, and information. They explain the challenges encountered when judging different types of targets and examine how different kinds of information contribute to the judgment process. The applications and implications of this work for relationships, workplace interactions, and evaluations of psychological health and functioning are discussed. Ways to improve accuracy and future directions for research on trait accuracy are offered.

An Improved Framework for Modelling Data Warehouse Systems Using UML Profile

Data Warehouse (DW) applications provide past detail for judgment process for the companies. It is acknowledged that these systems depend on Multidimensional (MD) modelling different from traditional database modelling. MD modelling keeps data in the form of facts and dimensions. Some proposals have been presented to achieve the modelling of these systems, but none of them covers the MD modelling completely. There is no any approach which considers all the major components of MD systems. Some proposals provide their proprietary visual notations, which force the architects to gain knowledge of new precise model. This paper describes a framework which is in the form of an extension to Unified Modelling Language (UML). UML is worldwide known to design a variety of perspectives of software systems. Therefore, any method using the UML reduces the endeavour of designers in understanding the novel notations. Another exceptional characteristic of the UML is that it can be extended to bring in novel elements for different domains. In addition, the proposed UML profile focuses on the accurate representations of the properties of the MD systems based on domain specific information. The proposed framework is validated using a specific case study. Moreover, an evaluation and comparative analysis of the proposed framework is also provided to show the efficiency of the proposed work

(In)efficiency of the judiciary in the matter of commercial disputes

In the introduction to the paper, the author presents views on the importance and role of law as an achievement of importance for life, economy and relations in every society and state. With his presentations, he tries to find the connection between the law and justice, briefly referring to the understandings of Plato, Aristotle, and Socrates. The author believes that the judge's task is to perform his difficult, honourable and responsible function in the judgment process, based primarily on the legal interpretation of regulations, in order to resolve disputes and establish a state of peace in the country, including the economy. The aim of the paper is, in accordance with the theme of the Conference of the Association of Business Lawyers in Serbia - "Companies and Commercial Judiciary", with the analysis of selected cases, which are examples of bad case law, to indicate the need to take measures in order to prevent and eliminate existing shortcomings in the activities of judicial authorities in Serbia.

Perspective Taking Abilities among Persons with Autism: A Preliminary Study

This preliminary study examined persons with autism’s perspective taking abilities. Participants were 28 persons with autism and 27 controls. Among the persons with autism, 15 presented the Asperger Syndrome that was described in the DSM4. Scenarios in which persons were about to buy a piece of clothing were presented to participants who assessed the extent to which these persons were going to buy it as a function of suitability and price (situational factors), and what is known about their purchasing habits (the personality factor). In the same way as controls, participants with autism were able to integrate personality information into their judgments. However, only participants presenting the Asperger Syndrome described in the DSM4 were, in the same way as controls, able to vary, as a function of personality information, the importance given to situational factors during the judgment process.

Integrated priority decision index for risk assessment in chaos: cost overruns in transport projects

Purpose Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model. Design/methodology/approach A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives. Findings The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network. Research limitations/implications This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers. Practical implications These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high. Originality/value This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.