839 research outputs found

    The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts

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    In important conflicts such as wars and labor-management disputes, people typically rely on the judgment of experts to predict the decisions that will be made. We compared the accuracy of 106 forecasts by experts and 169 forecasts by novices about eight real conflicts. The forecasts of experts who used their unaided judgment were little better than those of novices. Moreover, neither group\u27s forecasts were much more accurate than simply guessing. The forecasts of experienced experts were no more accurate than the forecasts of those with less experience. The experts were nevertheless confident in the accuracy of their forecasts. Speculating that consideration of the relative frequency of decisions across similar conflicts might improve accuracy, we obtained 89 sets of frequencies from novices instructed to assume there were 100 similar situations. Forecasts based on the frequencies were no more accurate than 96 forecasts from novices asked to pick the single most likely decision. We conclude that expert judgment should not be used for predicting decisions that people will make in conflicts. When decision makers ask experts for their opinions, they are likely to overlook other, more useful, approaches

    Stability and innovation in the use of forecasting systems:a case study in a supply-chain company

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    Computer-based demand forecasting systems have been widely adopted in supply chain companies, but little research has studied how these systems are actually used in the forecasting process. We report the findings of a case study of demand forecasting in a pharmaceutical company over a fifteen-year period. At the start of the study managers believed that they were making extensive use of their forecasting system that was marketed on the basis of the accuracy of its advanced statistical methods. Yet the majority of forecasts were obtained by using the system’s facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of an S & OP process, yet these often only served to reduce forecast accuracy. This study uses observations of the forecasting process, interviews with participants and data on the accuracy of forecasts to investigate the reasons underlying the managers’ use of the system at two levels, the individual and the organizational. This evidence is then interpreted using various theories to understand the longevity of the company’s forecasting process, despite potential economic benefits that could be achieved through change. However, 10 years after the original case observations radical transformations of the forecasting system were introduced. The paper concludes by considering the impetus for adopting the new system and processes, and the changes in organizational practices this has led to

    Judgmental forecasting: Factors affecting lay people's expectations of inflation

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    In this thesis, laypeople’s judgmental forecasting about inflation is reviewed and experimentally explored in six chapters. Inflation is defined as the Consumer Price Index (CPI) across the whole thesis. In Chapter 1, I review work on the formation of inflation expectations, drawing mainly from the economic literature. In Chapter 2, I review research on judgmental forecasting, drawing mainly from the literature in cognitive psychology and management science. In Chapter 3, three experiments are presented that were designed to determine how and when people employ internal information of experienced price changes to form inflation expectations. In Chapter 4, three experiments are used to investigate the effects of providing within-series and across-series historical information (inflation rates, interest rates and unemployment rates) on inflation expectations. In Chapter 5, two experiments are reported that examine how training using simple outcome feedback increases the accuracy of inflation judgments and improves the calibration of confidence in those judgments. Chapter 6 reports experiments designed to examine the effects of using different elicitation methods (point forecasts, interval forecasts and density forecasts) on the accuracy of inflation judgments. Chapter 7 is a concluding chapter that summarises findings from these experiments and suggests avenues for future work

    Modelling credit and investment decisions based on AI algorithmic behavioral pathways

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    This paper provides a new approach to understanding bankers' risk-taking behavior. We build upon prior studies that suggest artificial intelligence algorithms are an effective approach to obtaining this understanding. Our approach uses behavioral finance and a unique decision-making model. Although the decision-making literature is replete with descriptions and explanations of creditors and investors' perceptions and judgment, it does not provide an algorithmic model that incorporates a more flexible approach to how creditors subjectively valuate risky projects. Specifically, a model is presented where 33 corporate bankers realized ex ante that they were unable to accurately model the underlying uncertainty that characterizes a company's need for a loan. The results indicate that bankers' risk assessments result in different evaluations of financial information regarding loans. This approach depicts an integrative algorithmic modelling process, whereby limits in the amount of historical conditional information prohibit the use of more complex econometric techniques

    Forecasting from time series subject to sporadic perturbations : effectiveness of different types of forecasting support

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    How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile

    Predictive maintenance for industry 5.0:behavioural inquiries from a work system perspective

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    Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition’s relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations.</p

    Confidence Interval Estimation Tasks and the Economics of Overconfidence

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    Experiments in psychology, where subjects estimate confidence intervals to a series of factual questions, have shown that individuals report far too narrow intervals. This has been interpreted as evidence of overconfidence in the preciseness of knowledge, a potentially serious violation of the rationality assumption in economics. Following these results a growing literature in economics has incorporated overconfidence in models of, for instance, financial markets. In this paper we investigate the robustness of results from confidence interval estimation tasks with respect to a number of manipulations: frequency assessments, peer frequency assessments, iteration, and monetary incentives. Our results suggest that a large share of the overconfidence in interval estimation tasks is an artifact of the response format. Using frequencies and monetary incentives reduces the measured overconfidence in the confidence interval method by about 65%. The results are consistent with the notion that subjects have a deep aversion to setting broad confidence intervals, a reluctance that we attribute to a socially rational trade-off between informativeness and accuracy.overconfidence; uncertainty; monetary incentives; experiments

    Mind the Gap between Demand and Supply. A behavioral perspective on demand forecasting

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    The anchoring heuristic and overconfidence bias among frontline employees in supply chain organizations

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    This study assesses the extent the anchoring heuristic and overconfidence bias leads to inaccurate judgments among frontline employees in complex multi-stakeholder supply chain organizations. Data is obtained from an experiment-based questionnaire in a United Kingdom based freight forward and materials handling company. Analysis is undertaken using descriptive and inferential statistics. Results suggest that frontline employees within consistently overestimate probabilities when framed in a conjunctive manner. They also consistently underestimate probabilities when framed in a disjunctive manner and also exhibit considerable overconfidence in their judgements. Mixed evidence was found regarding susceptibility to anchoring and overconfidence in terms of level of expertise and geographical location. Findings highlight the critical role of communication in establishing reflective monitoring of, and improvements to, heuristics usage in daily supply chain decisions
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