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Forecasting processes in organizational settings largely rely on human judgment, which makes it important to examine ways to improve the accuracy of these judgmental forecasts. The purpose of this paper is to test the effect of providing relative performance feedback on judgmental forecasting accuracy.

This paper is based on a controlled laboratory experiment.

The authors show that feedback that ranks the forecasting performance of participants improves their accuracy compared with the forecasting accuracy of participants who do not get such feedback. The authors also find that the effectiveness of such relative performance feedback depends on the content of the feedback information as well as on whether accurate forecasting performance is linked to additional financial rewards. Relative performance feedback becomes more effective when subjects are told they rank behind other participants than when they are told they rank higher than other participants. This finding is consistent with loss aversion: low-ranked individuals view their performance as a loss and work harder to avoid it. By contrast, top performers tend to slack off. Finally, the authors find that the addition of monetary rewards for top performers reduces the effectiveness of relative performance feedback, particularly for individuals whose performance ranks near the bottom.

One way to improve forecasting accuracy when forecasts rely on human judgment is to design an effective incentive system. Despite the crucial role of judgmental forecasts in organizations, little attention has been devoted to this topic. The aim of this study is to add to the literature in this field.

This paper aims to examine the relationship between the characteristics of strategic decision-making team’s mental model and its performance. The authors propose that the relationship between mental models and performance is two-way, rather than one-way. Thus, performance feedback should, in turn, influence strategic behavior and future performance by either triggering or hindering the learning process.

The authors conduct the research in the setting of a simulation experiment. A longitudinal data set was collected from 36 teams functioning as strategic decision makers over three periods.

This study provides support for the positive impacts of both the complexity and centrality of a team’s mental model on its performance. The authors also find that positive performance feedback reduces changes in complexity and centrality of team mental models due to cognitive inertia.

The study contributes to the literature by investigating the specific mechanisms that underlie mental model evolution. Different from the existing studies on team mental models that mainly focus on similarity of these shared cognitive structures, this study examines another two characteristics of team mental model, complexity and centrality, that are more relevant to the strategic decision-making process but has not been extensively studied in the team literature. In addition, this study reveals that performance feedback has different effects on team mental models depending on the referents – past performance or social comparison – which advances the understanding of the learning effects of performance feedback.

In large-scale group decision-making (LSGDM) situations, existing TODIM group decision-making methods often fail to account for the influence of social network relationships and the bounded rationality of decision-makers (DMs). To address this issue, a new TODIM-based group decision-making method is proposed that considers the current trust relationships among DMs in a large-scale trust relationship network.

This method consists of two main stages. In the first stage, the large-scale group is partitioned into several sub-clusters based on trust relationships among DMs. The dominance degree matrix of each sub-cluster is then aggregated into the large-scale group dominance degree. In the second stage, after aggregating the large-scale group dominance degree, the consensus index is calculated to identify any inconsistent sub-clusters. Feedback adjustments are made based on trust relationships until a consensus is reached. The TODIM method is then applied to calculate the corresponding ranking results. Finally, an illustrative example is applied to show the feasibility of the proposed model.

The proposed method is practical and effective which is verified by the real case study. By taking into account the trust relationships among DMs in the core process of LSGDM, it indeed has an impact on the decision outcomes. We also specifically address this issue in Chapter Five. The proposed method fully incorporates the bounded rationality of DMs, namely their tendency to accept the opinions of trusted experts, which aligns more with their psychology. The two-stage consensus model proposed in this paper effectively addresses the limitations of traditional assessment-based methods.

This study establishes a two-stage consensus model based on trust relationships among DMs, which can assist DMs in better understanding trust issues in complex decision-making, enhancing the accuracy and efficiency of decisions, and providing more scientific decision support for organizations such as businesses and governments.

This paper aims to propose and evaluate an intrusion sensitivity (IS)-based approach regarding the detection of pollution attacks in collaborative intrusion detection networks (CIDNs) based on the observation that each intrusion detection system may have different levels of sensitivity in detecting specific types of intrusions.

In this work, the authors first introduce their adopted CIDN framework and a newly designed aggregation component, which aims to collect feedback, aggregate alarms and identify important alarms. The authors then describe the details of trust computation and alarm aggregation.

The evaluation on the simulated pollution attacks indicates that the proposed approach is more effective in detecting malicious nodes and reducing the negative impact on alarm aggregation as compared to similar approaches.

More efforts can be made in improving the mapping of the satisfaction level, enhancing the allocation, evaluation and update of IS and evaluating the trust models in a large-scale network.

This work investigates the effect of the proposed IS-based approach in defending against pollution attacks. The results would be of interest for security specialists in deciding whether to implement such a mechanism for enhancing CIDNs.

The experimental results demonstrate that the proposed approach is more effective in decreasing the trust values of malicious nodes and reducing the impact of pollution attacks on the accuracy of alarm aggregation as compare to similar approaches.

This bibliometric mapping study aimed to provide comprehensive insights into the global research landscape of cybernetics. Utilizing the biblioshiny function in R Studio, we conducted an analysis spanning 1958 to 2023, sourcing data from Scopus. This research focuses on key terms such as cybernetics, cybernetics systems, complex adaptive systems, viable system models (VSM), agent-based modeling, feedback loops and complexity systems.

The analysis leveraged R Studio’s biblioshiny function to perform bibliometric mapping. Keyword searches were conducted within titles, abstracts and keywords, targeting terms central to cybernetics. The timespan, 1958–2023, provides a comprehensive overview of the evolution of cybernetics-related literature. The data were extracted from Scopus to ensure a robust and widely recognized source.

The results revealed a rich and interconnected global research network in cybernetics. The word cloud analysis highlights prominent terms such as “agent-based modeling,” “complex adaptive systems,” “feedback loop,” “viable system model” and “cybernetics.” Notably, the journal Kybernetes has emerged as a focal point, with significant citations, solidifying its position as a key source within the cybernetics research domain. The bibliometric map provides visual clarity regarding the relationships between various concepts and their evolution over time.

This study contributes original insights by employing advanced bibliometric techniques in R Studio to map the cybernetics research landscape. The comprehensive analysis sheds light on the evolution of key concepts and the global collaborative networks shaping cybernetics research. The identification of influential sources, such as Kybernetes, adds value to researchers seeking to navigate and contribute to the dynamic field of cybernetics. Furthermore, this study highlights that cybernetics not only provides a useful framework for understanding and managing major economic shocks but also offers perspectives for understanding phenomena in various fields such as economics, medicine, environmental sciences and climate change.

A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology‐generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain‐specific documents for general users. In addition, it aims to test several effectiveness measures of rank order to determine if the top‐ranked lists recommended by the experts were reliable.

In the approach, expert groups evaluate web documents to provide a recommender system for general users. The authority and make‐up of the expert group are adjusted through user feedback. The system also uses various measures to gauge the difference between the opinions of experts and those of general users to improve the evaluation effectiveness.

The proposed system is efficient when there is major support from experts and general users. The recommender system is especially effective where there is a limited amount of evaluation data from general users.

This is an original study of how to effectively recommend web documents to users based on the opinions of human experts. Simulation results were provided to show the effectiveness of the dynamic expert group for recommender systems.

This paper reports experiments with a term weighting model incorporating relevance information in which it is assumed that index terms are distributed dependently. Initially this model was tested with complete relevance information against a similar model which assumes index terms are distributed independently. The experiments demonstrated conclusively that index terms are not independent for a number of diverse document collections. It was concluded that the use of relevance information together with dependence information could potentially improve retrieval effectiveness. As a result of further experiments the initial strict dependence model was modified and in particular a new relevance‐based term weight was developed. This modified dependence model was then used as the basis for relevance feedback, i.e. with partial relevance information only, and significant increases in retrieval effectiveness were achieved. The evaluation method used in the feedback experiments emphasized the effect of the feedback on documents which the potential user would not previously have seen. Finally the incorporation of relevance feedback in an operational system is considered and in particular it is argued that if high recall searches are required, relevance feedback based on the modified dependence model may be superior to the widely used Boolean search.

The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback.

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.

Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.

The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users.

The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.

This paper is a study of how people with heterogonous individual characteristics self-select into different compensation schemes. A laboratory experiment is designed in which “workers” can join “companies” that pay according to various schemes: piece rate, revenue sharing, individual tournament, and team tournament. The main findings are: (1) Subjects with high relative performance always prefer individual tournament. (2) Risk-averse subjects are less likely to choose competitive schemes. (3) Individual tournament attracts fewer women than men, which is partially explained by gender-specific social preferences. (4) Compared to people with siblings, only children are less likely to accept any team-based schemes without information about their teammates. (5) The provision of feedback about relative performance can adjust individuals’ biased self-beliefs and then influence their self-selections.