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Behavioral solutions to our cognitive biases have long been studied in the literature. However, there is still ample evidence of behavioral biases in decision-making, with only limited improvement in the medium/long term even when debiasing methods are applied. The purpose of this paper is to describe how financial investors could benefit from a proactive management of emotions combined with a set of learning and decision-making heuristics to make more efficient investments in the long run.

First, the authors offer a classification of the appropriate quantitative and qualitative methodologies to use in different ecological environments. Then, the authors offer a list of detailed heuristics to be implemented as behavioral principles intended to induce more long-lasting changes than the original rules offered by the adaptive toolbox literature. Finally, the authors provide guidelines on how to embed artificial intelligence and cognitive diversity within the investment decision architecture.

Improvements in decision skills involve changes that rarely succeed through a single event but through a succession of steps that must be habitualized. This paper argues that implementing a more conscious set of personal and group principles is necessary for long-lasting changes and provides guidelines on how to minimize systematic errors with adaptive heuristics. To maximize their positive effects, the principles outlined in this paper should be embedded in an architecture that fosters cognitively diverse teams. Moreover, when using artificial intelligence, the authors advise to maximize the interpretability/accuracy ratio in building decision support systems.

The paper proposes a theoretical reflection on the field of behavioral research and decision-making in finance, where the chief goal is to offer practical advices to investors. The literature on debiasing cognitive biases is limited to the detection and correction of immediate effects. The authors go beyond the traditional three building blocks developed by the behavioral finance literature (search rules, stopping rules and decision rules) and aim at helping investors who are interested in finding long-term solutions to their cognitive biases.

Are there smart ways to find heuristics? What are the common principles behind heuristics? We propose an integrative definition of heuristics, based on insights that apply to all heuristics, and put forward meta-heuristics for discovering heuristics.

We employ Herbert Simon’s metaphor that human behavior is shaped by the scissors of the mind and its environment. We present heuristics from different domains and multiple sources, including scholarly literature, practitioner-reports and ancient texts.

Heuristics are simple, actionable principles for behavior that can take different forms, including that of computational algorithms and qualitative rules-of-thumb, cast into proverbs or folk-wisdom. We introduce heuristics for tasks ranging from management to writing and warfare. We report 13 meta-heuristics for discovering new heuristics and identify four principles behind them and all other heuristics: Those principles concern the (1) plurality, (2) correspondence, (3) connectedness of heuristics and environments and (4) the interdisciplinary nature of the scissors’ blades with respect to research fields and methodology.

We take a fresh look at Simon’s scissors-metaphor and employ it to derive an integrative perspective that includes a study of meta-heuristics.

This paper aims to investigate the role of AI in facilitating knowledge sharing and learning in organizations and the redesign of AI-enabled knowledge workers’ roles and processes.

This paper develops a framework for analyzing AI’s role in different knowledge management activities, explores the impact of AI in transforming knowledge workers’ roles and processes in knowledge sharing and learning and presents recommendations for tailored AI-enabled knowledge management systems for modern knowledge worker environments.

The authors synthesize the elements from different parts of the relevant literature and develop a unified framework consisting of three dimensions of AI systems, three knowledge management (KM) activities and two types of AI–human interactions. Based on this framework, the authors summarize the primary use cases supported by AI-enabled knowledge management systems (KMS) and compare them with the traditional KMS use cases. The authors find that a single type of AI system is insufficient to support the increasingly complex nature of knowledge workers’ activities, manifested in three dimensions – process, engagement and content; a tailored AI system should be developed to support knowledge workers in their unique roles and processes.

With the growing interest in AI and its applications to KM, this research provides managerial insights for practitioners to effectively adopt AI in managing knowledge assets in organizations.