An agent-based debiasing framework for investment decision-support systems

Though researchers agree on the role of psychological forces on individuals' decision-making and emphasise the need for developing decision-support systems (DSS) that make individuals aware of these forces, a framework that can guide us in building such systems is still non-existent. In this article, we attempt to bridge this gap by proposing an agent-based debiasing framework for developing investment DSS. Identifying the primary characteristics of major biases influencing investment decisions through a thorough literature review, we propose a taxonomy to categorise them as cognitive, affective or conative. Cognitive biases are information-processing biases. Affective biases involve general moods and emotions. Conative biases are relatively stable personality traits such as overconfidence and inertia. We then outline debiasing strategies for each of these bias categories and identify decision-support characteristics necessary in software agents to carry out the appropriate debiasing tasks. An agent-based DSS architecture is then proposed, and a detailed trading example triggering a sample bias detection and debiasing algorithm is discussed to demonstrate the feasibility of the proposed system.