Sub-Optimal Behavior in Family Business Management: The Role of Individual Distorted Perception

The paper describes in detail the cognitive distortions derived from the studies of behavioural finance (BF) and moves biases, heuristics and framing effects into the context of family business management (FBM), with the goal of formally embedding them into cognitive agents, for computational modeling and simulation. BF is an approach for studying finance, economics and business management, based on the interactions among cognitive sciences and decision-making models, and explores the main cognitive distortions that could lead to sub-optimal decisions and behaviors. FBM is often characterized by sub-optimal decisions that derive from risk aversion, emotional components and the will of leaving the enterprise to the next family generation. This is the reason why the study of systematic cognitive errors brings to an improvement of FBM researches. In this paper, the most frequent behavioral distortions are analyzed in terms of FBM, and their use within computational models is proposed. A simulation model can be employed as tools for better understanding the aggregate and complex effects of emotionally distorted behaviors, as opposed to pure rational ones, when dealing with management topics of family firms. A new method for cognitive action selection is formally introduced, keeping into consideration an individual bias: ego biased learning. It allows the agents to adapt their behavior according to a payoff coming from the action they performed at time t-1, by converting an action pattern into a synthetic value, updated at each time, but keeping into account their individual preferences towards specific actions. The contribution to the field of FBM is represented by the possibility of measure how strategic choices depend on the way in which the owners/managers act, by identifying the main distortions that characterize their actions (what is good for a family and good for the enterprise, conservationism, risk aversion, irrational capital allocation and so on). The model proposes an approach to risk management and to strategic process analysis, based on the knowledge of these systematic errors, with the purpose of avoiding them and moving towards an optimal aggregate outcome