The Effect of Relationship Biases on AOCC Performance

Abstract

Passengers are frequently affected by airline disruptions, leading to a poorer than expected passenger experience. Airlines are affected by disruptions in the order of billions of dollars. Managing disruptions effectively is therefore paramount for an airline’s long-term commercial success. In spite of decision-support tools being introduced to facilitate Airline Operational Control Center (AOCC) decision-making, their adoption rate is low. For the foreseeable future, humans will unquestionably remain in the loop when it comes to AOCC
disruption management and human-factors will continue to come into play in AOCC decision-making. To
improve AOCC decision-making, the effects of human factors on decision-making must be well understood. Bias is a human factor that affects decision-making and a relationship bias is a bias where previous negative experiences, between two individuals, will negatively affect future interactions they may have. A lack of trust, unwillingness to concede (in negotiations) or even a reluctance to interact, are a few examples on how a relationship bias may operationally manifest itself. AOCC decision-makers collaborate with one another to arrive at a integrated solution that mitigates an airline’s disruption. If a relationship biases exist within the AOCC, this negatively affects collaboration among AOCC decision-makers the development of solutions. The effect of the relationship bias on the solutions selected to mitigate an airline’s disruptions motivates the study of the effect of the relationship bias on AOCC performance. The fact that the relationship bias on AOCC decision-making has never been research, further motivates its study. We hope to address this research gap by evaluating the effects of the relationship bias on AOCC performance. More precisely, the research objective is to evaluate the effects of the relationship bias on AOCC performance, by modelling AOCC decision-making through a Naturalistic Decision-making framework using Klein’s Extended Recognition-Primed Decision model, and modelling AOCC social decision-making and interactions
using Chow’s Co-Ladder model. The methodology involves formalizing AOCC goals through a framework [Popova and Sharpanskykh, 2008] which enables us to measure organizational performance. Furthermore, it involves formally integrating Bruce’s extension [Bruce, 2011a] of Klein’s extended Recognition-primed Decision (RPD) model with Chow’s social interaction model [Chow et al., 2000]. The model is finally simulated for a scenario where a scheduled flight suffers a mechanical disruption and the performance is evaluated based on a goal satisfaction and operational costs. There are three main contributions of this research. Firstly, the individual cognition model developed makes it possible to model AOCC decision-maker’s individual cognition within the complex and dynamic AOCC environment. The second contribution is the proposed integrated model, which make it possible to integrate agent individual cognition and agent social decision-making and interactions. The third contribution is the evaluation of various possible relationship biases, which makes it possible evaluate the effect of different relationship types on AOCC performance. The research conducted led to a few interesting findings. For example, only some relationship biases lead to a significant decreased in AOCC performance, whereas other relationships have a negligible effect. Another interesting finding was that if there is a relationship bais between two agent, they are both not equally affected. The relationship bias only affects the AOCC control agent who requires information from a counterpart in order to develop their partial solution.